Current methods of meteorological forecasting produce predictions with unknown levels of uncertainty, particularly in regions with few observational assets. Forecast errors and uncertainties also arise from shortcomings in model physics. With the ability to estimate the uncertainty in predictions, forecasters would have a powerful tool to make decisions and to judge the likelihood of mission success.
The goals of our project are to develop methods for evaluating the uncertainty of mesoscale meteorological model predictions, and to create methods for the integration and visualization of multisource information derived from model output, observations and expert knowledge. We assess uncertainty by developing probability distributions of future weather states. We have been developing several approaches to this, building on the University of Washington ensemble system: Bayesian model averaging (BMA) and Ensemble Model Output Statistics (EMOS) for individual weather quantities, and the Geostatistical Output Perturbation (GOP) method for probabilistic forecasting of entire weather fields simultaneously.
We are also developing tools and methods for visualizing predictions of quantities of interest and the uncertainty about them by (i) choosing appropriate quantities of interest for display based on cognitive factors, and (ii) developing appropriate plots, maps, three-dimensional displays, and video displays for decision support. Some examples can be found in the links on this page.