sea ice drift product validation plan

ESA UNCLASSIFIED - For Official Use

Sea Ice Climate Change Initiative: Phase 2

Sea Ice Drift Product Validation Plan

Doc Ref: SICCI-PVP-05-16

Version: 1.0

Date: 30 May 2016

Product Validation Plan

Ref. SICCI-PVP-05-16

Version 1.0 dated 30 May 2016

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Change Record

Issue Date Reason for Change Author

1.0 30 May 2016 First Issue Fanny Ardhuin, Thomas Lavergne, Leif Toudal Pedersen, Roberto Saldo, Thomas Hollands, Stefan Muckenhuber

Authorship

Role Name Signature

Written by: Fanny Ardhuin, Thomas Lavergne, Leif Toudal Pedersen, Roberto Saldo, Thomas Hollands, Stefan Muckenhuber

Checked by: Gary Timms (CGI)

Approved by: Stein Sandven (NERSC)

Authorised by: Pascal Lecomte (ESA)

Distribution

Organisation Names Contact Details

ESA Pascal Lecomte [email protected]

NERSC Stein Sandven, Natalia Ivanova, Kirill Khvorostovsky

[email protected]; [email protected]

CGI Gary Timms, Sabrina Mbajon, Clive Farquhar

[email protected]; [email protected]; [email protected]

MET Norway Thomas Lavergne, Atle

Sørensen [email protected]; [email protected]

DMI Leif Toudal Pedersen, Rasmus Tonboe


DTU Roberto Saldo, Henriette Skourup

[email protected]; mailto:[email protected]

FMI Marko Mäkynen, Eero Rinne [email protected]; [email protected];

University of Hamburg

Stefan Kern, Lars Kaleschke, Xiangshan Tian-Kunze

[email protected]; [email protected]; [email protected]

University of Bremen

Georg Heygster [email protected]

MPI-M Dirk Notz, Felix Bunzel [email protected]; [email protected]

mailto:[email protected]







mailto:

mailto:


mailto:[email protected];







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Organisation Names Contact Details

Ifremer Fanny Ardhuin [email protected]

AWI Marcel Nicolaus, Stefan

Hendricks, Thomas Hollands

[email protected];







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Table of Contents

1 Introduction ................................................................................... 7 1.1 Document Structure ........................................................................7 1.2 Document Status ............................................................................7 1.3 Applicable Documents .....................................................................7 1.4 Reference Documents ......................................................................7 1.5 Acronyms and Abbreviations ............................................................7

2 Sea Ice Drift (SID) ECV ................................................................... 9

3 RRDP activities ............................................................................. 10 3.1 Inputs ......................................................................................... 10 3.2 Reference data ............................................................................. 12 3.3 Format of the RRDP ...................................................................... 14 3.4 Collocation method ....................................................................... 14 3.5 Algorithms evaluation: parameters/statistics, criteria ......................... 14 3.6 Sensitivity ................................................................................... 15 3.7 Uncertainties ................................................................................ 15

4 Roles and Responsibilities ............................................................. 16

5 Master Time Schedule ................................................................... 17

6 Summary ...................................................................................... 18

Appendix A References ................................................................................ 19




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List of Figures

Figure 3-1: Geographical distribution of buoys (and some land data) ....................... 13

Figure 3-2: Number of observations distributed per year of the current SICCI2 in situ

data record. Left is NH and right is SH. .......................................................... 14




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List of Tables

Table 1-1: Applicable Documents ......................................................................... 7

Table 1-2: Acronyms .......................................................................................... 8




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1 Introduction

1.1 Document Structure

This document describes the validation plan for the Sea Ice Drift (SID)

Essential Climate Variable algorithm to be selected in the ESA's Sea Ice

Climate Change Initiative project.

This document is composed of the presentation of the SID activities in Phase

2 of the project, a Round Robin Data Package (RRDP) detailed presentation,

definition of responsibilities of the team, and master time schedule of these

activities.

