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Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model.
- Source :
- Remote Sensing; Oct2023, Vol. 15 Issue 19, p4745, 21p
- Publication Year :
- 2023
-
Abstract
- Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events' temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region. [ABSTRACT FROM AUTHOR]
- Subjects :
- SPATIAL variation
SEVERE storms
Subjects
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 19
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 172983485
- Full Text :
- https://doi.org/10.3390/rs15194745