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

Snow water equivalent (SWE), as one of the land initial or boundary conditions, plays a crucial role in global or regional energy and water balance, thereby exerting a considerable impact on seasonal and subseasonal-scale predictions owing to its enduring persistence over 1 to 2 months. Despite its...

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Bibliographic Details
Published in:Geoscientific Model Development 2024-12, Vol.17 (23), p.8799-8816
Main Authors: Lee, Joonlee, Lee, Myong-In, Tak, Sunlae, Seo, Eunkyo, Lee, Yong-Keun
Format: Article
Language:English
Subjects:
Accumulation
Algorithms
Analysis
Artificial satellites in remote sensing
Boundary conditions
Data assimilation
Data collection
Equivalence
Errors
Ice mapping
Interactive systems
Kalman filters
Land surface models
Latitude
Microwave radiometers
Radiometers
Regional development
Regions
Remote sensing
Satellite data
Satellite observation
Satellites
Simulation methods
Simulators
Snow
Snow accumulation
Snow and ice
Snow cover
Snow-water equivalent
Temporal variations
Uncertainty
Water balance
Water purification
Winter
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Summary:Snow water equivalent (SWE), as one of the land initial or boundary conditions, plays a crucial role in global or regional energy and water balance, thereby exerting a considerable impact on seasonal and subseasonal-scale predictions owing to its enduring persistence over 1 to 2 months. Despite its importance, most SWE initialization remains challenging due to its reliance on simple approaches based on spatially limited observations. Therefore, this study developed an advanced SWE data assimilation framework with satellite remote sensing data utilizing the local ensemble transform Kalman filter (LETKF) and the Joint UK Land Environment Simulator (JULES) land model. This approach constitutes an objective method that optimally combines two previously unattempted incomplete data sources: the satellite SWE retrieval from the Advanced Microwave Scanning Radiometer 2 (AMSR2) and dynamically balanced SWE from the JULES land surface model. In this framework, an algorithm is additionally considered to determine the assimilation process based on the presence or absence of snow cover from the Interactive Multisensor Snow and Ice Mapping System (IMS) satellite, renowned for its superior reliability.
ISSN:1991-959X
1991-962X
1991-962X
DOI:10.5194/gmd-17-8799-2024