Satellite precipitation estimates data are widely used for a variety of studies, including the hydrologic and climate modeling, weather forecasting, and agriculture management or extreme events prediction. However, satellite precipitation estimation is inevitably followed with errors which are caused by different factors, therefore it is essential to evaluate the relative errors of satellite precipitation data. A realizable method which can be used to quantify the relative errors in large-scale datasets is triple collocation. This method can objectively obtain the relative errors for at least three or more independent products. But before estimation of relative errors, the bias of the products relative to each other should be reduced or removed. This study tests the cumulative distribution function (CDF) matching approach which aims to reduce the bias among three precipitation products over the Netherlands. Afterwards, the triple collocation technique is applied to determine the relative errors of these precipitation products. The three precipitation datasets are, the Climate Prediction Center morphing method (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and the gridded rain gauge data interpolated from in situ rain gauge measurement data provided by the Royal Netherlands Meteorological Institute (KNMI).
Determining Relative Errors of Satellite Precipitation Data over The Netherlands
Published: 22 March 2018 by MDPI in 2nd International Electronic Conference on Remote Sensing session Big Data Handling
Keywords: precipitation products, bias correction, triple collocation, relative errors.