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Atlantic -Iberian Biscay Irish- IBI Production Centre

IBI_ANALYSIS_FORECAST_PHYS_005_001

Issue: 4.1

Contributors: Marcos G. Sotillo, Bruno Levier, Pablo Lorente, Karen Guihou

Approval date by the CMEMS product quality coordination team: 06/12/2019

QUID for IBI MFC Products


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CHANGE RECORD

When the quality of the products changes, the QuID is updated and a row is added to this table. The third column specifies which sections or sub-sections have been updated. The fourth column should mention the version of the product to which the change applies.

Issue Date § Product Version

Description of Change Author Validated By

1.0

08/01/2013

All

IBI-V3 Creation and MyO2 update of the document.

Marcos G. Sotillo Pablo Lorente Bruno Levier Marie Drevillon Jérôme Chanut

Enrique Álvarez Fanjul

1.1 05/02/2013 All IBI-V3 Minor Changes after IBI V3 Acceptance Review.

Marcos G Sotillo Enrique Álvarez Fanjul

1.2.1 20/02/2014 All IBI-V4 Changes concerning new IBI-V4 operational launch.

Bruno Levier Marcos G Sotillo Pablo Lorente Arancha Amo


1.2.2 17/03/2014 All IBI-V4 Changes after QUARK review.

Marcos G Sotillo Bruno Levier


1.3 May 1 2015 All Change format to fit CMEMS graphical rules.

L. Crosnier

2.0 12/01/2016 All CMEMS IBI-V2

Upgrade for including inputs from new CMEMS V2 IBI-MFC Operational release.

Marcos G Sotillo Bruno Levier Pablo Lorente


2.1 12/09/2016 All Change: new CMEMS GLOBAL system as boundary condition

Information on the change of data imposed ad IBI boundary condition (related to the use of a new upgraded CMEMS GLOBAL system).

Bruno Levier Guillaume Reffray Marcos G Sotillo




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3.0 23/12/2016 All CMEMS IBI-V3

Upgrade to include product quality information from the new CMEMS V3 IBI-MFC Operational release



4.0 23/12/2017 All CMEMS IBI-V4

Upgrade to include product quality information from the new CMEMS V4 IBI-MFC Operational release



4.1 03/12/2019 All CMEMS December 2019 release

IBI NRT PHY Product Upgrade: Delivery of new dataset with 15 minutes frequency data for sea level and surface currents.

Karen Guihou Roland Aznar Arancha Amo Pablo Lorente Marcos G Sotillo

Marcos García Sotillo



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TABLE OF CONTENTS

Change Record............................................................................................................................................................ 2

Table of contents ........................................................................................................................................................ 4

I Executive summary ............................................................................................................................................ 5

I.1 Products covered by this document................................................................................................................... 5

I.2 Summary of the results ..................................................................................................................................... 5

I.3 Estimated Accuracy Numbers ............................................................................................................................ 8

II Production system description ..........................................................................................................................13

III Validation framework .......................................................................................................................................16

IV Validation results ..............................................................................................................................................27

IV.1 NRT Operational Validation ............................................................................................................................27

IV.1.1 Sea Surface Temperature ....................................................................................................................... 28

IV.1.2 Temperature and Salinity ....................................................................................................................... 51

IV.1.3 Surface Currents ..................................................................................................................................... 60

IV.1.4 Sea Surface Height .................................................................................................................................. 73

IV.2 Offline R&D mode Validation .........................................................................................................................77

IV.2.1 Sea Surface Height .................................................................................................................................. 77

IV.2.2 Surface currents ..................................................................................................................................... 80

IV.2.3 Surface temperature .............................................................................................................................. 82

IV.2.4 Temperature and salinity profiles .......................................................................................................... 85

V System’s Noticeable events, outages or changes ...............................................................................................88

VI Quality changes since previous version ........................................................................................................... 101

VII References .................................................................................................................................................. 103



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I EXECUTIVE SUMMARY

I.1 Products covered by this document

This document describes the quality of the CMEMS Atlantic IBI -Iberian Biscay Irish- Ocean Analysis and Forecast products (CMEMS product catalogue identifier: IBI_ANALYSIS_FORECAST_PHYS_005_001).

The operational IBI Ocean Analysis and Forecasting system provides a daily updated of a 5-day hydrodynamic forecast, including high frequency processes of paramount importance to characterize regional scale marine processes (i.e. tidal forcing, surges and high frequency atmospheric forcing, fresh water river discharge inputs, etc.). A weekly update of IBI analysis is also delivered as historic IBI best estimates. The system is based on a (eddy-resolving) NEMO model application run at 1/36º horizontal resolution, driven by high frequency meteorological and oceanographical forcing. The data assimilation system (Mercator Ocean assimilation system SAM2) allows constraining the model in a multivariate way with Sea Surface Temperature, together with all available satellite Sea Level Anomalies, and with in situ observations. The CMEMS IBI MFC Production Unit (run by Nologin in coordination with Puertos del Estado and with the support in terms of supercomputing resources of CESGA) is responsible of the generation and delivery of the CMEMS IBI_ANALYSIS_FORECAST_PHYS_005_001 products. Further information on the latest version of the IBI-MFC Forecast System and its associated product (the IBI_ANALYSIS_FORECAST_PHYS_005_001) is provided in the Products User Manual Document (CMEMS-IBI-PUM-005-001-v6.2).

I.2 Summary of the results

Quality assessment is a key issue in the CMEMS Service. In that sense, before any new operational release, the CMEMS IBI Monitoring & Forecasting Centre (IBI-MFC) performs a qualification of the proposed future IBI system before this new model system version becomes fully operational. This qualification phase is based on scientific assessment of the IBI products, measuring the quality of any new updated version of the IBI forecast system and evaluating their products together with the previous IBI operational ones to quantify potential added values associated to the novelties, as well as to verify the existence of non-regression in terms of quality with respect to the previous IBI solutions.

Together with this pre-operational Qualification Phase, the IBI MFC performs an exhaustive NRT validation of the operational products generated by the IBI operational system. This Operational Validation Phase is based on a routine monitoring of the quality of the IBI ocean forecast products on a daily, monthly, seasonal and yearly basis. The quality of the IBI Ocean Forecast System and its operational products are routinely assessed from year 2011 through the NARVAL tool (Lorente et al, 2012, Sotillo et al., 2015).

These qualification and validation works allow to the IBI scientific team to have a better knowledge and objective evaluation of the quality and accuracy of IBI ocean forecast products. Outcomes from both Phases



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(pre-operational V4 Qualification and NRT operational Validation) are shown in the present IBI QUID document. From the IBI operational NRT validation, it is shown the metrics that illustrate in detail how the IBI forecast system performed in the past 2017 year (See results in Section IV.1). Also, derived from the IBI operational products available, it has been estimated the EANs (Estimated Accuracy Numbers). The IBI EANs (provided in Section I.3) are essential statistics, obtained from long-term comparisons with reference observations, that gives a measure of the IBI performance along the historical time series of products available in the CMEMS Catalogue.

Together with these metrics derived from the routine IBI validation, there are also some quality indicators extracted from the pre-operational qualification phase performed mainly for testing the new proposal of IBI system update before its operational release (see results in Section IV.2). More specifically, the CMEMS V4 IBI operational release has been qualified, comparing its performance with the previous IBI operational set-up. Quality indicators and some discussion on how the V4 novelty (i.e.: the generation of IBI analysis by means of the activation of a new data assimilation scheme) impacts in the IBI solution are also provided. Finally, point out that in the latest CMEMS release of December 2019, the IBI-MFC started the delivery of a new IBI dataset with 15-minutes frequency data for sea level and surface currents. Section VI shows the consistency between this new 15-minutes IBI dataset and the one, based on hourly data, previously delivered as part of the IBI_ANALYSIS_FORECAST_PHYS_005_001 product.

The headline results from these Qualification/Validation efforts are the follows:

The qualification phase shows a good agreement in average between the system and the observations. This is particularly true for the sea level when compared to along track altimetry (which is now assimilated by the system), with low values of the RMS of the differences between the system and the measurements. Tidal and residual sea level is also closed to the tide gauges observations. The comparisons with the in-situ profiles show also a good agreement, especially below the thermocline. There are very few long time series of velocity measurements, so it is difficult to assess this variable. The comparisons with existing buoys measurements show small correlations. Concerning the Sea Surface Temperature, the IBI system performs well in average, but can locally display high biases.

The skill of IBI operational products are routinely assessed through NARVAL web tool and also in ‘offline’ mode by the IBI MFC Validation Team from April 2011 to the present. The EANs give a measure of the IBI performance along the 2012-2016 period. Specific metrics for last year (2017) computed from the IBI operations are provided in this document. Likewise, the new CMEMS IBI-V4 System has been evaluated in the pre-operational qualification phase using to this aim reference observational sources along two years (March-2013 to March-2015). The specific pre-operational qualification tests performed to evaluate the numerical code update impact have confirmed that there is non-degradation in the solution provided by the new upgraded CMEMS IBI V4 system.

Sea Level and Tides: Comparisons with in-situ observations or tides atlases showed very good agreement in terms of sea surface elevation. The mean RMS differences for the M2 constituent is smaller than 12 cm at tide-gauge locations over the whole IBI domain. The residual elevation is closed to observations at high frequencies (RMS difference generally lower than 10 cm; correlation higher than .8). The new CMEMS IBI V4 system presents an analogous tidal solution to previous releases.



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Currents: According to the statistical results (correlation coefficients generally above 0.4), basic oceanographic features related to the surface circulation are statistically reproduced on a monthly and quarterly basis by IBI system in terms of mean and variance, despite some model drawbacks detected when dealing with specific phenomena (i.e. overestimation of the zonal Atlantic inflow in the Strait of Gibraltar). Broadly speaking, time-averaged maps of Eulerian surface currents provided by IBI satisfactorily reproduce the prevalent circulation patterns during 2017, as reflected by the CLASS1 metrics derived from IBI-HF radar comparisons over three different study regions. Local comparisons with in-situ current observations from moorings shows that the IBI performance quality varies locally (IBI Quality differs from one buoy to another, and even from month to month in same location). In this context, the current roses reveal a rather good performance of IBI. The new IBI-V4 release shows very similar results than the previous model application, being difficult to conclude which system provides a better performance in term of currents or tidal currents.

Sea Surface Temperature: The performance of IBI system for the sea surface temperature is context-dependent since regions dominated by tidal fronts or upwelling phenomena (in the Western Iberian coast) can be affected by a model misrepresentation of such events, giving rise to potential misfits in the model-observation comparison. Likewise, some periods of the year are more challenging from a modelling perspective, like the summertime in the Strait of Gibraltar. Leaving aside some local differences, an overall good agreement between IBI model data and the satellite-derived observations has been proved, in terms of averaged values and variability, with the mean bias, RMSD, and correlation (CLASS1 metrics) in the ranges [-0.19, 0.20] ᵒC, [0.52, 0.82] ᵒC and [0.96, 1] respectively. The CLASS2 statistical results derived from the validation of IBI simulations against a broad range of in-situ sensors (on board of mooring buoys) reinforce the previous statement about the significant accuracy of IBI performance during 2017. This high accuracy in terms of SST performance is confirmed by the IBI SST EAN (derived from the comparison of hourly IBI best estimates with CMEMS L3 satellite for years 2013-2016, averaged over the whole IBI service domain) values: a RMSD of 0.53ᵒC and a bias of 0.07ᵒC.

Temperature & Salinity: the level of accuracy shown by the IBI products in reproducing temperature and salinity conditions on the whole water column are provided by the new accuracy numbers estimated along the time period covered by the specific IBI qualification run (April 2013 – April 2015). These numbers are estimated from comparisons of IBI solution with data from in-situ ARGO profiles on the whole water column (considering it as the layer between surface and the 2000-m depth, where most of the ARGO profiler observations are taken). The comparison of the full profile observations with the IBI best estimates shows rms difference and bias values of 0.55 and 0.08 ᵒC, respectively. Related to salinity, rms differences and biases of 0.46 and 0.01 for surface and of 0.12 and 0.02 for the whole column are obtained (all in psu units).

CLASS4 metrics, computed over the entire year 2017, show that IBI seems to capture the vertical variability of temperature, with RMSD and correlation values generally in the ranges [0.2ᵒ-0.5ᵒ] and [0.85-0.98] for the full ARGO profiles. The daily evolution of IBI performance to simulate the temperature at different layers reveals that correlation values remain above 0.9 most of the time and RMSD values are generally lower for the deepest levels. In the case of salinity, the skill metrics are not as good as those



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derived from the comparison of temperature profiles: the correlation and RMSD values emerged in the ranges [0.4-0.9] and [0.2-0.8 PSU], and rather constant with independence of the depth level considered.

I.3 Estimated Accuracy Numbers

This section is devoted to providing essential statistics obtained from long-term comparisons with reference information on ocean dynamics. A consistent estimation of the relevant accuracy levels for the IBI-MFC products of the CMEMS catalogue is provided. Most of the validation procedures are based on comparisons of the model solution with in-situ and satellite observational data delivered by the CMEMS Thematic Assembly Centres (SST and In-Situ TACs).

The SST accuracy numbers shown in Table 1 were derived from the comparison of IBI best estimates with CMEMS L3 satellite SST data (CMEMS product identifier: SST-EUR-SST-L3S-NRT-OBSERVATIONS-010-009_a). The provided EANs are derived from statistics, averaged over the whole IBI service domain, computed using best estimates of the IBI long run used in the Qualification phase for the years 2010-2016. For such a long period and large region, the mean difference is almost zero, but it can be higher locally and on short time scale.

Sea Surface Temperature (K)

IBI Service Domain

RMSD difference

Whole period

Winter Spring Summer Autumn

Surface RMSD 0.64 0.54 0.61 0.74 0.64

Surface difference 0 0.02 -0.02 -0.01 0.01

Table 1: IBI EAN related to SST. Time period: 01/01/2010-31/12/2016). Daily IBI best estimates (hindcast) compared with the observational source used as reference (Daily SST MyOcean/CMEMS L3 satellite SST product). Statistics averaged over the whole IBI Service Domain.

The following Estimated Accuracy Numbers (EAN) numbers have been calculated for a 2-year period (April 2013-April 2015).

The EAN for residual sea surface height (the tidal signal has been removed), shown in Table 2, have been computed using as reference observations from data provided by the tide-gauges network on the IBI service domain for a 2-year period (April 2013-April 2015) (MyOcean/CMEMS product identifier: INSITU_IBI_NRT_OBSERVATIONS_013_033).



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Sea Surface Height (cm)

IBI Service Domain

RMSD difference Mean difference

Residual SSH 6 1

Table 2: IBI EAN related to SSH residuals. Daily IBI best estimates (hindcast) used in the metric computation. Observational source used as reference: Sea level data from in-situ tide-gauge stations avail in the IBI domain (CMEMS IBI-InSituTAC product). Statistics averaged over the whole IBI Service Domain.

