Abstract:
This study attempts to model the daily rainfall climatology of Sri Lanka using Markov models at
three selected weather stations where long term data records were available. Both the first and
the second order Markov models were able to forecast the occurrence of daily rainfall to an
accuracy of ∼72±4%. The accuracy of predicting wet days during the wet season was very much
higher than in the dry season, while predicting dry days during the dry season was very much
higher than in the wet season. The mean number of wet spells per month and mean length of a
wet spell per month has been forecasted using standard probability distributions combined with
transition probabilities based on the first order Markov model. The deviation of the simulated
values from the observed values was lower than the statistical variations which indicate that the
model is suitable for simulating wet and dry spells in Sri Lanka.
