HR: 0800h
AN: C21B-0463    [Abstracts]
TI: Land Surface Model Biases and their Impacts on the Assimilation of Snow-related Observations
AU: * Arsenault, K R
EM: kristi@hsb.gsfc.nasa.gov
AF: UMBC Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center (Code 614.3), Greenbelt, MD 20771, United States
AU: Kumar, S
EM: sujay@hsb.gsfc.nasa.gov
AF: UMBC Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center (Code 614.3), Greenbelt, MD 20771, United States
AU: Hunter, S M
EM: smhunter@do.usbr.gov
AF: US Bureau of Reclamation Technical Services Center, Denver Federal Center, Building 67, Mail Code 86-68510, Denver, CO 80225, United States
AU: Aman, R
EM: raaman5@yahoo.com
AF: US Bureau of Reclamation Technical Services Center, Denver Federal Center, Building 67, Mail Code 86-68510, Denver, CO 80225, United States
AU: Houser, P R
EM: phouser@gmu.edu
AF: Center for Research on Environment & Water; George Mason University, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705, United States
AU: Toll, D
EM: david.l.toll@nasa.gov
AF: NASA Goddard Space Flight Center, Mail Code 614.3, Greenbelt, MD 20771, United States
AU: Engman, T
EM: tengman@hsb.gsfc.nasa.gov
AF: SAIC; NASA Goddard Space Flight Center, 4600 Powder Mill Road, Beltsville, MD 20705, United States
AU: Nigro, J
EM: jnigro@hsb.gsfc.nasa.gov
AF: SSAI; NASA Goddard Space Flight Center, 10210 Greenbelt Road, Suite 600, Lanham, MD 20706, United States
AB: Some recent snow modeling studies have employed a wide range of assimilation methods to incorporate snow cover or other snow-related observations into different hydrological or land surface models. These methods often include taking both model and observation biases into account throughout the model integration. This study focuses more on diagnosing the model biases and presenting their subsequent impacts on assimilating snow observations and modeled snowmelt processes. In this study, the land surface model, the Community Land Model (CLM), is used within the Land Information System (LIS) modeling framework to show how such biases impact the assimilation of MODIS snow cover observations. Alternative in-situ and satellite-based observations are used to help guide the CLM LSM in better predicting snowpack conditions and more realistic timing of snowmelt for a western US mountainous region. Also, MODIS snow cover observation biases will be discussed, and validation results will be provided. The issues faced with inserting or assimilating MODIS snow cover at moderate spatial resolutions (like 1km or less) will be addressed, and the impacts on CLM will be presented.
DE: 0700 CRYOSPHERE (4540)
DE: 0736 Snow (1827, 1863)
DE: 0742 Avalanches
DE: 0764 Energy balance
SC: Cryosphere [C]
MN: 2007 Fall Meeting