Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events
We explore the characteristics of 96,190 wet events (WEs) defined as consecutive 3‐hourly rainfall ≥ 1 mm/3 hr from a network of 34 stations across Bangladesh. Nearly 60% (5%) of WEs last ≤ 3 (≥ 15) hr. The WEs are dynamically clustered into four “canonical” storm types (STs), mostly discretized by...
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| Veröffentlicht in: | International journal of climatology 2023-02, Vol.43 (2), p.850-873 |
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| creator | Moron, Vincent Acharya, Nachiketa Hassan, S. M. Quamrul |
| description | We explore the characteristics of 96,190 wet events (WEs) defined as consecutive 3‐hourly rainfall ≥ 1 mm/3 hr from a network of 34 stations across Bangladesh. Nearly 60% (5%) of WEs last ≤ 3 (≥ 15) hr. The WEs are dynamically clustered into four “canonical” storm types (STs), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. Approximately 60% of WEs are associated with ST#1, characterized by short and small WEs and very low rainfall amounts (usually |
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Exactly 96,190 wet events (WEs) (= consecutive 3‐hourly rainfall ≥1 mm/3 hr) across Bangladesh were analysed and synthesized into four storm types (STs), which were discretized by their duration and intensity. While durations, total amounts and wet contiguous areas are positively related, their mean intensity is nearly independent of them.(Grey dots) Surface of IMERG wet area (in km2) receiving at least 10 mm versus (a) duration of wet events (in 3‐hr slots), (b) total amounts (in 1/10 mm) and (c) mean intensity (in 1/10 mm per 3 hr) at the target Bangladesh Meteorological Department (BMD) rain gauges. The median (coloured large dots) with the 25th–75th (large coloured lines) and 5th–95th (thin coloured lines) percentiles are superimposed for the four STs. The black circles in panels (a) and (b) represent the linear fit between the variables shown in each panel with the upper and lower triangles showing the two‐sided 90% interval of confidence. Due to Y‐axis log scale, negative values of the lower confidence bound are not shown in panels (a) and (b). Only WEs receiving at least 10 mm at the BMD rain gauge and at the co‐located IMERG grid‐points are considered. Note that the linear fit line is not indicated in panel (c) because the common variance is <2%.</description><identifier>ISSN: 0899-8418</identifier><identifier>EISSN: 1097-0088</identifier><identifier>DOI: 10.1002/joc.7835</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>3‐hourly rainfall ; Climatology ; clustering ; Earth Sciences ; Hourly rainfall ; Meteorology ; Precipitation ; predictability ; Probability theory ; Rain ; Rainfall ; Rainfall amount ; Sciences of the Universe ; Seasonal variation ; Seasonal variations ; Thunderstorms</subject><ispartof>International journal of climatology, 2023-02, Vol.43 (2), p.850-873</ispartof><rights>2022 The Authors. published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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M. Quamrul</creatorcontrib><title>Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events</title><title>International journal of climatology</title><description>We explore the characteristics of 96,190 wet events (WEs) defined as consecutive 3‐hourly rainfall ≥ 1 mm/3 hr from a network of 34 stations across Bangladesh. Nearly 60% (5%) of WEs last ≤ 3 (≥ 15) hr. The WEs are dynamically clustered into four “canonical” storm types (STs), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. Approximately 60% of WEs are associated with ST#1, characterized by short and small WEs and very low rainfall amounts (usually <10 mm), ~30% of WEs are associated with either (ST#2) short/small WEs but with intense rainfall, probably mostly related to scattered thunderstorms or (ST#3) longer/larger WEs but with less intense rainfall. The last ST (ST#4) is rare (~6%), related to very long durations and large wet areas and includes the wettest WEs. It is especially frequent over southeastern Bangladesh. ST#2–ST#4 contribute almost equally to the local‐scale total amount of rainfall (27–29% each in mean) while ST#1, despite its individual low rainfall amount, still includes ~15% of it. ST#2 (ST#4) is related to the highest probability of occurrence of 3‐hourly (daily) extremes. ST#4 occurrence is the most impacted by synoptic Indian lows/depressions as well as the main modes of intra‐seasonal variation, while ST#1 and ST#2 are also significantly impacted by intra‐seasonal modes but in reverse manner than ST#4.
