Preprints
https://doi.org/10.5194/egusphere-2022-957
https://doi.org/10.5194/egusphere-2022-957
 
27 Sep 2022
27 Sep 2022

Improving the SST in a regional ocean model through refined SST assimilation

Silje Christine Iversen1, Ann Kristin Sperrevik2, and Olivier Goux3 Silje Christine Iversen et al.
  • 1Department of Physics and Technology, Arctic University of Norway, Tromsø, Norway
  • 2Division for Ocean and Ice, Norwegian Meteorological Institute, Oslo, Norway
  • 3CERFACS/CECI CNRS UMR 5318, Toulouse, France

Abstract. Infrared (IR) and passive microwave (PMW) satellite sea surface temperature (SST) retrievals are valuable to assimilate into high-resolution regional ocean forecast models. Still, there are issues related to these SSTs that need to be addressed to achieve improved ocean forecasts. Firstly, satellite SST products tend to be biased. Assimilating SSTs from different providers can thus cause the ocean model to receive inconsistent information. Secondly, while the PMW SSTs are valuable for constraining the model in cloudy regions, the spatial resolution of these retrievals is rather coarse. Assimilating PMW SSTs into high-resolution ocean models will spatially smooth the modeled SST and consequently remove finer SST structures. In this study, we implement a bias correction scheme that corrects the satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation algorithm. This supermod operator handles the resolution mismatch between the coarse observations and the finer model. We test the bias correction scheme and the supermod operator using a setup of ROMS covering the shelf seas and shelf break off Norway. The results show that the validation statistics in the modeled SST improve if we apply the bias correction scheme. We also find improvements in the validation statistics when we assimilate PMW SSTs in conjunction with the IR SSTs. However, our supermod operator must be activated to avoid smoothing the modeled SST structures of spatial scales smaller than twice the PMW SST footprint. Both the bias correction scheme and the supermod operator are easy to apply, and the supermod operator can be adapted for other observation variables.

Silje Christine Iversen et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-957', Anonymous Referee #1, 20 Oct 2022
  • RC2: 'Comment on egusphere-2022-957', Anonymous Referee #2, 08 Nov 2022

Silje Christine Iversen et al.

Data sets

Model data from experiments Silje Christine Iversen https://thredds.met.no/thredds/projects/supermodop.html

Model code and software

Supermod operator Olivier Goux and Silje Christine Iversen https://github.com/siljeci/ROMS_supermod

Silje Christine Iversen et al.

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Short summary
We present two methods to refine the assimilation of satellite sea surface temperatures (SSTs) into a regional ocean model. First, we correct the SSTs for biases and show that this correction reduces the model SST errors. Then, we implement a special observation operator that handles the spatial resolution mismatch between coarse passive microwave SSTs and the high-resolution model. We find that excluding this operator spatially smooths the modeled SST, whereas its inclusion prevents this.