Abstract
AbstractForecasts from NOAA's Global Forecast System (GFS) and the High‐Resolution Rapid Refresh (HRRR) weather models are matched to surface observations for the winter season of November 2019 to March 2020 at 210 airports across the United States. The 2‐m temperature errors, conditioned on observed weather conditions such as cloud cover amount and wind speed, are used to determine the nature of systematic model biases. We observe a strong diurnal cycle in 2‐m temperature errors in the GFS in conditions with 50% and 25% sky cover, with a 1°C warm bias at night and a 2°C cold bias during the day. The HRRR, which uses a different set of physical parameterizations, does not have a clear diurnal cycle in errors under the same conditions. These results highlight the utility of weather‐conditional comparisons across the diurnal cycle to diagnose sources of model weaknesses and to target model improvements.
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