A Machine Learning-Based Bias Correction Method for Global Forecast System Products

Linlin Cui (https://orcid.org/0009-0000-7997-9845); Jun Wang (https://orcid.org/0009-0007-9030-3417); Sadegh Sadeghi Tabas (https://orcid.org/0000-0001-9157-3397); Jacob R. Carley (https://orcid.org/0000-0003-4763-6666)

doi:10.25923/6266-e822

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A Machine Learning-Based Bias Correction Method for Global Forecast System Products

[PDF-2.57 MB]

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Personal Author:

Linlin Cui (https://orcid.org/0009-0000-7997-9845) ; Jun Wang (https://orcid.org/0009-0007-9030-3417) ; Sadegh Sadeghi Tabas (https://orcid.org/0000-0001-9157-3397) ; Jacob R. Carley (https://orcid.org/0000-0003-4763-6666)

Linlin Cui (https://orcid.org/0009-0000-7997-9845) ; Jun Wang (https://orcid.org/0009-0007-9030-3417) ; Sadegh Sadeghi Tabas (https://orcid.org/0000-0001-9157-3397) ; Jacob R. Carley (https://orcid.org/0000-0003-4763-6666) Less -
Corporate Authors:

National Centers for Environmental Prediction (U.S.) ; Lynker. ; Axiom.

National Centers for Environmental Prediction (U.S.) ; Lynker. ; Axiom. Less -
NOAA Program & Office:

NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)

NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) Less -
Description:

Accurate numerical weather forecasting is essential for forecasters to make predictions. However, operational forecast models often exhibit systematic biases. In this study, we present a machine learning-based approach called BC-Unet, a convolutional neural network (CNN) based on the renowned U-Net architecture, to correct biases in National Centers for Environmental Prediction (NCEP) operational Global Forecast System (GFS) v16 products over the contiguous United States (CONUS).
Keywords:

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Machine Learning Numerical Weather Forecasting
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Office note (National Centers for Environmental Prediction (U.S.)) ; 520
DOI:

https://doi.org/10.25923/6266-e822
Format:

pdf
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Office Note
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United States
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https://creativecommons.org/publicdomain/zero/1.0/
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CC0 Public Domain
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Submitted
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urn:sha-512:e6bed60b5c9435b4bc4b4ebf540369e13689f34ffb6d73fdf0fc2fca86265847e603a0520fe3a26bd34aa7fcd718dd97e509814d590e891fd4dfb13b63f951bc
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https://repository.library.noaa.gov/view/noaa/67181/noaa_67181_DS1.pdf
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A Machine Learning-Based Bias Correction Method for Global Forecast System Products

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