Simulations of extreme rainfall in the Yarlung Tsangbo Grand Canyon, China Article Swipe
Xuelong Chen
,
Dianbin Cao
,
Qiang Zhang
,
Xin Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/ems2024-3
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/ems2024-3
The Yarlung Tsangbo Grand Canyon (YGC), one of the world’s deepest canyons, is located in the southeastern Tibetan Plateau (SETP). The YGC exhibits the highest frequency of convective activity in China. Due to frequent rainstorms in the wet season, natural disasters such as landslides and debris flows frequently occur, and often block traffic corridors. Thus, understanding the relationship between water vapor changes, convective cloud activity, and extreme rainfall events in the YGC is critical. A comprehensive observation network for water vapor variations, cloud activity, local circulation, and land-air interactions in the YGC was installed to help us to determine the relationship between the water vapor transport and heavy precipitation in the YGC and the physical process that determines the precipitation intensity, especially for cases of strong precipitation. We analyzed 35 years observation data of daily precipitation to objectively classify the weather systems responsible for the heavy precipitation. Hierarchical clustering method divided the atmospheric circulation of the regional heavy precipitation into two representative patterns: the Tibetan Plateau vortex type (TPVT, accounting for 56.6% of the heavy precipitation events) and the mid-latitude trough type (MLTT，43.4%). The comprehensive analysis of the two patterns shows a clear connection between the heavy precipitation and positive vorticity anomaly, moisture convergence and the southeastward shift of the westerly jet core. Specifically, TPVT heavy precipitation events are caused by potential vorticity dry-to-wet processes during its eastward movement, while MLTT events are associated with the intrusion of deeply extratropical trough-ridge circulations into the SETP. We used the Weather Research and Forecasting (WRF) model to simulate the water vapor flux during extreme rainfall events. The general shortcoming of the WRF precipitation simulation nudged with the European Centre for Medium-Range Weather Forecasts’ reanalysis dataset version 5 (ERA5), is that it cannot capture strong rainfall period. We tested many WRF parameterization schemes at a 1 km grid resolution. The Multiscale Kain-Fritsch (MSKF) scheme outperforms explicit calculations (NO_CU) in capturing heavy precipitation events accurately, thanks to its enhanced treatment of cloud-radiation interactions. The incorporation of the Turbulence Orographic Form Drag (TOFD) scheme significantly improves precipitation simulation accuracy. This is particularly evident in the better representation of local circulation. A noticeable improvement in near surface wind speeds and the vertical profile of horizontal winds was presented by the TOFD scheme. It was found that when an optimized combination of parameterization schemes in WRF can better capture the variations in the wind and water vapor concentration in the YGC channel, the model produced the best simulation results for extreme rainfall in the YGC. These analyses have help us understanding the impacts of YGC valley on the water vapor transport and extreme rainfall outbreak mechanism.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.5194/ems2024-3
- OA Status
- gold
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400365450
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400365450Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5194/ems2024-3Digital Object Identifier
- Title
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Simulations of extreme rainfall in the Yarlung Tsangbo Grand Canyon, ChinaWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-05Full publication date if available
- Authors
-
Xuelong Chen, Dianbin Cao, Qiang Zhang, Xin XuList of authors in order
- Landing page
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https://doi.org/10.5194/ems2024-3Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5194/ems2024-3Direct OA link when available
- Concepts
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Precipitation, Climatology, Environmental science, Plateau (mathematics), Water vapor, Trough (economics), Subtropical ridge, Orographic lift, Atmospheric sciences, Geology, Meteorology, Geography, Economics, Macroeconomics, Mathematics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.European | 275 |
| abstract_inverted_index.Research | 249 |
| abstract_inverted_index.activity | 28 |
| abstract_inverted_index.analyses | 419 |
| abstract_inverted_index.analysis | 185 |
| abstract_inverted_index.analyzed | 128 |
| abstract_inverted_index.anomaly, | 201 |
| abstract_inverted_index.canyons, | 11 |
| abstract_inverted_index.changes, | 61 |
| abstract_inverted_index.channel, | 404 |
