Estimating point rainfall statistics from gridded large scale rainfall data

Many stochastic rainfall generators rely on the structure of rainfall considered a spatio-temporal point process. Ordinary practice is to infer these parameters from data observed on a group of rain gauges over some reference time. However, gridded estimates of rainfall (out of meteorological radar, satellites, or meteorological models) give a distinct perspective on the same precipitation, albeit with scaling issues and biases, and a question is how to value this information even if we target the urban scale. This contribution is about how the scaling issue can be handled in a geostatistical perspective, deriving a point variability model consistent with the variability observed at grid scale. Available local raingauge data, even in moderate amount, can be used to checked the suggested methodology. Expected uses are in data merging, downscaling climate scenarios, and establishing local rainfall models, also in local data-poor contextes.

 

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