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Proceedings Paper

Assimilation of MODIS snow cover fraction for improving snow variables estimation in west China
Author(s): Chunlin Huang
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Paper Abstract

Accurate estimation of snow properties is important for effective water resources management especially in mountainous areas. In this work, we develop a snow data assimilation scheme based on ensemble Kalman filter (EnKF), which can assimilate remotely sensed snow observations into the Common Land Model (CoLM) to produce spatially continuous and temporally consistent snow variables. The snow cover fraction (SCF) product (MOD10C1) from the moderate resolution imaging spectroradiometer (MODIS) aboard the NASA Terra satellite was used to update CoLM snow properties. The assimilation experiment is conducted during 2003-2004, in Xingjiang province, west China. The preliminary results are very promising and show that distributions of snow variables (such as SCF, snow depth, and SWE) are more reasonable and reliable after assimilating MODIS SCF data. The results also indicate that EnKF is an effective and operationally feasible solution for improve snow properties prediction.

Paper Details

Date Published: 19 October 2012
PDF: 7 pages
Proc. SPIE 8531, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV, 853111 (19 October 2012); doi: 10.1117/12.974512
Show Author Affiliations
Chunlin Huang, Cold and Arid Regions Environmental and Engineering Research Institute (China)

Published in SPIE Proceedings Vol. 8531:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
Christopher M. U. Neale; Antonino Maltese, Editor(s)

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