Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture

Andrew R. Wade aCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
bNOAA/NCEP/Storm Prediction Center, Norman, Oklahoma

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Israel L. Jirak bNOAA/NCEP/Storm Prediction Center, Norman, Oklahoma

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Anthony W. Lyza aCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
cNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

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Abstract

This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.

Significance Statement

The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Andrew R. Wade, andrew.wade@noaa.gov

Abstract

This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics.

Significance Statement

The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Andrew R. Wade, andrew.wade@noaa.gov
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