At the coastal boundary of northeastern Australia, during periods where trade wind flow predominated, potential predictors from the Australian regional limited area model were matched with a 1129-day high quality rainfall dataset. With the aid of classification trees, objective sorting criteria were developed to stratify the numerical model dataset into weather regimes prior to the development of a series of rainfall prediction equations through screening regression. Rainfall equations were also developed by seasonal stratification of the development data. These relationships, together with other subjective and objective techniques, were tested on a 226-day independent dataset for the verification periods 0-24 and 24-48-h beyond the defining model analysis. Objective probability of precipitation (PoP) and Quantititative Precipitation Forecasts (QPFs) were produced on a regional and individual city basis. The synoptically stratified PoP forecasts, tuned for measurable rain prediction, showed skill against reference methods and against subjective Bureau of Meteorology operational forecasts at the 24-48-h projection. The synoptically stratified QPF forecasts also displayed skill against climatology and persistence at this projection. For rain events greater or equal to 2.5 mm per day, the synoptically stratified methodology was significantly more skillful than other objective techniques at the 95% confidence interval.
Synoptic stratification was also applied to a matched observed-surface-wind/ numerical-model dataset for the development of a wind prediction scheme for application to the sailing events of the 2000 Olympic Games in Sydney, Australia. The scheme was designed to provide hourly wind vectors for both inshore and offshore locations. The resulting wind prediction equations exhibited skill over both climatology and persistence and provided useful input into the official wind forecasts for Sydney Harbour during the Olympic and Paralympic Games.