Abstract/Summary

Weather forecasts play a vital part in our lives, with major impacts on society and the economy. Forecasts are obtained by combining observations of the weather with computational predictions using a data assimilation process. Forecast accuracy relies on accurate estimates of the uncertainty in these weather observations. Remotely sensed observations, for example from satellites and ground-based instruments such as radar, provide the most benefit to forecast accuracy, but these are the most expensive data to acquire and their errors are not well understood. Research at the University of Reading has developed new methodology for estimating observation error statistics and has contributed important theoretical advances in understanding the role of observation uncertainty in weather forecasting. These methods are now being used in operational systems worldwide by the UK Met Office, European Centre for Medium-Range Weather Forecasts, US Naval Research Laboratory and NASA, amongst others. Thanks to these advances, there have been significant improvements in forecast accuracy and cost-effectiveness of observations for forecasting agencies, without loss of computational efficiency. The research has also influenced strategies for system enhancement, diagnostic activities and training at these centres and has raised the quality of weather information provided to government, business and the public for decision-making.