Table 3 and Table 4 show the EAN set for temperature and salinity, computed using reprocessed (CORA) and near real time in-situ observational data sources for the period January 2011 to December 2016. The EAN for temperature and salinity have been computed using as reference observational data from any in-situ platform available on the IBI service domain along the period. Data for years before 2013 is sourced from the CMEMS product INSITU_GLO_TS_REP_OBSERVATIONS_013_001_b, whereas the observational data for year 2014 to 2016 was obtained from the CMEMS INSITU_GLO_NRT_OBSERVATIONS_013_030 product.

Information for the whole water column (full profiles considered) is provided. Likewise, information for different levels is provided, being the specific levels considered in the IBI EAN estimation: 0-5 m, 5-200 m, 200-600 m, 600-1500 m, 1500-2000 m.

Together with the EANs obtained from the comparisons between the IBI solution and the in-situ observations available in the whole IBI service domain, some regional EANs for specific regions within the IBI service domain are provided. Only 4 of these areas (out of the 10 selected for other different validation purposes shown in Figure 1): the one centred in the Canary Island region, the Gulf of Cadiz, the Bay of Biscay and the western Med have been considered for computing these validation indicators. The other IBI regional validation zone (more focused on coastal and shelf areas, have not been considered to compute regional EAN due to the scarce or unavailability of ARGO floats in those areas.



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Figure 1: IBI Service Domain and the different zones selected to make regional metrics for the IBI validation.



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Temperature (K)

IBI Service Domain Cadiz (CADIZ)

RMSD difference

Mean difference

RMSD difference

Mean difference

FULL 0.51 0.08 0.71 0.25

0-5 m 0.55 0.11 0.75 0.30

5-200 m 0.53 0.09 0.53 0.07

200-600 m 0.34 0.08 0.35 0

600-1500 m 0.60 0.12 0.60 0.07

1500-2000 m 0.39 0.13 0.53 0.15

Canary Island Zone (ICANA) Bay of Biscay (GOBIS)

FULL 0.45 0.07 0.46 0.08

0-5 m 0.44 0.12 0.48 0.10

5-200 m 0.53 0.01 0.44 0.05

200-600 m 0.36 0.02 0.16 0

600-1500 m 0.38 0.02 0.38 0.02

1500-2000 m 0.22 0.03 0.28 0.01

Western Mediterranean

(WSMED)

FULL 0.25 0.03

0-5 m 0.70 0.16

5-200 m 0.53 0.12

200-600 m 0.12 0.01

600-1500 m 0.08 0.02

1500-2000 m 0.03 0.02

Table 3: IBI EAN related to Temperature for the period April 2013- April 2015. Daily IBI best estimates (hindcast) used in the metric computation. Observational source used as reference: Temperature profiles and time-series from all platforms available in the IBI service domain. Also specific regional EAN in the western Mediterranean, Canary Island, Bay of Biscay and Cadiz regions are shown.



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Salinity (PSU)

IBI Service Domain Cadiz (CADIZ)

RMSD

difference

Mean

difference

RMSD

difference

Mean

difference

FULL 0.13 0.02 0.19 0.02

0-5 m 0.39 0.04 0.24 0

5-200 m 0.15 0.03 0.10 0

200-600 m 0.07 0.01 0.08 0

600-1500 m 0.14 0.03 0.16 0

1500-2000 m 0.07 0.03 0.11 0.02

Canary Island Zone (ICANA) Bay of Biscay (GOBIS)

FULL 0.15 -0.01 0.20 0.04

0-5 m 0.29 -0.09 0.34 0.03

5-200 m 0.10 0.01 0.15 0.04

200-600 m 0.08 0.01 0.03 0

600-1500 m 0.08 0 0.08 0.01

1500-2000 m 0.04 0 0.05 0.01

Western Mediterranean

(WSMED)

FULL 0.11 0

0-5 m 0.53 -0.11

5-200 m 0.20 0

200-600 m 0.04 0

600-1500 m 0.03 0.01

1500-2000 m 0.02 0.01

Table 4: IBI EAN related to Salinity for the period January 2012- December 2016. Daily IBI best

estimates (hindcast) used in the metric computation. Observational source used as reference: Salinity profiles and time-series from all platforms available in the IBI service domain. Also specific regional metrics in the western Mediterranean, Canary Island, Bay of Biscay and Cadiz regions are shown.



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II PRODUCTION SYSTEM DESCRIPTION

Production Center

The IBI MFC NRT ocean forecast service is generated by the IBI-MFC Production Centre. Nologin, in coordination with Puertos del Estado, is responsible of the operational suites to produce, disseminate, and validate the near real time IBI ocean forecast products. The CMEMS IBI MFC Production line is:

CMEMS IBI MFC PU/DU: Puertos del Estado (PdE) IBI-PUERTOS-MADRID-SP (Production Line)

Production System

The CMEMS IBI-MFC V4 NEMO System

The CMEMS IBI-MFC forecast product (IBI_ANALYSIS_FORECAST_PHYS_005_001) composed of five datasets: daily mean fields (including 3D daily means fields of Temperature, Salinity, Zonal Velocity and Meridional Velocity together with daily means of Sea Surface Height, Mix Layer Depth and Sea Bottom Temperature); hourly means of surface and single-level fields (such as sea surface temperature, mix layer depth, surface current, barotropic velocities and sea surface height); also a 3-D hourly dataset (only for forecasts) together with a higher temporal frequency (15-minutes) for sea level and surface current is provided; the product is completed with a monthly mean dataset.

The NEMO model configuration is covering the whole IBI region (including Ireland shelves) at 1/36°with 50 vertical levels, and it is nested in CMEMS GLOBAL 1/12° NEMO System. Main characteristics of the system are summarized in Table 6. A more detailed description of the model system is provided in the CMEMS Product User Manual Document (CMEMS-IBI-PUM-005-001; Sotillo et al. 2018) and in Sotillo et al. 2015. From CMEMS V4 release (April 2018), IBI counts with a data assimilation system and a regional analysis is weekly performed. An aggregation of this IBI analysis solution is kept as IBI best-estimate historic product.

The IBI run is forced every 3 hours with atmospheric fields from the ECMWF. Lateral open boundary data (temperature, salinity, velocities, and sea level) are interpolated from the daily outputs from the CMEMS GLOBAL eddy resolving system at 1/12º. These are complemented by 11 tidal harmonics (M2, S2, N2, K1, O1, Q1, M4, K2, P1, Mf, Mm). The river fresh water discharge inputs in the IBI area was prescribed through 33 point sources corresponding to the main rivers present in the area. An extra coastal runoff rate (derived from climatology; in monthly basis) added to complement the high frequency fresh water forcing associated to the main 33 rivers.

The most outstanding novelty introduced in the IBI-MFC NRT Forecast System at CMEMS V4 (March 2018) consisted on the implementation of a new data assimilation scheme that generates regional IBI analysis on weekly basis, substituting the previous downscaling methodologies used (based on periodic re-initialization



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firstly and on spectral nudging technique methods after CMEMS V2 release). Finally, point out that in the latest CMEMS release of December 2019, the IBI-MFC started the delivery of a new IBI dataset with 15-minutes frequency data for sea level and surface currents. Further details on this latest change can be seen in Section VI.

Domain IBI Iberia-Biscay-Ireland

Geographical coverage (19°W 5°E ; 26°N 56°N)

Code NEMO v3.6

Resolution 1/36° (2 km)

Vertical coordinates

Z* (50 levels)

Free surface Non-linear split explicit

Vertical mixing k- ε (Umlauf and Burchard, 2003)

Tracer horizontal advection

Quickest (Leonard, 1979)

Tides 11 tidal harmonics as open boundary forcing from TPX07.1 (Egbert and Erofeeva, 2002) inverse model.

+ tidal potential body forcing

Short wave radiation penetration

2 bands scheme. Variable climatological PAR absorption depth.

Atmospheric forcing

3 h ECMWF outputs, with a 1/12ᵒ of spatial resolution

Surface atmospheric pressure forcing (surge component) included

Open boundaries Daily output from CMEMS GLOBAL System HR GLOBAL_ANALYSIS_FORECAST_PHYS_001_001_d

Initial conditions Output from CMEMS GLOBAL System HR zoom GLOBAL_ANALYSIS_FORECAST_PHYS_001_001_c

Table 5: Main characteristics of the IBI system at CMEMS V4 Release.



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River inputs 33 rivers (daily flowrates from PREVIMER project (http://www.previmer.org) and SMHI E-HYPE hydrological model (http://e-hypeweb.smhi.se), or monthly climatological flowrates) from GRDC (http://www.bafg.de/GRDC) and French “Banque Hydro” (http://www.hydro.eaufrance.fr/) datasets.

From CMEMS V3, an extra coastal runoff rate climatology (in monthly basis) is used as IBI forcing to complement the high frequency forcing associated to the main 33 rivers. This added fresh water input make the IBI forcing consistent with the ones imposed in the global system (derived from the Dai and Trenberth climatology).

Data Assimilation SAMv2 Data Assimilation scheme: a reduced-order Kalman filter. It is based on the Singular Evolutive Extended Kalman Filter (SEEK) formulation

Assimilated Observations

Altimeter data, in situ temperature and salinity vertical profiles and satellite sea surface temperature are assimilated to estimate the initial conditions for numerical ocean forecasting.

Table 6 (continued): Main characteristics of the IBI system at CMEMS V4 Release.

http://www.previmer.org/

http://e-hypeweb.smhi.se/

http://www.bafg.de/GRDC

http://www.hydro.eaufrance.fr/



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III VALIDATION FRAMEWORK

One of the primary objectives of the CMEMS IBI-MFC Team is to assess the quality of the IBI operational products provided. Evaluating the quality of the IBI oceanographic forecast system operational performances in terms of reliability and accuracy is mandatory for the CMEMS IBI-MFC (and among their main objectives) in order to inform its end-users about the products confidence level. Furthermore, increasing knowledge on the model solution aids to identify areas where potential improvements in the IBI system can be achieved, making evolve it towards more advanced versions thanks to reliable upgrades, and encourage interoperability and collaboration between the scientists focused on the IBI R&D and the operations teams. Therefore, qualification of the IBI system releases and validation of their operational products quality constitute a core activity in the CMEMS IBI-MFC.

Within this general framework, a validation tool named NARVAL (North Atlantic Regional VALidation; Lorente et al. 2012, Lorente et al. 2016a) has been developed along the last MyOcean/CMEMS times to evaluate IBI performance in terms of accuracy, robustness, variability and reliability: 3D comparisons of the main oceanographic variables are carried out on different basis using all the available observational sources together with other model solutions (global / regional / local) working in overlapped areas.

NARVAL has proved to be a powerful tool to provide routinely a variety of objective product quality indicators in an automatic way. This tool generates and organizes valuable information to help the IBI-MFC team to validate IBI model solution and products, increasing knowledge of the system and its model simulations, and allowing detection and understanding of potential sources of discrepancies in IBI predictions. It is also useful to reduce uncertainties and to identify areas where potential improvements in the system can be achieved, helping to make the system evolve towards more sophisticated versions thanks to reliable upgrades.

A dedicated validation website has been created in order to routinely monitor the involved systems accuracy and to disseminate (currently only open to the IBI MFC developers and identified users) results from this comprehensive comparative exercise performed both on daily basis and on delayed mode. Currently, the NARVAL web page (Figure 2 and Figure 3) organizes and displays information and metrics generated by the IBI-MFC Teams.

The automated IBI scientific validation process, routinely performed by NARVAL, consists of two different modes according to their time frequency performance:

1. On-line (“Real-time”) Mode (Figure 2): procedures launched on a daily basis after finishing the daily IBI forecast cycle in order to validate the latest IBI operational forecast bulletin available. 2. Delayed mode (Figure 3): It will include validation procedures launched to compute specific metrics and statistics covering longer periods. Information extracted from IBI product quality analysis is automatically computed on a monthly, quarterly and annual basis.



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The NARVAL On-line Mode performs routinely a product quality assessment to daily monitor quality of CMEMS IBI products with computation of daily updated metrics, and providing statistics (15-days evolution of statistical parameters) and quality indicators organized in 3 blocks:

Validation of IBI system: IBI-VS-Observations

IBI Forecast Consistency assessment: IBI-VS-Climatology

Automated comparisons of the IBI solution with other model products (provided by other CMEMS MFCs or by other CMEMS-external systems) in overlapped regions.

The “real-time” Validation Mode is daily performed on-line with the following objectives:

To check consistency of IBI products of the day (as soon as they are generated).

To verify quality of the IBI best estimate against available observations from the previous day Note that IBI System has no data assimilation, therefore, the here so-called IBI best estimate consists on the ocean state obtained from a model run that uses as forcing atmospheric analysis. These best IBI estimates are compared against independent measures (since there is no IBI data assimilation, any observational source represents an independent category to compare with).



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Figure 2: Partial snapshot of NARVAL Near-Real-Time Online-Mode Website.

The main objectives of the Delayed Mode Validation Component (Figure 3) are:

To provide a global review of the IBI system performance for longer time periods. The information on longer time scales (i.e.: monthly, seasonal, or annual) automatically generated is the base of user-oriented IBI bulletins and internal IBI-PU-oriented long-term system logs.

This delay mode validation processes allow us also to compute specific metrics or diagnostics focused on particular physical processes that needs observational data which are available only in delayed mode. Also, automated comparisons of IBI solution with other model systems are performed.

o IBI is compared with “parent” CMEMS GLOBAL System, other CMEMS model solutions over overlapping regions (i.e. MED & NWS), or other non-CMEMS high resolution local systems nested into IBI.



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Figure 3: Partial snapshot of NARVAL delayed-mode website.

Table 7 summarizes the metrics performed that use observations or products derived from observations (i.e. climatologies, optimal interpolated products, etc.). Likewise, Table 8 summarizes the metrics used to check the consistency with other model systems (i.e. the “parent” CMEMS GLOBAL System, and the regional CMEMS NWS and MED solutions on overlapping areas).



