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Mesoscale Analysis by Numerical Modeling Coupled with Sounding Retrieval from Satellites

Alan E. LiptonDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Thomas H. Vonder HaarDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Abstract

The development and evaluation of a system for time-continuous mesoscale analysis is presented, with a focus on retrieving water vapor concentrations and ground surface temperatures from VISSR Atmospheric Sounder (VAS) data. The analysis system is distinguished by an intimate coupling of retrieval and numerical modeling processes that avoids some of the problem researchers have encountered when satellite-retrieved parameters have been input to models. The system incorporates virtually all of the temporal, vertical and horizontal structure that can be resolved in VAS soundings while maintaining model-generated gradients. The two primary components of the system are a version of the CSU Regional Atmospheric Modeling System (RAMS) and an algorithm for retrieving meteorological parameters from VAS data.

The analysis system was evaluated by means of simulations, with a domain that consisted of a vertical cross section through a broad mountain slope. The purposes were to determine the accuracy of coupled analysis results under controlled conditions and to compare results of the coupled scheme with those of other analysis schemes. For water vapor analysis, vertical gradients were more accurately resolved with the coupled method than with conventional retrieval from satellite data. The coupled method's incorporation of VAS data from multiple observation times was valuable for making mesoscale horizontal gradients stand out more clearly amid the noise in the water vapor analysis. In addition, the method was relatively robust when confronted with a common problem in analysis of the preconvective atmosphere—contamination of the satellite data by increasing amounts of small convective clouds. Analyses in which surface temperatures were derived from satellite-based retrievals were compared with the alternative of relying on energy balance computations without mesoscale data about soil characteristics. The surface temperatures from the two methods differed by as much as 5 K, giving rise to prominent differences in the induced mesoscale circulations. The energy balance computations were so sensitive to soil characteristics that the satellite retrieval method gave more accurate results even with cloud contamination.

Abstract

The development and evaluation of a system for time-continuous mesoscale analysis is presented, with a focus on retrieving water vapor concentrations and ground surface temperatures from VISSR Atmospheric Sounder (VAS) data. The analysis system is distinguished by an intimate coupling of retrieval and numerical modeling processes that avoids some of the problem researchers have encountered when satellite-retrieved parameters have been input to models. The system incorporates virtually all of the temporal, vertical and horizontal structure that can be resolved in VAS soundings while maintaining model-generated gradients. The two primary components of the system are a version of the CSU Regional Atmospheric Modeling System (RAMS) and an algorithm for retrieving meteorological parameters from VAS data.

The analysis system was evaluated by means of simulations, with a domain that consisted of a vertical cross section through a broad mountain slope. The purposes were to determine the accuracy of coupled analysis results under controlled conditions and to compare results of the coupled scheme with those of other analysis schemes. For water vapor analysis, vertical gradients were more accurately resolved with the coupled method than with conventional retrieval from satellite data. The coupled method's incorporation of VAS data from multiple observation times was valuable for making mesoscale horizontal gradients stand out more clearly amid the noise in the water vapor analysis. In addition, the method was relatively robust when confronted with a common problem in analysis of the preconvective atmosphere—contamination of the satellite data by increasing amounts of small convective clouds. Analyses in which surface temperatures were derived from satellite-based retrievals were compared with the alternative of relying on energy balance computations without mesoscale data about soil characteristics. The surface temperatures from the two methods differed by as much as 5 K, giving rise to prominent differences in the induced mesoscale circulations. The energy balance computations were so sensitive to soil characteristics that the satellite retrieval method gave more accurate results even with cloud contamination.

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