Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/8666
Title: The Forecast Interpretation Tool – a Monte Carlo technique for blending climatic distributions with probabilistic forecasts
Authors: Husak, Gregory J.
Michaelsen, Joel
Kyriakidis, Phaedon
Verdin, James P.
Funk, Chris
Galu, Gideon
Keywords: Rainfall
Forecast
Probability
Monte Carlo simulation
Issue Date: Jan-2011
Publisher: Wiley-Blackwell
Source: International Journal of Climatology, 2011, Volume 31, pages 461-467
Abstract: Probabilistic forecasts are produced from a variety of outlets to help predict rainfall, and other meteorological events, for periods of 1 month or more. Such forecasts are expressed as probabilities of a rainfall event, e.g. being in the upper, middle, or lower third of the relevant distribution of rainfall in the region. The impact of these forecasts on the expectation for the event is not always clear or easily conveyed. This article proposes a technique based on Monte Carlo simulation for adjusting existing climatologic statistical parameters to match forecast information, resulting in new parameters defining the probability of events for the forecast interval. The resulting parameters are shown to approximate the forecasts with reasonable accuracy. To show the value of the technique as an application for seasonal rainfall, it is used with consensus forecast developed for the Greater Horn of Africa for the 2009 March-April-May season. An alternative, analytical approach is also proposed, and discussed in comparison to the first simulation-based technique.
URI: http://ktisis.cut.ac.cy/jspui/handle/10488/8666
ISSN: 0899-8418
1097-0088 (Online)
DOI: 10.1002/joc.2074
Rights: Copyright  2010 Royal Meteorological Society
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