Ingesting Land Surface Temperature differences to improve Downwelling Solar Radiation using Artificial Neural Network: A Case Study

Friday, 19 December 2014
Nabin K Malakar1, Mark Bailey2, Becca Latto2, Emmanuel Ekwedike2, Barry Gross2, Jorge Gonzalez2, Charles J Vorosmarty2 and Glynn C Hulley1, (1)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (2)CUNY City College, New York, NY, United States
In order to study the effects of global climate change on regional scales, we need high resolution models that can be injected into local ecosystem models. Although the injection of regional Meteorological Models such as Weather Research and Forecasting (WRF) can be attempted where the Global Circulation Model (GCM) conditions and the forecasted land surface properties are encoded into future time slices - this approach is extremely computer intensive.
We present a two-step mechanism in which low resolution meteorological data including both surface and column integrated parameters are combined with high resolution land surface classification parameters to improve on purely interpolative approaches by using machine learning techniques. In particular, we explore the improvement of surface radiation estimates critical for ecosystem modeling by combining both model and satellite based surface radiation together with land surface temperature differences.