Loading…

A climatological benchmark for operational radar rainfall bias reduction

The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factor...

Full description

Saved in:
Bibliographic Details
Published in:Hydrology and earth system sciences 2021-07, Vol.25 (7), p.4061-4080
Main Authors: Imhoff, Ruben, Brauer, Claudia, van Heeringen, Klaas-Jan, Leijnse, Hidde, Overeem, Aart, Weerts, Albrecht, Uijlenhoet, Remko
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913
cites cdi_FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913
container_end_page 4080
container_issue 7
container_start_page 4061
container_title Hydrology and earth system sciences
container_volume 25
creator Imhoff, Ruben
Brauer, Claudia
van Heeringen, Klaas-Jan
Leijnse, Hidde
Overeem, Aart
Weerts, Albrecht
Uijlenhoet, Remko
description The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.
doi_str_mv 10.5194/hess-25-4061-2021
format article
fullrecord <record><control><sourceid>gale_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_e87aff19b8944ef1ac11cb5d8506571c</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A668379071</galeid><doaj_id>oai_doaj_org_article_e87aff19b8944ef1ac11cb5d8506571c</doaj_id><sourcerecordid>A668379071</sourcerecordid><originalsourceid>FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913</originalsourceid><addsrcrecordid>eNptUU1rGzEQXUoCzdcP6G2hpx421be0RxPSxhAIpM1ZzI4kR-7acqU1NP--2rq0MQSBNLx58_SG1zQfKLmWtBefn30pHZOdIIp2jDD6rjmjiuhO99ycvKrfN-elrAlhxih21twtWhzjBqY0plVEGNvBb_F5A_lHG1Ju085nmGLa1k4GB7necRtgrMQIpc3e7XHuXzanFS3-6u970Tx9uf1-c9fdP3xd3izuOxSGT53mQWkOzgdKidRQfQlAxpUjQoIJTLjgFB0GzxGl8EY64sFpLqlH7Cm_aJYHXZdgbXe5es8vNkG0f4CUVxbyFHH01hsNIdB-ML0Q9UNASnGQzkiipKZYtT4etHY5_dz7Mtl12ue6arFMSiIN04b_Z62gitbl05QBN7GgXShluO6Jnn1dv8Gqx_lNxLT1IVb8aODT0UDlTP7XtIJ9KXb57fGYSw9czKmU7MO_xSmxc_52zr-atnP-ds6f_waMW6Hc</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2550582783</pqid></control><display><type>article</type><title>A climatological benchmark for operational radar rainfall bias reduction</title><source>Publicly Available Content Database</source><source>DOAJ Directory of Open Access Journals</source><creator>Imhoff, Ruben ; Brauer, Claudia ; van Heeringen, Klaas-Jan ; Leijnse, Hidde ; Overeem, Aart ; Weerts, Albrecht ; Uijlenhoet, Remko</creator><creatorcontrib>Imhoff, Ruben ; Brauer, Claudia ; van Heeringen, Klaas-Jan ; Leijnse, Hidde ; Overeem, Aart ; Weerts, Albrecht ; Uijlenhoet, Remko</creatorcontrib><description>The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.</description><identifier>ISSN: 1607-7938</identifier><identifier>ISSN: 1027-5606</identifier><identifier>EISSN: 1607-7938</identifier><identifier>DOI: 10.5194/hess-25-4061-2021</identifier><language>eng</language><publisher>Katlenburg-Lindau: Copernicus GmbH</publisher><subject>Algorithms ; Annual rainfall ; Archives &amp; records ; Benchmarks ; Bias ; Carrots ; Catchment area ; Catchments ; Climatology ; Discharge ; Estimates ; Gauges ; Hydrologic data ; Hydrologic models ; Hydrology ; Hydrometeorology ; Methods ; Polders ; Precipitation ; Radar ; Radar data ; Radar rainfall ; Rain ; Rain and rainfall ; Rain gauges ; Rainfall ; Rainfall data ; Rainfall simulators ; Real time ; Reduction ; Seasons ; Simulation ; Vegetables ; Weather forecasting</subject><ispartof>Hydrology and earth system sciences, 2021-07, Vol.25 (7), p.4061-4080</ispartof><rights>COPYRIGHT 2021 Copernicus GmbH</rights><rights>2021. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913</citedby><cites>FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913</cites><orcidid>0000-0002-4096-3528 ; 0000-0002-3249-8363 ; 0000-0001-7418-4445 ; 0000-0001-7835-4480 ; 0000-0001-5550-8141 ; 0000-0002-6459-9230</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2550582783/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2550582783?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Imhoff, Ruben</creatorcontrib><creatorcontrib>Brauer, Claudia</creatorcontrib><creatorcontrib>van Heeringen, Klaas-Jan</creatorcontrib><creatorcontrib>Leijnse, Hidde</creatorcontrib><creatorcontrib>Overeem, Aart</creatorcontrib><creatorcontrib>Weerts, Albrecht</creatorcontrib><creatorcontrib>Uijlenhoet, Remko</creatorcontrib><title>A climatological benchmark for operational radar rainfall bias reduction</title><title>Hydrology and earth system sciences</title><description>The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.