On the Importance of Regime-Specific Evaluations for Numerical Weather Prediction Models as Demonstrated using the High Resolution Rapid Refresh (HRRR) Model

Temple R. Lee aNOAA/Air Resources Laboratory, Oak Ridge, Tennessee

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Sandip Pal bAtmospheric Science Group, Department of Geosciences, Texas Tech University, Lubbock, Texas

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Ronald D. Leeper cNorth Carolina Institute for Climate Studies, Asheville, North Carolina
dNOAA/National Centers for Environmental Information, Asheville, North Carolina
eCenter for Weather and Climate, Asheville, North Carolina

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Tim Wilson aNOAA/Air Resources Laboratory, Oak Ridge, Tennessee
fOak Ridge Associated Universities, Oak Ridge, Tennessee

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Howard J. Diamond gNOAA/Air Resources Laboratory, College Park, Maryland

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Tilden P. Meyers hNOAA/Air Resources Laboratory, Boulder, Colorado

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David D. Turner iNOAA/Global Systems Laboratory, Boulder, Colorado

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Abstract

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings which can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different scenarios, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions (i.e., different near-surface heating rates (dTdt), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)) to evaluate the High-Resolution Rapid Refresh (HRRR) model, which is a 3-km model used for operational weather forecasting in the U.S. On days with small (large) dTdt, we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identifying model deficiencies which supports improvements to the parameterization schemes used therein. A similar, regime-specific model verification approach may also be used to help evaluate other geophysical models.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Temple R. Lee, temple.lee@noaa.gov

Abstract

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings which can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different scenarios, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions (i.e., different near-surface heating rates (dTdt), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)) to evaluate the High-Resolution Rapid Refresh (HRRR) model, which is a 3-km model used for operational weather forecasting in the U.S. On days with small (large) dTdt, we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identifying model deficiencies which supports improvements to the parameterization schemes used therein. A similar, regime-specific model verification approach may also be used to help evaluate other geophysical models.

© 2024 American Meteorological Society. This is an Author Accepted Manuscript distributed under the terms of the default AMS reuse license. For information regarding reuse and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dr. Temple R. Lee, temple.lee@noaa.gov
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