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Richardson, D. (2000) Skill and Relative Economic Value of the ECMWF Ensemble Prediction System. Quarterly Journal of Royal Meteorological Society, 126, 649-667.
http://dx.doi.org/10.1002/qj.49712656313

has been cited by the following article:

  • TITLE: Use of Decision Theory to Predict Dust Storms over New Delhi, India

    AUTHORS: Nirmala D. Desouza, Rajnish Kurchania, Muhammad S. Qureshi

    KEYWORDS: Aerosol Index, Decision Matrix, Impact Factor, Meteorological Parameters, Probabilistic Forecast

    JOURNAL NAME: Natural Science, Vol.6 No.8, April 28, 2014

    ABSTRACT: The north and northwest parts of India experience dust/sandstorms during the pre-monsoon season (April to May). We studied dust storms occurring over New Delhi, India (2001 to 2012) to develop a probabilistic forecast method. A probabilistic forecast method is discussed in this paper as a decision making tool that can be used to meet the needs of the users. The application of decision theory to forecast an event is that the end user of the forecast takes a decision for action on the basis of each forecast. This study stems from an elementary decision theory based on three interlocking procedures to follow viz. 1) identification of meteorological parameters responsible for dust storms, 2) determining the impact of each meteorological parameter in the initiation of a dust storm and 3) finally using the first two steps an action is recommended. Among the meteorological parameters, temperature, wind speed, pressure, number of sunny hours and evaporation had a positive impact on dust storm occurrence as compared to other variables selected. Using the concept of utility, which is an integral part of decision theory, a decision matrix is constructed. This decision matrix contains the threshold value above which a dust storm has occurred followed by each state of weather and the course of action. Thus, in this paper, a different concept of forecasting is discussed and optional rules for decision making based on the availability of a limited amount of meteorological data are presented. This forecast is of the very short range (0 - 3 hours) based on the meteorological conditions just prior to the occurrence of a dust event. We validated our findings with the OMI Aerosol index obtained from AERONET.