Exactly 96,190 wet events (WEs) (= consecutive 3‐hourly rainfall ≥1 mm/3 hr) across Bangladesh were analysed and synthesized into four storm types (STs), which were discretized by their duration and intensity. While durations, total amounts and wet contiguous areas are positively related, their mean intensity is nearly independent of them.(Grey dots) Surface of IMERG wet area (in km2) receiving at least 10 mm versus (a) duration of wet events (in 3‐hr slots), (b) total amounts (in 1/10 mm) and (c) mean intensity (in 1/10 mm per 3 hr) at the target Bangladesh Meteorological Department (BMD) rain gauges. The median (coloured large dots) with the 25th–75th (large coloured lines) and 5th–95th (thin coloured lines) percentiles are superimposed for the four STs. The black circles in panels (a) and (b) represent the linear fit between the variables shown in each panel with the upper and lower triangles showing the two‐sided 90% interval of confidence. Due to Y‐axis log scale, negative values of the lower confidence bound are not shown in panels (a) and (b). Only WEs receiving at least 10 mm at the BMD rain gauge and at the co‐located IMERG grid‐points are considered. Note that the linear fit line is not indicated in panel (c) because the common variance is <2%.</description><subject>3‐hourly rainfall</subject><subject>Climatology</subject><subject>clustering</subject><subject>Earth Sciences</subject><subject>Hourly rainfall</subject><subject>Meteorology</subject><subject>Precipitation</subject><subject>predictability</subject><subject>Probability theory</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall amount</subject><subject>Sciences of the Universe</subject><subject>Seasonal variation</subject><subject>Seasonal variations</subject><subject>Thunderstorms</subject><issn>0899-8418</issn><issn>1097-0088</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp10FFLwzAQB_AgCs4p-BECvijYmTRN2_g2hzplsIfpc7g2qevomplkG33zI_gZ_SS2Vnzz6eDud8fxR-ickhElJLxZmXyUpIwfoAElIgkISdNDNCCpEEEa0fQYnTi3IoQIQeMBWiy8sWvsm412uKzxHdRvFSjtlrdYbS340tTX7cDr2pW-wVArDFYDNkXXtfD18amgrBq81x7rna69O0VHBVROn_3WIXp9uH-ZTIPZ_PFpMp4FOYspD-KCcIhjQSkrOFAuGM9okXEmqI4hzGJGC6G4hjwLo0QREulMQKpzxYCKRLEhuurvLqGSG1uuwTbSQCmn45nseiRiIeMi3NHWXvR2Y837VjsvV2Zr6_Y9GSZJFDHeulZd9iq3xjmri7-zlMgu3nYrl128LQ16ui8r3fzr5PN88uO_ARnRezU</recordid><startdate>202302</startdate><enddate>202302</enddate><creator>Moron, Vincent</creator><creator>Acharya, Nachiketa</creator><creator>Hassan, S. 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Quamrul</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3615-6f05a669113f5a15935b1fb5391e6a2b631f9d5eacb247d004eb9a8ecd3a197d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>3‐hourly rainfall</topic><topic>Climatology</topic><topic>clustering</topic><topic>Earth Sciences</topic><topic>Hourly rainfall</topic><topic>Meteorology</topic><topic>Precipitation</topic><topic>predictability</topic><topic>Probability theory</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainfall amount</topic><topic>Sciences of the Universe</topic><topic>Seasonal variation</topic><topic>Seasonal variations</topic><topic>Thunderstorms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moron, Vincent</creatorcontrib><creatorcontrib>Acharya, Nachiketa</creatorcontrib><creatorcontrib>Hassan, S. M. Quamrul</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>International journal of climatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moron, Vincent</au><au>Acharya, Nachiketa</au><au>Hassan, S. M. Quamrul</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events</atitle><jtitle>International journal of climatology</jtitle><date>2023-02</date><risdate>2023</risdate><volume>43</volume><issue>2</issue><spage>850</spage><epage>873</epage><pages>850-873</pages><issn>0899-8418</issn><eissn>1097-0088</eissn><abstract>We explore the characteristics of 96,190 wet events (WEs) defined as consecutive 3‐hourly rainfall ≥ 1 mm/3 hr from a network of 34 stations across Bangladesh. Nearly 60% (5%) of WEs last ≤ 3 (≥ 15) hr. The WEs are dynamically clustered into four “canonical” storm types (STs), mostly discretized by their duration, but also their mean and maximal intensity. While durations, total amounts and wet contiguous areas of WEs are positively related, their mean intensity is nearly independent of them. Approximately 60% of WEs are associated with ST#1, characterized by short and small WEs and very low rainfall amounts (usually <10 mm), ~30% of WEs are associated with either (ST#2) short/small WEs but with intense rainfall, probably mostly related to scattered thunderstorms or (ST#3) longer/larger WEs but with less intense rainfall. The last ST (ST#4) is rare (~6%), related to very long durations and large wet areas and includes the wettest WEs. It is especially frequent over southeastern Bangladesh. ST#2–ST#4 contribute almost equally to the local‐scale total amount of rainfall (27–29% each in mean) while ST#1, despite its individual low rainfall amount, still includes ~15% of it. ST#2 (ST#4) is related to the highest probability of occurrence of 3‐hourly (daily) extremes. ST#4 occurrence is the most impacted by synoptic Indian lows/depressions as well as the main modes of intra‐seasonal variation, while ST#1 and ST#2 are also significantly impacted by intra‐seasonal modes but in reverse manner than ST#4.
Exactly 96,190 wet events (WEs) (= consecutive 3‐hourly rainfall ≥1 mm/3 hr) across Bangladesh were analysed and synthesized into four storm types (STs), which were discretized by their duration and intensity. While durations, total amounts and wet contiguous areas are positively related, their mean intensity is nearly independent of them.(Grey dots) Surface of IMERG wet area (in km2) receiving at least 10 mm versus (a) duration of wet events (in 3‐hr slots), (b) total amounts (in 1/10 mm) and (c) mean intensity (in 1/10 mm per 3 hr) at the target Bangladesh Meteorological Department (BMD) rain gauges. The median (coloured large dots) with the 25th–75th (large coloured lines) and 5th–95th (thin coloured lines) percentiles are superimposed for the four STs. The black circles in panels (a) and (b) represent the linear fit between the variables shown in each panel with the upper and lower triangles showing the two‐sided 90% interval of confidence. Due to Y‐axis log scale, negative values of the lower confidence bound are not shown in panels (a) and (b). Only WEs receiving at least 10 mm at the BMD rain gauge and at the co‐located IMERG grid‐points are considered. Note that the linear fit line is not indicated in panel (c) because the common variance is <2%.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/joc.7835</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0002-4981-9530</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | 3‐hourly rainfall Climatology clustering Earth Sciences Hourly rainfall Meteorology Precipitation predictability Probability theory Rain Rainfall Rainfall amount Sciences of the Universe Seasonal variation Seasonal variations Thunderstorms |
| title | Storm types in Bangladesh: duration, intensity and area of intra‐daily wet events |
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