Assessing impacts of integrating MODIS vegetation data in Weather Research Forecasting (WRF) model coupled to two types of Rc scheme

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Tuesday, 19 January 2010: 11:30 AM
B207 (GWCC)
Anil Kumar, NASA/GSFC, Greenbelt, MD; and F. Chen, D. Niyogi, M. B. Ek, and C. D. Peters-Lidard

The MODIS products provide a number of vegetation parameters at higher spatial and temporal resolution than the AVHRR-based climatology data currently used in WRF. The objective of this investigation is to assess impacts of incorporating MODIS 8-day, 1-km leaf area index (LAI), green vegetation fraction (GVF), and land-use data in WRF on regional weather prediction. These high-resolution, near-real-time MODIS data are hypothesized to be more accurate to reflect variations in vegetation characteristics. Our main focus is to investigate the degree to which the use of MODIS data can improve WRF model forecast skill for surface fluxes, boundary layer structures, and precipitation. We first assessed the impact of MODIS data assimilation in potentially improving the surface energy and water budgets within the Noah land model. We then conducted a 13-member ensemble using latest version of WRFV3.0.1 for a typical summertime convection episode over the Southern Great Plains that occurred during the IHOP_2002 field experiment. The model was run for 2831 May 2002 (4 days simulation). These experiments were performed with the Noah coupled to two canopy resistance schemes, namely the default Jarvis scheme and a more interactive scheme based on photosynthesis gas-exchange (GEM). We will discuss these modeling results, identify the role of using MODIS in two difference canopy resistance approaches, and assess the impact of scaling LAI by GVF (to ensure the consistency of treating vegetation characteristics in Noah) on model simulations.