NDLI: An analysis of meteorological services under extreme weather conditions based on a Bayesian decision-support model: a case study of the thunderstorms in Beijing on July 21, 2012

Content Provider	Springer Nature Link
Author	Hu, Haibo Liang, Xudong You, Fengchun Sun, Jisong
Copyright Year	2015
Abstract	The decision-making procedures of the meteorological service concerning the extreme thunderstorm in Beijing on July 21, 2012, were simulated and analyzed in a scenario using a Bayesian decision-support model. A thorough analysis of the decision-making process during that terrible thunderstorm demonstrated that a decision-support model can be used to make optimal decisions regarding uncertainty problems in the meteorological service supported by current meteorological technology and data resources, e.g., the mesoscale numeric weather prediction (NWP) system and observational data. Using NWP grid data, we assessed the flooding and debris flow risks on that day, and the high risks were clearly apparent. Consulting the historical flooding records, we also recognized the high thunderstorm risk that day even though the predicted precipitation was reported as 100–200 mm in most areas. Because of the low probability of extreme precipitation indicated by climate data, the posteriori probability estimated by the Bayesian model was only 23.1 %. For the differences between expected losses in a disaster and a non-disaster state, issuing a prediction for a non-disaster state could obviously lead to greater expected losses than predicting a disaster state. Therefore, it would be advisable to provide a disaster state prediction and take a correspondingly worst case scenario outlook in the meteorological service, which was the optimal decision-making strategy at that time. This study reveals that (1) the objective promotion of an emergency response level corresponding to a severe weather warning is recommended to realize the advantages of a worst case scenario prediction, even if the forecasters underestimate the devastating impact of the weather, and thus, it can obviously relieve unnecessary pressure on forecasters, and (2) the public should be provided with uncertainty information along with severe weather forecasts and warnings so, as the end users of meteorological services, they can make better informed decisions.
Starting Page	1225
Ending Page	1241
Page Count	17
File Format	PDF
ISSN	0921030X
Journal	Natural Hazards
Volume Number	78
Issue Number	2
e-ISSN	15730840
Language	English
Publisher	Springer Netherlands
Publisher Date	2015-04-24
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Weather forecast uncertainty Bayesian Decision making Flood Natural Hazards Hydrogeology Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Atmospheric Science Water Science and Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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