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  • Using Machine Learning to Improve Forecasting of Deep Convection

    PI Christopher Hennon

    CO-I Ronny Schroeder

    CO-I Curtis James

    CO-I Abd AlRahman AlMomani

    We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).

    This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.

    The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research. 

    Tags: thunderstorms monsoon machine learning forecasting tool

    Categories: Faculty-Staff

  • Environmental Analysis of Convective Initiation Events in Central Florida using Integrated Mobile Observation

    PI Shawn Milrad

    PI Daniel Halperin

    This research collaboration with the National Weather Service (NWS) Weather Forecast Office Tampa Bay aims to develop an ingredients-based methodology to help improve forecasts of first-strike cloud-to-ground lightning strikes in summer thunderstorms across Central Florida. Results will be used to construct a new forecast tool that will aid NWS forecasters in protecting the region’s life and property from these dangerous lightning events.

    Lightning is a major hazard to life and property in Florida and annually leads the nation in lightning strikes and fatalities. The proposed research collaboration with the National Weather Service (NWS) Weather Forecast Office Tampa Bay aims to develop an ingredients-based methodology to help forecast first strike cloud-to-ground lightning strikes in warm-season thunderstorms across Central Florida. A comprehensive environmental analysis of these convective initiation events is being performed using numerous observational datasets, including mobile radar and surface observations from recent ERAU field courses and campaigns. The environmental analysis will examine first-strike events across the eight large-scale flow regimes previously identified by NWS Tampa Bay. A particular focus is placed on events that occurred during four weeks of ERAU field courses/campaigns in 2015 and 2018, allowing for the unique integration of mobile observations. Results are being used to construct a new forecast tool integrated with existing radar- and satellite-based lightning tools, to improve first-strike alert lead times. Also, the proposed project has established a fruitful collaborative research relationship between ERAU and NWS Tampa Bay while providing research experience and training for several ERAU undergraduate meteorology majors. These undergraduate students have completed much of the work on the project and have gotten to interact with NWS Tampa Bay personnel. It is expected that this project will also stimulate future more significant research collaborations between ERAU Meteorology and regional NWS forecast offices.

    Tags: weather forecasting tool lightning strikes NAtional Weather Servcice - Tampa Bay

    Categories: Faculty-Staff

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