Abstract/Summary

Efficient methods for predicting weather-related hazards are crucial for the effective management of environ- mental risk. Many environmental hazards depend on the evo- lution of meteorological conditions over protracted periods, requiring assessments that account for evolving conditions. The TAMSAT-ALERT approach addresses this challenge by combining observational monitoring with a weighted multi-year ensemble. In this way, it enhances the utility of existing systems by enabling users to combine multiple streams of monitoring and meteorological forecasting data into holistic hazard assessments. TAMSAT-ALERT forecasts are now used in a number of regions in the Global South for soil moisture forecasting, drought early warning and agri- cultural decision support. The model presented here, Gen- eral TAMSAT-ALERT, represents a significant scientific and functional advance on previous implementations. Notably, General TAMSAT-ALERT is applicable to any variable for which time series data are available. In addition, function- ality has been introduced to account for climatological non- stationarity (for example due to climate change), large-scale modes of variability (for example El Niño) and persistence (for example of land-surface conditions). In this paper, we present a full description of the model, along with case studies of its application to the prediction of central England temperature, Pakistan vegetation conditions and African precipitation.