peter oke, pavel sakov, et al. · seamless data assimilation: one size can fit all peter oke,...

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Seamless data assimilation: one size can fit all

Peter Oke, Pavel Sakov, et al.

CSIRO Marine and Atmospheric Research

Presented by Richard Matear

http://www.cmar.csiro.au/staff/oke/


Bluelink Data Assimilation Philosophy

Data assimilation development under Bluelink started with the following

applications in mind:

Large-scale eddy-resolving models using MOM4 OFAM;

Global coarse-resolution models using MOM2 POAMA;

Global coarse-resolution for climate using MOM4 AusCOM;

Coupled regional model and DA (MOM and UK Met atmosphere);

(Sandary)

Coastal applications using SHOC ROAM. (Sokov)

A model-independent ocean data assimilation code was therefore pursued.

Bluelink evolved into a project addressing problems in the littoral zone &

coupled ocean-atmosphere applications … and now ROMS and NEMO.

An application independent (i.e., not ocean, not atmosphere, not waves …

just related 2-, 3- and 4-dimensional variables) was subsequently developed.



Bluelink Data Assimilation Developments

2007-10: Enhanced version developed & tested for BRAN1p5 & 2p1 &

POAMA

Oke, P. R., G. B. Brassington, D. A. Griffin and A. Schiller, 2008: The Bluelink Ocean Data Assimilation System

(BODAS). Ocean Modelling, 20, 46-70, doi:10.1016/j.ocemod.2007.11.002.

Schiller, A., P. R. Oke, G. B. Brassington, M. Entel, R. Fiedler, D. A. Griffin, and J. V. Mansbridge, 2008: Eddy-

resolving ocean circulation in the Asian-Australian region inferred from an ocean reanalysis effort. Progress

in Oceanography, 76, 334-365.

Yin, Y., O. Alves, P. R. Oke, 2011: An ensemble ocean data assimilation system for seasonal prediction.

Monthly Weather Review, 139, 786-808.

2011-14: BODAS modified for ROAM & OFAM; & EnKF-C system developed

Oke, P. R., M. L. Cahill, D. A. Griffin, M. Herzfeld, 2013: Constraining a regional ocean model with climatology

and observations: Application to the Hawaiian Islands. 10, 20-26, CAWCR Research Letters.

Sakov, P. 2014: EnKF-C user guide, Version (2014), p. 41 (https://code.google.com/p/enkf-c).

Sandery, P. A., Sakov, P., & Majewski, L. (2014). The impact of open boundary forcing on forecasting the East

Australian Current using ensemble data assimilation. Ocean Modelling, 84, 1-11.

Ensemble and Coupled Data Assimilation

O'Kane, T. J., Oke, P. R. and Sandery, P. A.: Predicting the East Australian Current, Ocean Modelling, 38(3),

251–266, doi:10.1016/j.ocemod.2011.04.003, 2011.

Sandery, P. A. and O'Kane, T. J.: Coupled initialization in an ocean–atmosphere tropical cyclone prediction

system, Q J Roy Meteor Soc, 2013.

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https://code.google.com/p/enkf-c





Assimilated data

The grey bar for GFO indicates that data were with-held (because of large errors)

BRAN2p1 (with OFAM2)



BRAN4 (with OFAM3)

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Correlation with Topex/Poseidon and Jason-1 SLA

BRAN3p5 and 3p6

generally have higher

correlation with

observations everywhere

All reanalyses are poorer at

higher latitudes

5



Salinity increments

BRAN2p1 BRAN3p5

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Future

OFAM4

Global 1/10 degree eddy resolving model for climate and

regional studies (based on MOM5)

Includes sea-ice and biogeochemical cycles

Will be used to produce global reanalysis products for both

physical and biogeochemical variables

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Data Assimilation Methodologies - opportunities

Multi-scale ensemble

Hybrid EnOI-EnKF (Sakov)

Alternative variables:

Currently: T, S, U, V, eta

Possible: SST, SSS, Depth of isopycnals

Coupled data assimilation

Ocean-atmosphere; ocean-ice; ocean-BGC; ocean-

atmosphere-ice-BGC …

Assimilation of unconventional observations (e.g., ocean colour)

Can we develop DA systems that can exploit both physical and

biogeochemical observations to improve the simulated physics and

biology?

8



Hawaii case study 9

Constraining ROAM

with only

climatology and

observations

Enables rapid deployment

with minimal dependency

on external data



Hawaii case study

Comparison of mean SLA (top) and SST (bottom)

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Hawaii case study

Comparison of standard deviation of SLA (top) and SST (bottom)

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Hawaii case study

Comparison between

modelled (top) and

observed (middle; XBT)

temperature section;

and difference (bottom)

12



BRAN3p6 – SST RMSD

… based on comparisons with AMSR-E SST (AMSR-E error ~ 0.25-0.5 deg C)

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RMS Error with Topex/Poseidon and Jason-1 SLA

BRAN3p5 and 3p6

are very similar …

both are better

than BRAN2p1

BRAN2p1 and 3p5

comparisons are

with T/P

BRAN3p6

comparisons are

with Jason-1

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