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Using GOES-16 to Characterize Thunderstorms : = Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using GOES-16 to Characterize Thunderstorms :/
其他題名:	Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms.
作者:	Whiteside, Abigail Elizabeth.
面頁冊數:	1 online resource (127 pages)
附註:	Source: Masters Abstracts International, Volume: 83-03.
Contained By:	Masters Abstracts International83-03.
標題:	Atmospheric sciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647802click for full text (PQDT)
ISBN:	9798535595870

Using GOES-16 to Characterize Thunderstorms : = Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms.
Whiteside, Abigail Elizabeth.

Using GOES-16 to Characterize Thunderstorms :Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms. - 1 online resource (127 pages)

Source: Masters Abstracts International, Volume: 83-03.

Thesis (M.S.)--The University of Alabama in Huntsville, 2021.

Includes bibliographical references

Every year in North America, severe thunderstorms produce copious amounts of damage to agriculture, infrastructure, and lives. The United States relies heavily on the Next Generation Weather Radar for weather information. The U.S.'s reliance on radar has led to one of the most extensive radar networks in the world. However, this network has gaps in coverage that could put many at risk. Multiple studies have shown that satellite data provides valuable storm information to forecasters. The GOES-R series offers high resolution imagery of cloud tops. An important variable to examine is the overshooting top (OT). One variable that stems from an OT is the Above Anvil Cirrus Plume (AACP). Both elements have been shown to be indicators of severe storms. Another aspect to examine is Flash Extnet Density (FED). The Geostationary Lightning Mapper (GLM) is a valuable tool for tracking lightning in severe storms. It has been shown that increases in lightning correlates to increases in storm intensity. This project aims to bridge the gap between radar data and satellite data. OT and AACP frequency and duration will be examined in both hail scar producing storms and non-hail scar producing storms. MESH values will be used to compare minimum cloud top temperatures (CTT) and maximum FED between hail scar producing storms and non-hail scar producing storms. Finally, a probability will be computed of a hail scar occurring, a severe storm occurring, and a non-severe storm occurring given specific CTT and FED. Within hail scars, OT appeared 100% of the time and AACP appeared 80% of the time. Severe storms that did not produce a hail scar had OT appear 70.8% and AACP form 27% of the time. Non-severe storms also saw OT (10.1%) and AACP (15.8%). There was not a high distinction between hail scar storms, severe storms, and non-severe storms CTT and FED values. Many of these values overlapped with each other and their distributions were close. Maximum MESH showed the highest distinction between the storm types. Hail scar producing storms had a mean MESH value of 49 mm and non-hail scar storms had a mean MESH value of 10 mm.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798535595870Subjects--Topical Terms:

3168354
Atmospheric sciences.
Subjects--Index Terms:

GOES-16Index Terms--Genre/Form:

542853
Electronic books.

Using GOES-16 to Characterize Thunderstorms : = Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms.
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245 1 0 $a Using GOES-16 to Characterize Thunderstorms : $b Hail Scar Producing Storms vs. Non-Hail Scar Producing Storms.
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500 $a Source: Masters Abstracts International, Volume: 83-03.
500 $a Advisor: Mecikalski, John R.
502 $a Thesis (M.S.)--The University of Alabama in Huntsville, 2021.
504 $a Includes bibliographical references
520 $a Every year in North America, severe thunderstorms produce copious amounts of damage to agriculture, infrastructure, and lives. The United States relies heavily on the Next Generation Weather Radar for weather information. The U.S.'s reliance on radar has led to one of the most extensive radar networks in the world. However, this network has gaps in coverage that could put many at risk. Multiple studies have shown that satellite data provides valuable storm information to forecasters. The GOES-R series offers high resolution imagery of cloud tops. An important variable to examine is the overshooting top (OT). One variable that stems from an OT is the Above Anvil Cirrus Plume (AACP). Both elements have been shown to be indicators of severe storms. Another aspect to examine is Flash Extnet Density (FED). The Geostationary Lightning Mapper (GLM) is a valuable tool for tracking lightning in severe storms. It has been shown that increases in lightning correlates to increases in storm intensity. This project aims to bridge the gap between radar data and satellite data. OT and AACP frequency and duration will be examined in both hail scar producing storms and non-hail scar producing storms. MESH values will be used to compare minimum cloud top temperatures (CTT) and maximum FED between hail scar producing storms and non-hail scar producing storms. Finally, a probability will be computed of a hail scar occurring, a severe storm occurring, and a non-severe storm occurring given specific CTT and FED. Within hail scars, OT appeared 100% of the time and AACP appeared 80% of the time. Severe storms that did not produce a hail scar had OT appear 70.8% and AACP form 27% of the time. Non-severe storms also saw OT (10.1%) and AACP (15.8%). There was not a high distinction between hail scar storms, severe storms, and non-severe storms CTT and FED values. Many of these values overlapped with each other and their distributions were close. Maximum MESH showed the highest distinction between the storm types. Hail scar producing storms had a mean MESH value of 49 mm and non-hail scar storms had a mean MESH value of 10 mm.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Meteorology. $3 542822
653 $a GOES-16
653 $a Hail
653 $a Lightning
653 $a Thunderstorms
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690 $a 0725
690 $a 0557
710 2 $a ProQuest Information and Learning Co. $3 783688
710 2 $a The University of Alabama in Huntsville. $b Atmospheric Science. $3 3694374
773 0 $t Masters Abstracts International $g 83-03.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28647802 $z click for full text (PQDT)