×H3. Enhancing real-time rainfall information: retrievals, analyses and forecasts

Real-time rainfall information in the form of analyses and forecasts, of intensity and probability of exceedance, serve many societal benefits particularly through increased preparedness against impending severe weather. It is recognised that no single rainfall data source possesses the accuracy, timeliness, spatial resolution and coverage to meet the needs of a nation-wide real-time extreme rainfall prediction capability. This has led researchers to propose methods to combine multiple, diverse rainfall sources, develop better retrieval methods, and progress modelling capability to resolve convective-scale processes.

The session invites contributions from research and operational communities around real-time rainfall retrieval, analyses and forecasts. We especially encourage submissions on novel methods of blending multiple sources of rainfall information for improved national-scale analyses and forecasting capability. Evaluations of rainfall estimation methods, including comparisons of traditional retrieval and geostatistical approaches with machine learning techniques are most welcome. The session also seeks contributions on the derivation and use of ensemble information, including uncertainty and probabilistic rainfall information. Finally, we invite user community to describe their experiences in using existing real-time products with a focus on the desirability of features and a wish list of future rainfall information products and services.

Key topics: Real-time, Rainfall forecasts, Blended rainfall analyses, Ensembles