Open AccessStatistically and Dynamically Downscaled, Calibrated, Probabilistic 10-m Wind Vector Forecasts Using Ensemble Model Output Statistics
Author(s) -
Bryan P. Holman,
S. M. Lazarus,
M. E. Splitt
Publication year - 2018
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-17-0338.1
Subject(s) - downscaling , weather research and forecasting model , ensemble forecasting , probabilistic logic , meteorology , computer science , north american mesoscale model , wind speed , mesoscale meteorology , environmental science , statistics , mathematics , global forecast system , precipitation , geography
A computationally efficient method is developed that performs gridded postprocessing of ensemble 10-m wind vector forecasts. An expansive set of idealized WRF Model simulations are generated to provide physically consistent, high-resolution winds over a coastal domain characterized by an intricate land/water mask. The ensemble model output statistics (EMOS) technique is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. In a yearlong study, the method is applied to 24-h wind forecasts from the Global Ensemble Forecast System (GEFS) at 28 east-central Florida stations. Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicates that the postprocessed forecasts are calibrated. A downscaling case study illustrates the method as applied to a quiescent easterly flow event. Strengths and weaknesses of the approach are presented and discussed.