Short-Term Single-Station Forecasting of Precipitation

Abstract

Forecast probabilities of rain were calculated up to 12 hours in advance using a Markov chain model applied to three-hourly observations from five major Australian cities. The four weather states chosen in this first study were three cloudiness states (0?2 oktas, 3?5 oktas and 6?8 oktas) and a rain state. Second-order Markov models with time-of-day dependent transition probabilities were fitted after appropriate statistical testing. Forecasts were made using transition probabilities for summer and winter seasons. The skill of the Markov chain forecast probabilities of rain was evaluated in terms of Brier scores using to years of independent data, and compared with forecasts based upon persistence and climatology. The skill of the Markov model forecasts appreciably exceeded that of persistence and climatology and a real time trial of the procedure is being planned.