Spatial variability of daily weather variables in the high plains of the USA

Abstract Confidence in network measurements is more than a question of sensor performance. Measurements represent conditions at a station and are also used to infer conditions between station sites. Site measurements are incorporated into basinwide and regionwide values as well. The confidence associated with these applications is network dependent and a function of the variability imposed on the network by the atmosphere. This study was undertaken in the High Plains of the United States to determine the spatial variability of daily measurements taken in a multivariable network. The fraction of variation explained in a given weather variable at one site by that weather variable at a second site is characterized in terms of the distance of separation. It is shown that 1 year of data is not sufficient to characterize the seasonal patterns in spatial variability. To explain more than 90% of the variation in maximum temperatures between sites, a spacing of 60 km is sufficient on a year-round basis. Minimum temperature, relative humidity, solar radiation, and evapotranspiration require closer spacing (~30 km) to achieve this criterion while wind and soil temperature require 10 and 20 km, respectively. Spacing of precipitation gauges, for this criterion, would be less than 5 km. All results are specific to the High Plains study area. Seasonality of specific variables at this latitude is suggested as the underlying cause for observed differences in spatial variability from month to month.