Weather models are the basic building blocks of a forecast. Researchers leverage machine learning techniques in an effort to improve these tools.
Researchers leverage machine learning to improve forecasting tools
Tag: CIMMS
Women of NSSL: Jian Zhang
For the month of October NOAA National Severe Storms Laboratory is publishing a series of stories highlighting some of the women working at the lab. One Q&A segment will be published each Monday in October.…
Gab at the Lab: Alexander Ryzhkov
Alexander Ryzhkov is a senior research scientist with over 20 years of experience at CIMMS/NSSL. Learn more.
New research improves water hazard forecasting
A new program supported by NSSL is testing the use of stream radar to improve flood forecasting. Learn more.
Gab at the Lab: Yunheng Wang
Yunheng Wang is part of the Warn-on-Forecast team, developing software for atmospheric applications. Learn more.
Gab at the Lab: Heather Reeves
Heather works with NSSL’s Warning Research Development Division, managing NSSL’s FAA research portfolio. Learn more.
Gab at the Lab: Matt Mahalik
Matt is part of the Severe Weather Warning Applications and Technology Transfer group in WRDD. Learn More.
Gab at the Lab: John Lawson
John’s passion is in chaos theory and the predictability of weather. Learn what he does with CIMMS/NSSL!
Gab at the Lab: Katie Bowden
Katie is a Ph.D. candidate at OU, studying the impact of rapid-scan radar data on forecaster warning decision-making. Learn more.
Significant Paper: The HMT Multi-Radar Multi-Sensor Hydro Experiment
This paper describes how NSSL scientists and NWS forecasters evaluate new tools and techniques through real-time testbed operations for the improvement of flash flood detection and warning operations.