Nowcasting tracks of severe convective storms in West Africa from observations of land surface state
In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land...
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creator | Taylor, Christopher M Klein, Cornelia Dione, Cheikh Parker, Douglas J Marsham, John Abdoulahat Diop, Cheikh Fletcher, Jennifer Saidou Chaibou, Abdoul Aziz Nafissa, Dignon Bertin Semeena, Valiyaveetil Shamsudheen Cole, Steven J Anderson, Seonaid R |
description | In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6–18 h, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics. |
doi_str_mv | 10.1088/1748-9326/ac536d |
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In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6–18 h, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.</description><identifier>ISSN: 1748-9326</identifier><identifier>EISSN: 1748-9326</identifier><identifier>DOI: 10.1088/1748-9326/ac536d</identifier><identifier>CODEN: ERLNAL</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Aridity ; Convection ; Convective clouds ; Emergency warning programs ; Energy balance ; Land surface temperature ; mesoscale convective systems ; Meteorological satellites ; Moisture control ; Nowcasting ; Numerical prediction ; Numerical weather forecasting ; Rain ; Rainfall ; Rainy season ; Sahel ; Satellite observation ; Severe weather ; Soil moisture ; Soil temperature ; Spatial filtering ; Storms ; Surface energy ; Surface properties ; Tropical environments ; Weather forecasting</subject><ispartof>Environmental research letters, 2022-03, Vol.17 (3), p.34016</ispartof><rights>2022 The Author(s). Published by IOP Publishing Ltd</rights><rights>2022 The Author(s). Published by IOP Publishing Ltd. This work is published under http://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-c448t-8a8b08e3a9ca9ea2270efc9bfdff5726a7c6c9bebe5916baba9f7ca8c45808e03</citedby><cites>FETCH-LOGICAL-c448t-8a8b08e3a9ca9ea2270efc9bfdff5726a7c6c9bebe5916baba9f7ca8c45808e03</cites><orcidid>0000-0002-4892-3344 ; 0000-0003-4294-8687 ; 0000-0002-8457-6175 ; 0000-0003-2335-8198 ; 0000-0002-1895-449X ; 0000-0001-6686-0458 ; 0000-0003-3219-8472 ; 0000-0001-9309-885X ; 0000-0002-0560-7050 ; 0000-0001-8556-577X ; 0000-0002-0120-3198</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1748-9326/ac536d/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,776,780,860,2096,27903,27904,38847,38869,53818,53845</link.rule.ids></links><search><creatorcontrib>Taylor, Christopher M</creatorcontrib><creatorcontrib>Klein, Cornelia</creatorcontrib><creatorcontrib>Dione, Cheikh</creatorcontrib><creatorcontrib>Parker, Douglas J</creatorcontrib><creatorcontrib>Marsham, John</creatorcontrib><creatorcontrib>Abdoulahat Diop, Cheikh</creatorcontrib><creatorcontrib>Fletcher, Jennifer</creatorcontrib><creatorcontrib>Saidou Chaibou, Abdoul Aziz</creatorcontrib><creatorcontrib>Nafissa, Dignon Bertin</creatorcontrib><creatorcontrib>Semeena, Valiyaveetil Shamsudheen</creatorcontrib><creatorcontrib>Cole, Steven J</creatorcontrib><creatorcontrib>Anderson, Seonaid R</creatorcontrib><title>Nowcasting tracks of severe convective storms in West Africa from observations of land surface state</title><title>Environmental research letters</title><addtitle>ERL</addtitle><addtitle>Environ. Res. Lett</addtitle><description>In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. The strong sensitivity of Sahelian MCSs to soil moisture, in combination with MCS life times of typically 6–18 h, opens up the opportunity for nowcasting of hazardous weather well beyond what is possible from atmospheric observations alone, and could be applied elsewhere in the semi-arid tropics.