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Efi Foufoula-Georgiou, Fellow

Research on Space-Time Precipitation Variability and Predictability

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Accurate forecasting of the onset, duration, location, intensity, and type of precipitation is one of the most difficult challenges facing modern-day meteorology. The economic and societal impacts of such forecasts are enormous, ranging from the mitigation of life and property loss associated with flash floods to the application of effective management strategies in hydroelectric power generation. It has become clear that the next quantum leap in Quantitative Precipitation Forecasting (QPF) will arise from the explicit representation of storm-scale features in non-hydrostatic numerical models and from development of methodologies for quantifying and interpreting differences in the statistical structures of predicted and observed fields that can guide further model improvements. Towards these efforts, this research addresses three issues. First, a suite of multiscale statistical measures of forecast performance are being developed and tested. These measures can help to detect and correct deficiencies in microphysical parameterizations and obtain forecast assessments at application-appropriate scales (e.g., basin-average rainfall). Second, a probabilistic framework is being developed under which forecast improvements and the limits of predictability of current numerical weather prediction (NWP) models can be assessed while explicitly acknowledging forecast uncertainty and dependence on scale. Finally, a probabilistic framework for assessing the practical utility of deterministic forecasts for risk-based decision making is being developed.

In order to interpret the effects of climate variability or climate change on water resources at the basin scale, the predictions of global circulation models, which are run at resolutions of the order of 100 km-200 km must be disaggregated down to smaller scales of the order of 2 km-10 km. Towards this objective, these researchers have significantly contributed over the past several years by investigating the fine-scale space-time structure of precipitation fields and in developing methodologies for reconstructing this small-time structure given large-scale averages from a prediction model. This research builds on previous results of the group and addresses four new points. The first is investigation of the subgrid scale rainfall variability of several storm types and development of a comprehensive classification scheme of the statistical-physical parameterization of this variability at a range of spatial scales. Second is the investigation of the effect of orography on the subgrid scale rainfall variability and its statistical/physical parameterization. Third is to follow an atmospheric modeling approach to understand the spatial and temporal variability of the storm environmental characteristics (e.g., Convective Available Potential Energy or Cloud Liquid Water Content), relatively to the statistical characteristics of the produced precipitation. Last is to investigate, via coupled atmospheric-hydrologic modeling, the effect of subgrid scale rainfall variability on water and energy partitioning and to suggest improvements in model or data requirements for better hydrologic and atmospheric predictions.

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