Evaluating the Experimental High-Resolution Rapid Refresh-Alaska Modeling System Using USArray Pressure Observations
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Evaluating the Experimental High-Resolution Rapid Refresh-Alaska Modeling System Using USArray Pressure Observations

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  • Journal Title:
    Weather and Forecasting
  • Description:
    The High-Resolution Rapid Refresh-Alaska (HRRR-AK) modeling system provides 3-km horizontal resolution and 0-36-h forecast guidance for weather conditions over Alaska. This study evaluated the experimental version of the HRRR-AK system available from December 2016 to June 2017, prior to its operational deployment by the National Centers for Environmental Prediction in July 2018. Surface pressure observations from 158 National Weather Service (NWS) stations assimilated during the model's production cycle and pressure observations from 101 USArray Transportable Array (TA) stations that were not assimilated were used to evaluate 265 complete 0-36-h forecasts of the altimeter setting (surface pressure reduced to sea level). The TA network is the largest recent expansion of Alaskan weather observations and provides an independent evaluation of the model's performance during this period. Throughout the study period, systematic differences in altimeter setting between the HRRR-AK 0-h forecasts were larger relative to the unassimilated TA observations than relative to the assimilated NWS observations. Upon removal of these initial biases from each of the subsequent 1-36-h altimeter setting forecasts, the model's 36-h forecast root-mean-square errors at the NWS and TA locations were comparable. The model's treatment of rapid warming and downslope winds that developed in the lee of the Alaska Range during 12-15 February is examined. The HRRR-AK 0-h forecasts were used to diagnose the synoptic and mesoscale conditions during this period. The model forecasts underestimated the abrupt increases in the temperature and intensity of the downslope winds with smaller errors as the downslope wind events evolved.
  • Source:
    Weather and Forecasting, 33(4), 933-953.
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