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Metric Name Ocean parameter Supporting reference dataset

T-CLASS1-SST_L4-MEAN

T-CLASS1-SST_L4-RMSE

T-CLASS1-SST_L4-CORR

T-CLASS1-SST_L4-VARI

Sea Surface Temperature (SST)

CMEMS product:

SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001

T-CLASS1-SST_L3-MEAN

T-CLASS1-SST_L3-RMSE

T-CLASS1-SST_L3-CORR

T-CLASS1-SST_L3-VARI


CMEMS product:

SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010

T-CLASS2-SST_MOORINGS-MEAN T-CLASS2-SSS_MOORINGS-RMSE T-CLASS2-SST_MOORINGS-CORR


Moorings INSITU-TAC, CMEMS product:

INSITU_IBI_TS_REP_OBSERVATIONS_013_040

T-CLASS1-T3D_WOA-MEAN 3-D Temperature Climatology WOA 2009

T-CLASS4-ARGO-MEAN

T-CLASS4-ARGO-RMSE

T-CLASS4-ARGO-CORR

Temperature profiles CMEMS product:


S-CLASS1-SSS_SMOS-MEAN S-CLASS1-SSS_SMOS-RMSE

Sea Surface Salinity (SSS)

SMOS satellite-derived product

S-CLASS2-SSS_MOORINGS-MEAN

S-CLASS2-SSS_MOORINGS-RMSE

S-CLASS2-SSS_MOORINGS-CORR




S-CLASS1-S3D_WOA-MEAN 3-D Salinity Climatology WOA 2009

S-CLASS4-ARGO-MEAN

S-CLASS4-ARGO-RMSE

S-CLASS4-ARGO-CORR

Salinity profiles CMEMS product:


UV-CLASS1-SSC_HFR-MEAN UV-CLASS1-SSC_HFR-RMSE UV-CLASS1-SSC_HFR-CORR

Sea Surface Currents (SSC). Zonal /

Meridional velocity

High Frequency (HF) CODAR SeaSonde radars

UV-CLASS2-SSC_MOORINGS-MEAN

UV-CLASS2-SSC_MOORINGS-RMSE

UV-CLASS2-SSC_MOORINGS-CORR

Sea Surface Currents (SSC). Zonal /

meridional velocity



SL-CLASS2-SSH_TIDE_GAUGE-MEAN

SL-CLASS2-SSH_TIDE_GAUGE-RMSE

SL-CLASS2-SSH_TIDE_GAUGE-CORR

Sea Level Moorings INSITU-TAC, CMEMS product:


Table 7: IBI metrics performed and observational data sources used.



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Names Ocean parameter Supporting reference dataset

SST-D-CLASS1-MOD_GLO-MEAN

SST-D-CLASS1-MOD_GLO-RMSD

SST-D-CLASS1-MOD_GLO-CORR


CMEMS product: GLOBAL ocean forecasting system

GLOBAL_ANALYSIS_FORECAST_PHYS_001_002

SST-D-CLASS1-MOD_NWS-MEAN

SST-D-CLASS1-MOD_NWS-RMSD

SST-D-CLASS1-MOD_NWS-CORR


CMEMS product: North-West-Shelf ocean forecasting system

NORTHWESTSHELF_ANALYSIS_FORECAST_PHYS_004_001_b

SST-D-CLASS1-MOD_MED-MEAN

SST-D-CLASS1-MOD_MED-RMSD

SST-D-CLASS1-MOD_MED-CORR


CMEMS product: MEDSEA ocean forecasting system

MEDSEA_ANALYSIS_FORECAST_PHYS_006_001_a

SSS-D-CLASS1-MOD_GLO-MEAN

SSS-D-CLASS1-MOD_GLO-RMSD

SSS-D-CLASS1-MOD_GLO-CORR




SSS-D-CLASS1-MOD_NWS-MEAN

SSS-D-CLASS1-MOD_NSW-RMSD

SSS-D-CLASS1-MOD_NSW-CORR




SSS-D--CLASS1-MOD_MED-MEAN

SSS-D-CLASS1-MOD_MED-RMSD

SSS-D-CLASS1-MOD_MED-CORR




UV-SURF-D-CLASS1-MOD_GLO-MEAN

UV-SURF-D-CLASS1-MOD_GLO-RMSD

UV-SURF-D-CLASS1-MOD_GLO-CORR

Sea Surface Currents Zonal /

meridional velocity



UV-SURF-D-CLASS1-MOD_NWS-MEAN

UV-SURF-D-CLASS1-MOD_NWS-RMSD

UV-SURF-D-CLASS1-MOD_NWS-CORR


meridional velocity



UV-SURF-D-CLASS1-MOD_MED-MEAN

UV-SURF-D-CLASS1-MOD_MED-RMSD

UV-SURF-D-CLASS1-MOD_MED-CORR


meridional velocity



UV-15m-D-CLASS2-BUOY-MEAN Zonal / meridional

velocity CMEMS product:

INSITU_GLO_NRT_OBSERVATIONS_013_030

Table 8: Metrics performed with other CMEMS Forecast Systems for consistency check.



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A conceptual scheme of the operational validation performed through NARVAL, both on Near-real-time and delayed modes, is depicted in Figure 4. In addition, a summary of availability of each IBI validation metric performed by means of the NARVAL tool through its delayed Mode during the entire IBI-MFC Service period (April 2011 – December 2017) is presented in Figure 5.

Erreur ! Signet non défini.

Figure 4: Validation of IBI products. Comparisons of IBI solution with observations and other model solutions performed by NARVAL on online (daily basis) and delayed mode (on a monthly, quarterly and annual basis). List of Acronyms used: SST (Sea Surface Temperature), SSS (Sea Surface Temperature), SSC (Sea Surface currents), SSH (Sea Surface Height), TMP (Temperature), SAL (salinity), NWS (North-West Shelf) and MED (Mediterranean).



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Figure 5: Availability of IBI validation metrics (performed with NARVAL through its Delayed Mode) during the entire IBI-MFC service (April 2011 – December 2016). All metrics performed through NARVAL -Delayed Mode- for sea surface temperature (SST, red lines), sea surface currents (SSC, blue lines) and temperature/salinity profiles form Argo-floats (green line). SST Satelite products used: MyO/CMEMS L3 and L4 (OSTIA) together with the L4 Odyssea Product from MeteoFrance (L4-MF). High Frequency Radars (HFR) related to the surface currents (enumerated according to the place of deployment: Galicia, Gibraltar Strait, Ebro Delta and Huelva-Algarve, 1 to 4 respectively). ARGO data from the CMEMS product: INSITU_IBI_NRT_OBSERVATIONS_013_033

The IBI validation metrics included in the CMEMS Quarterly Validation Reports are also automatized and computed by means of NARVAL tool, included in its Delayed Mode. In these CMEMS reports users can display the following IBI metrics:

IBI-Vs-CMEMS L3 SST Product (metrics for the whole IBI service domain and for different sub-regions)

IBI-Vs-CMEMS L4 OSTIA SST Product (metrics for different regions and for different horizons: HC, FC+3d and FC+5d)

All these quarterly validation metrics can be displayed through the CMEMS Quarterly Validation Web Page (freely accessible at http://marine.copernicus.eu/web/103-validation-statistics.php). This web page compiles very useful information on quality level of the different CMEMS products and has been steadily evolving to offer new valuable features, such as zooming capability, choice of type of figures through dedicated buttons, etc. Figure 6 shows an example of the IBI metrics displayed through this CMEMS Quarterly Validation Webpage.

http://marine.copernicus.eu/web/103-validation-statistics.php

https://correo.puertos.es/owa/redir.aspx?SURL=cW--wWxtI45dZlMKQJ2wKMJDriCdf2NTuOPo2pEV0e3lJHgdqxrTCGgAdAB0AHAAOgAvAC8AbQBhAHIAaQBuAGUALgBjAG8AcABlAHIAbgBpAGMAdQBzAC4AZQB1AC8AdwBlAGIALwAxADAAMwAtAHYAYQBsAGkAZABhAHQAaQBvAG4ALQBzAHQAYQB0AGkAcwB0AGkAYwBzAC4AcABoAHAA&URL=http%3A%2F%2Fmarine.copernicus.eu%2Fweb%2F103-validation-statistics.php



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Figure 6: Example of IBI validation metrics computed by NARVAL for further dissemination to users through the CMEMS Quarterly Validation reports. SST RMSD computed by region and for 3 different IBI forecast horizons (analysis -HC01-, +60 hours – FC03- and +108 hours – FC05-).

An additional characteristic of the CMEMS Quarterly Validation website is the visual inspection of the metrics evolution for the study-period, focused on the full IBI domain (as shown below in Figure 7) or specific sub-regions of interest.



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Figure 7: Example of time evolution of IBI validation metrics for a period comprised between January 2013 and August 2016. RMSD/Bias (upper panel) and correlation (lower panel) computed for the full IBI Service Domain are depicted for three different IBI forecast horizons (analysis -HC01-, +60hs – FC03- and +108h –FC05-).

Finally, a variety of validation exercises have been conducted through NARVAL and also ‘offline’ in order to showcase the multi-parameter skill assessment of IBI. For instance, an investigation focused on a specific subdomain (NW Mediterranean) where several observational networks (encompassing both in situ and remote sensing instruments) are available has been addressed in Lorente et al. (2016-a). In particular, class-1 and class-2 skill metrics have been used in concert to quantitatively evaluate the quality of hourly IBI surface fields. Equally, the ability of IBI to detect the presence of coastal eddies is also qualitatively estimated by calculating surface vorticity maps.



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Complementarily, quality-controlled High Frequency radar (HFR) surface current data have been used as benchmark for a rigorous validation of the IBI regional system in the Ebro River Delta (Lorente et al., 2016-b). The analysis of skill metrics and monthly averaged current maps showed that IBI reasonably captured the prevailing dynamic features of the coastal circulation previously observed by the HFR, according to the moderate resemblance found in circulation patterns and the spatial distribution of eddy kinetic energy. The model skill assessment was completed with an exploration of dominant modes of spatiotemporal variability (Empirical Orthogonal Function analysis).

Among the ‘offline’ validation exercises, Aznar et al. (2016) was aimed at intercomparing the ocean physical daily forecast and 10-year (2002–2012) reanalysis products provided by the IBI-MFC over an overlapping 9-month period (April–December 2011). The comparison emphasized the possible benefits of the data assimilation scheme in areas away from the coastline, but also its limitations in complex coastal regions. Spatial resolution seemed to play a key role in such areas, especially around the Iberian Peninsula, where the higher resolution forecast brings in general better results than the coarser resolution reanalysis. The study suggests that the observational data assimilation represents a crucial step towards improving the performance of regional modelled solutions, as long as the spatial resolution is kept at fine-enough meshes in order to prevent higher uncertainties in coastal and shelf areas.

Finally, it is worth to mention how IBI MFC ocean products are being validated in the context of other Research Project and model intercomparison exercises, outside of the CMEMS framework. Among others, it is mentioned here the exhaustive validation of IBI MFC products performed within the frame of the MEDESS-4MS (MEditerranean DEcision Support System for Marine Safety) project. The IBI forecast product has been extensively validated in the Gibraltar Strait and the Alboran Seas, by means of using in-situ measurements from the MEDESS-GIB drifter buoy database (comprising Lagrangian positions, derived velocities and sea surface temperature) for a 3-month period (September-December 2014. The analysis revealed some strengths of IBI such as the realistic values of the Atlantic Jet and the reliable simulation of the Algerian current (Sotillo et al. (2016)). In terms of models intercomparison, the capabilities of diverse operational ocean forecasting systems running in the Balearic sub-basin (NW Mediterranean) have been recently evaluated by Capó et al. (2016). In this study, performed also within the frame of the MEDESS-4MS, the lagrangian skill assessment of IBI and other models has been achieved by using observations from ships. Results indicated the importance of reproducing the sub-mesoscale structures in the Balearic Sea in order to develop accurate tracking systems.



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IV VALIDATION RESULTS

This Section shows relevant diagnostics, based on the metrics detailed in the previous section, and provide a synthesis of the product quality for the different IBI variables. Some of the product quality metrics have been computed from the specific works done for the pre-operational V4 IBI qualification phase (results shown in Section IV.2), whereas some others come from the operational NRT IBI Validation and are shown in Section IV.1.

Both phases (the pre-operational V4 Qualification and the operational NRT Validation) contribute to the scientific assessment of the IBI products, allowing the IBI MFC to have a better knowledge and an objective evaluation of the quality and accuracy of the IBI ocean forecast products.

IV.1 NRT Operational Validation

This section provides a comprehensive view of the IBI performance quality through validation metrics computed using the IBI operational products delivered along the last service year (the 2017 year). Figure 8 shows a summary of the availability of current existing metrics computed on a monthly basis through this NARVAL Delayed Mode Module for the entire 2017. The observational data used by the NARVAL –Delayed Mode- along this 2017 year encompass the following sources: for SST, CMEMS L3 and L4 (OSTIA) satellite-derived products have been used; for temperature and salinity profiles, ARGO floats quality-controlled record (CMEMS product) have been used; for surface currents, a high-frequency (HF) coastal radar network along the Iberian Peninsula coast (composed by four different systems and operated by PdE) has been employed.



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Figure 8: Availability of IBI validation metrics in 2016. All metrics performed through NARVAL -Delayed Mode- for sea surface temperature (SST, red lines), sea surface currents (SSC, blue lines) and temperature/salinity profiles form Argo-floats (green line). SST Satellite-derived products used: CMEMS L3 and L4 (OSTIA). High Frequency Radars (HFR) related to the surface currents (enumerated according to the place of deployment: Galicia, Gibraltar Strait, Ebro Delta and Huelva-Algarve, 1 to 4 respectively).

As example of the metrics computed, some information from the IBI performance validation review is provided below. All the information provided in the following examples has been directly extracted from the NARVAL Validation Website.

IV.1.1 Sea Surface Temperature

The following figures illustrates the kind of metrics (class1-type) implemented to validate the IBI SST field with the CMEMS L3 satellite-derived daily products. Metrics are computed for the entire year 2016. The IBI operational products delivered and used in the validation of the year 2016 correspond to the IBI-V4 version (implemented in MyOcean2 and active until the new CMEMS V3 release). It is worth mentioning that L3 satellite SST product provides maps derived from the original along-track data from satellite tracks, where the spatial gaps have not been spatially interpolated. That means that the class-1 metric maps presented below should be interpreted according to the number of available data for each grid point of the study-domain.



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Focusing on yearly metrics and leaving aside some local differences, Figure 9: CLASS1 metrics of SST for year 2017: Mean field derived from daily IBI products (upper left panel) and from daily CMEMS L3 satellite product (upper central panel). The temporal availability of L3 data (in days) for 2017 is depicted in the upper right panel. Spatial distribution of SST bias (middle left panel), RMSD (middle central panel), correlation index (middle right panel), variance of IBI (lower left panel) and L3 (lower central panel) computed from the daily values of IBI best estimates and L3 satellite data over 1-year period (2017).

shows the overall good agreement existing between the IBI model data and the satellite observations, in terms not only of mean, but also of variability (represented by the variance). Maps of bias, root mean squared errors (RMSD), and temporal correlation coefficient are also depicted in order to infer the areas where the model differs more from the observations. As it can be seen, the main discrepancies are in the Strait of Gibraltar and the Alboran Sea, two challenging areas from a modelling perspective.



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Figure 9: CLASS1 metrics of SST for year 2017: Mean field derived from daily IBI products (upper left panel) and from daily CMEMS L3 satellite product (upper central panel). The temporal availability of L3 data (in days) for 2017 is depicted in the upper right panel. Spatial distribution of SST bias (middle left panel), RMSD (middle central panel), correlation index (middle right panel), variance of IBI (lower left panel) and L3 (lower central panel) computed from the daily values of IBI best estimates and L3 satellite data over 1-year period (2017).



