Preprints
https://doi.org/10.5194/gmd-2023-221
https://doi.org/10.5194/gmd-2023-221
Submitted as: methods for assessment of models
 | 
05 Dec 2023
Submitted as: methods for assessment of models |  | 05 Dec 2023
Status: this preprint is currently under review for the journal GMD.

Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter

Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee

Abstract. The advanced snow data assimilation is developed in this study with satellite remote-sensing retrievals of snow water equivalent(SWE) and snow cover fraction(SCF) utilizing the local ensemble transform Kalman filter based on the Joint U.K. Land Environment Simulator(JULES) land model. The system assimilates SWE from the Advanced Microwave Scanning Radiometer 2(AMSR2) and SCF from the Interactive Multisensor Snow and Ice Mapping System(IMS) during April 2013–2020. The performance is evaluated by the validations with independent data assimilation products derived from in-situ observation.

The baseline model simulation from JULES without satellite data assimilation shows a superior performance in high-latitude regions with heavy snow accumulation, but relatively inferior in the transition regions with less snow and high spatial and temporal variation. Contrastingly, the AMSR2 satellite data exhibit a superior performance in the transition regions, but poor performance in the high latitudes, presumably due to the limitation in the penetrating depth of satellite retrieval. The data assimilation(DA) that combines AMSR2 and IMS satellite data with the JULES model backgrounds demonstrates the positive impacts by reducing uncertainty in both satellite-derived snow data in penetrating deep snow and the model simulations in the transition regions. While DA shows superior performance in most regions, it specifically improves the analysis in the mid-latitude transition regions where the model background errors from the ensemble runs are significantly larger than the observation errors, emphasizing the substantial influence of satellite information. The long-term analysis of snow manifests a pronounced variability in the continental interior at the interannual timescales, which implies large uncertainty in the snow initialization for the sub-seasonal to seasonal predictions of the climate models, potentially degrading prediction skills without satellite snow data assimilation.

Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-221', Anonymous Referee #1, 13 Dec 2023
  • RC2: 'Comment on gmd-2023-221', Anonymous Referee #2, 23 Dec 2023
  • RC3: 'Comment on gmd-2023-221', Chih-Chi Hu, 27 Dec 2023
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee

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Short summary
We developed a snow assimilation system using satellite data based on a land surface model. The snow states produced by the assimilation system demonstrate high performance in all regions, including transition regions, compared to the satellite data and land model. As snow significantly influences energy and water balance at the atmosphere-land boundary, this approach allows for a more accurate prediction of atmospheric conditions by realistically representing atmosphere-land interactions.