Rochelle Worsnop

Image of Rochelle Worsnop
Publications

Position

Research Scientist

Division

Hydrology Applications

Affiliation

NOAA

Contact

rochelle.worsnop@noaa.gov

About

Rochelle is a Research Physical Scientist in NOAA's Physical Sciences Lab. Her research focuses on the understanding and probabilistic prediction of fire-weather variables and wildfire indicators at medium-to-subseasonal timescales. These variables range from temperature, precipitation, wind speed, relative humidity, as well as fire indicators such as the Hot-Dry-Windy Index and components from the National Fire Danger Rating System. She leverages large hindcast datasets in combination with conventional and novel statistical post-processing techniques such as deep learning methods to improve the skill and reliability of forecasts output from dynamical weather models. The goal of these improved probabilistic forecasts are to help forecasters and end-users make more informed decisions.

Rochelle has lead the development of two near real-time experimental forecast tools:

(1) Climate Prediction Center's Fire-weather Week 2 (8-14 Day) Forecasts: https://www.cpc.ncep.noaa.gov/products/people/mchen/fireWeather/cpc_wk2fw_index.html

(2) Physical Sciences Laboratory's Experimental Subseasonal Precipitation Accumulation Outlooks
https://psl.noaa.gov/forecasts/s2s_NNprecip/

Rochelle came to NOAA as PSL's first federal Pathways intern in the last year of her graduate studies at the University of Colorado-Boulder. She joined NOAA as a CIRES Research Scientist in the summer of 2018 in the Attribution and Predictability Assessments Team and joined NOAA's federal workforce in 2023 within the Hydrology Applications Division.

Ph.D., Atmospheric and Oceanic Sciences, University of Colorado-Boulder
M.S., Atmospheric and Oceanic Sciences, University of Colorado-Boulder
B.S., Meteorology, Florida State University

Research Interests

  • Extended-range fire weather forecasting
  • Statistical postprocessing of numerical weather forecasts
  • Deep learning for postprocessing and prediction
  • Probabilistic prediction and verification
  • Research-to-operations

Selected Publications

  • Worsnop, R. P., M. Scheuerer, F. DiGiuseppe, C. Barnard, T. M. Hamill, and C. Vitolo, 2021: Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical postprocessing techniques and the Global ECMWF Fire Forecast System (GEFF). Wea. Forecasting, 36, 2113–2125. https://doi.org/10.1175/WAF-D-21-0075.1.
  • Worsnop, R. P, M. Scheuerer, T. M. Hamill, 2020: Extended–range probabilistic fire-weather forecasting based on Ensemble Model Output Statistics and Ensemble Copula Coupling. Mon. Weather Rev., 148, 499-521, https://doi.org/10.1175/MWR-D-19-0217.1.
  • Scheuerer, M., M. B. Switanek, R. P. Worsnop, and T. M. Hamill, 2020: Using artificial neural networks for generating probabilistic subseasonal precipitation forecasts over California. Mon. Wea. Rev., 148 (8), 3489–3506, https://doi.org/10.1175/MWR-D-20- 0096.1.
  • Worsnop, R. P., M. Scheuerer, T. M. Hamill, and J. K. Lundquist, 2018: Generating wind power scenarios for probabilistic ramp event prediction using multivariate statistical post-processing. Wind Energy Sci., 3, 371-393. doi: 10.5194/wes-3-371-2018
  • Worsnop, R. P., J. K. Lundquist, G. H. Bryan, W. Musial, and R. Damiani, 2017: Gusts and shear within hurricane eyewalls can exceed offshore wind turbine design standards. Geophys. Res. Lett., 44, 6413-6420. doi: 10.1002/2017GL073537

Professional Activities

Honors and Awards