12B.2 Rapid Cycling in the Last Frontier: Improving Model Initialization over Alaska

Thursday, 7 June 2018: 8:30 AM
Colorado B (Grand Hyatt Denver)
Trevor Alcott, ESRL, Boulder, CO; and M. Hu, D. C. Dowell, and C. R. Alexander

The 3-km convection-allowing High-Resolution Rapid Refresh (HRRR), Alaska version (HRRR-AK) is a rapidly updating weather forecast model that uses a specially configured version of the Advanced Research WRF (ARW) model and assimilates many novel and conventional observation datasets, including radar reflectivity, using Gridpoint Statistical Interpolation (GSI). HRRR-AK incorporates recent enhancements of the HRRR model physics suite, including improved land-surface and boundary layer prediction using the updated Mellor-Yamada-Nakanishi-Niino (MYNN) parameterization scheme, aerosol-aware Thompson microphysics and an upgraded Rapid Update Cycle (RUC) land-surface model.

In its operational configuration HRRR-AK is initialized every 3 h as follows: 1) 13-km Rapid Refresh (RAP) forecasts are interpolated to 3-km to provide initial and boundary conditions valid 1 h before initialization time, 2) a 1-h “pre-forecast” is executed during which radar reflectivity data from the Multi-Radar, Multi-Sensor (MRMS) system are assimilated at 15-min intervals, and 3) satellite and conventional observations are assimilated at initialization time using a hybrid 3-D Ensemble Kalman Filter approach. Only the land-surface state is cycled from one run to the next.

Noting a delay in the development of precipitation over interior Alaska, and difficulty with resolving small-scale features early in the forecast with only a one-hour spin-up period, a series of experiments were conducted to evaluate the impact of partial cycling of atmospheric fields in HRRR-AK. Various cycling periods were tested, along with several options for the timing and frequency of radar data assimilation. This presentation will describe the configuration and reasoning for the full cycling experiments, provide verification of real-time forecasts and retrospective case studies, and conclude with a recommendation for an optimal method for initializing HRRR-AK in future.

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