2020-01-10 | The first Unified Forecast System (UFS) Users’ Workshop will take place on July 27-29, 2020 at NCAR's Center Green Campus in Boulder, Colorado, with the aim to establish an annual forum to exchange ideas, advance education on applying the UFS, and embrace the full potential of the community to support and help improve the UFS modeling and prediction system. It will feature lively engagement and discussions with diverse groups of participants from across the Weather Enterprise including public… Read More

Autumn 2019 | The Earth Prediction Innovation Center, or “EPIC,” will advance Earth system modeling skills, reclaim and maintain international leadership in Earth system prediction, and improve the transition of research to operations (R2O) and operations to research (O2R) within NOAA by working closely with partners across the weather enterprise.  EPIC’s legislative language is included as an amendment to the Weather Research and Forecasting Innovation Act (WRFIA) of 2017 (Public Law 115-25… Read More

Autumn 2019 | What does unified modeling mean to you? Perhaps an obvious meaning is that “unified” implies one single model: one choice for dynamics, physics, data assimilation, and postprocessing. Everyone uses the same code: universities, private companies, research labs, and operational prediction centers. It is a grand vision. As a community, we are not there yet. I argue that there is a more practical, and potentially more useful definition of unified modeling: codes are easily shared and interoperable. Why do I say this? At… Read More

Autumn 2019 | Tell us a little about yourself and your career path. I’m a Wisconsin native and grew up fascinated by severe storms. Naturally, when it came time to think about college, I looked for meteorology programs in the midwest. I settled on Saint Louis University for my B.S. in Atmospheric Sciences and then earned my M.S. in Mathematics from the University of Wisconsin - Milwaukee (UWM).  After graduation, I stayed on at UWM as a researcher while also working for a UWM-affiliated meteorological consulting company called… Read More

Autumn 2019 | Ensembles are useful forecast tools because they account for uncertainties in initial conditions, lateral boundary conditions, and/or model physics, and they provide probabilistic information to users. However, many ensembles suffer from under-dispersion, sub-optimal reliability, and systematic biases. Machine learning (ML) can help remedy these shortcomings by post-processing raw ensemble output. Conceptually, ML identifies (nonlinear and linear) patterns in… Read More