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

Abstract

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 publicationhttps://ecrs-2.sciforum.net/
PublisherSciforum.net
Volume22
Edition2
DOIs
Publication statusPublished - 22 May 2018

Fingerprint

artificial neural network
gauge
climate prediction
product
method
rain
distribution
test
in situ

Keywords

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

Cite this

Wang, Q., Zeng, Y., Mannaerts, C. M., & Golroudbary, V. R. (2018). Determining Relative Errors of Satellite Precipitation Data over The Netherlands. In 2nd International Electronic Conference onf Remote Sensing: https://ecrs-2.sciforum.net/ (2 ed., Vol. 22). Sciforum.net. https://doi.org/10.3390/ecrs-2-05139
Wang, Qingyu ; Zeng, Yijian ; Mannaerts, C.M. ; Golroudbary, Vahid Rahimpour. / Determining Relative Errors of Satellite Precipitation Data over The Netherlands. 2nd International Electronic Conference onf Remote Sensing: https://ecrs-2.sciforum.net/. Vol. 22 2. ed. Sciforum.net, 2018.
@inproceedings{20c4938a14654ad3974fedc5f0ff7128,
title = "Determining Relative Errors of Satellite Precipitation Data over The Netherlands",
abstract = "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.",
keywords = "bias correction, precipitation products, relative errors, triple collocation",
author = "Qingyu Wang and Yijian Zeng and C.M. Mannaerts and Golroudbary, {Vahid Rahimpour}",
year = "2018",
month = "5",
day = "22",
doi = "10.3390/ecrs-2-05139",
language = "English",
volume = "22",
booktitle = "2nd International Electronic Conference onf Remote Sensing",
publisher = "Sciforum.net",
address = "Switzerland",
edition = "2",

}

Wang, Q, Zeng, Y, Mannaerts, CM & Golroudbary, VR 2018, Determining Relative Errors of Satellite Precipitation Data over The Netherlands. in 2nd International Electronic Conference onf Remote Sensing: https://ecrs-2.sciforum.net/. 2 edn, vol. 22, Sciforum.net. https://doi.org/10.3390/ecrs-2-05139

Determining Relative Errors of Satellite Precipitation Data over The Netherlands. / Wang, Qingyu; Zeng, Yijian; Mannaerts, C.M.; Golroudbary, Vahid Rahimpour.

2nd International Electronic Conference onf Remote Sensing: https://ecrs-2.sciforum.net/. Vol. 22 2. ed. Sciforum.net, 2018.

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

TY - GEN

T1 - Determining Relative Errors of Satellite Precipitation Data over The Netherlands

AU - Wang, Qingyu

AU - Zeng, Yijian

AU - Mannaerts, C.M.

AU - Golroudbary, Vahid Rahimpour

PY - 2018/5/22

Y1 - 2018/5/22

N2 - 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.

AB - 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.

KW - bias correction

KW - precipitation products

KW - relative errors

KW - triple collocation

UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2018/conf/wang_det.pdf

U2 - 10.3390/ecrs-2-05139

DO - 10.3390/ecrs-2-05139

M3 - Conference contribution

VL - 22

BT - 2nd International Electronic Conference onf Remote Sensing

PB - Sciforum.net

ER -

Wang Q, Zeng Y, Mannaerts CM, Golroudbary VR. Determining Relative Errors of Satellite Precipitation Data over The Netherlands. In 2nd International Electronic Conference onf Remote Sensing: https://ecrs-2.sciforum.net/. 2 ed. Vol. 22. Sciforum.net. 2018 https://doi.org/10.3390/ecrs-2-05139