Seminar: Improving Flood Prediction with Cutting-Edge Data Assimilation
11:00 am – 12:00 pm MST
Abstract
Increased occurrences of inland and flash flooding, driven by intense rainfall and hurricanes, highlight the critical need for accurate and timely streamflow predictions to safeguard lives and infrastructure. Combining streamflow gauge data with physics-based simulations through data assimilation (DA) has proven effective in enhancing prediction accuracy. However, traditional DA methods often falter under extreme rainfall events due to challenges such as model uncertainties, forcing biases, sampling error, and noisy observations.
This seminar introduces Hydro-DART, a state-of-the-art flood prediction system that couples the National Water Model configuration of the WRF-Hydro modeling system with the Data Assimilation Research Testbed (DART). Hydro-DART provides a robust, efficient ensemble prediction framework, featuring innovative DA techniques such as adaptive spatial and temporal covariance inflation, topologically based stream localization, multi-configuration ensembles, and adaptive hybrid ensemble-variational filtering. The system's performance is demonstrated through case studies of catastrophic hurricanes impacting regions like North Carolina and Florida. Ensemble forecasts generated by Hydro-DART are rigorously evaluated across low-flow and high-flow conditions, showcasing its robustness and accuracy. Notably, Hydro-DART delivers improved predictive capabilities with reduced computational burden, extending streamflow forecasts up to 18 hours ahead of flood peaks – marking a significant advancement in flood prediction.
This seminar is for staff only.
Name
Moha Gharamti
Moha Gharamti is a Scientist at the Computational and Information Systems Laboratory (CISL) at the NSF National Center for Atmospheric Research (NSF NCAR). He earned his bachelor's degree in Geological Engineering from the Middle East Technical University in Ankara, Turkey, and later obtained his PhD in Earth Sciences and Engineering from King Abdullah University of Science and Technology in Thuwal, Saudi Arabia. Following his PhD, he completed an 18-month postdoctoral fellowship at the Nansen Environmental and Remote Sensing Center in Bergen, Norway. Gharamti has been an integral part of NSF NCAR for 8 years, contributing significantly to the advancement of data assimilation (DA) science.
Gharamti's expertise lies in both sequential and variational data assimilation methodologies, with a particular focus on the ensemble Kalman filter. His research centers on developing innovative, efficient, and robust DA algorithms applicable across a wide range of Earth system models. His contributions span various geoscience applications, including atmospheric science, ocean biogeochemistry, hydrologic forecasting, and flood prediction. He is one of the lead developers of the Data Assimilation Research Testbed (DART), a widely used tool within the academic community and internationally.