</description><subject>Aridity</subject><subject>Convection</subject><subject>Convective clouds</subject><subject>Emergency warning programs</subject><subject>Energy balance</subject><subject>Land surface temperature</subject><subject>mesoscale convective systems</subject><subject>Meteorological satellites</subject><subject>Moisture control</subject><subject>Nowcasting</subject><subject>Numerical prediction</subject><subject>Numerical weather forecasting</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainy season</subject><subject>Sahel</subject><subject>Satellite observation</subject><subject>Severe weather</subject><subject>Soil moisture</subject><subject>Soil temperature</subject><subject>Spatial filtering</subject><subject>Storms</subject><subject>Surface energy</subject><subject>Surface properties</subject><subject>Tropical environments</subject><subject>Weather forecasting</subject><issn>1748-9326</issn><issn>1748-9326</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNp1kU1PGzEQhleoSKSBO0dLHHppinfXX3tECFokVC4gjtbs7DhySNap7aTqv6_DVpRDexrP6H3eGc9U1XnNv9TcmMtaC7Po2kZdAspWDUfV7K304d37pPqY0opzKaQ2s2r4Hn4ipOzHJcsR8CWx4FiiPUViGMY9YfZ7YimHuEnMj-yZUmZXLnoE5mLYsNAninvIPoyv8BrGgaVddIAHDjKdVscO1onO_sR59XR783j9bXH_8PXu-up-gUKYvDBgem6ohQ6hI2gazclh17vBOakbBRpVSakn2dWqhx46pxEMCmkKx9t5dTf5DgFWdhv9BuIvG8Db10KISwsxe1yT5YorCQRkWi660nYgdMZoTVqSxKZ4XUxe2xh-7Mqf7Srs4ljGt41qpRRGaFFUfFJhDClFcm9da24Pd7GHxdvD4u10l4J8mhAftn89Ka6L1LaWt4LXym4HV5Sf_6H8r_FvC6id-Q</recordid><startdate>20220301</startdate><enddate>20220301</enddate><creator>Taylor, Christopher M</creator><creator>Klein, Cornelia</creator><creator>Dione, Cheikh</creator><creator>Parker, Douglas J</creator><creator>Marsham, John</creator><creator>Abdoulahat Diop, Cheikh</creator><creator>Fletcher, Jennifer</creator><creator>Saidou Chaibou, Abdoul Aziz</creator><creator>Nafissa, Dignon Bertin</creator><creator>Semeena, Valiyaveetil Shamsudheen</creator><creator>Cole, Steven J</creator><creator>Anderson, Seonaid R</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-4892-3344</orcidid><orcidid>https://orcid.org/0000-0003-4294-8687</orcidid><orcidid>https://orcid.org/0000-0002-8457-6175</orcidid><orcidid>https://orcid.org/0000-0003-2335-8198</orcidid><orcidid>https://orcid.org/0000-0002-1895-449X</orcidid><orcidid>https://orcid.org/0000-0001-6686-0458</orcidid><orcidid>https://orcid.org/0000-0003-3219-8472</orcidid><orcidid>https://orcid.org/0000-0001-9309-885X</orcidid><orcidid>https://orcid.org/0000-0002-0560-7050</orcidid><orcidid>https://orcid.org/0000-0001-8556-577X</orcidid><orcidid>https://orcid.org/0000-0002-0120-3198</orcidid></search><sort><creationdate>20220301</creationdate><title>Nowcasting tracks of severe convective storms in West Africa from observations of land surface state</title><author>Taylor, Christopher M ; Klein, Cornelia ; Dione, Cheikh ; Parker, Douglas J ; Marsham, John ; Abdoulahat Diop, Cheikh ; Fletcher, Jennifer ; Saidou Chaibou, Abdoul Aziz ; Nafissa, Dignon Bertin ; Semeena, Valiyaveetil Shamsudheen ; Cole, Steven J ; Anderson, Seonaid R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c448t-8a8b08e3a9ca9ea2270efc9bfdff5726a7c6c9bebe5916baba9f7ca8c45808e03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aridity</topic><topic>Convection</topic><topic>Convective clouds</topic><topic>Emergency warning programs</topic><topic>Energy balance</topic><topic>Land surface temperature</topic><topic>mesoscale convective systems</topic><topic>Meteorological satellites</topic><topic>Moisture control</topic><topic>Nowcasting</topic><topic>Numerical prediction</topic><topic>Numerical weather forecasting</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainy season</topic><topic>Sahel</topic><topic>Satellite observation</topic><topic>Severe weather</topic><topic>Soil moisture</topic><topic>Soil temperature</topic><topic>Spatial filtering</topic><topic>Storms</topic><topic>Surface energy</topic><topic>Surface properties</topic><topic>Tropical environments</topic><topic>Weather forecasting</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Taylor, Christopher