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The Figure 10 Erreur ! Source du renvoi introuvable.displays the daily evolution of the statistical metrics, computed over the entire IBI domain, along the year 2017, whereas the Figure 11

Figure 11illustrates the number of L3 satellite-derived data available for each day of 2017 that have been considered to perform the validation. It can be noted that during the 2017 summer months (particularly in July and August) seems to occur an increase of the differences between the IBI model solution and the observational data, leading to higher RMSD values and slightly lower correlations.



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Figure 10: Daily evolution of L3 satellite-derived (blue line) and IBI simulated (red line) sea surface temperature (SST), averaged for the entire IBI service (IBISR) domain for the entire year 2017 (upper left panel). Time series of SST Bias (upper right panel), RMSD (lower left panel) and spatial correlation index (lower right panel) computed from daily IBI best estimates and L3 satellite data over the entire IBISR domain are also shown.



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Figure 11: 2017 daily evolution of the number of L3 data available within IBI domain to compute the validation metrics through NARVAL.

Time period IBI mean L3 mean Bias RMSD Spatial Corr N

ANNUAL 18.28 18.13 0.15 0.73 0.97 365

DJF 15.82 15.85 -0.04 0.48 0.99 91

MAM 16.15 15.90 0.25 0.59 0.99 90

JJA 20.85 20.41 0.44 0.88 0.96 91

SON 20.08 20.09 -0.02 0.76 0.97 91

JAN 16.62 15.69 -0.07 0.47 0.99 31

FEB 15.22 15.18 0.04 0.46 0.99 28

MAR 15.04 14.92 0.12 0.49 0.99 31

ABR 15.93 15.60 0.33 0.65 0.99 30

MAY 17.47 17.16 0.31 0.64 0.99 31



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JUN 19.71 19.20 0.52 0.89 0.97 30

JUL 21.23 20.77 0.46 0.91 0.96 31

AGO 21.56 21.33 0.33 0.84 0.96 31

SEP 21.11 21.04 0.07 0.80 0.96 30

OC 20.41 20.41 -0.01 0.73 0.97 31

NOV 18.71 18.84 -0.13 0.75 0.97 30

DEC 17.13 17.36 -0.23 0.69 0.98 31

Table 9 displays the averaged metrics computed over the whole IBI service domain for the year 2017 on an annual, seasonal, and monthly basis. IBI performance appears to be rather consistent, especially during winter and spring seasons when lower (higher) RMSD (spatial correlation) are obtained. In order to gain insight into the long-term skill assessment of IBI, Figure 12 shows a summary of quality indicators for the period October 2012-2017, firstly focused on the entire IBI service are (Figure 12, a-c), secondly focused on a number of sub-regions (Figure 12, d-f) and eventually focused on the Strait of Gibraltar and the Irish Sea (Figure 12, g-i). According to the results, IBI performance seems to be consistent in terms of RMSD and correlation evolution, not only for the entire domain but also for the rest of sub-regions. Some improvements have been detected in the Strait of Gibraltar in terms of lower RMSD monthly values during 2016-2017 (Figure 12 e, h), proving the added value of the spectral nudging methodology implemented operationally since the CMEMS V2 release. This behaviour has been also observed in the Irish Sea (Figure 12-i).

Time period IBI mean L3 mean Bias RMSD Spatial Corr N

ANNUAL 18.28 18.13 0.15 0.73 0.97 365

DJF 15.82 15.85 -0.04 0.48 0.99 91

MAM 16.15 15.90 0.25 0.59 0.99 90

JJA 20.85 20.41 0.44 0.88 0.96 91

SON 20.08 20.09 -0.02 0.76 0.97 91

JAN 16.62 15.69 -0.07 0.47 0.99 31

FEB 15.22 15.18 0.04 0.46 0.99 28

MAR 15.04 14.92 0.12 0.49 0.99 31



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ABR 15.93 15.60 0.33 0.65 0.99 30

MAY 17.47 17.16 0.31 0.64 0.99 31

JUN 19.71 19.20 0.52 0.89 0.97 30

JUL 21.23 20.77 0.46 0.91 0.96 31

AGO 21.56 21.33 0.33 0.84 0.96 31

SEP 21.11 21.04 0.07 0.80 0.96 30

OC 20.41 20.41 -0.01 0.73 0.97 31

NOV 18.71 18.84 -0.13 0.75 0.97 30

DEC 17.13 17.36 -0.23 0.69 0.98 31

Table 9: Statistical metrics derived from the comparison of SST provided by IBI and L3 satellite-derived product. Comparisons of SST fields were performed for the entire year 2017, averaged values computed for the IBI service domain, on a monthly, seasonal and annual basis.



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Figure 12: Long-term (2012-2017) skill assessment of sea surface temperature. Monthly metrics, averaged over the entire IBI service (IBISR) area (a) and specific sub-regions (d), are derived from the comparison of IBI against L3 satellite-derived product.

The following metrics are derived from the validation of IBI SST with an L4 satellite-derived product (OSTIA) on an annual basis (2017, Erreur ! Source du renvoi introuvable.). The first panel row depicts the close resemblance of the yearly-averaged maps of sea surface temperature (SST) provided by IBI (left) and L4 (right). The second panel row provides, from left to right, the bias, RMSD and temporal correlation. As it can be seen, the main discrepancies are located in limited coastal areas (such as the upwelling regions in western coasts of Spain and Morocco) and also in the Strait of Gibraltar, which constitutes a challenging area from a modelling perspective as a result of its complex ocean dynamics and the non-linear interactions between the Atlantic and Mediterranean waters. Finally, the third panel row shows the variability of both sources and the associated differences, which are by far found in the Mediterranean Sea. Broadly speaking, IBI exhibit a higher variability over the Western Mediterranean basin and also in coastal areas of Spain.



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Figure 13: CLASS1 metrics of SST for summer 2017. Mean field derived from daily IBI outputs (upper left panel) and from L4 OSTIA satellite daily data (upper right panel). Spatial distribution of SST bias (middle left panel), RMSD (middle central panel), time correlation index (middle right panel), variance of IBI (lower left panel), L4 (lower central panel) and the associated differences (lower right panel) computed from the daily values of IBI best estimates and OSTIA L4 satellite data over this 3-month period (JJA-2017).



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Daily metrics averaged over the entire IBI domain but computed from three distinct IBI forecast horizon data are depicted for the entire year 2017 in Figure 14Figure 14: Daily evolution of statistical metrics derived from IBI-L4 SST comparisons for the year 2017. The Bias (upper left panel), RMSD (upper right panel) and spatial correlation index (lower left panel) were computed from daily IBI best estimates or hindcast (HC01, red line) and two different forecast horizons (FC03 and FC05, in green and blue lines, respectively) and L4 satellite data, averaged over the entire IBI service (IBISR) domain.

with the aim of checking out the consistency of IBI solution and to evaluate the IBI skill and the potential loss of precision for longer time horizons. According to the metrics displayed, IBI system skill is generally kept in similar levels along the 5 days of the forecast run (using values averaged over the whole IBI service domain).



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Figure 14: Daily evolution of statistical metrics derived from IBI-L4 SST comparisons for the year 2017. The Bias (upper left panel), RMSD (upper right panel) and spatial correlation index (lower left panel) were computed from daily IBI best estimates or hindcast (HC01, red line) and two different forecast horizons (FC03 and FC05, in green and blue lines, respectively) and L4 satellite data, averaged over the entire IBI service (IBISR) domain.

Both the overall and quarterly statistical metrics corresponding to the year 2017 are presented in Table 10. As it can be seen, IBI solution slightly overestimates the mean L4 satellite derived SST. There is a slight increase in the accuracy of IBI performance during winter and spring seasons (as reflected by lower RMSD values).

Time period IBI mean SAT mean Bias RMSD Spatial Corr N

Annual 16.98 16.83 0.15 0.57 0.99 365

DJF 14.68 14.64 0.04 0.39 0.99 91

MAM 14.99 14.80 0.18 0.45 0.99 90

JJA 19.55 19.16 0.39 0.76 0.98 91

SON 18.70 18.66 0.04 0.61 0.99 91

Table 10: Statistical metrics derived from IBI-L4 SST comparison for summer (JJA) 2016. The values have been averaged for the entire IBI Service domain.

The following figures display as an example IBI-L4 SST metrics computed for winter (defined as D-J-F) conditions in 2017. Figure 15 shows the statistical maps derived from the comparison of the sea surface temperature provided by IBI solution and L4 OSTIA product. Complementarily, since special emphasis has been placed on the regional IBI performance evaluation, the IBI metrics have been computed over specific sub-regions of particular concern in order to evaluate uncertainty levels and delimit areas where discrepancies are mainly located. The statistical time series presented in Figure 16 illustrate the skill of IBI to predict the winter SST over different IBI areas. As expected, some regional discrepancies arise during the wintertime study-period. At first glance, the Northern Iberian Shelf area (NIBSH, dashed black line) seems the coastal region where IBI’s accuracy is not so high for 2017 winter season, according to the evolution of the spatial correlation averaged over that specific area. Notwithstanding, there are other sub-regions like the Western Mediterranean (WSMED, orange line) or the Canary Islands (ICANA, light green line) where IBI performance is rather consistent in terms of low RMSD and bias along with high correlation coefficient values. The metrics, representative of the 3-month period corresponding to the wintertime and gathered in Table 11: Statistical metrics derived from IBI-L4 SST comparison for winter (Dec’16 - J’17 – F’17). The values have been averaged for specific sub-regions (see Figure 1) within the IBI service (IBISR, in red) domain.



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A more detailed evaluation of IBI performance in two specific sub-regions (the Irish Sea and the Strait of Gibraltar, referred respectively as IRISH and GIBSTR hereinafter) is presented as opposing examples (Figure 17and Figure 18). As it can be derived from the daily evolution of the spatial correlation index, IBI reproduces more accurately the SST in the former region during January and February (the correlation remains rather constant above 0.9) whereas in the latter the IBI performance is not so good since the correlation coefficient regularly fluctuates between 0.8 and 0.9, especially by the end of the wintertime study-period. Regarding the different forecast horizons analysed, no significant discrepancies can be found in both examples in terms of the consistency and temporal evolution of the statistical metrics.

, confirm the previous statements.



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Figure 15: CLASS1 metrics of SST for winter 2017. Mean field derived from daily IBI outputs (upper left panel) and from L4 OSTIA satellite daily data (upper right panel). Spatial distribution of SST bias (middle left panel), RMSD (middle central panel), time correlation index (middle right panel), variance of IBI (lower left panel) and satellite L4 product (lower central panel) together with the associated differences (lower right panel) computed from the daily values of IBI best estimates and the L4 satellite data over this 3-month period (DJF).



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Figure 16: Daily evolution of statistical metrics derived from IBI-L4 SST comparison for winter (December 2016 - February 2017). The Bias (upper left panel), RMSD (upper right panel) and spatial correlation index (lower left panel) computed from daily IBI best estimates or hindcast and L4 satellite data over the entire IBI service (IBISR) domain (red line) and other nine additional sub-regions (marked with different colours) and previously presented in Figure 1.



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Area IBI mean SAT mean Bias RMSD Spatial Corr N

IBISR 14.68 14.64 0.04 0.39 0.99 91

ICANA 19.27 19.15 0.12 0.34 0.91 91

CADIZ 17.25 17.34 -0.09 0.42 0.80 91

ECHAN 9.14 9.52 -0.37 0.61 0.97 91

IRISH 10.09 10.16 -0.06 0.63 0.92 91

NIBSH (--) 13.53 13.54 -0.01 0.29 0.77 91

WIBSH 15.37 15.32 0.05 0.35 0.95 91

WSMED 15.58 15.65 -0.07 0.40 0.90 91

GOBIS 12.33 12.30 0.03 0.39 0.95 91

GIBST 16.35 16.42 -0.07 0.38 0.88 91

Table 11: Statistical metrics derived from IBI-L4 SST comparison for winter (Dec’16 - J’17 – F’17). The values have been averaged for specific sub-regions (see Figure 1) within the IBI service (IBISR, in red) domain.

A more detailed evaluation of IBI performance in two specific sub-regions (the Irish Sea and the Strait of Gibraltar, referred respectively as IRISH and GIBSTR hereinafter) is presented as opposing examples (Figure 17and Figure 18). As it can be derived from the daily evolution of the spatial correlation index, IBI reproduces more accurately the SST in the former region during January and February (the correlation remains rather constant above 0.9) whereas in the latter the IBI performance is not so good since the correlation coefficient regularly fluctuates between 0.8 and 0.9, especially by the end of the wintertime study-period. Regarding the different forecast horizons analysed, no significant discrepancies can be found in both examples in terms of the consistency and temporal evolution of the statistical metrics.



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Figure 17: Daily evolution of statistical metrics derived from IBI-L4 SST comparison for winter 2016-2017 (DJF) in the Irish Sea (IRISH) region. The Bias (upper left panel), RMSD (upper right panel) and spatial correlation index (lower left panel) computed from daily IBI best estimates or hindcast (HC01, red line) and two different forecast horizons (FC03 and FC05, in green and blue lines, respectively) and L4 satellite data, specifically averaged over the IRISH region.



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Figure 18: Daily evolution of statistical metrics derived from IBI-L4 SST comparison for winter 2016-2017 (DJF) in the Gibraltar Strait (GIBSTR) Region. The Bias (upper left panel), RMSD (upper right panel) and spatial correlation index (lower left panel) computed from daily IBI best estimates or hindcast (HC01, red line) and two different forecast horizons (FC03 and FC05, in green and blue lines, respectively) and L4 satellite data, specifically averaged over the Strait of Gibraltar (GIBSTR) region.

In order to provide a deeper insight into IBI model performance during 2017, a qualitative model-observation comparison was performed. In particular, Hovmöller diagrams were computed for selected transects of constant latitude and longitude with the main aim of analysing the temporal evolution of the daily sea surface temperature in key regions like the Strait of Gibraltar or the Galician upwelling system (Figure 19 and Figure 20). As it can be observed for each transect, the diagrams look rather alike and IBI appears to properly capture basic features like the annual cycle (Figure 19, a-c) or the African and Galician upwelling systems where sudden cooling events take place when northerly winds blow during summertime



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(Figure 19, d-f). Such wind regime moves surface waters away from the coast (also called Ekman transport), which are replaced by cooler water that wells up from below.

Figure 19: Transects of constant longitude within IBI domain (a, d, g) and the associated Hovmöller diagrams where the daily evolution of sea surface temperature during the entire 2017 is depicted for both the L4 satellite-derived observations and IBI forecast system.

A relevant model-observation resemblance is observed in the Strait of Gibraltar (6ᵒW-5ᵒW), where a quasi-permanent intrusion of Atlantic cool waters are satisfactorily reproduced by IBI along the entire year 2017



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(Figure 20, a-c). In the Alboran Sea (3ᵒW-0ᵒW), the SST resemblance is also noticeable, although IBI appear to slightly overestimate the daily values of temperature. Equally, the model seems to accurately replicate basic characteristics of the NW Iberian upwelling system such as the cooling of sea surface waters during specific summer time coastal upwelling events when northerly winds are predominant (Figure 20, d-f). Finally, IBI performance is also in accordance with remote-sensed estimations of SST in the French continental shelf (Atlantic facade), showing a number of well-documented upwelling events during summertime (J-J-A) and early autumn (September-October) in coastal areas (Figure 20, g-i).

