- Related Research Areas
- Climate Variability & Change, Water & Energy Cycles
This proposal focuses on the development of a modeling and data assimilation framework that will allow for downscaling of Global Precipitation Mission (GPM) and Soil Moisture Active and Passive (SMAP) observations to produce precipitation and soil moisture predictions at fine scales for hydrometeorological applications. The hypothesis of this research is that a high resolution regional climate model coupled to a physically-based representation of subgrid land-atmosphere feedbacks and used with data assimilation may be an optimal approach for downscaling of coarse scale remotely sensed precipitation and complementary soil moisture products. The modeling framework with data assimilation capabilities consists of tRIBS-VEGGIE (Triangulated Irregular Network (TIN)-based Real-time Integrated Basin Simulator with VEGetation Generator for Interactive Evolution) serving as the lower boundary for an advanced mesoscale regional atmospheric model known as the Weather Research and Forecasting (WRF) model. tRIBS-VEGGIE (tRIBS hereafter) offers the unique capability of capturing the fine structure of topography and its role in modulating the dynamics of soil moisture and vegetation. This in turn leads to improvement in the representation the sub-grid land surface fluxes in the coupled WRF-tRIBS model, which improves the simulation of dynamics within the atmospheric boundary layer at fine spatial and time resolutions. The original WRF model is designed for modeling mesoscale processes, but has limited ability to represent the land surface energy and water fluxes at high resolution and detailed topography. Accurate representation of the complex mesoscale feedback at the land atmosphere boundary is proven to increase the predictive skill of precipitation at fine spatial-temporal scales. A key component of the downscaling-prediction framework is the data assimilation function of satellite based observations. Specifically, we propose to assimilate GPM and SMAP rainfall and soil moisture using 4DVAR and/or EnKF data assimilation techniques. Assimilation of SMAP soil moisture within tRIBS has been proven to lead to better estimation of energy fluxes at the land atmosphere boundary while assimilation of rainfall within WRF and other atmospheric models has been proven useful. The expectation is that assimilation of both precipitation and soil moisture products within the proposed coupled atmospheric-landsurface model will result in further improvements of in the estimation of the two complementary variables: rainfall and soil moisture. The deliverables of this projects are (1) a methodology to downscale future GPM and SMAP products and improve the predictability of precipitation and soil moisture and (2) an index based on high resolution soil moisture estimates from tRIBS that will be useful for flash flood forecasting and hopefully improve on existing operational procedures.
Project PI: Rafael Bras/University of California, Irvine
University of California, Irvine 305 Rockwell Engineering Center
Irvine, CA 92697-2700
-Effective September 2010-
Rafael L. Bras - Provost and Executive Vice President for Academic Affairs Georgia Institute of Technology
225 North Avenue
Atlanta, GA 30332-0325
ph: +1 404 385 2700
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