Evaluation of Regional Climate Models with Remotely Sensed Data for CONUS (Contiguous United States)

Abstract:

Water is one of the most precious resources on Earth. Managing water resources is a complex discipline that requires accurate data, which in turn means that the managament of water resources is limited by the availability and quality of these datasets. Evapotranspiration (ET) is one of these key datasets, but is also one that is lacking in large-scale spatial distribution with traditional methods such as Penman-Monteith. This is also a quantity poorly handled at present in regional and global climate models. In order to overcome the limitations imposed by pointwise calculation of ET, a new dataset based on a surface energy balance model METRIC) constrained by MODIS satellite imagery have been developed. A Fully Automated Python implementation of METRIC model was needed and developed to cover the CONUS due to the high computational time for manual processing of METRIC. In this study, the new ET dataset will be used to evaluate the Weather Research and Forecasting Model coupled with Community Land Model's (WRF-CLM) as well as NOAH Land Surface Model. CLM and NOAH are the models which is used to understand the processes between land and climate and also climate change, and contains crucial but poorly constrained parameterizations for ET. In this study CLM and NOAH will be coupled with WRF. The WRF model is driven by North American Regional Reanalysis (NARR) data. The results between Metric ET and WRF-CLM ET and WRF-NOAH ET are different since they use different approaches for obtaining ET. The difference will be checked with statistical approaches. For this study 3 years are selected: A relatively dry year, 2012, a relatively wet year, 2007 and a regular year 2005. The results of this study will improve our understanding of climate-land interactions which will lead to better present simulations and future predictions of water resource-related issues since ET has the biggest consumption in the water budget.