HR: 0800h
AN: C31A-0279    [Abstracts]
TI: A Meteorological Distribution System for High Resolution Terrestrial Modeling (MicroMet)
AU: * Liston, G E
EM: liston@atmos.colostate.edu
AF: Colorado State University, Department of Atmospheric Science, Fort Collins, CO 80523-1371 United States
AU: Elder, K
EM: kelder@fs.fed.us
AF: Rocky Mountain Research Station, USDA Forest Service, Fort Collins, CO 80526 United States
AB: Spatially distributed terrestrial models generally require atmospheric forcing data on horizontal grids that are of higher resolution than available meteorological data. Furthermore, the meteorological data collected may not necessarily represent the area of interest's meteorological variability. To address these deficiencies, computationally efficient and physically realistic methods must be developed to take available meteorological data sets (e.g., meteorological tower observations) and generate high-resolution atmospheric-forcing distributions. This poster describes MicroMet, a quasi-physically-based, but simple meteorological distribution model designed to produce high-resolution (e.g., 5-m to 1-km horizontal grid increments) meteorological data distributions required to run spatially distributed terrestrial models over a wide variety of landscapes. The model produces distributions of the seven fundamental atmospheric forcing variables required to run most terrestrial models: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, and precipitation. MicroMet includes a preprocessor that analyzes meteorological station data and identifies and repairs potential data deficiencies. The model uses known relationships between meteorological variables and the surrounding area (primarily topography) to distribute those variables over any given landscape. MicroMet performs two kinds of adjustments to available meteorological data: 1) when there are data at more than one location, at a given time, the data are spatially interpolated over the domain using a Barnes objective analysis scheme, and 2) physical sub-models are applied to each MicroMet variable to improve its realism at a given point in space and time with respect to the terrain. The three, 25-km by 25-km, Cold Land Processes Experiment (CLPX) mesoscale study areas (MSAs: Fraser, North Park, and Rabbit Ears) will be used as example MicroMet simulation domains, to highlight model strengths, weaknesses, and applications.
DE: 3307 Boundary layer processes
SC: Cryosphere [C]
MN: 2004 AGU Fall Meeting