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Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture
被引:0
|作者:
Wade, Andrew R.
[1
,2
]
Jirak, Israel L.
[2
]
Lyza, Nthony W.
[1
,3
]
机构:
[1] Univ Oklahoma, Cooperat Inst Severe & High Impact Weather Res & O, Norman, OK 73019 USA
[2] NOAA, NCEP, Storm Predict Ctr, Norman, OK 73072 USA
[3] NOAA, Natl Severe Storms Lab, OAR, Norman, OK USA
关键词:
Cloud-resolving models;
Model errors;
Model evaluation/performance;
Numerical weather prediction/forecasting;
Regional models;
VERIFICATION;
PREDICTION;
D O I:
10.1175/WAF-D-23-0120.1
中图分类号:
P4 [大气科学(气象学)];
学科分类号:
0706 ;
070601 ;
摘要:
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.
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页码:2415 / 2426
页数:12
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