Using Satellite-Derived Land Use Data to Improve Meso- and Storm-Scale Numerical Weather Prediction
Improvements in numerical weather prediction (NWP) have been hindered by deficiencies in the availability and quality of land cover data. Capabilities to inventory and map land cover conditions and to monitor land surface changes at higher spatial and temporal resolution are needed. To address these issues, a collaborative study is proposed to improve the simulation of surface heat and moisture fluxes in mesoscale NWP models. This proposal will focus upon weather events that are not forecast well by present operational NWP models, such as extreme high and low temperature events, where the incorporation of improved land cover data is likely to have the greatest impact.
Four specific objectives are defined: (1) evaluate the ability to improve upon the daily coarse-resolution (1 km) satellite data by blending in very high-resolution satellite data, (2) evaluate the improvements for using a detailed land surface parameterization scheme in mesoscale model simulations by comparing modeled fluxes to the unique flux observations available over Oklahoma, (3) document the improvements in simulating several extreme temperature events that have occurred over the United States by comparing mesoscale model simulations with and without the detailed land cover data, and (4) incorporate the improved land surface parameterization scheme into the Advanced Regional Prediction System (ARPS) for use in operational NWP efforts.