USING BAYESIAN STATISTICAL POSTPROCESSING METHODS TO IMPROVE LOCAL WIND FORECASTS
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Authors
Maier, Darby J.
Subjects
wind
forecast
Bayesian
WRF
model
accuracy
precision
small-scale
local
forecast
Bayesian
WRF
model
accuracy
precision
small-scale
local
Advisors
Nuss, Wendell A.
Date of Issue
2021-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This thesis explores the use of Bayesian statistical postprocessing to rapidly train a highly accurate forecast from a 1 km resolution gridded WRF model forecast over a 100 km by 100 km area. These methods leverage three modeled forecast variables—10 m winds, sea-level pressure, and terrain elevation—in conjunction with downstream observations and prior model runs to identify model inaccuracies. Using only three days of data, a Bayesian corrected forecast is produced and analyzed for accuracy and improvement over the original model run relative to real-world observations. Over 90% of the resulting forecasts saw improvement over the raw model forecasts in root mean squared error, and over 87% of the forecasts saw improvement in mean error over the raw model forecasts. Extreme circumstances saw improvements in accuracy of over 9 knots while overall improvements were reliably seen both in accuracy and precision among Bayesian corrected forecasts. These findings are significant as they suggest that Bayesian statistical postprocessing methods work and should be both employable at rapid rates, and result in more accurate forecasts.
Type
Thesis
Description
Series/Report No
Department
Meteorology (MR)
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release. distribution is unlimited
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.