Figure 20: Transects of constant latitude within IBI domain (a, d, g) and the associated Hovmöller diagrams where the daily evolution of sea surface temperature during the entire 2017 is depicted for both the L4 satellite-derived observations and IBI forecast system.



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Complementarily, a number of examples of IBI validation with in-situ hourly SST data, observed at different moorings, are presented below. CLASS-2 metrics are defined in order to monitor the IBI system output quality. Class-2 metrics are quite useful since aims to concentrate at particular locations (virtual moorings within the IBI spatial domain), usually with a finer resolution than the Class-1 metrics approach. To this purpose, a new module of NARVAL has been implemented to compare IBI solution with all observed data available from the mooring buoys existing in the IBI area, provided by the CMEMS IBI INSITU-TAC (filled purple -green- squares in the map shown in Figure 21 represent mooring buoy -tide gauges- location). The CLASS2 metrics are quite interesting since they provide the possibility of validating IBI using quality-controlled SST hourly estimations.

A specific case is presented as example for a buoy moored in the Irish Sea, which reveals the close resemblance of both IBI solution and in-situ measurements for the entire 2017 (Figure 21-a). IBI correctly reproduces the annual cycle and the high-frequency fluctuations, especially during the central part of the year 2017. As reflected by the overall metrics attached below, IBI solution slightly overestimates the annual mean SST and it is also significantly correlated (0.96) with independent in-situ records.

An additional example focused on the Mediterranean Sea is presented for autumn 2017 in Figure 21-bErreur ! Source du renvoi introuvable.. In this seasonal study-case, IBI solution (red line) seems to slightly underestimate the SST during the first days of September but rapidly correct their evolution until matching the observations (blue scatter). A close data-model agreement is exhibited for the rest of the autumn (as highlighted by the metrics presented, with RMSD and correlation around 0.49ᵒC and 0.97 respectively), with SST values steadily decreasing despite of high-frequency variations. Likewise, Figure 21-c illustrates the agreement between IBI SST prediction (red line) and observations (blue scatter) from a buoy moored close to the SE coast of the Iberian Peninsula during summer 2017, as indicated by the significantly high correlation coefficient (0.95). Indeed, IBI was able to replicate the sharp drop in SST (about 5ᵒC) that took place during early August.



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Figure 21: (a) Annual (2017) validation of IBI SST solution (red line) against in-situ hourly measurements from a buoy moored in the Irish Sea (red circle); (b) Quarterly validation (autumn) in the NE of the Iberian Peninsula; (c) Quarterly validation (summer) in the SE of the Iberian Peninsula. CLASS-2 metrics are gathered in grey boxes, revealing the high data-model concordance.



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Special emphasis has been also placed on the comparison of IBI with its parent system: the CMEMS GLOBAL system. Metrics comparing both system versus same observational data reference sources are periodically (i.e. on a monthly, seasonal and yearly basis) computed. Here we present a validation example of SST for the month of October 2017, using as reference the SST field from the CMEMS L4 OSTIA SST product (Figure 22 and Figure 23). As it can be derived from the CLASS1 maps, both forecasting systems behave similarly, although IBI seems to outperform GLOBAL solution on specific coastal areas (in terms of lower bias and RMSD), like the Strait of Gibraltar, the Galician region or the Irish Sea (Figure 23). By contrast, in other sub-regions GLOBAL (such as the African coastal upwelling system or the Alboran Sea) appears to performance slightly better. This fact is expectable since the GLOBAL system assimilates a variety of observations, among others, those provided by ARGO floats and the satellite-derived SST.

Figure 22: CLASS1 metrics of SST for October 2017. Mean field derived from daily IBI outputs (upper left panel), GLOBAL (upper central panel) and from L4-OSTIA satellite data (upper right panel). Spatial distribution of bias (lower panel) was computed from the daily values of IBI and GLOBAL best estimates and L4 satellite data over this 1-month period.



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Figure 23: CLASS1 metrics of SST for October 2017. Spatial distribution of SST RMSD (upper panel) and time correlation index (lower panel) were computed from the daily values of IBI and GLOBAL best estimates and L4-OSTIA satellite data over this 1-month period.

Figure 24 shows the temporal evolution of CLASS1 metrics (averaged over specific sub-regions within IBI domain) for October 2017. IBI seems to better perform in some areas such as the Strait of Gibraltar (GIBST, cyan line) or the North Iberian Shelf (NIBSH, dashed black line) according to the lower (higher) RMSD (correlation) values obtained. By contrast, GLOBAL outperforms IBI simulation in other regions like the Western Mediterranean (WSMED, orange line). The previous statements are confirmed by the monthly-averaged metrics presented in Figure 25, where a more detailed evaluation of the strengths and weaknesses of both foresting systems (for the specific month of October 2017) is presented.



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Figure 24: Daily evolution of statistical metrics derived from IBI-L4 (left column) and GLOBAL-L4 (right column) SST comparisons for October 2017. The Bias (upper panel), RMSD (middel panel) and spatial correlation index (lower panel) computed from daily IBI and GLOBAL best estimates (or hindcast) and L4 OSTIA satellite data over the entire IBI service (IBISR) domain (red line) and other nine additional sub-regions (marked with different colours) and previously presented in Figure 1.



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Figure 25: Statistical metrics derived from IBI-L4 and GLOBAL-L4 SST comparisons for October 2017. The values have been averaged for specific sub-regions (see Figure 1) within the IBI service (IBISR, in red) domain.

As a brief example, Figure 26 presents a specific case of CLASS-2 metrics computed for the SST registered in the aforementioned Gascogne buoy and used as reference field to benchmark both CMEMS IBI and GLOBAL model solutions. As it can be seen, the resemblance of both simulations (GLOBAL and IBI are represented by green and red lines, respectively) where observational data (blue scatter) is rather close to the latter, which performs a little bit better according to the slightly higher correlation coefficient obtained. The standardized validation methodology presented here is particularly useful to judge the strengths and weaknesses of operational ocean forecasting systems (OOFSs) and could encompass the future intercomparison of global configurations (CMEMS GLOBAL), regional applications (CMEMS IBI) and higher resolution models at coastal/shelf scales (SAMOA, dynamically embedded in IBI) with the aim of quantifying the added value of downscaling in local downstream approaches and evaluating their ability to out-perform larger scale systems.



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Figure 26: Autumn 2016, seasonal model intercomparison of IBI and GLOBAL SST solutions (red and green lines, respectively) against in-situ hourly measurements provided by Gascogne moored buoy (blue scatter) in the Gulf of Biscay region.

IV.1.2 Temperature and Salinity

A variety of CLASS-2 monthly validation examples against in-situ measurements for 2017 is provided in Figure 27. Broadly speaking, IBI captures basic features of the sea surface salinity field, as reflected by the overall metrics attached in grey boxes.



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Figure 27: Monthly validations of IBI predictions of sea surface salinity (red line) against in-situ hourly measurements provided by three buoys moored in different regions within IBI spatial domain. CLASS-2 metrics are gathered in grey boxes, revealing the high data-model concordance.



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For the sake of completeness, supplementary validation works in the entire three-dimensional water column have been undertaken to achieve a comprehensive model skill assessment. A routine validation of IBI temperature and salinity profiles against ARGO floats (used as reference observational data source; and extracted from the CMEMS INSITU_IBI_NRT_OBSERVATIONS_013_033 product) within IBI spatial domain has been carried out on a monthly basis thanks to NARVAL web tool. Through this NARVAL component, class-4 metrics have been computed for the whole water column (full ARGO profiles considered) and for different layers, being the specific levels considered in the IBI validation process: 0-5 m, 5-200 m, 200-600 m, 600-1500 m, 1500-2000 m.

Figure 28 shows the qualitative validation of temperature and salinity full profiles predicted by IBI and measured by 36 Argo floats deployed within IBISR region during June 2017. The resemblance between both datasets is significantly high, and the Temperature-Salinity (TS) diagrams look rather alike.

Figure 28: Monthly comparison (June 2017) of temperature (left column) and salinity (central column) profiles provided by ARGO floats (upper panels) and IBI predictions (lower panels) for the entire IBI service (IBISR) domain. TS diagrams are provided in the right column.

Likewise, Figure 29 exhibits the same sort of results but focused on the Western Mediterranean Sea for May 2017. The data-model concordance is again noticeable, and IBI can capture not only the high concentrations of salinity, representative of the Mediterranean waters, but also the extremely limited variability of the temperature and the salinity fields for different depth levels. The values extracted at



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specific depth levels are also presented and compared, highlighting the good accordance. Finally, the TS diagrams are very similar.

Figure 29: Monthly comparison (May 2017) of temperature (left column) and salinity (central column) profiles provided by ARGO floats (upper panels) and IBI predictions (lower panels) for the Western Mediterranean (WESMED) sub-region. TS diagrams are provided in the right column.

Figure 30 presents the validation results for August 2017 in the Gulf of Biscay. Once again, overall results confirm the consistency of IBI model solution for the entire water column. The temperature profiles show more variability along the z-axis than in the Western Mediterranean sub-region. Obviously, the TS diagrams are also different since they are representative of different water masses.



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Figure 30: Monthly comparison (August 2017) of temperature (left column) and salinity (central column) profiles provided by ARGO floats (upper panels) and IBI predictions (lower panels) for the Gulf of Biscay (GOBIS) sub-region. TS diagrams are provided in the right column.

Figure 31 presents the validation results for June 2017 in the Canarias Islands (ICANA) sub-region. Widely speaking, IBI is able to reproduce in a detailed way not only the mean temperature and salinity profiles but also the significant variability in the 1000-1500 metres layer. There is also a good data-model agreement of values extracted at specific depth levels.



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Figure 31: Monthly comparison (June 2017) of temperature (left column) and salinity (central column) profiles provided by ARGO floats (upper panels) and IBI predictions (lower panels) for the Canarias Islands (ICANA) sub-region. TS diagrams are provided in the right column.

Figure 32 and Figure 33 illustrate the metrics corresponding to November 2017 and computed for temperature, using data from the full profile and only the 1500-2000 m layer, respectively. Broadly speaking, it seems that IBI is able to capture the vertical variability of temperature, with RMSD and correlation values generally in the ranges [0.2ᵒ-0.5ᵒ] and [0.85-0.98] for the full ARGO profile. In the case of the 1500-2000 m layer, slightly lower correlation coefficients are obtained in the Western Mediterranean (Figure 33). Figure 34 illustrates the daily evolution during November 2017 of IBI performance to simulate the temperature at different layers. As it can be observed, correlation values remain above 0.9 most of the time and RMSD values are generally lower for the deepest levels.

An example of the comparisons performed for salinity, considering the whole water column (full profiles), the 200-600 m and 600-1500 m layers are depicted respectively in Figure 35, Figure 36 and Figure 37,Figure 37 respectively. The skill metrics are not as good as those derived from the comparison of temperature profiles: the correlation and RMSD values emerged in the ranges [0.4-0.9] and [0.2-0.8 PSU], and rather constant with independence of the depth level considered.



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Figure 32: November 2017, monthly validation of temperature. Observational source used as

reference: Temperature profiles from ARGO floats available within IBI service domain. Daily IBI best estimates (hindcast) used in the metric computation. Bias, RMSD and correlations computed using FULL profiles.

Figure 33: November 2017, monthly validation of temperature. Observational source used as reference: Temperature profiles from ARGO floats available within the IBI service domain. Daily IBI best estimates (hindcast) used in the metric computation. Bias, RMSD and correlations computed for the layer 1500-2000 meters.



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Figure 34: November 2017, daily comparisons of IBI best estimates with temperature profiles from

ARGO floats available in the IBI service domain. Time series of temperature Bias (upper left panel), RMSD (upper right panel) and correlation (lower left panel) computed for different validation layers. Lower right panel shows number of ARGO profilers available per day in the IBI service domain and used to compute the IBI-ARGO statistics.



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Figure 35: July 2017, monthly validation of salinity (PSU). Daily IBI best estimates (hindcast) used in

the metric computation. Observational source used as reference: Salinity profiles from ARGO floats available in the IBI service domain. Bias, RMSD and correlations computed using FULL profiles.


the metric computation. Observational source used as reference: Salinity profiles from ARGO floats available in the IBI service domain. Bias, RMSD and correlations computed for the 200-600 m layer.



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the metric computation. Observational source used as reference: Salinity profiles from ARGO floats available in the IBI service domain. Bias, RMSD and correlations computed for the 600-1500 m layer.

IV.1.3 Surface Currents

There is a noticeable lack of permanent eulerian measurements of the ocean currents in the IBI area. Apart of some current-meters devices on-board of the Puertos del Estado moorings along the Spanish coasts there are not very much information available on real-time or near-real-time basis. In recent years, PdE has made noticeable efforts to establish an operational network of High-Frequency (HF) coastal radars, which consists of four radar systems deployed in four different regions of the Spanish coastline (Figure 38). This technology has been steadily gaining recognition as an effective shore-based remote sensing instrument for measuring coastal surface currents. They can provide near-real time 2-D synoptic maps of the surface flow for distances up to 200 km offshore over a wide variety of spatial and temporal scales, enabling the continuous monitoring of gyres and complex currents structures. The HF radar networks can be used for a broad range of practical applications: for Search and Rescue (SAR) operations, hazardous pollution spills response or the validation of operational ocean forecasting systems like IBI.



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Figure 38: HF coastal radar network currently operated by Puertos del Estado. Such network is based on four different CODAR SeaSonde systems (GALICIA, GIBRALTAR, EBRO-DELTA and HUELVA-ALGARVE) enumerated accordingly in Figure 8 (HFR-1, HFR-2, HFR-3, HFR-4).

In this context, the IBI-MFC Validation Team has included the HF radar observations in both on-line and delayed mode of NARVAL validation web tool with the aim of performing CLASS-1 validation exercises with hourly surface current maps. Since HF radar land-based technology is prone to errors and radar estimations are subject to a variety of potential uncertainties (among others: radio frequency interferences, ionosphere clutter, ship echoes, antenna pattern distortions or environmental noise) the integrity of HF radar data must be previously assured by carrying out regular validation exercises with independent in situ observations from buoys moored within the radar footprint. This integrated approach studies gives added confidence to HF radar measurements as a solid benchmark for the rigorous skill assessment of IBI performance.

The following figures illustrate the kind of information generated by NARVAL: monthly, quarterly and annual-averaged maps of Eulerian surface currents have been calculated for HF radar and IBI in order to explore prevalent circulation patterns during 2017 and also to compute CLASS1 metrics. Figure 39 shows the annually-averaged circulation patterns obtained in the Strait of Gibraltar for IBI and the HF radar during the entire 2017. IBI properly captures the Atlantic inflow from SW to NE and the intensity of the Atlantic jet, with current speeds above 120 cm/s. However, IBI clearly overrates the extent of maximum velocities as reflected by the maps of differences in the current speed. Figure 39 (g-h)Erreur ! Source du renvoi introuvable. manifests the significant radar-IBI agreement in terms of high temporal complex correlation (above 0.8) and generally low veering (or phase) angles (below 10 degrees), except in the outer edges of



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the radar domain (and also inside of the Algeciras Bay) mainly due to a lower data availability and thus higher radar uncertainties.