| abstract_inverted_index.classify | 138 |
| abstract_inverted_index.eastward | 227 |
| abstract_inverted_index.enhanced | 324 |
| abstract_inverted_index.exhibits | 22 |
| abstract_inverted_index.explicit | 312 |
| abstract_inverted_index.frequent | 33 |
| abstract_inverted_index.improves | 340 |
| abstract_inverted_index.land-air | 87 |
| abstract_inverted_index.moisture | 202 |
| abstract_inverted_index.outbreak | 437 |
| abstract_inverted_index.patterns | 189 |
| abstract_inverted_index.physical | 114 |
| abstract_inverted_index.positive | 199 |
| abstract_inverted_index.produced | 407 |
| abstract_inverted_index.rainfall | 67, 262, 292, 414, 436 |
| abstract_inverted_index.regional | 156 |
| abstract_inverted_index.simulate | 255 |
| abstract_inverted_index.vertical | 365 |
| abstract_inverted_index.westerly | 210 |
| abstract_inverted_index.accuracy. | 343 |
| abstract_inverted_index.activity, | 64, 83 |
| abstract_inverted_index.capturing | 316 |
| abstract_inverted_index.critical. | 73 |
| abstract_inverted_index.determine | 98 |
| abstract_inverted_index.disasters | 40 |
| abstract_inverted_index.frequency | 25 |
| abstract_inverted_index.installed | 93 |
| abstract_inverted_index.intrusion | 236 |
| abstract_inverted_index.movement, | 228 |
| abstract_inverted_index.optimized | 382 |
| abstract_inverted_index.patterns: | 162 |
| abstract_inverted_index.potential | 221 |
| abstract_inverted_index.presented | 371 |
| abstract_inverted_index.processes | 224 |
| abstract_inverted_index.transport | 105, 433 |
| abstract_inverted_index.treatment | 325 |
| abstract_inverted_index.vorticity | 200, 222 |
| abstract_inverted_index.Multiscale | 307 |
| abstract_inverted_index.Orographic | 334 |
| abstract_inverted_index.Turbulence | 333 |
| abstract_inverted_index.accounting | 169 |
| abstract_inverted_index.associated | 233 |
| abstract_inverted_index.clustering | 148 |
| abstract_inverted_index.connection | 193 |
| abstract_inverted_index.convective | 27, 62 |
| abstract_inverted_index.corridors. | 53 |
| abstract_inverted_index.determines | 117 |
| abstract_inverted_index.dry-to-wet | 223 |
| abstract_inverted_index.especially | 121 |
| abstract_inverted_index.frequently | 47 |
| abstract_inverted_index.horizontal | 368 |
| abstract_inverted_index.intensity, | 120 |
| abstract_inverted_index.landslides | 43 |
| abstract_inverted_index.mechanism. | 438 |
| abstract_inverted_index.noticeable | 356 |
| abstract_inverted_index.rainstorms | 34 |
| abstract_inverted_index.reanalysis | 281 |
| abstract_inverted_index.simulation | 271, 342, 410 |
| abstract_inverted_index.variations | 393 |
| abstract_inverted_index.Forecasting | 251 |
| abstract_inverted_index.accurately, | 320 |
| abstract_inverted_index.atmospheric | 152 |
| abstract_inverted_index.circulation | 153 |
| abstract_inverted_index.combination | 383 |
| abstract_inverted_index.convergence | 203 |
| abstract_inverted_index.improvement | 357 |
| abstract_inverted_index.objectively | 137 |
| abstract_inverted_index.observation | 76, 131 |
| abstract_inverted_index.outperforms | 311 |
| abstract_inverted_index.resolution. | 305 |
| abstract_inverted_index.responsible | 142 |
| abstract_inverted_index.shortcoming | 266 |
| abstract_inverted_index.variations, | 81 |
| abstract_inverted_index.Hierarchical | 147 |
| abstract_inverted_index.Kain-Fritsch | 308 |
| abstract_inverted_index.Medium-Range | 278 |
| abstract_inverted_index.calculations | 313 |
| abstract_inverted_index.circulation, | 85 |
| abstract_inverted_index.circulation. | 354 |
| abstract_inverted_index.circulations | 241 |
| abstract_inverted_index.interactions | 88 |
| abstract_inverted_index.mid-latitude | 179 |
| abstract_inverted_index.particularly | 346 |
| abstract_inverted_index.relationship | 57, 100 |
| abstract_inverted_index.southeastern | 16 |
| abstract_inverted_index.trough-ridge | 240 |
| abstract_inverted_index.Specifically, | 213 |
| abstract_inverted_index.comprehensive | 75, 184 |
| abstract_inverted_index.concentration | 400 |
| abstract_inverted_index.extratropical | 239 |
| abstract_inverted_index.incorporation | 330 |
| abstract_inverted_index.interactions. | 328 |
| abstract_inverted_index.precipitation | 108, 119, 135, 158, 175, 197, 216, 270, 318, 341 |
| abstract_inverted_index.significantly | 339 |
| abstract_inverted_index.southeastward | 206 |
| abstract_inverted_index.understanding | 55, 423 |
| abstract_inverted_index.precipitation. | 126, 146 |
| abstract_inverted_index.representation | 351 |
| abstract_inverted_index.representative | 161 |
| abstract_inverted_index.cloud-radiation | 327 |
| abstract_inverted_index.parameterization | 298, 385 |
| abstract_inverted_index.world’s | 9 |
| abstract_inverted_index.Forecasts’ | 280 |
| abstract_inverted_index.(MLTT，43.4%). | 182 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.16993365 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |