An Objective High-Resolution Hail Climatology of the Contiguous United States
The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Dat...
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description | The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed. |
doi_str_mv | 10.1175/WAF-D-11-00151.1 |
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Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.</description><identifier>ISSN: 0882-8156</identifier><identifier>EISSN: 1520-0434</identifier><identifier>DOI: 10.1175/WAF-D-11-00151.1</identifier><identifier>CODEN: WEFOE3</identifier><language>eng</language><publisher>Boston, MA: American Meteorological Society</publisher><subject>Algorithms ; Climate ; Climate change ; Climatic data ; Climatology ; Data centers ; Datasets ; Earth, ocean, space ; Economic impact ; Economic indicators ; Exact sciences and technology ; External geophysics ; Hail ; Hail damage ; Meteorological radar ; Meteorology ; Neural networks ; Population density ; Radar ; Remote sensing ; Servers ; Severe storms ; Severe thunderstorms ; Spatial discrimination ; Spatial resolution ; Storm data ; Storms ; Studies ; Temporal resolution ; Thunderstorms ; Velocity ; Warm seasons ; Water in the atmosphere (humidity, clouds, evaporation, precipitation) ; Weather forecasting ; Weather radar</subject><ispartof>Weather and forecasting, 2012-10, Vol.27 (5), p.1235-1248</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright American Meteorological Society Oct 2012</rights><rights>Copyright American Meteorological Society 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c451t-ead193381eadc3c21761f2e45741071dc3d607ff64ff678de3ea514e62f349443</citedby><cites>FETCH-LOGICAL-c451t-ead193381eadc3c21761f2e45741071dc3d607ff64ff678de3ea514e62f349443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3667,27903,27904</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26515695$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>CINTINEO, John L</creatorcontrib><creatorcontrib>SMITH, Travis M</creatorcontrib><creatorcontrib>LAKSHMANAN, Valliappa</creatorcontrib><creatorcontrib>BROOKS, Harold E</creatorcontrib><creatorcontrib>ORTEGA, Kiel L</creatorcontrib><title>An Objective High-Resolution Hail Climatology of the Contiguous United States</title><title>Weather and forecasting</title><description>The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.</description><subject>Algorithms</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climatic data</subject><subject>Climatology</subject><subject>Data centers</subject><subject>Datasets</subject><subject>Earth, ocean, space</subject><subject>Economic impact</subject><subject>Economic indicators</subject><subject>Exact sciences and technology</subject><subject>External geophysics</subject><subject>Hail</subject><subject>Hail damage</subject><subject>Meteorological radar</subject><subject>Meteorology</subject><subject>Neural networks</subject><subject>Population density</subject><subject>Radar</subject><subject>Remote sensing</subject><subject>Servers</subject><subject>Severe storms</subject><subject>Severe thunderstorms</subject><subject>Spatial discrimination</subject><subject>Spatial resolution</subject><subject>Storm 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Harold E</au><au>ORTEGA, Kiel L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Objective High-Resolution Hail Climatology of the Contiguous United States</atitle><jtitle>Weather and forecasting</jtitle><date>2012-10-01</date><risdate>2012</risdate><volume>27</volume><issue>5</issue><spage>1235</spage><epage>1248</epage><pages>1235-1248</pages><issn>0882-8156</issn><eissn>1520-0434</eissn><coden>WEFOE3</coden><abstract>The threat of damaging hail from severe thunderstorms affects many communities and industries on a yearly basis, with annual economic losses in excess of $1 billion (U.S. dollars). Past hail climatology has typically relied on the National Oceanic and Atmospheric Administration/National Climatic Data Center’s (NOAA/NCDC) Storm Data publication, which has numerous reporting biases and nonmeteorological artifacts. This research seeks to quantify the spatial and temporal characteristics of contiguous United States (CONUS) hail fall, derived from multiradar multisensor (MRMS) algorithms for several years during the Next-Generation Weather Radar (NEXRAD) era, leveraging the Multiyear Reanalysis of Remotely Sensed Storms (MYRORSS) dataset at NOAA’s National Severe Storms Laboratory (NSSL). The primary MRMS product used in this study is the maximum expected size of hail (MESH). The preliminary climatology includes 42 months of quality controlled and reprocessed MESH grids, which spans the warm seasons for four years (2007–10), covering 98% of all Storm Data hail reports during that time. The dataset has 0.01° latitude × 0.01° longitude × 31 vertical levels spatial resolution, and 5-min temporal resolution. Radar-based and reports-based methods of hail climatology are compared. MRMS MESH demonstrates superior coverage and resolution over Storm Data hail reports, and is largely unbiased. The results reveal a broad maximum of annual hail fall in the Great Plains and a diminished secondary maximum in the Southeast United States. Potential explanations for the differences in the two methods of hail climatology are also discussed.</abstract><cop>Boston, MA</cop><pub>American Meteorological Society</pub><doi>10.1175/WAF-D-11-00151.1</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Climate Climate change Climatic data Climatology Data centers Datasets Earth, ocean, space Economic impact Economic indicators Exact sciences and technology External geophysics Hail Hail damage Meteorological radar Meteorology Neural networks Population density Radar Remote sensing Servers Severe storms Severe thunderstorms Spatial discrimination Spatial resolution Storm data Storms Studies Temporal resolution Thunderstorms Velocity Warm seasons Water in the atmosphere (humidity, clouds, evaporation, precipitation) Weather forecasting Weather radar |
title | An Objective High-Resolution Hail Climatology of the Contiguous United States |
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