Figure 39: Year 2017, annually-averaged surface current fields derived from hourly IBI outputs and HF radar current data in the strait of Gibraltar. Speed and direction of seasonal mean surface current (m/s) from IBI System (a), from the HF Radar system (b) and the availability of radar data (c, in %). The speed field difference between both sources (d), the RMSD (e) and the averaged directions (blue and red unitary vectors represent HF radar and IBI, respectively, in f) are also presented for this 3-month period. Map of complex correlation (g) and the associated veering (h) where negative (positive) values represent a counter-clockwise (clockwise) shift of IBI vectors respect HF radar vectors.

The temporal evolution of daily metrics indicates a rather stable performance of IBI in terms of the spatially-averaged bias and RMSD values (Figure 40), although some fluctuations in the complex correlation index are observed as a consequence of periodic fluctuations of HF radar spatial coverage due to day/night cycles. Broadly speaking, mean metrics exhibit a quite consistent and accurate model performance.

Likewise, Figure 41 reveals that IBI correctly reproduces the prevailing circulation features in the Ebro Delta during autumn (defined as S-O-N) 2017 as derived from HF radar observations, namely: the predominant south-westward flow (also denominated “The North Current”), the inversion of the coastal flow to the NE and the presence of a clockwise gyre in front of the Ebro Delta estuary.

The monthly evolution of spatially averaged metrics (Figure 42) demonstrates a close data-model match during the selected study-period, with moderate RMSD values and monthly spatially-averaged correlation



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coefficients up to 0.6. A decrease in model skill is observable during October (reaching minimum values), followed by a later recovery during November 2017.

Figure 40: Daily evolution of statistical metrics derived from IBI-HF radar comparison of sea surface currents in the Strait of Gibraltar for the entire 2017. The bias (a), RMSD (b) and spatial correlation index (c) computed from hourly IBI best estimates (or hindcast) and radar estimations, averaged over the radar domain.



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Figure 41: Autumn 2017, quarterly comparison of surface current fields derived from hourly IBI outputs and HF radar current data in the Ebro Delta (NW Mediterranean). Speed and direction of seasonal mean surface current (m/s) from IBI System (a), from the HF Radar system (b) and the availability of radar data (c, in %). The speed field difference between both sources (d), the RMSD (e), the averaged directions (blue and red unitary vectors represent HF radar and IBI, respectively, in e), the complex correlation (g) and the associated veering (h) are also presented.



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Figure 42: Daily evolution of statistical metrics derived from IBI-HF radar comparison of sea surface currents in the Ebro Delta (NE Spain) for autumn 2017. The bias (a), RMSD (b) and spatial correlation index (c) computed from hourly IBI best estimates (or hindcast) and radar estimations, averaged over the radar domain.

Furthermore, in Lorente et al. (2016-a) two perpendicular transects encompassing the HFR domain were selected along lines of constant latitude and longitude to correspond with HFR grid cells. The meridional and zonal components of hourly total current vectors estimated by HFR and modelled by IBI along the longitudinal and latitudinal transects, respectively, were qualitatively compared for the entire year 2014 (Figure 43).

According to the qualitative HFR-IBI comparison on two selected transects, IBI satisfactory captures basic oceanographic characteristics such as the southwestward North Current (NC), represented by persistent and negative meridional (V) velocities along the interval 1.3–1.7 longitude degrees (Figure 43 b-c). IBI is also able to represent the reversal of the inshore flow during different stages of the year 2014, as reflected by positive V values along 0.5–1 longitude degrees, although it seems to underestimate the length of the summertime current inversion detected by HFR from June to September. The visual inspection of observed and modelled zonal currents along the latitudinal transect confirms the overall resemblance in terms of the predominant westward velocities in January and from September onwards. Furthermore, IBI simulations adequately reproduce the complete current reversal flowing to the east, prevalent during February, March and August (Figure 43, e-f).

Regarding the third HF radar system, deployed in the Huelva-Algarve coastline (SW Spain, Figure 38), Figure 44 highlights that in general terms IBI shows a similar spatial pattern of surface current, with analogous speed current values and south-eastward velocities located along the westernmost and easternmost regions of the radar domain.

In order to summarize the statistical results derived from diverse radar-model comparisons performed during 2017 on a seasonal basis, Table 12 is presented below. The Galician HF radar system worked erratically so no quarterly metrics are provided. Seasonal IBI precision seems rather constant in the Strait of Gibraltar, with complex correlation values in the range [0.60-0.71] and phase angles below 11 degrees for each quarter of 2017. It is noteworthy that the scalar zonal (west-east) correlation is significantly higher than the meridional (north-south) correlation, because of the predominant zonal flow from the Atlantic Ocean into the Mediterranean basin. Likewise, the RMSD values are the highest of the four HF radar



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systems as a consequence of the specific ocean dynamics at Gibraltar, characterised by extremely intense current pulses that reach velocities up to 2 m/s. Finally, IBI performance is also rather consistent in the two other HF radar regions (Ebro Delta and Huelva-Algarve), with complex correlation values emerging in the range [0.26-0.55] and the associated veering angles (in absolute value) below 12 degrees.

Figure 43: (a) Position of the longitudinal transect within the HF radar domain. (b,c) Time evolution of the meridional component of hourly total current vectors provided by HFR and IBI for 2014, respectively, along the longitudinal transect. (d) Position of the latitudinal transect. (e,f) Time evolution of the zonal component of total vectors provided by HFR and IBI for 2014, respectively, along the latitudinal transect.



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Figure 44: June 2017, comparison of monthly-averaged surface current fields derived from hourly IBI outputs and Huelva-Algarve HF radar current data (SW Spain). Speed and direction of seasonal mean surface current (m/s) from IBI System (upper left panel), from the HF Radar system (upper central panel) and the availability of radar data (in %, upper right panel). The speed field difference between both sources (lower left panel) and the averaged directions (blue and red unitary vectors represent HF radar and IBI, respectively, in the lower right panel) are also presented.



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GALICIA RMSD U RMSD V Corr. U Corr. V CC Coef CC Phase

Winter NA NA NA NA NA NA

Spring NA NA NA NA NA NA

Summer NA NA NA NA NA NA

Autumn NA NA NA NA NA NA

GIBRALTAR RMSD U RMSD V Corr. U Corr. V CC. Phase

Winter 0.47 0.21 0.54 0.46 0.60 -13.11

Spring 0.43 0.30 0.47 0.32 0.69 -6.58

Summer 0.35 0.30 0.65 0.37 0.71 -9.74

Autumn 0.32 0.27 0.49 0.30 0.66 -12.62

EBRO DELTA RMSD U RMSD V Corr. U Corr. V CC. Phase

Winter 0.12 0.14 0.34 0.16 0.43 1.59

Spring 0.09 0.10 0.45 0.30 0.41 -6.63

Summer 0.09 0.09 0.50 0.30 0.55 9.47

Autumn 0.10 0.11 0.21 0.43 0.53 11.28

HUELVA-ALGARVE RMSD U RMSD V Corr. U Corr. V CC. Phase

Winter 0.12 0.09 0.41 0.42 0.41 9.32

Spring 0.11 0.08 0.39 0.67 0.50 10.69

Summer 0.11 0.09 0.25 0.41 0.47 10.12

Autumn 0.14 0.08 0.36 0.48 0.26 12.01

Table 12: Statistical metrics derived from IBI-HF radar seasonal comparison of sea surface currents

during 2017. Four different HF radar systems (in blue) have been employed (Figure 38). The values

have been averaged over each HF radar domain and each specific time-period. The units of RMSD and phase are m/s and degrees, respectively. The CC and NA acronyms mean “Complex Correlation” and “Not Available” (as a result of a radar break-down), respectively.

Figure 45 shows a quarterly example (Summer 2016) of model intercomparison between IBI and its parent forecast system (GLOBAL) by using the HF radar as benchmark in order to illustrate strengths and weaknesses of downscaling approaches. In particular, both model solutions reproduce not only the North current flowing south-westward but also the full current inversion in coastal areas. Notwithstanding, IBI seems to properly capture the presence of a fully-developed clockwise eddy (dotted black box) in the central part of the radar domain, whereas GLOBAL shows a more simplified pattern of the surface circulation.



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Figure 45: Quarterly (Summer 2016) model intercomparison of sea surface currents in the Ebro Delta area (NE Spain). Averaged maps of GLOBAL (left) and IBI (central) model solutions are compared with the pattern remotely-observed by a High-Frequency radar (right). The presence of a clockwise eddy marked with a dotted black box.

Complementarily, an example of CLASS-2 validation exercise for the sea surface currents is presented below. As it was described before with the SST case, NARVAL has currently active a module specially dedicate to validate IBI solution in specific locations where a mooring buoy or tide gauge is available. As example, results for a buoy moored in the NW Spanish coastline (Silleiro, Figure 46) are shown to illustrate the validation of modelled currents provided by IBI. It can be stated that IBI captures the basic features of the current speed existing during the selected month (April 2017) with similar mean speeds (around 0.2 m/s) and current roses, reproducing also some of the more prominent peaks of velocity along the month.



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Figure 46: April 2017, monthly validation of sea surface speed modelled by IBI (red line) against in-situ hourly measurements from Silleiro buoy (red circle). Current roses are also provided.

Finally, a brief example of CLASS2 metrics can be found below (Figure 47) regarding the intercomparison of the IBI solution with its parent system (CMEMS GLOBAL) one. This study-case is focused on the sea surface current velocities modelled by both operational forecasting systems during November 2016 and those registered in-situ by Bilbao moored buoy. As it can be observed, the resemblance of IBI simulation (red line) with observational data (blue scatter) is better than the obtained for the GLOBAL system (green line). Likewise, the comparison of current roses reveals that IBI can reproduce the predominant current regime more properly, with a prevailing flow to the east (Figure 47, lower panel). This is mainly attributable to the fact that this area is a tidal-dominated region and the GLOBAL system does not include these tidal processes on its predictions. As a consequence, the obtained metrics for GLOBAL are slightly worse.



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Figure 47: November 2016, monthly validation of sea surface speed modelled by IBI (red line) and GLOBAL (green line) against in-situ hourly measurements provided by Bilbao moored buoy (blue scatter). Lower panel: Comparison of sea surface direction and speed registered at Bilbao buoy (left current rose) and modelled by IBI (central current rose) and GLOBAL (right current rose)

A NARVAL module is still being implemented to verify the IBI performance with in-situ information derived from drifter buoys (using the CMEMS product: INSITU_IBI_NRT_OBSERVATIONS_013_033). Information from this satellite-tracked surface drifters provide a useful information on the surface circulation and related water property transports. The objective of this validation is to compare model simulated drifter trajectories computed from the CMEMS IBI current fields (i.e. “virtual” trajectories) with corresponding independent drifter observations. The lagrangian separation distance between endpoints of each IBI simulated “virtual” and the “real” observed drifter trajectory is used to assess the performances of the IBI model product. The validation with drifters is periodically performed from monthly to annual basis.

Figure 48 shows the CMEMS IBI metrics, based on lagrangian distances (in Km), computed along track of the surface drifter buoys available within the IBI service Domain along the whole year 2015. In order to



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obtain such distance metrics, IBI virtual trajectories were computed using the current velocity fields provided by the CMEMS IBI model along the tracks of the different drifters. IBI virtual tracks are computed every 24h with a temporal extension of 24h, 48h and 72h. In the figure is shown only the distance metrics computed for a virtual trajectory extended until 48h.

Figure 48: December 2015, example of monthly metrics based on lagrangian distances from virtual IBI trajectories (left) and related scores (middle and right).

Once the IBI virtual trajectories are computed, the lagrangian separation distance between them and their corresponding observed drifter trajectories can be used as a metric to quantify the IBI model performance. The left panel of the figure shows the map of lagrangian separation distances, whereas the middle one shows the histograms of the lagrangian separation distance computed for +48h virtual IBI trajectories, showing this distance histogram a peak of frequency occurrence in the 10 to 40 km range, with a mean value of 28 Km.

A skill score error based on these lagrangian separation distances normalized by the length of the observed trajectory (and therefore to the existing current velocity at each point) is computed, following the formulation proposed in Liu and Weisberg (2011). This skill score is suitable to validate a model with a very limited number of drifters distributed over a big geographical domain such as the IBI one. The size of the IBI domain implies the existence of a big spatial variability in terms of dynamical patterns, existing areas within the IBI region with very fast currents together with regions marked by much slower ones. The right panel of the figure illustrates the spatial distribution of this IBI performance indicator (the normalized skill scores is non-dimensional with 0 value for the poorest quality, and 1 for the best agreement between model and observed trajectories).



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IV.1.4 Sea Surface Height

A CLASS-2 validation has been carried out through NARVAL with the aim of assessing the ability of IBI system to accurately predict sea surface height (SSH). To this aim, it has been used the observational data product generates by the IBI INSITU TAC which compiles a significant number of real-time tide-gauges (their locations are depicted by filled green squares in the map shown in Figure 49). Some examples of these IBI validation metrics against tide gauge observational data are shown in the following figures, focus not only on different sub-regions but also in distinct time periods.

Figure 49-a shows the monthly validation of SSH in the English Channel, giving rise to a correlation (RMSD) of 0.95 (0.79 m) as reflected by the close visual resemblance of both time series. A quarterly comparison in the Irish Sea is presented for summer 2017 (Figure 49-b), highlighting again the noticeable data-model concordance. Finally, Figure 49-c reveals the consistent performance of IBI in the North of Spain, as indicated by the moderate RMSD value (0.23 m) and the significantly high temporal correlation (0.97).

Analogously, Figure 50 presents the same type of statistical information, confirming the overall accuracy and robustness of IBI model solution in terms of sea surface height prediction. It is also true IBI seems to slightly underestimate the amplitude of the tidal oscillations in the Strait of Gibraltar (Figure 50-c)

Table 13 summarizes the annual (2017) CLASS2 metrics derived from the SSH comparison between IBI and the set of tide-gauges (some of them already aforementioned in this document). Each tide-gauge belongs to a specific sub-region previously mentioned and indicated in Figure 1. Although the analysed tide-gauges are not fully representative of their respective areas, the results are good enough to state that IBI properly predicts the sea surface level.



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Figure 49: Examples of sea surface height validation for different periods of 2017. IBI model solution (red line) is compared against quality-controlled in situ SSH observations provided by tide-gauges (blue dots).



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Figure 50: Examples of sea surface height validation for different periods of 2017. IBI model solution (red line) is compared against quality-controlled in situ SSH observations provided by tide-gauges (blue dots).



