4DVAR assimilation of surface velocities destroy sst

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xavierC
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4DVAR assimilation of surface velocities destroy sst

#1 Post by xavierC »

Capture d’écran 2020-01-30 à 10.58.13.png
Hi,
I am running some I4DVAR experiments where i assimilate surface velocities from radar measurement. But while i tend to improve my surface current i am inducing a strong cooling of the SST as illustrated in the attached figure. This is after a 24 hour run (with data every 30min) but after few days sst gradient keep increasing and can eventually blowup the model.

Any advice welcome
Thanks
Xavier

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jivica
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Location: The University of Western Australia, Perth, Australia

Re: 4DVAR assimilation of surface velocities destroy sst

#2 Post by jivica »

I was waiting for someone else to response, but none did, so here it goes.
What I think is happening is that you have defined too loose model forcing standard deviations (i.e. std file you use in your s4var.in STDnameF).
The "easy" way for model to get closer to your obs (in your case surface currents) is to alter heatflux as std are too big and cool the surface. You should be careful how you construct them (not to use diurnal variations which gives you large values) as they are "penalizing" model deviations.

Cheers
Ivica

xavierC
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Location: Brest

Re: 4DVAR assimilation of surface velocities destroy sst

#3 Post by xavierC »

thanks for your answer Jivica,

unfortunately even by reducing drastically by 90% my standard deviation i don't solve it. Plus i did not compile with -DADJUST_STFLUX.

i think the problems come from the data i assimilate, which are not divergence free and therefore may induce some spurious upwelling.

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arango
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Re: 4DVAR assimilation of surface velocities destroy sst

#4 Post by arango »

It is all a matter of the assumed background error covariance hypothesis and its parameters. What do you trust more the model or the observations? HF radar velocity observations are very tricky since it is a very noisy data, and depends on the processing: averaging, detiding, super-observations, errors of representation, etc. I am not an expert on HF radar, but our group is actively working on their impact in the circulation 4D-Var analysis. We are assimilating the radials instead of the biased gridded product. ROMS observation operator can assimilate the original HF radar radials. We are also working on quality control and standardization of this observation asset within the U.S. In general, assimilating velocity observations are very tricky because it requires sophisticated processing and error models. I know of several people that question how useful are the HF radar observations in data assimilation. As you have found out, it is a difficult dataset that requires special considerations. We have been carrying out observation impacts and observation sensitivities about these observations that we publish shortly.

One thing that we recommend is to use the dual (W4DPSAS plus RPCG) instead of the primal (IS4DVAR) formulation. The primal formulation is becomming deprecated everywhere. The dual formulation is much better because it allows strong and weak constraint, and several useful analysis tools to examine the analysis and forecast observations impacts and observations seinsitivities (4D-Var)T. See :arrow: WikiROMS for more information.

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