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Today, the Department of Commerce and NOAA announced a $7 million funding opportunity through President Biden’s Investing in America agenda to establish a new multi-university Data Assimilation Consortium that will improve weather predictions.
FREE Virtual Training Workshops are being held July 24-26 during UIFCW23. Help advance the Unified Forecast System (UFS) through EPIC’s hands-on workshops and accelerate weather modeling and innovations through open science. There is limited space.
Meet Renee Richardson, a Program Coordinator at WPO. She is featured in an article by NOAA Global Ocean Montioring and Observation for being one of seven women to advance hurricane research and forecasting at NOAA.
The Airborne Phased Array Radar (APAR) will be the world’s first phased array C-band, dual-Doppler, dual-polarization radar. WPO helped fund the initial research and development of APAR which received $91.8 million in June from the National Science Foundation.
UCAR | CPAESS and NOAA’s Weather Program Office are excited to welcome the inaugural class of the Weather Program Office (WPO) Innovation for Next Generation Scientists (WINGS) Dissertation Fellowship of 2023-2024.
In recognition of the Year of Open Science, NOAA (National Oceanic and Atmospheric Administration) and EPIC (Earth Prediction Innovation Center) have been featured on Open.Science.gov for the collaborative and open science work on the Unified Forecast System (UFS).
The 5-day workshop will engage the greater Weather Enterprise in the ongoing effort to accelerate contributions to the Unified Forecast System.
How do different racial and socioeconomic groups in the United States receive, understand, and respond to severe weather information? A new study by three NOAA scientists and a member of the Coast Guard takes a look at the demographics of severe weather communication.
This two-day hybrid workshop will be hosted out of the NOAA Center for Weather and Climate Prediction in College Park, MD. Day 1 activities will provide a historical perspective on the NMME and highlight existing use/needs cases. Day 2 participants will delve into how the NMME fits into the research community and collaboratively set the future for NMME to explore unmet needs.
Over the last several years, 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. First trained on historical records of excessive rainfall, the model is now smart enough to make accurate predictions of events like tornadoes and hail four to eight days in advance – the crucial sweet spot for forecasters to get information out to the public so they can prepare. The model is called CSU-MLP, or Colorado State University-Machine Learning Probabilities.