Preprints
https://doi.org/10.5194/tc-2023-115
https://doi.org/10.5194/tc-2023-115
30 Aug 2023
 | 30 Aug 2023
Status: a revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean-sea ice model

Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin

Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the satellite individual swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (specially critical on periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic is not used, providing less temporal resolution in the data assimilation (DA) analysis and therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenge (i) and (ii) can be avoided by assimilating satellite individual swaths (Level-3 Uncollated) instead of daily means (Level-3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and The Los Alamos Sea Ice Model (CICE) together with the Ensemble Kalman Filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the Mean Absolute Difference (MAD) shows a 10 % improvement at the end of the assimilation period, and a 7 % improvement at the end of the 7-day forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.

Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-115', Anonymous Referee #1, 18 Sep 2023
    • AC1: 'Reply on RC1', Marina Duran Moro, 04 Dec 2023
  • RC2: 'Comment on tc-2023-115', Anonymous Referee #2, 06 Nov 2023
    • AC2: 'Reply on RC2', Marina Duran Moro, 04 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2023-115', Anonymous Referee #1, 18 Sep 2023
    • AC1: 'Reply on RC1', Marina Duran Moro, 04 Dec 2023
  • RC2: 'Comment on tc-2023-115', Anonymous Referee #2, 06 Nov 2023
    • AC2: 'Reply on RC2', Marina Duran Moro, 04 Dec 2023
Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin

Data sets

Ice-charts MET Norway https://doi.org/10.48670/moi-00128

Model code and software

metno/metroms: Version 0.4.1 Jens Debernard, Nils Melsom Kristensen, Sebastian Maartensson, Keguang Wang, Kate Hedstrom, Jostein Brændshøi, and Nicholas Szapiro https://doi.org/10.5281/zenodo.5067164

EnKF-C v.2.9.9 Pavel Sakov https://github.com/sakov/EnKF-C.git

Marina Durán Moro, Ann Kristin Sperrevik, Thomas Lavergne, Laurent Bertino, Yvonne Gusdal, Silje Christine Iversen, and Jozef Rusin

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Latest update: 14 Feb 2024
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Short summary
Individual satellite passes instead of daily means of sea ice concentration are used to correct the sea ice model forecast in the Barents Sea. The use of passes provides a significantly larger improvement of the forecasts even after a 7-day period due to the more precise information on temporal and spatial variability contained in the passes. One major advantage of the use of satellite passes is that there is no need to wait for the daily means availability in order to update the forecast.