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Sherman Lo
Forecasting of precipitation in the British Isles
We present a statistical methodology to do forecasting of precipitation at the 0.1° resolution using computer simulations (air temperature, geopotential, specific humidity, total column water vapour, wind velocity) at a lower resolution (5/9° and 5/6° lon/lat). Observed precipitation at the 0.1° resolution for the past 4 decades was used to fit our model onto in order to do forecasting with quantifiable uncertainty.
Our model is the compound Poisson distribution (Revfeim, K.J.A., 1984, Dunn, P.K., 2004) which can model both occurrence and quantity of precipitation as a random variable. We impose time autocorrelation by introducing auto-regressive and moving average terms into the model. Spatial dependencies were introduced by putting a Gaussian process prior on the parameters.
The model fields were interpolated, with uncertainty bars, from low resolution to high resolution using a Gaussian process. The entire Bayesian inference (or model fitting) was done using a Gibbs sampling scheme consisting of Metropolis-Hastings, slice sampling and elliptical slice sampling.