A comprehensive approach from the raw and normalized anomalies to the analysis and prediction of the Beijing extreme rainfall on July 21, 2012

Ning Jiang (), Weihong Qian (), Jun Du, Richard H. Grumm and Jiaolan Fu
Additional contact information
Ning Jiang: Peking University
Weihong Qian: Peking University
Jun Du: NOAA/NCEP/Environmental Modeling Center
Richard H. Grumm: National Weather Service
Jiaolan Fu: National Meteorological Center

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 84, issue 3, No 4, 1567 pages

Abstract: Abstract Two anomaly-based fields, raw anomaly (RA) and normalized anomaly (NA), have been proven to be useful to indicate different aspects of weather extremes compared to conventional total fields. Based on the previous studies and a case analysis of the Beijing extreme rainfall on July 21, 2012, several unique features of the anomaly-based fields such as maximum height anomaly, positive humidity anomalous axes and zero temperature anomaly isoline are derived to indicate the extreme rain event effectively. Furthermore, complementary roles of the anomaly-based fields are summarized: RA fields can enlarge signals of an anomalous weather system with a clear structure, while NA fields can be used to quantitatively measure the degree of rarity or abnormality of a high-impact weather event. After a comparison between RA and NA variables, a comprehensive approach which combines these two types of anomalies is proposed to improve the extreme weather prediction using the ECMWF model output data. The results show that, for this Beijing extreme rainfall case, the proposed approach made a successful forecast with 156-h lead-time, which is 72 h earlier than the model precipitation forecast. Thus, anomaly-based fields are useful for forecasters to identify severe weather event warnings in practice.

Keywords: Anomaly-based fields; Forecast skill; Extreme weather events; Beijing 7.21 rainfall (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11069-016-2500-0

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