MSPA-CSE: Statistical Methods for Precipitation Nowcasting and Verification

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This project seeks to develop a new approach to short-term forecasts of heavy precipitation based on a multi-resolution statistical representation of the relevant atmospheric dynamics incorporated within a Bayesian hierarchical modeling framework. The primary input data will be reflection data from weather radars and the dynamics of the evolution of the precipitation will be represented as a low-dimensional stochastic process. One set of research questions includes the nature of the map between the hidden dynamics and the precipitation field, of the propagator determining the evolution of the hidden dynamics and its relation to atmospheric physics, and of the parametric dependence of the map and propagator functions on meteorological variables. A variety of linear and nonlinear maps, conditioned on meteorological parameters, between the hidden low-dimensional process and the multi-scale precipitation field will be tested. The propagator will be approximated by a quasi-stationary first-order Markov process where the limited time dependence is represented as a parametric dependence on the meteorological regime. The model will be applied to and tested with radar and other meteorological data collected during the THOR observing project over Illinois, Ohio and Indiana. The method will be compared with short-term forecasts for this region obtained from existing prognostic tools. Later in the project, the approach will be extended to the development of a sequential prediction algorithm using sequential Monte Carlo techniques. A second set of research questions relate to the development of effective verification techniques. Methods for automatically identifying storm cells in data and forecasts and making cell-by-cell comparisons of their shape, location and intensity will be explored. Finally the project will include research into hypothesis testing techniques for storm cell predictions. The methodology developed could lead to improvements in flood forecasting and a quantitative estimate of the uncertainty in these forecasts with consequent benefit to emergency services.