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The Joint Technology Transfer Initiative supported the development and completion of the “WoFS ML Severe” severe weather probability suite, which is based on the Warn-on-Forecast System and machine learning. WoFS ML Severe uses inputs from the Warn-on-Forecast System and is trained on a combination of local storm reports and radar-based detections of severe weather to…
Russ Schumacher, professor in the Department of Atmospheric Science and Colorado State Climatologist, has led a team developing a sophisticated machine learning model for advancing skillful prediction of hazardous weather across the continental United States.
This project implements an algorithm to estimate drift of the dropsondes in the data assimilation system of the operational Hurricane Weather Research and Forecasting model.
Within the last decade, operational meteorologists have raised concerns that the availability of weather information from a variety of sources may contribute to a perception that weather risk messages are inconsistent and result in negative consequences among end users.
The primary objective of this project is to improve predictions of water levels caused by extratropical tide and storm surge within the the Global Extratropical Storm and Tide Operational Forecast System (Global ESTOFS), by developing improved meshes and directly incorporating baroclinic and hydrologic physics.
Forecasters at NOAA’s Weather Prediction Center (WPC) are responsible for producing Excessive Rainfall Outlooks, which brings awareness to the potential for flood-inducing rains up to three days in advance. However, the amount of rain that qualifies as “excessive” varies from region to region.