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Tide-Gauge Sub-region RMSD (m) CORRELATION N

St. Helier (Jersey) ECHAN 0.79 0.90 7766

CastleTownnbere IRISH 0.27 0.92 8295

Bilbao-3 GOBIS 0.28 0.96 8664

Gijon-2 NIBSH 0.25 0.96 8637

Ferrol-1 WIBSH 0.24 0.95 8664

Huelva-5 CADIZ 0.23 0.95 8615

Algeciras GIBST 0.09 0.92 8455

Lanzarote-Arrecife ICANA 0.16 0.95 8558

Valencia WSMED 0.05 0.85 8484

Gandia WSMED 0.11 0.46 8203

Arrecife ICANA 0.11 0.94 8515

Table 13: Annual statistical metrics derived from the SSH comparison of IBI against tide-gauges

during the entire 2017. Different sub-regions have been considered (Figure 1) and subsequently the

values have been averaged over each sub-domain. N represents the number of available hourly observations.



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IV.2 Offline R&D mode Validation

In order to qualify the new IBI system, and to verify the impact of the changes related to the previous one, it was performed the IBI R&D Mode Qualification phase. This qualification phase was focused on evaluating the direct impact of the main IBI novelty in the V4 release (in this case, the activation of a data assimilation scheme to generate regional IBI analysis). Following sub-sections are devoted to describe the tests performed and the main conclusions from this IBI Qualification Phase. The qualification run covers the period from January 2010 to the May 2017. The metrics are mainly applied on to the April 2013 – April 2015 period (2 years so we get 2 annual cycles; this is also the period covered by the qualification run of the previous version). Data assimilation metrics (computed in-line) are presented for the whole period (2010-2017).

IV.2.1 Sea Surface Height

IV.2.1.1 Tidal elevation

The amplitudes and phases of the tidal constituents are derived from a harmonic analysis performed on the hourly model elevations outputs. Comparisons between the IBI system and the TPXO 7.2 atlas for the M2 constituent are presented in Figure 51 (for a part of the domain, where the amplitude of tide is most important). The overall performance of IBI is good. The major differences in amplitude appear in the eastern entrance of the Channel (amplitude over-estimated), and in the German Bight (amplitude under-estimated). The phase for M2 is well reproduced except in the Baltic Sea and in the western part of the Mediterranean Sea (not shown).

Figure 51: Difference of amplitude in cm (left panel) and phase in degree (right panel) of the M2 tidal constituent between TPXO 7.2 and the IBI system.



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Figure 52 displays the RMS difference between the sea level observations at tide gauges and the IBI system over the whole domain for nine tidal constituents. The RMS difference of the IBI system is around 12 cm for the M2 constituent and 4 cm for the S2 constituent.

Figure 52: RMS difference (cm) between observations and systems tidal elevation (9 components) at buoys locations within the whole system domain.

IV.2.1.2 Residual elevation

Comparisons between residual elevations (e.g. elevations corrected from the tidal signal) from the systems and the observations at tide gauges are presented in Figure 53. The RMS difference is smaller than 10 cm at almost every tide gauges. The highest values are located in the north part of the domain. Correlation are higher than 0.80 almost everywhere.



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Figure 53: Correlation (left) and RMS difference (cm)(right) between the IBI system and the observations of the residual elevation at tide gauges´ locations (April 2013 to April 2015).

IV.2.1.3 Data Assimilation metrics

Figure 54 shows the RMS of the difference between the IBI system and the along track sea level anomalies (assimilated by the Data Assimilation system). The difference is around 6 cm in average.

Figure 54: Seal level innovation (assimilated along track altimetry – model analyse)(cm) RMS over the whole domain. Colours represent different altimetry satellites (ERS, T/P, Jason, Envisat, GFO, …). Covered time period: 2010 - 2017.



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IV.2.2 Surface currents

IV.2.2.1 Tidal currents

The amplitudes and phases of tidal constituents are derived from the harmonic analysis of hourly modelled surface currents. Figure 55 displays the surface tidal ellipse for the IBI system and the observations at buoys for the M2 and S2 tidal constituents. Results differ from one buoy to another. In the Med Sea, at Cadiz (6.95°W 36.49°N), Donostia (2°W 43.57°N) and Bilbao (3.04°W 43.62°N) buoys, the M2 amplitudes and directions of the system ellipses are close to the observation. At Estaca de Bares buoy (7.6°W 44°N) and Villano-Sisargas buoy (9.21°W 43.50°N), the system over-estimates the amplitude of the current while at Santander (3.76°W 43.85°N) the amplitude is under-estimated. At Santander and Cabo Silleiro (9.4°W 42.1°N) the buoys’ direction is wrong. If we look now at the S2 constituent, the amplitude of the system is higher than the observations in the northern part of Spain (except at Donostia buoy). The system is closer to the observations in the Mediterranean part of the domain.

M2 constituent

S2 constituent

Figure 55: Modelled (blue) and observed (red) surface tidal ellipses at buoys locations for the M2 constituent (left) and the S2 constituent (right).

IV.2.2.2 Residual currents

Comparisons between residual currents (e.g. currents corrected from the tidal signal) from the systems and the observations at buoys are presented in Figure 56 for the zonal and meridional components. The RMS difference is relatively high with most of the values higher than 10 cm/s. The correlation is generally poor, except for the zonal component in the north west corner of Spain.



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U correlation

U RMSD

V correlation

V RMSD

Figure 56: Correlation (left) and RMS difference (cm)(right) between the IBI system and the observations of the residual current components (the frequencies higher than one day are filtered out) at buoys locations (April 2013 to April 2015). Top panels: zonal component. Bottom panels: meridional component.



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IV.2.3 Surface temperature

IV.2.3.1 time series of temperature

Comparisons between near surface temperature from the systems and the observations at buoys are presented in Figure 57. Correlation is very good, higher than 0.96 at most of the buoys. RMS difference values are lower than 0.5°C away from the shelf, and between 0.5 and 1°C along the coasts.

T correlation

T RMSD

Figure 57: Correlation (left) and RMS difference (Celsius)(right) between the IBI system and the observations of the residual elevation at buoys locations (April 2013 to April 2015).

IV.2.3.2 SST fields

Model SST are compared with satellite observations (Figure 58) and calculate quarterly statistics averaged for different areas (see geographical limits of these areas in Figure 1). In the Gibraltar and Cadiz regions the system has less skill than in other regions (and also in the English Channel in summer), according to the statistical metrics exposed in the Taylor diagrams. Gibraltar has been long recognized as a challenging area from a modelling perspective due to the confluence of Atlantic and Mediterranean water masses, with



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distinct salinity and temperature properties. The uncertainties in reproducing the Atlantic jet into the Mediterranean basin (in terms of location, orientation and intensity of such jet, with surface current speeds usually above 150 cm/s) could partially explain the differences detected in the SST field. Discrepancies observed in Cadiz might be attributed to a potential misrepresentation of the coastal upwelling and the subsequent Ekman pumping of cooler and nutrients-enriched deeper waters in Algarve area. Equally, the cooling effect of enhanced summer tidal mixing could explain the SST differences in the English Channel .

Jan/Feb/Mar 2014

Apr/May/June 2014

Jul/Aug/Dep 2014

Oct/Nov/Dec 2014

Figure 58: Taylor diagrams of the SST for Jan/Feb/Mar 2014 (upper left), Apr/May/June 2014 (upper right), Jul/Aug/Sep 2014 (lower left) and Oct/Nov/Dec 2014 (lower right). The observation is indicated with the black point. The circle, asterisk, square, up-triangle, cross, down-triangle and diamond symbols



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are for comparisons within the Bay of Biscay shelf region (BISSH), the Cadiz region (CADIZ), the channel region (ECHAN), the Bay of Biscay (GOBIS), the Canary region (ICANA) and the Western Mediterranean Sea (WSMED) respectively.

IV.2.3.3 Data Assimilation metrics

Figure 59 displays the RMS of the difference of sea surface temperature between the IBI system and the assimilated datasets (satellite and in-situ). For the satellite SST, the RMS difference is less than 0.5°C in average for the whole period. For the in-situ SST, the RMS difference is slightly higher than for the satellite SST during the first half part of the time series. Then, for the second half part, the RMS difference increases. This is due to the change of the dataset. The dataset is the reprocessed one (CORA4.1) until 2014. In 2014 the Data Assimilation system switches to the NRT product.

Figure 59: Time evolution of the domain average innovation (assimilated SST – model analyse)(°C) in term of RMS. In black AVHRR SST data, in orange, CORA SST data (in °C). Covered time period: 2010 - 2016.



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IV.2.4 Temperature and salinity profiles

The CLASS4 results are summarized in Figure 60 to Figure 63 for the IBI service domain. Statistics are averaged spatially (by regions and for the vertical layer 5-200 m, 200-600 m, 600-1500 m and 1500-2000 m); time series are smoothed for clarity (31-days smooth).

Temperature in the 5-200 m layer (Figure 60, left panel): RMS difference values are between 0.4 in winter and 0.8 in summer, far lower than the climatology values.

Salinity in the 5-200 m layer (Figure 60, right panel): RMS difference values are between 0.10 and 0.25 psu, slightly lower than for the climatology.

Temperature in the 200-600 m layer (Figure 61, left panel): RMS difference values are between 0.3 and 0.5 (0.5 and 0.7 fir the climatology).

Salinity in the 200-600 m layer (Figure 61, right panel): RMS difference values are between 0.05 and 0.10 psu, slightly lower than for the climatology (especially in spring 2014).

Temperature in the 600-1500 m layer (Figure 62, left panel): RMS difference values are between 0.5 and 0.7, almost the same than for the climatology.

Salinity in the 600-1500 m layer (Figure 62, right panel): RMS difference values are between 0.1 and 0.2, slightly higher than for the climatology, especially in 2015.

Temperature in the 1500-2000 m layer (Figure 63, left panel): The RMS difference values are lower than 0.5°C, higher than for the climatology, especially in the second half period.

Salinity in the 1500-2000 m layer (Figure 63, right panel): the RMS difference values are between 0.05 and 0.10 psu for the IBI system, higher than for the climatology.

Temperature 5-200m

Salinity 5-200m

Figure 60: Time evolution of the RMS error of temperature (°C, left panels) and salinity (psu, right panels) for the whole domain between 5 and 200 m. The blue for the IBI system, the green for the WOA 2009 climatology.



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Temperature 200-600m

Salinity 200-600m



Salinity 600-1500m




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Salinity 1500-2000m


Data Assimilation Metrics

Figure 64 displays the RMS difference between the IBI system and the in-situ profiles for the whole period of the qualification run. The maximum is located near the surface as expected, with a seasonal cycle for temperature. There is also a secondary maximum near 1000m depth where the Mediterranean water flows.

Figure 64: Time evolution of the domain average innovation (assimilated profiles – model analyse)(°C) in term of RMS difference for temperature (left) and salinity (right). Observations: CORA in-situ temperature profiles. Covered time period: 2010 – 2016.



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V SYSTEM’S NOTICEABLE EVENTS, OUTAGES OR CHANGES

This section is dedicated to track and describe changes that have occurred in the operational system (including major system version upgrades, changes related to upstream data changes). Specific validation results associated to some dedicated assessment that focus on these changes are also provided.

The IBI operational forecast products have been generated by means of an evolving service based on a

continuously updated operational suite and numerical model application. Table 15 shows the evolution of the

IBI service (in terms of main operational versions and main changes associated to each updated IBI system) along the MyOcean projects and the CMEMS service.

System Version

(Project/Service)

Operational launch

End of operations

Description of novelties associated to the change/event

IBI-V0 (MyO) 01/04/2010 30/03/2011 PdE ESEOAT System (based on POLCOMS model application)

IBI-V1

(MyO)

01/04/2011 09/01/2012 New IBI system based on NEMO code forced with atmospheric data from ECMWF and IC & BC from MyOcean GLOBAL system.

IBI-V2

(MyO)

10/01/2012 22/04/2013 Some minor improvements on the model code related to atmospheric forcing interpolation, lateral friction parameterization and the use of a variable bottom friction parameter. Modified bathymetry.

IBI-V3

(MyO-2)

23/04/2013 14/04/2014 Improvement in the fresh water river forcing data (inclusion of tributaries)

IBI-V4

(MyO-2)

15/04/2014 14/04/2016 Update the NEMO code use as base of the IBI Model Application (from NEMO v2.3 to the most recent NEMOv3.4)

IBI-V2

(CMEMS)

15/04/2016 19/10/2016 Update the NEMO code use as base of the IBI Model Application (from NEMO v3.4 to the most stable NEMOv3.6). Change of the periodic initialization scheme by a new spectral nudging solution. Warning to users: a jump in ssh values exists with respect to previous IBI solutions.

Table 14: Historical evolution of the IBI MFC operational forecast system (along the different MyOcean Projects –MyOcean, MyOcean-2 and MyOcean-FO - and the CMEMS Service times). For each system version is provided the time period in operations for production of IBI MFC NRT forecast, as well as its main novelties with respect to the previous one.



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System Version

(Project/Service)

Operational launch

End of operations

Description of novelties associated to the change/event

IBI-V2 (CMEMS)

19/10/2016 19/04/2017 Change in the imposed data as IBI OBC.

An upgrade of the CMEMS Global System, used as boundary condition in the IBI run, was launched the 19

th

October 2016. This change in the imposed data used as IBI boundary condition has an impact in the IBI solution, and particularly in the sea surface height. Therefore, a discontinuity in this variable is found for this date in the IBI SSH variable provided through the CMEMS catalogue. Further details on this issue can be found in the present section.

IBI-V3 (CMEMS)

19/04/2017 22/03/2018 V3 IBI Upgrade. Inclusion of new fresh water discharge inputs.

IBI-V4 (CMEMS)

22/03/2018 02/12/2019 V4 IBI Upgrade. Activation of a new Data Assimilation

scheme in IBI to generate IBI regional analysis.

CMEMS December 2019

Release

03/12/2019 …. IBI NRT PHY Product Upgrade: Delivery of new dataset

with 15 minutes frequency data for sea level and

surface currents

Table 15 (continued): Historical evolution of the IBI MFC operational forecast system (along the different MyOcean Projects –MyOcean, MyOcean-2 and MyOcean-FO - and the CMEMS Service times). For each system version is provided the time period in operations for production of IBI MFC NRT forecast, as well as its main novelties with respect to the previous one.

A description of the major IBI set-up changes occurred along the CMEMS IBI service is provided bellow. This historical review does not include the latest change implemented in the IBI NRT ocean forecast product (December 2019 release), related to the delivery of a new 15-minute dataset for sea-level and surface currents, what it is described in detail in the next section).

CMEMS V2 Release (April 2016). Change in the IBI set-up: from a periodic reinitialization to a continuous run with spectral nudging

The main change introduced in the CMEMS V2 release in the IBI system (April 2016) was related to the NEMO code version upgrade (from NEMOv3.4 to NEMOv3.6) and to the upgrade of the downscaling methodology, substituting a periodic weekly re-initialization by a new spectral nudging technique method, applied to downscale the CMEMS GLOBAL analysis. The resulting regionalized analysis are used as the IBI MFC best estimates. The chosen spectral nudging method permits to “nudge” the low frequency IBI system solution towards the large scale GLOBAL analysis in those areas where this global solution is supposed to be more accurate (mainly off the shelf and in deep waters) due to the assimilation of lower frequency signals.