</description><subject>Algorithms</subject><subject>Annual rainfall</subject><subject>Archives &amp; records</subject><subject>Benchmarks</subject><subject>Bias</subject><subject>Carrots</subject><subject>Catchment area</subject><subject>Catchments</subject><subject>Climatology</subject><subject>Discharge</subject><subject>Estimates</subject><subject>Gauges</subject><subject>Hydrologic data</subject><subject>Hydrologic models</subject><subject>Hydrology</subject><subject>Hydrometeorology</subject><subject>Methods</subject><subject>Polders</subject><subject>Precipitation</subject><subject>Radar</subject><subject>Radar data</subject><subject>Radar rainfall</subject><subject>Rain</subject><subject>Rain and rainfall</subject><subject>Rain gauges</subject><subject>Rainfall</subject><subject>Rainfall data</subject><subject>Rainfall simulators</subject><subject>Real time</subject><subject>Reduction</subject><subject>Seasons</subject><subject>Simulation</subject><subject>Vegetables</subject><subject>Weather forecasting</subject><issn>1607-7938</issn><issn>1027-5606</issn><issn>1607-7938</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUU1rGzEQXUoCzdcP6G2hpx421be0RxPSxhAIpM1ZzI4kR-7acqU1NP--2rq0MQSBNLx58_SG1zQfKLmWtBefn30pHZOdIIp2jDD6rjmjiuhO99ycvKrfN-elrAlhxih21twtWhzjBqY0plVEGNvBb_F5A_lHG1Ju085nmGLa1k4GB7necRtgrMQIpc3e7XHuXzanFS3-6u970Tx9uf1-c9fdP3xd3izuOxSGT53mQWkOzgdKidRQfQlAxpUjQoIJTLjgFB0GzxGl8EY64sFpLqlH7Cm_aJYHXZdgbXe5es8vNkG0f4CUVxbyFHH01hsNIdB-ML0Q9UNASnGQzkiipKZYtT4etHY5_dz7Mtl12ue6arFMSiIN04b_Z62gitbl05QBN7GgXShluO6Jnn1dv8Gqx_lNxLT1IVb8aODT0UDlTP7XtIJ9KXb57fGYSw9czKmU7MO_xSmxc_52zr-atnP-ds6f_waMW6Hc</recordid><startdate>20210713</startdate><enddate>20210713</enddate><creator>Imhoff, Ruben</creator><creator>Brauer, Claudia</creator><creator>van Heeringen, Klaas-Jan</creator><creator>Leijnse, Hidde</creator><creator>Overeem, Aart</creator><creator>Weerts, Albrecht</creator><creator>Uijlenhoet, Remko</creator><general>Copernicus GmbH</general><general>Copernicus Publications</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4096-3528</orcidid><orcidid>https://orcid.org/0000-0002-3249-8363</orcidid><orcidid>https://orcid.org/0000-0001-7418-4445</orcidid><orcidid>https://orcid.org/0000-0001-7835-4480</orcidid><orcidid>https://orcid.org/0000-0001-5550-8141</orcidid><orcidid>https://orcid.org/0000-0002-6459-9230</orcidid></search><sort><creationdate>20210713</creationdate><title>A climatological benchmark for operational radar rainfall bias reduction</title><author>Imhoff, Ruben ; Brauer, Claudia ; van Heeringen, Klaas-Jan ; Leijnse, Hidde ; Overeem, Aart ; Weerts, Albrecht ; Uijlenhoet, Remko</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Annual rainfall</topic><topic>Archives &amp; records</topic><topic>Benchmarks</topic><topic>Bias</topic><topic>Carrots</topic><topic>Catchment area</topic><topic>Catchments</topic><topic>Climatology</topic><topic>Discharge</topic><topic>Estimates</topic><topic>Gauges</topic><topic>Hydrologic data</topic><topic>Hydrologic models</topic><topic>Hydrology</topic><topic>Hydrometeorology</topic><topic>Methods</topic><topic>Polders</topic><topic>Precipitation</topic><topic>Radar</topic><topic>Radar data</topic><topic>Radar rainfall</topic><topic>Rain</topic><topic>Rain and rainfall</topic><topic>Rain gauges</topic><topic>Rainfall</topic><topic>Rainfall data</topic><topic>Rainfall simulators</topic><topic>Real time</topic><topic>Reduction</topic><topic>Seasons</topic><topic>Simulation</topic><topic>Vegetables</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Imhoff, Ruben</creatorcontrib><creatorcontrib>Brauer, Claudia</creatorcontrib><creatorcontrib>van Heeringen, Klaas-Jan</creatorcontrib><creatorcontrib>Leijnse, Hidde</creatorcontrib><creatorcontrib>Overeem, Aart</creatorcontrib><creatorcontrib>Weerts, Albrecht</creatorcontrib><creatorcontrib>Uijlenhoet, Remko</creatorcontrib><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>Aqualine</collection><collection>Meteorological &amp; Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological &amp; Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric &amp; Aquatic Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Hydrology and earth system sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Imhoff, Ruben</au><au>Brauer, Claudia</au><au>van Heeringen, Klaas-Jan</au><au>Leijnse, Hidde</au><au>Overeem, Aart</au><au>Weerts, Albrecht</au><au>Uijlenhoet, Remko</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A climatological benchmark for operational radar rainfall bias reduction</atitle><jtitle>Hydrology and earth system