Development of a Relationship between Station and Grid-Box Rainday Frequencies for Climate Model Evaluation

The validation of climate model simulations creates substantial demands for comprehensive observed climate datasets. These datasets need not only to be historically and geographically extensive, but need also to be describing areally averaged climate, akin to that generated by climate models. This paper addresses one particular difficulty found when attempting to evaluate the daily precipitation characteristics of a global climate model, namely the problem of aggregating daily precipitation characteristics from station to area. Methodologies are developed for estimating the standard deviation and rainday frequency of grid-box mean daily precipitation time series from relatively few individual station time series. Temporal statistics of such areal-mean time series depend on the number of stations used to construct the areal means and are shown to be biased (standard deviations too high, too few raindays) if insufficient stations are available. It is shown that these biases can be largely removed by using parameters that describe the spatial characteristics of daily precipitation anomalies. These spatial parameters (the mean interstation correlation between station time series and the mean interstation probability of coincident dry days) are calculated from a relatively small number of available station time series for Europe, China, and Zimbabwe. The relationships that use these parameters are able to successfully reproduce the statistics of grid-box means from the statistics of individual stations. They are then used to estimate the statistics of grid-box means as if constructed from an infinite number of stations (for standard deviations) or 15 stations (for rainday frequencies), even if substantially fewer stations are actually available. These estimated statistics can be used for the evaluation of daily precipitation characteristics in climate model simulations, and an example is given using a simulation by the Commonwealth Scientific and Industrial Research Organisation atmosphere general circulation model. Applying the authors’ aggregation methodology to observed station data is a more faithful form of model validation than using unadjusted station time series.