Climatology and predictability of the spatial coverage of 5-day rainfall over Indian subdivisions

Abstract

Six‐state discrete simple Markov chain models are applied to the 5‐day spatial rainfall features during the summer monsoon for 30 years (1964–1993) over 33 meteorological subdivisions of India to understand the persistence behaviour of the spatial coverage of rainfall and the underlying time‐evolutionary processes on the synoptic scale. The stochastic models are cross validated on 5 years of independent data (1994–1998) by evaluating various measures of forecast skill. It is revealed that the spatial coverage of rainfall does exhibit first order Markov persistence for all features but diminishes for longer intervals for some features. The stochastic matrix, together with the climatic information about the spatial coverage of 5‐day rainfall features, could be an aid to the operational forecaster to judge the evolution of specific areal rainfall events for qualitative prediction on a medium to extended range scale over a subdivision. Copyright © 2000 Royal Meteorological Society

Markov chain models for pre‐monsoon season thunderstorms over Pune

“…Such a tendency can be best explained by using a Markov chain model (MCM) of a particular order of conditional dependence on a physical process. MCMs have been extensively used by different scientists (Gabriel and Neumann, 1962;Chin, 1976Chin, , 1977Bishoni and Saxena, 1979;Sarkar 1994;Pant and Shivhare, 1998;Dahale and Puranik, 2000;Dasgupta and De, 2001) to forecast the occurrence of precipitation, the distribution of wet and dry spells, etc. A common conclusion of the above studies suggests that, on the majority of occasions, the occurrence of precipitation can be best described by a first-order Markov chain.…”