Improving sea surface temperature in a regional ocean model through refined sea surface temperature assimilation
-
Published:2023-05-26
Issue:3
Volume:19
Page:729-744
-
ISSN:1812-0792
-
Container-title:Ocean Science
-
language:en
-
Short-container-title:Ocean Sci.
Author:
Iversen Silje ChristineORCID, Sperrevik Ann Kristin, Goux Olivier
Abstract
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 PMW SSTs are valuable for constraining models during cloudy conditions, 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 satellite SSTs before assimilation. We also introduce a special observation operator, called the supermod operator, into the Regional Ocean Modeling System (ROMS) four-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 on 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 easily be adapted for other observation variables.
Funder
Norges Forskningsråd
Publisher
Copernicus GmbH
Subject
Cell Biology,Developmental Biology,Embryology,Anatomy
Reference52 articles.
1. Alerskans, E., Høyer, J. L., Gentemann, C. L., Pedersen, L. T.,
Nielsen-Englyst, P., and Donlon, C.: Construction of a climate data record of
sea surface temperature from passive microwave measurements, Remote Sens.
Environ., 236, 111485, https://doi.org/10.1016/j.rse.2019.111485, 2020. a 2. Beldring, S., Engeland, K., Roald, L. A., Sælthun, N. R., and Voksø, A.: Estimation of parameters in a distributed precipitation-runoff model for Norway, Hydrol. Earth Syst. Sci., 7, 304–316, https://doi.org/10.5194/hess-7-304-2003, 2003. a 3. Brasnett, B. and Colan, D. S.: Assimilating Retrievals of Sea Surface
Temperature from VIIRS and AMSR2, J. Atmos. Ocean Tech., 33, 361–375,
https://doi.org/10.1175/JTECH-D-15-0093.1, 2016. a, b 4. Buehner, M., Caya, A., Pogson, L., Carrieres, T., and Pestieau, P.: A New
Environment Canada Regional Ice Analysis System, Atmos. Ocean, 51, 18–34,
https://doi.org/10.1080/07055900.2012.747171, 2013. a 5. Cabanes, C., Grouazel, A., von Schuckmann, K., Hamon, M., Turpin, V., Coatanoan, C., Paris, F., Guinehut, S., Boone, C., Ferry, N., de Boyer Montégut, C., Carval, T., Reverdin, G., Pouliquen, S., and Le Traon, P.-Y.: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements, Ocean Sci., 9, 1–18, https://doi.org/10.5194/os-9-1-2013, 2013. a
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|