A Mesoscale Data Analysis and Downscaling Method over Complex Terrain

Abstract

A mesoscale data analysis method for meteorological station reports is presented. Irregularly distributed measured values are combined with measurement-independent a priori information about the modification of analysis fields due to topographic forcing. As a physical constraint to a thin-plate spline interpolation, the so-called ?fingerprint method? recognizes patterns of topographic impact in the data and allows for the transfer of information to data-sparse areas. The results of the method are small-scale interpolation fields on a regular grid including topographically induced patterns that are not resolved by the station network. Presently, the fingerprint method is designed for the analysis of scalar meteorological variables like reduced pressure or air temperature. The principles for the fingerprint technique are based on idealized influence fields. They are calculated for thermal and dynamic surface forcing. For the former, the effects of reduced air volumes in valleys, the elevated heat sources, and the stability of the valley atmosphere are taken into account. The increase of temperature under ideal conditions in comparison to flat terrain is determined on a 1-km grid using height and surface geometry information. For the latter, a perturbation of an originally constant cross-Alpine temperature gradient is calculated by a topographical weighting. As a result, the gradient is steep where the mountain range is high and steep. If, during the interpolation process, some signal of the idealized patterns is found in the station data, it is used to downscale the analysis. It is shown by a cross validation of a case study that the interpolation of a mean sea level pressure field over the Alpine region is improved objectively by the method. Thermally induced mesoscale patterns are visible in the interpolated pressure field.