1.2 Document Status

This is the first version of the PVP for SID which was not included in Phase 1

of the project. This includes additional internal (consortium) review

comments.

1.3 Applicable Documents

The following table lists the Applicable Documents that have a direct impact

on the contents of this document.

Acronym Title Reference Issue

AD-1 Sea Ice ECV Project

Management Plan

ESA-CCI_SICCI_PMP_D6.1_v1.1 1.1

Table 1-1: Applicable Documents

1.4 Reference Documents

All references are listed at the end of the document

1.5 Acronyms and Abbreviations

Acronym Meaning

AMSR Advanced Microwave Scanning Radiometer

ASCAT Advanced Scatterometer

ASCII American Standard Code for Information Interchange

ATBD Algorithm Theoretical Basis Document

CRREL Cold Region Research and Engineering Laboratory

DMSP Defence Meteorological Satellite Program

ECV Essential Climate Variable

Envisat Environmental Satellite

ESA European Space Agency

FY First Year

GPS Global Positioning System

H Horizontal polarization




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Acronym Meaning

IABP International Arctic Buoys Program

IPAB International Program for Antarctic Buoys

ITP Ice Tethered Profiler

MCC Max Cross Correlation

MIZ Marginal ice zone

MY MultiYear

n.a. Not applicable

NetCDF Network Common Data Format

NH Northern hemisphere

NSIDC National Snow and Ice Data Center

OSI-SAF Ocean and Sea Ice Satellite Application Facility

PMW Passive Microwave

PVP Product Validation Plan

RADAR Radio Detection and Ranging

RRDP Round Robin Data Package

SAR Synthetic Aperture Radar

SID Sea Ice Drift

SH Southern hemisphere

SMMR Satellite Multichannel Microwave Radiometer

SMOS Soil Moisture and Ocean Salinity

SSM/I Special Sensor Microwave / Imager

SSM/IS Special Sensor Microwave / Imager+Sounder

V Vertical polarization

Table 1-2: Acronyms




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2 Sea Ice Drift (SID) ECV

The SID maps exist from different sensors, at different time and space

scales. Each existing dataset has been validated with references, some have

also been inter-compared at the basin scale (Sumata et al, 2014) or on

specific area (Rozman et al, 2011). The goal here is not to do again a

product comparison we will focus here on the algorithms, independently of

the data and scale used. At the end of this study we will be able to select an

algorithm (which we call “the best one”) that could be applied to any

dataset for a climate record (the project does not include the production of

this dataset).

The goal of the Product Validation Plan (PVP) is to define a strategy of

validation to select the “best” algorithm(s), this includes:

- choose some metrics

- choose satellite data and reference data

- design the RRDP

Main existing SID datasets are defined as displacement estimate, we choose

here to apply the RRDP with the same parameter: a time integrated

displacement rather than speed values which are used by modellers.




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3 RRDP activities

The RRDP aims to compare algorithms on several satellite datasets in order

to select the “best” algorithm. This requires a number of input satellite

datasets, algorithms, and validation datasets compiled in a so-called Round

Robin data package (RRDP).

This consists in collecting available algorithms as well as establishing a

validation dataset by which the algorithms can be tested for performance

against a number of performance criteria. This requires also a collection of

satellite data. The main challenges of the RRDP are the compilation of

algorithms, satellite and reference datasets.

This section describes the selection and comparison of algorithms with the

satellite and reference data.

The protocols contain:

1. Specification of input data (satellites, algorithms)

2. Specification of validation data (reference data)

3. Definition of format of RRDP

4. Collocation methods

5. Specification of validation parameters/statistics, criteria.

6. Sensitivity of the results

3.1 Inputs

3.1.1 Satellite data

The database will consist of daily averaged maps of

1) Gridded brightness temperature (TB) data measured by passive

microwave radiometers (PMW) onboard satellites:

SMMR (if needed)

SSM/I(S) onboard DMSP at low resolution (the successive SSMI

sensors onboard F8 to F17 are calibrated)

AMSR-E at medium resolution

AMSR2 at medium resolution

2) Gridded low resolution backscatter data :

SeaWinds/QuikSCAT

ASCAT/MetOp (-A, -B if needed)

3) High resolution radar sensor (SAR)

EnviSATSentinel-1

Other high resolution data exist but have not the potential for a climate data

record.




