Cohort Level: Cohort - IV

Career Goal: After obtaining my Bachelors of Science in Computer Science I aim to apply my skills in the field of Software Systems Engineering. Specifically I want to work on low level parallel systems as while as distributed computing. These tools are vital in the current project I am working on since WRF is built with parallelism using MPI and is made portable using Docker.

Expected Graduation Date: December 18, 2020

Degree: B.S Computer Science

Research Title: Simulation of Remote Sensing Data in WRF-HYDRO

Research Synopsis: This project aims to use WRF HYDRO's simulation ability to serve as an early warning system for flash floods. Using widely available data such as USGS Gage data alongside meteorological data we can build a robust WRF Simulation to predict possible flood events. Using known flood events such as Hurricane Maria we can then verify the parameters to the WRF Hydro simulation to increase its accuracy. WRF Hydro allows a piecemeal approach to simulating Puerto Rico. Instead of simulating the entire island at once, WRF can simulate a watershed at a time, greatly reducing the needed computational ability and overall training time. These aspects together mean that a finely tuned WRF HYDRO model can be a quick and simple tool to predict massive flood events.

This project aims to use WRF HYDRO's simulation ability to serve as an early warning system for flash floods. Using widely available data such as USGS Gage data alongside meteorological data we can build a robust WRF Simulation to predict possible flood events. Using known flood events such as Hurricane Maria we can then verify the parameters to the WRF Hydro simulation to increase its accuracy. WRF Hydro allows a piecemeal approach to simulating Puerto Rico. Instead of simulating the entire island at once, WRF can simulate a watershed at a time, greatly reducing the needed computational ability and overall training time. These aspects together mean that a finely tuned WRF HYDRO model can be a quick and simple tool to predict massive flood events.