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Early warning products for severe weather events derived from operational medium-range ensemble forecasts.

Authors :
Matsueda, Mio
Nakazawa, Tetsuo
Source :
Meteorological Applications; Apr2015, Vol. 22 Issue 2, p213-222, 10p
Publication Year :
2015

Abstract

ABSTRACT Accurate predictions of severe weather events are important for the society, economy, and environment in regions affected by such events. In the present study, the development and testing of a suite of prototype ensemble-based early warning products for severe weather events, which are now routinely available at , are reported. The early warning products are based on operational medium-range ensemble forecasts from four of the leading global numerical weather centres: the European Centre for Medium-Range Weather Forecasts, the Japan Meteorological Agency, the UK Meteorological Office, and the National Centers for Environmental Prediction in USA. In these products, the forecast probability of the occurrence of severe weather events, including heavy rainfall, strong surface winds, and high/low surface temperatures, is defined based on each model's climatological probabilistic density function. Several case studies have demonstrated the ability of the products to successfully predict severe events, including the Russian heatwave in 2010, the 2010 Pakistan floods, and Hurricane Sandy in 2012. The construction of a grand ensemble by combining four single-centre ensembles can improve the probabilistic skills of forecasts of severe events, up to a lead time of +360 h. The improvements in forecast skills are more pronounced for severe surface temperature and precipitation. The grand ensemble provides more reliable forecasts than single-centre ensembles, particularly with respect to strong wind speeds and severe temperature, aiding the advance detection of severe weather events to help mitigate the associated catastrophic damage, especially in developing countries. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13504827
Volume :
22
Issue :
2
Database :
Complementary Index
Journal :
Meteorological Applications
Publication Type :
Academic Journal
Accession number :
102319580
Full Text :
https://doi.org/10.1002/met.1444