James Belanger Email & Phone Number

Atlanta, Georgia, United States

Greater Atlanta Area

Greater Atlanta Area

·Developed new prediction techniques for producing probabilistic forecasts of tropical cyclone impacts·Evaluated strategies to reduce the electric grid’s vulnerability to weather-related hazards through the development of a power outage forecast model via a 4-yr, $434K National Science Foundation Science, Engineering, and Education for Sustainability Fellowship ·Designed and taught Synoptic Meteorology to undergraduate students by implementing and utilizing Georgia Tech WRF—a daily, Weather, Research and Forecasting (WRF) model forced with initial and boundary conditions from the ECMWF High-Resolution global model. Automated the production of isobaric diagnostics, quasigeostrophic and isentropic analyses using GEMPAK shell scripts of NCEP model data acquired via LDM·Managed Georgia Tech’s WxChallenge forecast team (ranked 5th out of 42 schools)·Designed and taught Mesoscale Dynamics to undergraduate and graduate students on topics including: hydrostatic and nonhydrostatic pressure changes, mesoscale instabilities, air mass boundaries, convective organization and evolution, and modes of deep moist convection·Mentored undergraduate students (Lauren Merritt – NWS Atlanta; Molly Fink – GT EE Masters's student; Bruno Rodriquez – San Jose State Master's student; Shitian Liu – GT Master's student) and graduate students (Robert Haynes – Florida Power & Light)·Designed and taught an Atmospheric Dynamics laboratory to undergraduate students based on computer modules using IDV and MATLAB for topics including: METAR/SPECI decoding, surface and upper-air analysis, 1D cloud modeling, isobaric analyses and QG theory·Created a new easterly wave tracking algorithm that was applied to several atmospheric reanalyses to produce the first, open-access climatology of easterly waves in the tropical Western Hemisphere

Atlanta, Ga

·Conducted predictability studies of tropical cyclones in the North Atlantic and North Indian Ocean using the ECMWF ensemble forecast systems to characterize the average and conditional skill of TC genesis, track and intensity forecasts ·Created a proof-of-concept, probabilistic forecast system for tropical cyclones in the North Atlantic and North Indian Oceans using machine learning ·Evaluated improvements to the Holland (2010) wind model using SFMR and flight-level wind data·Developed an operational, statistical tornado model for U.S. landfalling tropical cyclones that has been used to reconstruct the historical TC tornado climatology

Greater Atlanta Area

·Implemented and managed an operational, short-term to subseasonal, probabilistic tropical cyclone (TC) forecast system for the North Atlantic, North Indian Ocean, and Northwest Pacific from 2008—2015 based on a customized, TC identification and tracking algorithm on global ensemble models that were statistically-post-processed using diagnostic and predictive machine learning techniques including regression analysis, filtering, clustering, and Monte Carlo methods·Established a multi-year agreement with Florida Power and Light to deliver customized TC forecasts based on the ECMWF and GFS ensemble forecast systems. Invented and evaluated a surface wind field algorithm based on the statistical downscaling of dynamical model forecasts. Implemented and managed the 4x-daily operational production and delivery of the forecasts ·Provided scientific expertise in the design and implementation of CFAN’s operational, short-term and extended-range temperature forecast system for U.S. and European cities based on global ensemble models calibrated utilizing machine learning techniques including quantile regression, Kalman filtering, and weather regime clustering ·Designed and implemented a forecast algorithm to produce probabilistic quantitative precipitation forecasts for the U.S. Calibration algorithm combined ECMWF model forecasts and high-resolution observations with machine learning techniques including quantile regression from weather regime clustering and logistic regression based on L-moment parameters·Developed and implemented an operational, probabilistic forecast scheme for hub-height wind speed, power, and ramp likelihood forecasts for Texas and the Great Plains·Established multi-year contracts with the Indian Space Research Organization and Georgia Emergency Management Agency to deliver customized TC forecasts based on the ECMWF ensemble forecast systems for the North Indian Ocean and North Atlantic Ocean

Doctor Of Philosophy (Ph.D.), Earth & Atmospheric Sciences, 4.0

Bachelor Of Science (B.S.) Earth & Atmospheric Sciences, 4.0