HR: 08:00h
AN: H41I-01 INVITED    [Abstracts]
TI: New methods for assimilating remotely sensed observations of snow covered area into land surface models
AU: * Zaitchik, B F
EM: ben.zaitchik@nasa.gov
AF: NASA GSFC/ University of Maryland, Code 614.3 NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AU: Rodell, M
EM: matthew.rodell@nasa.gov
AF: NASA GSFC, Code 614.3 NASA Goddard Space Flight Center, Greenbelt, MD 20771, United States
AB: Snow cover is the fastest varying land surface feature on Earth, and it has a dramatic impact on atmospheric processes and hydrology at the local, regional, and global scales. Snow is also an important memory component of the climate system, as seasonal establishment of the snow pack has a strong influence on further snow accumulation, while seasonal melt can impact soil moisture for months into the warm season. For these reasons it is essential that land surface models (LSMs) provide an accurate representation of snow cover for forecast initialization and retrospective analysis. Data assimilation provides a tool for updating snow within an LSM, but assimilation algorithms that utilize observations of snow covered area (SCA) are complicated by (1) an information deficit between the retrieved variable and those simulated by the LSM, and (2) by the potential to inadvertently disturb other water storage variables during the assimilation update. The results of two assimilation algorithms are presented. In the first, remotely sensed snow cover observations are introduced to the Noah LSM using a rule-based function that converts SCA to snow water equivalent (SWE). In the second, observations are introduced to the model one day in advance of the observation time. These advance observations are used to adjust the atmospheric forcing fields within the LSM, pulling the model into agreement with satellite data. In global simulations, both algorithms improved the LSM's simulation of total snow covered area, as evaluated against independent datasets, and both provided some improvement in the simulation of SWE. The algorithms differed in their impact on surface energy fluxes and the local hydrologic budget, and the implications of these differences will be discussed.
DE: 1807 Climate impacts
DE: 1843 Land/atmosphere interactions (1218, 1631, 3322)
DE: 1847 Modeling
DE: 1863 Snow and ice (0736, 0738, 0776, 1827)
DE: 3315 Data assimilation
SC: Hydrology [H]
MN: 2007 Fall Meeting