M</creatorcontrib><creatorcontrib>Klein, Cornelia</creatorcontrib><creatorcontrib>Dione, Cheikh</creatorcontrib><creatorcontrib>Parker, Douglas J</creatorcontrib><creatorcontrib>Marsham, John</creatorcontrib><creatorcontrib>Abdoulahat Diop, Cheikh</creatorcontrib><creatorcontrib>Fletcher, Jennifer</creatorcontrib><creatorcontrib>Saidou Chaibou, Abdoul Aziz</creatorcontrib><creatorcontrib>Nafissa, Dignon Bertin</creatorcontrib><creatorcontrib>Semeena, Valiyaveetil Shamsudheen</creatorcontrib><creatorcontrib>Cole, Steven J</creatorcontrib><creatorcontrib>Anderson, Seonaid R</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental 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>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Environmental research letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Taylor, Christopher M</au><au>Klein, Cornelia</au><au>Dione, Cheikh</au><au>Parker, Douglas J</au><au>Marsham, John</au><au>Abdoulahat Diop, Cheikh</au><au>Fletcher, Jennifer</au><au>Saidou Chaibou, Abdoul Aziz</au><au>Nafissa, Dignon Bertin</au><au>Semeena, Valiyaveetil Shamsudheen</au><au>Cole, Steven J</au><au>Anderson, Seonaid R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nowcasting tracks of severe convective storms in West Africa from observations of land surface state</atitle><jtitle>Environmental research letters</jtitle><stitle>ERL</stitle><addtitle>Environ. Res. Lett</addtitle><date>2022-03-01</date><risdate>2022</risdate><volume>17</volume><issue>3</issue><spage>34016</spage><pages>34016-</pages><issn>1748-9326</issn><eissn>1748-9326</eissn><coden>ERLNAL</coden><abstract>In tropical convective climates, where numerical weather prediction of rainfall has high uncertainty, nowcasting provides essential alerts of extreme events several hours ahead. In principle, short-term prediction of intense convective storms could benefit from knowledge of the slowly evolving land surface state in regions where soil moisture controls surface fluxes. Here we explore how near-real time (NRT) satellite observations of the land surface and convective clouds can be combined to aid early warning of severe weather in the Sahel on time scales of up to 12 h. Using land surface temperature (LST) as a proxy for soil moisture deficit, we characterise the state of the surface energy balance in NRT. We identify the most convectively active parts of mesoscale convective systems (MCSs) from spatial filtering of cloud-top temperature imagery. We find that predictive skill provided by LST data is maximised early in the rainy season, when soils are drier and vegetation less developed. Land-based skill in predicting intense convection extends well beyond the afternoon, with strong positive correlations between daytime LST and MCS activity persisting as far as the following morning in more arid conditions. For a Forecasting Testbed event during September 2021, we developed a simple technique to translate LST data into NRT maps quantifying the likelihood of convection based solely on land state. We used these maps in combination with convective features to nowcast the tracks of existing MCSs, and predict likely new initiation locations. This is the first time to our knowledge that nowcasting tools based principally on land observations have been developed. 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subjects | Aridity Convection Convective clouds Emergency warning programs Energy balance Land surface temperature mesoscale convective systems Meteorological satellites Moisture control Nowcasting Numerical prediction Numerical weather forecasting Rain Rainfall Rainy season Sahel Satellite observation Severe weather Soil moisture Soil temperature Spatial filtering Storms Surface energy Surface properties Tropical environments Weather forecasting |
title | Nowcasting tracks of severe convective storms in West Africa from observations of land surface state |
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