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These reference datasets will be detailed in the DBT2 document.

The possibilities of optical/infrared data are not considered because they are

highly contaminated by clouds and limited by the polar night (little potential

for climate data record).

The SID team will keep an eye on future/new sensors to continue the SID

ECV dataset if available (for example future European Scatterometer...).

Most of products are based on daily averaged maps of TB (radiometry) or

Sigma0 (scatterometry), we have to pay attention at the loss of accuracy of

the averaging of swaths data, this will be analysed on the uncertainties work

among other parameters to take into account.

3.1.2 Algorithms and methods

Several algorithms have been applied to infer SID. We will focus here on the

a) technics to infer vector estimate

b) but also pre-processing of the raw data

c) post-processing consistency check of the vectors

d) application to single or multiple data (channels/sensors)

a) We will test 2 main families of algorithms based on :

1) Pattern Matching / Correlation based methods

1.1. Max Cross Correlation (MCC)

This method is applied on successive and lagged averaged daily maps, it

consists in tracking common features on pairs of sequential images (Ninnis

et al 1986, Emery et al 1997). A correlation is estimated between two

arrays of data, one at a given day and another one lagged in time, and the

location of the maximum correlation is the location of the maximum

similarity between the two original subimages. The displacement is thus

inferred.

MCC is widely used since a long time for SID estimate, it has been used with

low resolution data (SSM/I – Agnew et al 1997, QuikSCAT - Girard-Ardhuin

and Ezraty 2012) but also medium resolution data (AMSRs, Girard-Ardhuin

and Ezraty 2012) and high resolution SAR data (Kwok et al 1990).

Lavergne et al (2010) applied the MCC with a continuous optimization step

resulting in a attenuation of the quantization noise due to the MCC technics.

1.2. Phase correlation

Another correlation based way to detect displacement is the analysis of

(time and space) varying signals through wavelet/Fourier transform. After

applications to wind patterns, Liu et al 1999 have been tested this method

to infer sea ice motion. Since the 2010's, others have applied this kind of

decomposition using high resolution data (SAR) (Thomas, 2005 and

Karvonen, 2012). While the application of phase correlation only can be

computationally more efficient than the correlation in the spatial domain, it

is less prone to high frequency noise.




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2) Feature-based detection

While pattern matching algorithms just correlate regular search windows,

feature tracking algorithms include a semantic step, identifying a list of

features to compare between two scenes. This method is described and

tested with Sentinel-1 SAR data by Muckenhuber et al (2015). It consists

first in a sampling of the data to lower resolution in order to decrease the

influence of speckle noise and increase the computational efficiency. Then a

tracking features method is applied with a new quality measure using the

amount and deviation of vectors in a grid cell.

Compared to patter matching, the advantage of feature tracking is that each

drift vector is independent of the surrounding motion; it is in particular of

interest for areas with high gradient displacements. The disadvantage

compared to purely correlation based methods is the irregular distribution

of vectors and a bias towards motion of clearly identifiable features

Sampling of the images, area of sub-window, correlation areas and

distances between inferred vectors are problems to be investigated in this

study.

b) Pre-processing of the images will also be tested, for example :

- Laplacian fields (Girard-Ardhuin & Ezraty 2012)

- Laplace (Komarov et al 2014)

- missing values filled with mean neighboured pixels (Kimura et al,

2013)

c) The existing SID datasets have been made using method for vector

detection but also using consistency check technics that could be tested

here, such as

- consistency with wind direction

- threshold of correlation coefficient

- consistency with averaged neighbours vectors

- consistency with lower resolution vectors pattern (for high

resolution)

