Determining Relative Errors of Satellite Precipitation Data over The Netherlands

Qingyu Wang, Yijian Zeng, C.M. Mannaerts, Vahid Rahimpour Golroudbary

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


Satellite precipitation data are widely used for a variety of studies. 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). For the relative errors among the three sets of precipitation data, it is found that the relative error of CMORPH is lower than the other two products', KNMI data is at the medium while PERSIANN is the highest one.
Original languageEnglish
Title of host publication2nd International Electronic Conference onf Remote Sensing
Subtitle of host publication
Publication statusPublished - 22 May 2018


  • bias correction
  • precipitation products
  • relative errors
  • triple collocation


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