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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...
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Published in: | Hydrology and earth system sciences 2021-07, Vol.25 (7), p.4061-4080 |
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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 |
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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 & 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 & 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 ; 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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> |
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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 |
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