The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites

This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.3019-3031
Hauptverfasser: Liu, Shuyan, Grassotti, Christopher, Liu, Quanhua, Lee, Yong-Keun, Honeyager, Ryan, Zhou, Yan, Fang, Ming
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 3031
container_issue
container_start_page 3019
container_title IEEE journal of selected topics in applied earth observations and remote sensing
container_volume 13
creator Liu, Shuyan
Grassotti, Christopher
Liu, Quanhua
Lee, Yong-Keun
Honeyager, Ryan
Zhou, Yan
Fang, Ming
description This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.
doi_str_mv 10.1109/JSTARS.2020.3000348
format Article
fullrecord <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_ieee_primary_9109712</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9109712</ieee_id><doaj_id>oai_doaj_org_article_3568c0019c324b9a892a3d90d0acaceb</doaj_id><sourcerecordid>2415962026</sourcerecordid><originalsourceid>FETCH-LOGICAL-c408t-684ca9fa30f6e51174c16b9207fac7e132689486251e40ea2c6e95caf06b7ddb3</originalsourceid><addsrcrecordid>eNo9kU9v00AQxS0EEqHwCXpZiQscHGb_eO3lFlUUghpSxYHrarIeh42cbLreFLWfvg6uehrp6b03o_ll2SWHKedgvvys17NVPRUgYCoBQKrqVTYRvOA5L2TxOptwI03OFai32bu-3wFoURo5yR7Xf4n9Ws5mbOFdDP_wntj8kGgbMVHDVpSip3vsWP3QJ9qzTwu_qj9_ZX-w8w0mHw4stOw2kvNHn0bhOoY9W5y65I8dsdvQYcyXceOTP2xZPfR2nU_Uv8_etNj19OF5XmS_r7-tr37kN8vv86vZTe4UVCnXlXJoWpTQaio4L5XjemMElC26krgUujKq0qLgpIBQOE2mcNiC3pRNs5EX2XzsbQLu7DH6PcYHG9Db_0KIW4sxedeRlYWuHAA3Tgq1MVgZgbIx0AA6dHTu-jh2HWO4O1Gf7C6c4mE43wrFC6MHBHpwydE1fLTvI7UvWznYMzA7ArNnYPYZ2JC6HFOeiF4SZrCXXMgnbX-SCw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2415962026</pqid></control><display><type>article</type><title>The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites</title><source>DOAJ Directory of Open Access Journals</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Liu, Shuyan ; Grassotti, Christopher ; Liu, Quanhua ; Lee, Yong-Keun ; Honeyager, Ryan ; Zhou, Yan ; Fang, Ming</creator><creatorcontrib>Liu, Shuyan ; Grassotti, Christopher ; Liu, Quanhua ; Lee, Yong-Keun ; Honeyager, Ryan ; Zhou, Yan ; Fang, Ming</creatorcontrib><description>This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.</description><identifier>ISSN: 1939-1404</identifier><identifier>EISSN: 2151-1535</identifier><identifier>DOI: 10.1109/JSTARS.2020.3000348</identifier><identifier>CODEN: IJSTHZ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Advanced microwave sounding unit-A (AMUSA)-microwave humidity sounder (MHS) ; advanced technology microwave sounder (ATMS) ; Algorithms ; Annual precipitation ; Atmospheric precipitations ; Cold season ; Instruments ; Microwave imaging ; microwave integrated retrieval system (MiRS) ; Microwave measurement ; Microwave theory and techniques ; Ocean temperature ; Performance measurement ; Polar orbiting satellites ; Precipitation ; Precipitation rate ; Radar ; Rain gauges ; Rainfall ; Rainfall intensity ; Retrieval ; satellite ; Satellite observation ; Satellites ; Seasons ; Statistical methods ; US Government agencies</subject><ispartof>IEEE journal of selected topics in applied earth observations and remote sensing, 2020, Vol.13, p.3019-3031</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-684ca9fa30f6e51174c16b9207fac7e132689486251e40ea2c6e95caf06b7ddb3</citedby><cites>FETCH-LOGICAL-c408t-684ca9fa30f6e51174c16b9207fac7e132689486251e40ea2c6e95caf06b7ddb3</cites><orcidid>0000-0003-0753-9511 ; 0000-0003-4924-6370 ; 0000-0001-8784-473X ; 0000-0002-3616-351X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,860,2095,4009,27902,27903,27904</link.rule.ids></links><search><creatorcontrib>Liu, Shuyan</creatorcontrib><creatorcontrib>Grassotti, Christopher</creatorcontrib><creatorcontrib>Liu, Quanhua</creatorcontrib><creatorcontrib>Lee, Yong-Keun</creatorcontrib><creatorcontrib>Honeyager, Ryan</creatorcontrib><creatorcontrib>Zhou, Yan</creatorcontrib><creatorcontrib>Fang, Ming</creatorcontrib><title>The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites</title><title>IEEE journal of selected topics in applied earth observations and remote sensing</title><addtitle>JSTARS</addtitle><description>This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.