Radar-guided interpolation of climatological precipitation data

Authors

  • Arthur T. DeGaetano,

    Corresponding author
    1. Northeast Regional Climate Center, Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
    • Northeast Regional Climate Center, 1119 Bradfield Hall, Department of Earth and Atmospheric Sciences, Cornell University, Ithaca NY 14853, USA.
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  • Daniel S. Wilks

    1. Northeast Regional Climate Center, Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY, USA
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Abstract

A refined approach for interpolating daily precipitation accumulations is presented, which combines radar-based information to characterize the spatial distribution and gross accumulation of precipitation with observed daily rain-gauge data to adjust for spatially varying errors in the radar estimates. Considering the rain gauge observations to be true values at each measurement location, daily radar errors are calculated at these points. These errors are then interpolated back to the radar grid, providing a spatially varying daily adjustment that can be applied across the radar domain. In contrast to similar techniques that are employed at hourly intervals to adjust radar-rainfall estimates operationally, this refined approach is intended to provide high-spatial-resolution precipitation data for climatological purposes, such as drought and environmental monitoring, retrospective impact analyses, and (when time series of sufficient length become available) assessment of temporal precipitation variations at high-spatial-resolution.

Compared to the Multisensor Precipitation Estimators (MPEs) used operationally, the refined method yields lower cross-validated interpolation errors regardless of season or daily precipitation amount. Comparisons between cross-validated radar estimates aggregated to monthly totals with operational (non-cross-validated) Parameter-elevation Regressions on Independent Slopes Model (PRISM) precipitation estimates are also favourable. The new method provides a radar-based alternative to similar climatologies based on the spatial interpolation of gauge data alone (e.g. PRISM). Copyright © 2008 Royal Meteorological Society

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