Evaluation of Satellite Derived Information as an Analysis Tool and to Improve Predictability Over Conventional Data Sparse Regions
NAVAL POSTGRADUATE SCHOOL MONTEREY CA DEPT OF METEOROLOGY
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The long-term goal of this research is to develop a method to assimilate satellite observations into a mesoscale model to improve prediction in data sparse areas. The specific objectives of this project are to assimilate satellite cloud drift winds, satellite soundings, and satellite irradiances into a mesoscale model using multiquadric interpolation. The system will be tested using data obtained during FASTEX to demonstrate its capability. The approach is to adapt the existing three-dimensional multiquadric interpolation based data assimilation technique developed at NPS to ingest satellite derived products. The satellite irradiances will be assimilated by applying a radiance model as a weak constraint in the assimilation method. This will be done for several cases during FASTEX and a series of numerical experiments will be done to test the impact of these data on the mesoscale forecasts. The work completed in 1998 has focused on the implementation of the three-dimensional multiquadric interpolation assimilation with the MM5 model and satellite-derived winds supplied by Chris Veldon of the University of Wisconsin. The MM5 model has been run on one of the cases from FASTEX, both with and without the satellite-derived winds. The model was run using a 108 and 36 km grid resolution from a cold start using NOGAPS analyses as the first guess in the data assimilation. The multiquadric data assimilation has been modified to accept the satellite-derived winds, and tests to optimize the use of this set of observations is being done. The model forecasts with the various sets of observational data are being examined to determine their impact on the mesoscale structure.
- Unmanned Spacecraft