Estimates of areal mean precipitation intensity derived from rain gages are commonly used to assess the performance of rainfall radars and satellite rainfall retrieval algorithms. Areal mean precipitation time series collected during short‐duration climate field studies are also used as inputs to water and energy balance models which simulate land‐atmosphere interactions during the experiments. In two recent field experiments (1987 First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) and the Multisensor Airborne Campaign for Hydrology 1990 (MAC‐HYDRO '90)) designed to investigate the climatic signatures of land‐surface forcings and to test airborne sensors, rain gages were placed over the watersheds of interest. These gages provide the sole means for estimating storm precipitation over these areas, and the gage densities present during these experiments indicate that there is a large uncertainty in estimating areal mean precipitation intensity for single storm events. Using a theoretical model of time‐ and area‐averaged space‐ time rainfall and a model rainfall generator, the error structure of areal mean precipitation intensity is studied for storms statistically similar to those observed in the FIFE and MAC‐HYDRO field experiments. Comparisons of the error versus gage density trade‐off curves to those calculated using the storm observations show that the rainfall simulator can provide good estimates of the expected measurement error given only the expected intensity, coefficient of variation, and rain cell diameter or correlation length scale, and that these errors can quickly become very large (in excess of 20%) for certain storms measured with a network whose size is below a “critical” gage density. Because the mean storm rainfall error is particularly sensitive to the correlation length, it is important that future field experiments include radar and/or dense rain gage networks capable of accurately characterizing the rainstorm spatial and temporal correlation structure.
All Science Journal Classification (ASJC) codes
- Water Science and Technology