</description><subject>Advanced microwave sounding unit-A (AMUSA)-microwave humidity sounder (MHS)</subject><subject>advanced technology microwave sounder (ATMS)</subject><subject>Algorithms</subject><subject>Annual precipitation</subject><subject>Atmospheric precipitations</subject><subject>Cold season</subject><subject>Instruments</subject><subject>Microwave imaging</subject><subject>microwave integrated retrieval system (MiRS)</subject><subject>Microwave measurement</subject><subject>Microwave theory and techniques</subject><subject>Ocean temperature</subject><subject>Performance measurement</subject><subject>Polar orbiting satellites</subject><subject>Precipitation</subject><subject>Precipitation rate</subject><subject>Radar</subject><subject>Rain gauges</subject><subject>Rainfall</subject><subject>Rainfall intensity</subject><subject>Retrieval</subject><subject>satellite</subject><subject>Satellite observation</subject><subject>Satellites</subject><subject>Seasons</subject><subject>Statistical methods</subject><subject>US Government agencies</subject><issn>1939-1404</issn><issn>2151-1535</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNo9kU9v00AQxS0EEqHwCXpZiQscHGb_eO3lFlUUghpSxYHrarIeh42cbLreFLWfvg6uehrp6b03o_ll2SWHKedgvvys17NVPRUgYCoBQKrqVTYRvOA5L2TxOptwI03OFai32bu-3wFoURo5yR7Xf4n9Ws5mbOFdDP_wntj8kGgbMVHDVpSip3vsWP3QJ9qzTwu_qj9_ZX-w8w0mHw4stOw2kvNHn0bhOoY9W5y65I8dsdvQYcyXceOTP2xZPfR2nU_Uv8_etNj19OF5XmS_r7-tr37kN8vv86vZTe4UVCnXlXJoWpTQaio4L5XjemMElC26krgUujKq0qLgpIBQOE2mcNiC3pRNs5EX2XzsbQLu7DH6PcYHG9Db_0KIW4sxedeRlYWuHAA3Tgq1MVgZgbIx0AA6dHTu-jh2HWO4O1Gf7C6c4mE43wrFC6MHBHpwydE1fLTvI7UvWznYMzA7ArNnYPYZ2JC6HFOeiF4SZrCXXMgnbX-SCw</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Liu, Shuyan</creator><creator>Grassotti, Christopher</creator><creator>Liu, Quanhua</creator><creator>Lee, Yong-Keun</creator><creator>Honeyager, Ryan</creator><creator>Zhou, Yan</creator><creator>Fang, Ming</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0753-9511</orcidid><orcidid>https://orcid.org/0000-0003-4924-6370</orcidid><orcidid>https://orcid.org/0000-0001-8784-473X</orcidid><orcidid>https://orcid.org/0000-0002-3616-351X</orcidid></search><sort><creationdate>2020</creationdate><title>The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites</title><author>Liu, Shuyan ; Grassotti, Christopher ; Liu, Quanhua ; Lee, Yong-Keun ; Honeyager, Ryan ; Zhou, Yan ; Fang, Ming</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c408t-684ca9fa30f6e51174c16b9207fac7e132689486251e40ea2c6e95caf06b7ddb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Advanced microwave sounding unit-A (AMUSA)-microwave humidity sounder (MHS)</topic><topic>advanced technology microwave sounder (ATMS)</topic><topic>Algorithms</topic><topic>Annual precipitation</topic><topic>Atmospheric precipitations</topic><topic>Cold season</topic><topic>Instruments</topic><topic>Microwave imaging</topic><topic>microwave integrated retrieval system (MiRS)</topic><topic>Microwave measurement</topic><topic>Microwave theory and techniques</topic><topic>Ocean temperature</topic><topic>Performance measurement</topic><topic>Polar orbiting satellites</topic><topic>Precipitation</topic><topic>Precipitation rate</topic><topic>Radar</topic><topic>Rain gauges</topic><topic>Rainfall</topic><topic>Rainfall intensity</topic><topic>Retrieval</topic><topic>satellite</topic><topic>Satellite observation</topic><topic>Satellites</topic><topic>Seasons</topic><topic>Statistical methods</topic><topic>US Government agencies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Shuyan</creatorcontrib><creatorcontrib>Grassotti, Christopher</creatorcontrib><creatorcontrib>Liu, Quanhua</creatorcontrib><creatorcontrib>Lee, Yong-Keun</creatorcontrib><creatorcontrib>Honeyager, Ryan</creatorcontrib><creatorcontrib>Zhou, Yan</creatorcontrib><creatorcontrib>Fang, Ming</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy &amp; Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>IEEE journal of selected topics in applied earth observations and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Shuyan</au><au>Grassotti, Christopher</au><au>Liu, Quanhua</au><au>Lee, Yong-Keun</au><au>Honeyager, Ryan</au><au>Zhou, Yan</au><au>Fang, Ming</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites</atitle><jtitle>IEEE