This new V2 SN IBI solution overcame two drawbacks of previous existing IBI versions: firstly, avoid the existence of temporal discontinuities in the IBI solution inherent to the periodic re-initialization, and



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secondly allows to minimize dependency from the GLOBAL parent solution on the shelf, where water properties are largely biased by the missing physics (tides and other high frequency physical processes).

The following issue should be considered by any IBI user. The CMEMS global model solution, in which IBI forecast system is nested have changed in the present CMEMS V2 release. Among other changes, it is worthy pointing out that exists some differences in terms of mean sea surface height values between the previous global solution (provided by the PSY2 system) and the new one (generated by means of the new PSY4 global model) that will consequently affect the IBI solution.

Figure 65 shows values of (ssh - mean_ssh) from each GLOBAL solution (provided by the PSY2 and PSY4 systems, respectively). Means are computed considering only the IBI geographical domain. It can be observed that spatial patterns are analogous and very close, nevertheless exist a difference of mean values of 18 cm. This bias is inherited by the IBI system and leads to a gap in the IBI sea surface height, shown when compares V2 solution and the previous IBI version ones, and definitively has to be taken into account for those users who use IBI data generated from previous versions together with the new V2 IBI products delivered from 13th April 2016."



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Figure 65: Upper panels: Maps of (ssh - mean_ssh) for previous CMEMS V1 Global system (PSY2 system) and the current V2 CMEMS Global solution (PsY4 system). Lower panel: Example of SSH timeseries from V1 & V2 IBI systems at a specific location.

Change in IBI boundary condition (new CMEMS GLOBAL data; 19th Oct 2016): impacts in the IBI sea level solution.

As in the previous case, the following issue should be considered by any IBI user. The CMEMS global model solution (PSY4), in which IBI forecast system is nested, have changed the 19th October 2016 due to a new GLOBAL system upgrade. Among other changes, it is worthy pointing out that exists some differences in terms of mean sea surface height values between the previous global solution (provided by the PSY2 system) and the new one (generated by means of the new PSY4 global model) that will consequently affect the IBI solution. Some metrics have been computed to compare the two global solutions (the old one: name hereafter GLO-1 and the new one from the PSY4 system, hereafter GLO-2) in the IBI area and thus assess the performance of the new CMEMS GLOBAL solution in which IBI system is nested.

Sea Surface Height

Figure 66 displays the monthly averaged gradient of the sea surface height between the Atlantic and the Western Mediterranean Sea for the previous and the new global solutions, and the difference between both. The gradient can control the transport in the Gibraltar strait. The time series of the two systems gradients are very similar, and the maximum difference is less than 2 cm (1 cm of standard deviation). On average, the differences in SSH (for year 2015) is about 2.8 cm (an example of local differences can be seen in the lower Panel of Figure 66).



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Figure 66: Upper Panel: Monthly averaged sea surface height gradient (m) between Atlantic and Western Mediterranean Sea for the previous global solution (in black) and the new one (in red). The blue curve is the difference between both. Lower panel: Example of SSH time series from GLO-V1 & GLO-V2 systems at a specific location.



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CMEMS V3 Release (April 2017). Inclusion of a runoff fresh water forcing to complement the river fresh water discharge input.

Runoff

Figure 67 displays an illustration of the inflow of fresh water along the boundaries that was included in the IBI-V3 system. This water flux was introduced as if it was a rain contribution in each coastal grid point. It is complementary to the 33 rivers runoffs already existing in the system and aims to take into account all the missing rivers.

Figure 67: Left: the black curve displays the total amount of daily river discharge (m3/s) of the 33 rivers implemented in the IBI-V2 system. The red curve displays the monthly climatological amount that is implemented in IBI- V3, in addition of the 33 rivers discharges. The sum of the black and red curves (not shown) is the total amount of river discharges in IBI-V3. Right: water inflow (kg/m2/s * 1000) for the month of June (Bay of Biscay zoom).

Besides the changes in the numerical code, the main change in the new IBI version is the new run-off forcing. This affects mainly the near coastal regions, but does not change the overall performance of the system as can be seen in Figure 68. Figure 68 shows the Taylor diagrams of the results of comparisons of the IBI system (V2 and V3) with observations at tide gauges and buoys (most of them are located near the coast). There is no (or very small) changes concerning the surface height (residual and also tidal (not shown)) and temperature. The current’s skill is slightly different between IBI-V2 and IBIV3 in a positive way. The main difference as expected concerns the salinity: IBI-V3 performs slightly better than IBI-V2. Figure 69 shows the comparisons of the IBI-V2 and IBI-V3 systems with observations from the PELGAS 2014 cruise. The low salinity tongue over the shelf is better reproduced with the IBI-V3 system than with the IBI-V2 one compared to the observations.



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Residual SSH

Temperature

Salinity

Zonal current

Figure 68: Taylor diagrams of residual elevation at tide gauges locations within the whole domain (upper left panel); of the temperature at buoys (upper right panel); of the salinity at buoys (lower left panel); of the zonal component at buoys (lower right). The observation is indicated with the black point. The light blue is for the IBI-V2 system, the dark blue for the IBI-V3 system.



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Observations

IBI-V2

IBI-V3

Figure 69: salinity at 5 m depth during May 2015 for observations (left), IBI-V2 (middle) and IBI-V3 (right). The observations come from the PELGAS 2014 cruise (DORAY Mathieu, DUHAMEL Erwan, HURET Martin, PETITGAS Pierre (2014) PELGAS 2014 cruise, RV Thalassa, http://dx.doi.org/10.17600/14001800). The system salinity field is collocated in space and time at the observations’ locations. Then the data are interpolated onto a regular grid.

CMEMS V4 Release (March 2018). New Data Assimilation scheme in IBI: First generation of analysis

Technically there is a huge difference between the new version of IBI (V4) and the previous one (V3). The new version now handles data assimilation. Satellite sea surface temperature, satellite sea level anomalies and in-situ profiles are assimilated by the IBI system.

In terms of results, the differences are not so huge. This is because the previous version uses the spectral nudging technique, which is a kind of assimilation (but for open sea only, not on the shelf). The most

http://dx.doi.org/10.17600/14001800



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noticeable enhancement is for sea level (as it can be seen in

Figure 76); the IBI system RMS difference for sea level anomalies is now 1 or 2 cm lower than for the previous system. One can expect the mesoscale features to be better reproduced by the new system.



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Figure 70: RMS difference (cm) between the IBI system and the observations of the along-track sea level anomalies. Upper: V3 system. Lower: V4 system.

For example, the comparison with sea surface temperature from satellite (Erreur ! Source du renvoi introuvable.) shows a slightly better agreement of the new system, especially in some of the regions (i.e. the Alboran Sea). The temperature and salinity are also slightly closer to the observations as can be seen is Erreur ! Source du renvoi introuvable. for temperature and Erreur ! Source du renvoi introuvable. for salinity. Some slight V4 regional improvement in reproducing SST variability is verified (as it is seen in the Taylor diagrams depicted in Erreur ! Source du renvoi introuvable.; the constellation of points, representing the V4 performance in the different IBI subregions is closer and more aligned with the optimum standard deviation curve than in V3). Despite the slightly better V4 SST general reproduction, the Erreur ! Source du renvoi introuvable. also shows that there can be a degradation of performance on some shelf areas (not so in open waters). This is the case for instance in the western Iberian Peninsula, the English Channel and the Irish Sea. This fact can be explained by the lack of data to be assimilated by the system in shelf seas, and also by the fact that satellite altimetry data are less accurate close to the coast. Erreur ! Source du renvoi introuvable. shows an example of IBI V4 & V3 model intercomparison in the latest region (at the M5 mooring location and using as reference dataset in-situ SST observations along 2015 year); the validation metrics obtained confirm this local V4 underperformance.



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Figure 71: RMS difference (°C) between the IBI system and the in-situ temperature assimilated profiles. Left: V3 system. Right: V4 system.

Figure 72: RMS difference between the IBI system and the in-situ salinity assimilated profiles. Left: V3 system. Right: V4 system.



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Figure 73: RMS difference of Sea Surface Temperature (°C) between the IBI system and the satellite L3 dataset (from March 2013 to Dec 2015). Left: V3 system. Right: V4 system.



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Figure 74: IBI-Vs-L3 SST Product (year 2015). Taylor diagrams for V3.1 & V4 solutions (upper & lower panels). Dots represents the different IBI validation sub-regions (shown in the Map).



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Figure 75: Example of validation (CLASS2 metrics for 2015 year) in the M5 Buoy (location shown in the Map). Red line for model (V3.1 on the left panel and V4.0 on the right one) and blue dots for observations. The table on the right show annual and seasonal metrics (bias, rmsd, r) for IBI V4 validation. The table on the left shows the differences in the statistics between V4 and V3 (values with worse V4 product quality metrics, shadowed in red).



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VI QUALITY CHANGES SINCE PREVIOUS VERSION

This section aims to provide a description of the impacts, in terms of solution and product quality, related to the last changes introduced the IBI PHY NRT system. In this case, the last IBI NRT ocean forecast product upgraded consists only in an extension of the final product delivered to end-users by means of including a new dataset with 15-minute frequency IBI model outputs.

Therefore, surface IBI outputs for sea level and horizontal velocities are now available with a 15-minute frequency. These new data correspond to a higher-frequency output, consistent with the already available, and delivered, hourly IBI data. Both IBI datasets, the previously delivered hourly one and the new 15 minute one, have been validated, confirming that the new 15-minute data do not present any divergence from the hourly outputs (as it was expected, since we are talking about the same IBI model solution).

In order to validate both datasets an IBI 1-month (March 2019) run has been performed, and the hourly and 15-minute outputs have been compared with in-situ observations from more than 30 tide gauges and 15 current-meters on board of fixed moorings. This validation exercise has confirmed the expected agreement between both IBI datasets. In order to illustrate such agreement, Figure 76a shows the sea level at Bilbao station (-3.05E/43.36N), from 26 to 28th March 2019. The 15-minutes frequency outputs of the model (blue dots) are in perfect agreement with the hourly outputs (red dots), and both reproduce with exactitude the variations of sea level at this location. Figure 76b shows the horizontal velocities at Silleiro buoy (-9.52E/41.62) over the same period. The main variations of current velocity amplitude are well reproduced by the two temporal frequency model output, being both datasets quite analogous.

Right now both datasets are quite consistent, however, in the future, and specially taking into account the context of expected future IBI model and forcing improvements (e.g. use of hourly atmospheric forcing instead of the present 3-hourly one), it may be expected to observe in the 15-minute outputs some more high-frequency signal, that would not appear in the hourly one, catching more detailed features and making thus the 15 minute dataset of more interest for end-users.



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Figure 76: (a) Observed and IBI Model (both 15 min and hourly) timeseries of sea level anomalies at Bilbao tide gauge station (3.05W/43.36N). Temporal coverage: 26th-28th March 2019. (b) Observed and IBI Model (both 15 min and hourly) timeseries of surface horizontal velocities at Silleiro buoy (9.52W/41.62N) over the same period.



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VII REFERENCES

Aznar R., Sotillo M.G., Cailleau S., Lorente P., Levier B., Amo-Baladrón A., Reffray G., Álvarez-Fanjul E. (2016). “Strengths and weaknesses of the Copernicus forecasted and reanalyzed solutions for the Iberia-Biscay-Ireland (IBI) waters”. Journal of Marine Systems, 159, pp. 1-14, 2016

Capó, E., Orfila, A., Sayol, J.M., Juza, M., Sotillo, M.G., Conti, D., Simarro, G., Mourre, B., Gómez-Puyol, L., Tintoré, J. (2016). “Assessment of operational models in the Balearic Sea during a MEDESS-4MS experiment”. Deep Sea Research Part II: Topical studies in oceanography. Volume 133, pp. 118-131.

Leclair, Matthieu and Madec, Gurvan (2009) A conservative leapfrog time stepping method. Ocean Modelling, 30, (2-3), 88-94. (doi:10.1016/j.ocemod.2009.06.006).

Lorente, P., B. Levier, M. Drevillon, C. Regnier, M.G.Sotillo (2012) "The IBI-MFC NARVAL: Products, operational status and progress". Conference: MyOcean Science Days (Hamburg, Germany). November, 19-21, 2012

Lorente P., Piedracoba S., Sotillo M.G., Aznar R., Amo-Balandron, A., Pascual, A., Soto-Navarro J., Alvarez-Fanjul, E. (2016-a). “Ocean model skill assessment in the NW Mediterranean using multi-sensor data. Journal of Operational Oceanography”. doi: 10.1080/1755876X.2016.1215224

Lorente P., Piedracoba S., Sotillo M.G., Aznar R., Amo-Balandron, A., Pascual, A., Soto-Navarro J., Alvarez-Fanjul E. (2016-b). “Characterizing the surface circulation in Ebro Delta (NW Mediterranean) with HF radar and modeled current data”. Journal of Marine Systems, 163, pp. 61-79, 2016

Maraldi, C., Chanut, J., Levier, B., Ayoub, N., De Mey, P., Reffray, G., Lyard, F., Cailleau, S., Drévillon, M., Fanjul, E. A., Sotillo, M. G., Marsaleix, P., and the Mercator Research and Development Team (2013): NEMO on the shelf: assessment of the Iberia–Biscay–Ireland configuration, Ocean Sci., 9, 745-771, doi:10.5194/os-9-745-2013.

Sotillo M G, S. Cailleau, P. Lorente, B. Levier, R. Aznar, G. Reffray, A. Amo-Baladrón, J. Chanut, M. Benkiran E. Alvarez-Fanjul (2015): The MyOcean IBI Ocean Forecast and Reanalysis Systems: operational products and roadmap to the future Copernicus Service, Journal of Operational Oceanography, DOI: 10.1080/1755876X.2015.1014663

Sotillo, M.G., Amo-Baladrón, A., Padorno, E., Garcia-Ladona, E., Orfila, A., Rodriguez-Rubio, P., Conti, D., Jiménez Madrid, J.A., De los Santos, F.J., Alvarez-Fanjul, E. (2016). “How is the surface Atlantic water inflow through the Gibraltar Strait forecasted? A lagrangian validation of operational oceanographic services in the Alboran Sea and the Western Mediterranean”. Deep Sea Research Part II: Topical studies in oceanography. Volume 133, pp. 100-117.

Sotillo MG, G Reffray, A amo, B Levier. 2017. CMEMS PRODUCT USER MANUAL for Atlantic -Iberian Biscay Irish- Ocean Physics Analysis and Forecast Product: IBI_ANALYSIS_FORECAST_PHYS_005_001. CMEMS Technical Report (www.marine.copernicus.eu)

http://dx.doi.org/10.1016/j.ocemod.2009.06.006

http://www.marine.copernicus.eu/

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