Geostatistical model averaging for locally calibrated probabilistic quantitative precipitation forecasting

Over the past two decades, ensembles of numerical weather prediction models have been developed, in which multiple estimates of the current state of the atmosphere are used to generate probabilistic forecasts for future weather events. However, ensemble systems are uncalibrated and biased, and thus need to be statistically postprocessed.

Bayesian model averaging (BMA) is a preferred way of doing this. Particularly for quantitative precipitation, biases and calibration errors depend critically on local terrain features. We introduce a geostatistical approach to modeling locally varying BMA parameters, as opposed to the extant method that holds parameters constant across the forecast domain.