1) Sensitivity analysis of the Weather Research and Forecasting (WRF) model simulations to land use and land cover changes.2) Validation of precipitation, temperature and soil moisture simulations through updated land use and vegetation information within the WRF model.3) Incorporating machine learning models and Noah-MP land surface model to improve surface heat fluxes and soil moisture/temperature simulations.4) An evaluation study of WRF-Hydro modeling system for simulation of runoff using near real-time remotely-sensed vegetation data.5) Evaluate the impacts of wildfire-related cascading hazards under a changing climate using Noah-MP land surface model, WRF simulations, and machine learning models.

The Summer Institute provided a truly unique opportunity to collaborate with national leaders in hydrology, deep learning and modeling to help advance the National Water Center’s goal of enhancing water prediction in the United States.1) Involved in developing and testing Long short-term memory (LSTM) deep learning models to predict stream-flow for use in the Next Gen National Water Model Framework.2) Worked with and applied scalable deep learning on distributed GPUs to train LSTM models for CAMELS catchments forced by NLDAS and observed data.

1) Estimation of surface net radiation using MODIS levle-1B products.2) Generated MODIS level 2 products using the NASA MODIS/AIRS data processing packages.3) Installed and configured the Regional Climate Model (RegCM) for use in AORC long-term precipitation estimations model over Fars province. This project was a unique opportunity to learn how to do parallel processing on the large data, and manager the concurrent users.Additional ProjectsI developed a web-based tool to search and analyze archived Moderate Resolution Imaging Spectroradiometer (MODIS) products acquired by the Oceanic and Atmospheric Research Center (AORC) MODIS receiver. It was the only web tool in the national scale that was developed to perform analysis on the remotely sensed data. I presented the application for Agricultural Organizations and other private companies.