journal of selected topics in applied earth observations and remote sensing</jtitle><stitle>JSTARS</stitle><date>2020</date><risdate>2020</risdate><volume>13</volume><spage>3019</spage><epage>3031</epage><pages>3019-3031</pages><issn>1939-1404</issn><eissn>2151-1535</eissn><coden>IJSTHZ</coden><abstract>This article describes a multisatellite validation study of precipitation from the microwave integrated retrieval system (MiRS). MiRS is a variational algorithm designed to process passive microwave measurements using a common core of retrieval software, and currently runs operationally on data from ten different earth-observing satellites. The primary validation was conducted for the polar-orbiting satellites SNPP, NOAA20 (both bearing the ATMS instrument), MetopB, and MetopC (both bearing the AMSUA and MHS instruments) during the period from December 1, 2018 through December 31, 2019. Validation was conducted using operational ground-based radar-rain gauge analyses from Stage-IV and multiradar multisensor (MRMS) over the continental United States. Results indicate that the precipitation estimates are largely consistent with one another and that agreement with ground-based analyses has a strong seasonal component. Warm season performance is generally higher and more stable than during the cold season. SNPP and NOAA20 estimates appeared to show slightly higher biases, outside of July and August. Frequency distributions of precipitation intensity also showed better agreement between MiRS and Stage-IV for higher precipitation rates in the warm season, highlighting the difficulty of estimating over land precipitation rates associated with stratiform precipitation systems that typically occur during the cold season. All satellites depicted the annual mean precipitation rate global distribution with good interconsistency, but with some differences possibly related to individual temporal sampling characteristics of each satellite. Summary validation statistics and scores stratified by season show that MiRS performance metrics using MRMS as a reference are largely similar to those based on using Stage-IV.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/JSTARS.2020.3000348</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-0753-9511</orcidid><orcidid>https://orcid.org/0000-0003-4924-6370</orcidid><orcidid>https://orcid.org/0000-0001-8784-473X</orcidid><orcidid>https://orcid.org/0000-0002-3616-351X</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1939-1404
ispartof IEEE journal of selected topics in applied earth observations and remote sensing, 2020, Vol.13, p.3019-3031
issn 1939-1404
2151-1535
language eng
recordid cdi_ieee_primary_9109712
source DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects Advanced microwave sounding unit-A (AMUSA)-microwave humidity sounder (MHS)
advanced technology microwave sounder (ATMS)
Algorithms
Annual precipitation
Atmospheric precipitations
Cold season
Instruments
Microwave imaging
microwave integrated retrieval system (MiRS)
Microwave measurement
Microwave theory and techniques
Ocean temperature
Performance measurement
Polar orbiting satellites
Precipitation
Precipitation rate
Radar
Rain gauges
Rainfall
Rainfall intensity
Retrieval
satellite
Satellite observation
Satellites
Seasons
Statistical methods
US Government agencies
title The NOAA Microwave Integrated Retrieval System (MiRS): Validation of Precipitation From Multiple Polar-Orbiting Satellites
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T10%3A38%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=The%20NOAA%20Microwave%20Integrated%20Retrieval%20System%20(MiRS):%20Validation%20of%20Precipitation%20From%20Multiple%20Polar-Orbiting%20Satellites&rft.jtitle=IEEE%20journal%20of%20selected%20topics%20in%20applied%20earth%20observations%20and%20remote%20sensing&rft.au=Liu,%20Shuyan&rft.date=2020&rft.volume=13&rft.spage=3019&rft.epage=3031&rft.pages=3019-3031&rft.issn=1939-1404&rft.eissn=2151-1535&rft.coden=IJSTHZ&rft_id=info:doi/10.1109/JSTARS.2020.3000348&rft_dat=%3Cproquest_ieee_%3E2415962026%3C/proquest_ieee_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2415962026&rft_id=info:pmid/&rft_ieee_id=9109712&rft_doaj_id=oai_doaj_org_article_3568c0019c324b9a892a3d90d0acaceb&rfr_iscdi=true