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The Advanced Study Program begins it's 2013 Seminar Series with David Gochis of RAL.
When: 11:00 a.m. Thursday, January 24thWhere: FL2 - 1022 (Large Auditorium)refreshments will be provided prior to the seminar.
Abstract:Improvement of predictions of water cycle processes such as precipitation, runoff, evapotranspiration and terrestrial storages remain as a grand challenge in Earth System science. For a long-time such predictions were handled in a two-step manner where predictions of precipitation were fed into an offline or ‘uncoupled’ hydrological model which would produce predictions of streamflow, aquifer recharge or inundated areas. While appropriate for many applications, recent research has suggested that important feedbacks between the land-atmosphere system can be neglected using such approaches. Also, use of uncoupled approaches do not guarantee that certain key processes that are shared between atmospheric and hydrological models, such as evapotranspiration are treated equally which can lead to inconsistencies in model predictions. Hence there is increasing demand for more complete and unified prediction systems which explicitly couple terrestrial hydrological processes with atmospheric processes. Similarly there is also a need to represent surface processes at increasingly fine scales which explicitly resolve key forcing mechanisms on the landscape. These needs have driven the development of a coupling architecture within the Weather Research and Forecasting (WRF) model which can accommodate some of the unique characteristics of hydrological models. This new architecture called WRF-Hydro aims to improve the study and prediction of coupled hydrometeorological processes by providing an environment for developing, coupling and testing new hydrological model components within the WRF model. This seminar will provide a thorough description of the WRF-Hydro system and its evolution as it is preparing for initial release in the spring of 2013. We will also provide some example applications of the WRF-Hydro system for several different prediction problems the model has been applied to around the world.