- back matching verification (Hollands, 2012, 2015, Komarov, 2014)

d) We will also test different ways to apply the algorithms over:

- single sensor or single channel sensor

- multi-channels or multi-sensors (Haarpainter 2006, Girard-Ardhuin &

Ezraty 2012, Lavergne, et al. 2010)

3.2 Reference data

An in situ reference data set is being compiled from mainly buoys and

drifters mainly available from different programs such as:

ITP (Woods Hole Oceanographic Institute,

http://www.whoi.edu/page.do?pid=20756)

IABP (International Arctic Buoy Programme,

http://iabp.apl.washington.edu/index.html)

CRREL IMB Ice Mass Balance Buoy Program

(http://www.erdc.usace.army.mil/Media/FactSheets/FactSheetArt

http://www.whoi.edu/page.do?pid=20756

http://iabp.apl.washington.edu/index.html

http://www.erdc.usace.army.mil/Media/FactSheets/FactSheetArticleView/tabid/9254/Article/553850/ice-mass-balance-imb-buoy-program.aspx




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icleView/tabid/9254/Article/553850/ice-mass-balance-imb-buoy-

program.aspx)

SAMS (The Scottish Association for Marine Science,

http://www.sams.ac.uk/)

IPAB (for southern hemisphere- SH)

GTS drifters (Global Telecommunication System from ECMWF

MARS archive)

Atlas of Antarctic Sea Ice Drift (http://imkbemu.physik.uni-

karlsruhe.de/~eisatlas/HTML/eisatlas_download.html)

SOOS (Southern Ocean Observing System,

http://soos.aq/data/overview)

AWI Buoy data (Alfred Wegener Institute)

Norwegian drift station (Yngve Christoffersen)

Marginal Ice zone Program (University of Washington,

(http://www.apl.washington.edu/project/project.php?id=miz)

ISTI (International surface temperature initiative,

http://www.surfacetemperatures.org/)UPMC Buoys

(http://www.iaoos-equipex.upmc.fr/fr/index.html)

University of Tasmania, Southern Ocean buoy data (if

possible....)

This list is neither complete nor final. It is a live list for the duration of the

project. The list does also contain doublets, but they will removed. The

reference data will be read and stored in uniform and consistent data

format, i.e. in netCDF with CF compliancy where possible. The reference

data will undergo a quality check in its final version.

The data set is under construction, but at any time a version is available for

testing. Careful attention will be drawn to the availability of the data (NH

and SH, MY/FY, period) and of the quality of them. When new data are

available and/or when improved data formats and/or better quality control

procedures are developed, the available data set will be reprocessed.

Statistics of the in situ data distribution of

the current in situ data set version is

presented in the figures x and y.

Figure 3-1: Geographical distribution of buoys (and some land data)



http://www.sams.ac.uk/

http://imkbemu.physik.uni-karlsruhe.de/~eisatlas/HTML/eisatlas_download.html

http://imkbemu.physik.uni-karlsruhe.de/~eisatlas/HTML/eisatlas_download.html

http://soos.aq/data/overview

http://www.apl.washington.edu/project/project.php?id=miz

http://www.iaoos-equipex.upmc.fr/fr/index.html




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Figure 3-2: Number of observations distributed per year of the current

SICCI2 in situ data record. Left is NH and right is SH.

(The current data set not complete, as mentioned, in particular drifters from

the southern hemisphere is anticipated to increase drastically in numbers)

These reference datasets will be detailed in the DBT2 document.

3.3 Format of the RRDP

We aim for a robust ASCII based (csv) flat file. Each line records information

for a unique (validation data, collocated) pair.

The source/institution, ref. SID value (+a confidence level) are part of the

information to be recorded. Follow the latitude, longitude, time, sensor

(<instr>-<platform>, e.g. ssmi-f15), channels if needed, polarization if

needed. The data format (e.g. order of the columns, number of decimals,

format for the date-time string) was not decided upon. This simple format

should enable easy exchange between the partners and reading from any

programming language or data-analysis tools.