sciences</jtitle><date>2021-07-13</date><risdate>2021</risdate><volume>25</volume><issue>7</issue><spage>4061</spage><epage>4080</epage><pages>4061-4080</pages><issn>1607-7938</issn><issn>1027-5606</issn><eissn>1607-7938</eissn><abstract>The presence of significant biases in real-time radar quantitative precipitation estimations (QPEs) limits its use in hydrometeorological forecasting systems. Here, we introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors, which vary per grid cell and day of the year. The factors are based on a historical set of 10 years of 5 min radar and reference rainfall data for the Netherlands. CARROTS is both operationally available and independent of real-time rain gauge availability and can thereby provide an alternative to current QPE adjustment practice. In addition, it can be used as benchmark for QPE algorithm development. We tested this method on the resulting rainfall estimates and discharge simulations for 12 Dutch catchments and polders. We validated the results against the operational mean field bias (MFB)-adjusted rainfall estimates and a reference dataset. This reference consists of the radar QPE, that combines an hourly MFB adjustment and a daily spatial adjustment using observations from 32 automatic and 319 manual rain gauges. Only the automatic gauges of this network are available in real time for the MFB adjustment. The resulting climatological correction factors show clear spatial and temporal patterns. Factors are higher away from the radars and higher from December through March than in other seasons, which is likely a result of sampling above the melting layer during the winter months. The MFB-adjusted QPE outperforms the CARROTS-corrected QPE when the country-average rainfall estimates are compared to the reference. However, annual rainfall sums from CARROTS are comparable to the reference and outperform the MFB-adjusted rainfall estimates for catchments away from the radars, where the MFB-adjusted QPE generally underestimates the rainfall amounts. This difference is absent for catchments closer to the radars. QPE underestimations are amplified when used in the hydrological model simulations. Discharge simulations using the QPE from CARROTS outperform those with the MFB-adjusted product for all but one basin. Moreover, the proposed factor derivation method is robust. It is hardly sensitive to leaving individual years out of the historical set and to the moving window length, given window sizes of more than a week.</abstract><cop>Katlenburg-Lindau</cop><pub>Copernicus GmbH</pub><doi>10.5194/hess-25-4061-2021</doi><tpages>20</tpages><orcidid>https://orcid.org/0000-0002-4096-3528</orcidid><orcidid>https://orcid.org/0000-0002-3249-8363</orcidid><orcidid>https://orcid.org/0000-0001-7418-4445</orcidid><orcidid>https://orcid.org/0000-0001-7835-4480</orcidid><orcidid>https://orcid.org/0000-0001-5550-8141</orcidid><orcidid>https://orcid.org/0000-0002-6459-9230</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1607-7938
ispartof Hydrology and earth system sciences, 2021-07, Vol.25 (7), p.4061-4080
issn 1607-7938
1027-5606
1607-7938
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_e87aff19b8944ef1ac11cb5d8506571c
source Publicly Available Content Database; DOAJ Directory of Open Access Journals
subjects Algorithms
Annual rainfall
Archives & records
Benchmarks
Bias
Carrots
Catchment area
Catchments
Climatology
Discharge
Estimates
Gauges
Hydrologic data
Hydrologic models
Hydrology
Hydrometeorology
Methods
Polders
Precipitation
Radar
Radar data
Radar rainfall
Rain
Rain and rainfall
Rain gauges
Rainfall
Rainfall data
Rainfall simulators
Real time
Reduction
Seasons
Simulation
Vegetables
Weather forecasting
title A climatological benchmark for operational radar rainfall bias reduction
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T21%3A05%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20climatological%20benchmark%20for%20operational%20radar%20rainfall%20bias%20reduction&rft.jtitle=Hydrology%20and%20earth%20system%20sciences&rft.au=Imhoff,%20Ruben&rft.date=2021-07-13&rft.volume=25&rft.issue=7&rft.spage=4061&rft.epage=4080&rft.pages=4061-4080&rft.issn=1607-7938&rft.eissn=1607-7938&rft_id=info:doi/10.5194/hess-25-4061-2021&rft_dat=%3Cgale_doaj_%3EA668379071%3C/gale_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c483t-73f673adef11057a1604ac236d045a8f24dfd61bbe3cc54e85d0ead7351ecc913%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2550582783&rft_id=info:pmid/&rft_galeid=A668379071&rfr_iscdi=true