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Use of the gamma distribution to represent monthly rainfall in Africa for drought monitoring applications
Author(s) -
Husak Gregory J.,
Michaelsen Joel,
Funk Chris
Publication year - 2006
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.1441
Subject(s) - weibull distribution , gamma distribution , range (aeronautics) , environmental science , probabilistic logic , meteorology , precipitation , probability distribution , goodness of fit , distribution (mathematics) , satellite , computer science , climatology , statistics , geography , mathematics , geology , machine learning , mathematical analysis , materials science , aerospace engineering , engineering , composite material
Evaluating a range of scenarios that accurately reflect precipitation variability is critical for water resource applications. Inputs to these applications can be provided using location‐ and interval‐specific probability distributions. These distributions make it possible to estimate the likelihood of rainfall being within a specified range. In this paper, we demonstrate the feasibility of fitting cell‐by‐cell probability distributions to grids of monthly interpolated, continent‐wide data. Future work will then detail applications of these grids to improved satellite‐remote sensing of drought and interpretations of probabilistic climate outlook forum forecasts. The gamma distribution is well suited to these applications because it is fairly familiar to African scientists, and capable of representing a variety of distribution shapes. This study tests the goodness‐of‐fit using the Kolmogorov–Smirnov (KS) test, and compares these results against another distribution commonly used in rainfall events, the Weibull. The gamma distribution is suitable for roughly 98% of the locations over all months. The techniques and results presented in this study provide a foundation for use of the gamma distribution to generate drivers for various rain‐related models. These models are used as decision support tools for the management of water and agricultural resources as well as food reserves by providing decision makers with ways to evaluate the likelihood of various rainfall accumulations and assess different scenarios in Africa. Copyright © 2006 Royal Meteorological Society