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The diurnal cycle of rainfall in two models is examined by comparing 5 km gridspacing, 48 hour forecasts of the Weather Research and Forecasting (WRF) model using the non-hydrostatic mesoscale model (NMM) dynamic core to 22 km grid-spacing forecasts from the WRF model using the Advanced Research WRF (ARW) dynamic core, with explicit and implicit representations of convection, respectively. Forecasts from 1 April to 25 July 2005 are examined for a domain encompassing the eastern two-thirds of the United States. Traditional skill measures and time-longitude, or Hovmoller, diagrams of daily and diurnally averaged forecast and observed rainfall are used to compare and evaluate the simulations. Forecast skill from both models is found to be significantly affected by the diurnal cycle. Equitable threat scores (ETSs) were highest around 0000 LST and 0300 LST for both models. However, the diurnal cycle of rainfall, shown using diurnally averaged Hovmoller diagrams, was very different between the two models. Bias scores were also indicative of this difference; the 22 km WRF-ARW had the highest bias scores around 1200 LST and the 5 km WRF-NMM around 1800 LST. Overall, the representation of the diurnal cycle in the 5 km WRF-NMM was superior to the 22 km WRF-ARW, especially during the second diurnal cycle, which was probably because of the advantages afforded by the explicit representation of convection in the 5 km WRF-NMM. However, overall, ETSs from the 22 km WRF-ARW were higher, reinforcing past studies indicating that comparing forecasts from high and low 5 resolution models can be misleading because of large errors caused by the misplacement of small areas of intense rainfall forecast by high resolution models that are not resolved by coarser models.

Examination of the diurnal cycle of rainfall and ensemble prediction strategies in WRF model simulations / by Adam Clark