Abstract

AbstractTo estimate daily catchment precipitation from point observations there is a need to understand the spatial pattern, particularly in mountainous regions. One of the most important processes occurring there is orographic enhancement, which is affected by, among other things, wind speed and wind direction. The objective of this paper was to investigate whether the relationship between precipitation, airflow and topography could be described by statistical relationships using data easily available in an operational environment. The purpose was to establish a statistical model to describe basic patterns of precipitation distribution. This model, if successful, can be used to account for the topographical influence in precipitation interpolation schemes. A statistical analysis was carried out to define the most relevant variables, and, based on that analysis, a regression model was established through stepwise regression. Some 15 years of precipitation data from 370 stations in Sweden were used for the analysis. The geostrophic wind, computed from pressure observations, was assumed to represent the airflow at the relevant altitude. Precipitation data for each station were divided into 48 classes representing different wind directions and wind speeds. Among the variables selected, the single most important one was found to be the location of a station with respect to a mountain range. On the upwind side, precipitation increased with increasing wind speed. On the leeward side there was less variation in precipitation, and wind speed did not affect the precipitation amounts to the same degree. For ascending air, slope multiplied by wind speed was another important factor. The effect of slope was enhanced close to the coast, and reduced for mountain valleys with upwind barriers. The stepwise procedure led to a regression model that also included the meridional and zonal wind components. Their inclusion might indicate the importance of air mass characteristics not explicitly accounted for. Copyright © 2003 Royal Meteorological Society

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