Merging radar and gauge information within a dynamical model combination framework for precipitation estimation in cold climates

Abstract

This study presents a dynamic forecast combination approach adapted to incorporate multiple sources of precipitation. Dynamic combination serves to utilise the varying merit each data source exhibits with time. The dynamic model combination framework presented merges a nonparametric k-nearest neighbour (k-nn) estimation of radar precipitation with Thin Plate Spline (TPS) interpolated gauge precipitation. Since air temperature is an essential variable to discriminate the phase of the precipitation in cold climates, this study uses radar precipitation and air temperature as the two variables in the dynamic combination algorithm. The merging of k-nn and TPS estimates is shown to reduce the RMSE by 25% compared to the original radar precipitation rates. The usefulness of air temperature is found not to be as significant in the combination as it is in the formulation of the nonparametric radar precipitation fields for cold incident temperatures.