DOI: 10.1002/2017JD027432
Scopus记录号: 2-s2.0-85044300863
论文题名: Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection From Infrared Imagery
作者: Klein C. ; Belušić D. ; Taylor C.M.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期: 6 起始页码: 3035
结束页码: 3050
语种: 英语
英文关键词: cloud top temperature
; deep convection
; extreme rain
; mesoscale convective system
; scale decomposition
; wavelets
Scopus关键词: atmospheric convection
; cloud cover
; convective system
; decomposition analysis
; extreme event
; global warming
; infrared imagery
; low temperature
; mesoscale meteorology
; Meteosat
; precipitation intensity
; stratiform cloud
; TRMM
; wavelet analysis
; West Africa
英文摘要: Mesoscale convective systems (MCSs) are frequently associated with rainfall extremes and are expected to further intensify under global warming. However, despite the significant impact of such extreme events, the dominant processes favoring their occurrence are still under debate. Meteosat geostationary satellites provide unique long-term subhourly records of cloud top temperatures, allowing to track changes in MCS structures that could be linked to rainfall intensification. Focusing on West Africa, we show that Meteosat cloud top temperatures are a useful proxy for rainfall intensities, as derived from snapshots from the Tropical Rainfall Measuring Mission 2A25 product: MCSs larger than 15,000 km2 at a temperature threshold of −40°C are found to produce 91% of all extreme rainfall occurrences in the study region, with 80% of the storms producing extreme rain when their minimum temperature drops below −80°C. Furthermore, we present a new method based on 2-D continuous wavelet transform to explore the relationship between cloud top temperature and rainfall intensity for subcloud features at different length scales. The method shows great potential for separating convective and stratiform cloud parts when combining information on temperature and scale, improving the common approach of using a temperature threshold only. We find that below −80°C, every fifth pixel is associated with deep convection. This frequency is doubled when looking at subcloud features smaller than 35 km. Scale analysis of subcloud features can thus help to better exploit cloud top temperature data sets, which provide much more spatiotemporal detail of MCS characteristics than available rainfall data sets alone. ©2018. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114246
Appears in Collections: 气候减缓与适应
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作者单位: Centre for Ecology and Hydrology, Wallingford, United Kingdom; Swedish Meteorological and Hydrological Institute, Norrköping, Sweden; National Centre for Earth Observation, Wallingford, United Kingdom
Recommended Citation:
Klein C.,Belušić D.,Taylor C.M.. Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection From Infrared Imagery[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(6)