3.4 Collocation method

The validation of the results with references needs to take the closest start

point for the vectors of the two datasets.

3.5 Algorithms evaluation: parameters/statistics, criteria

There are several ways to compare data to reference dataset, relevant

metrics for the SID are Firstly:

standard deviation estimate of the difference between the data and

the references

the bias of the difference between the data and the references (in

particular for high displacement values)

Secondly:

the computation time

the number of estimated vectors

the resolution, spatial and temporal coverage

The potential for back tracking will be also analysed and quantified.




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We will start these estimates on

the U and V components (East and North)

the X and Y components (grid)

magnitude and direction

The method will be applied only where reference data are available, which

has limitations (in particular to test the sensitivity to different ice conditions,

periods, areas...). One can note here that the RRDP is designed by the

reference data rather than the satellite data.

The method will be applied to compare algorithms, independently of the

satellites data (frequency, active/passive, resolution, etc...), the RRDP

should be designed to allow processing on gridded daily maps but also

swath data.

The goal of the activity is to select the “best” algorithm(s), it is not obvious

that only one algorithm has the best performance for each criteria and we

may select a pair of algorithms with description of advantages,

performances and limitations of each.

3.6 Sensitivity

During the validation of the final SID algorithm, if possible we will have a

look at the relative performance of the algorithms regarding

sea ice conditions (melt, pack ice, season...)

summer data analysis

hemisphere (Arctic and Antarctic areas)

drift values (areas with mean low values, and with mean high

values)

ice type (MY, FY...)

area (MIZ, pack ice, high and low values areas...)

grid

These analysis will be made if possible (i.e. if data available).

3.7 Uncertainties

A specific work will be conducted to deduce uncertainties for the inferred

vectors, depending on the time and space scales, algorithms, and other

parameters. The performace of the algorithm depends on the uniqueness of

the available patterns. It can therefore vary between for different datasets

and even within a scene. The Project partners will derive a proxy for the

performance and related uncertainty of the algorithm based on measures,

which describe:

texture itself (e.g. Hollands, 2015)

phase correlation surface (e.g. Karvonen, 2012)




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4 Roles and Responsibilities

Project partners are tasked with working on the RRDP, deriving high quality

SID information from a number of independent satellites and several

algorithms. The RRDP will be published on the project web site.

The following tasks will be mainly followed by

Prepare the database for RRDP : DMI (lead), DTU

Build the RRDP and implement the algorithms : DTU (lead),

MetNorway

Algorithm selection : MetNorway (lead), all

Uncertainties estimate : AWI (lead), all

Document the SID algorithms for future production : MetNorway

(lead), all

Future new sensors : NERSC (lead)




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5 Master Time Schedule

Compilation of database and algorithms of RRDP (Feb-May 2016)

Implement of algorithms (April-June 2016)

Test runs of algorithms (June-Sept 2016)

Evaluation of results and selection of algorithms (Sept 2016-March

2017)

Study of the uncertainties estimate of the ECV (Sept 2016-March 2017)

Document the whole processing and the results of the WP (August 2017)




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6 Summary

This report describes

how we will compose the RRDP dataset for SID algorithm comparison

and selection

how we will analyse the candidate SID algorithms to select the final SID

ECV algorithm.




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Appendix A References

Agnew, T., H. Le, T. Hirose, 1997 : Estimation of large scale sea ice motion

from SSM/I 85.5 GHz imagery, Ann. Glaciology, 24, 305-311.

Emery, W.J., C.W. Fowler, J.A. Maslanik, 1997: Satellite-derived maps of

Arctic and Antarctic sea ice motion : 1988-1994, Geophys. Res. Lett., 24

(8), 897-900. doi : 10.1029/97GL00755

Girard-Ardhuin, F., R. Ezraty, 2012 : Enhanced Arctic sea ice drift estimation

merging radiometer and scatterometer data. IEEE Trans. Geosci. Remote

Sensing, vol. 50 (7), pp 2639-2648. Doi : 10.1109/TGRS.2012.2184124.

