Copula-based assimilation of radar and gauge information to derive bias-corrected precipitation fields

摘要

This study addresses the problem of combining radar information and gaugemeasurements. Gauge measurements are the best available source of absoluterainfall intensity albeit their spatial availability is limited.Precipitation information obtained by radar mimics well the spatial patternsbut is biased for their absolute values.In this study copula models are used to describe the dependence structurebetween gauge observations and rainfall derived from radar reflectivity atthe corresponding grid cells. After appropriate time series transformation togenerate "iid" variates, only the positive pairs (radar >0,gauge >0) of the residuals are considered. As not each grid cellcan be assigned to one gauge, the integration of point information, i.e.gauge rainfall intensities, is achieved by considering the structure and thestrength of dependence between the radar pixels and all the gauges within theradar image. Two different approaches, namely Maximum Theta andMultiple Theta, are presented. They finally allow for generatingprecipitation fields that mimic the spatial patterns of the radar fields andcorrect them for biases in their absolute rainfall intensities. Theperformance of the approach, which can be seen as a bias-correction for radarfields, is demonstrated for the Bavarian Alps. The bias-corrected rainfallfields are compared to a field of interpolated gauge values (ordinarykriging) and are validated with available gauge measurements. The simulatedprecipitation fields are compared to an operationally corrected radarprecipitation field (RADOLAN). The copula-based approach performs similarlywell as indicated by different validation measures and successfully correctsfor errors in the radar precipitation.