Haarpaintner, J., 2006 : Arctic wide operational sea ice drift from enhanced

resolution QuikSCAT/SeaWinds scatterometry and its validation. IEEE Trans.

Geosci. Rem. Sens., 44 (1).

Hollands, T. (2012) : Motion tracking of sea ice with SAR satellite data , PhD

thesis, Universität Bremen. hdl:10013/epic.40814, http://nbn-

resolving.de/urn:nbn:de:gbv:46-00102948-13

Hollands, T., S. Linow, W. Dierking, 2015 : Reliability measures for sea ice

motion retrieval from synthetic aperture radar images, IEEE JSTARS, 8 (1),

pp. 67-75. doi: 10.1109/JSTARS.2014.2340572

Karvonen, J., 2012: Operational SAR-based sea ice drift monitoring over the

Baltic sea, Ocean Sci., 8, doi:10.5194/os-8-473-2012.

Kimura, N., A. Nishimura, Y. Tanaka, H. Yamaguchi, 2013: Influence of

winter sea ice motion on summer ice cover in the Arctic, Polar Res., 32,

doi:10.3402/polar.v32i0.20193.

Komarov A.S., D.G. Barber, 2014 : Sea ice motion tracking from sequential

dual-polarization RADARSAT-2 images. IEEE Trans. Geosci. Rem. Sens., 52

(1).

Kwok, R., J.C. Curlander, R. Mc Connell, S.S Pang, 1990 : An ice motion

tracking system at the Alaska SAR facility. IEEE J. Ocean. Eng, 15.

Lavergne T., S. Eastwood, Z. Teffah, H. Schyberg, L. Breivik, 2010: Sea ice

motion from low-resolution satellite sensors: an alternative method and its

validation in the Arctic. J. Geophys. Res., C10032, doi:

10.1029/2009JC005958

Linow S., T. Hollands, W. Dierking, 2015: An assessment of the reliability of

sea ice motion and deformation from synthetic aperture radar data, Annals

of Glaciology, 56 (69), pp. 229-234 . doi: 10.3189/2015AoG69A826

Liu, A. K., Y. Zhao and S. Y. Wu, 1999 : Arctic sea ice drift from wavelet

analysis of NSCAT and special sensor microwave imager data. J. Geophys.

Res., 104 (C5), 11529-11538.

Muckenhuber, S., A. Korosov, S. Sandven, 2015 : Sea ice drift from

Sentinel-1A SAR imagery using open source feature tracking. The

Cryosphere, under discussion




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Ninnis, R.M., W.J.Emery, M.J. Collins, 1986: Automated extraction of pack

ice motion from advanced very high resolution radiometer imagery. J.

Geophys. Res., 91, doi: 10.1029/JC091iC09p10725. issn: 0148-0227.

Rozman, P., J. A. Holemann, T. Krumpen, R. Gerdes, C. Koberle, T.

Lavergne, S. Adams, and F. Girard-Ardhuin, 2011: Validating satellite

derived and modelled sea-ice drift in the Laptev Sea with in situ

measurements from the winter of 2007/08, Polar Res., 30, 7218,

doi:10.3402/polar.v30i0.7218.

Sumata, H., T. Lavergne, F. Girard-Ardhuin, N. Kimura, M. A. Tschudi, F.

Kauker, M. Karcher, R. Gerdes, 2014: An intercomparison of Arctic ice drift

products to deduce uncertainty estimates, J. Geophys. Res. Oceans, 119,

4887–4921, doi:10.1002/2013JC009724.

Thomas, M., S. Misra, C. Kambhamettu and J.T. Kirby, 2005: A robust

motion estimation algorithm for PIV, Measurment Science and Technology,

16, pp. 865-877, doi:10.1088/0957-0233/16/3/031




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