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The High-Resolution Rapid Refresh Ensemble (HRRRE) is a 36-member ensemble analysis system with 9 forecast members that utilizes the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) dynamic core and the physics suite from the operational Rapid Refresh/High-Resolution Rapid Refresh deterministic modeling system. A goal of HRRRE development is a system with sufficient spread among members, comparable in magnitude to the random error in the ensemble mean, to represent the range of possible future atmospheric states.HRRRE member diversity has traditionally been obtained by perturbing the initial and lateral boundary conditions of each member, but recent development has focused on implementing stochastic approaches in HRRRE to generate additional spread. These techniques were tested in retrospective experiments and in the May 2019 Hazardous Weather Testbed Spring Experiment (HWT-SE). Results show a 6%-25% increase in the ensemble spread in 2-m temperature, 2-m mixing ratio, and 10-m wind speed when stochastic parameter perturbations are used in HRRRE (HRRRE-SPP). Case studies from HWT-SE demonstrate that HRRRE-SPP performed similar to or better than the operational High-Resolution Ensemble Forecast system, version 2 (HREFv2), and the nonstochastic HRRRE. However, subjective evaluations provided by HWTSE forecasters indicated that overall, HRRRE-SPP predicted lower probabilities of severe weather (using updraft helicity as a proxy) compared to HREFv2. A statistical analysis of the performance of HRRRE-SPP and HREFv2 from the 2019 summer convective season supports this claim, but also demonstrates that the two systems have similar reliability for prediction of severe weather using updraft helicity. [ABSTRACT FROM AUTHOR]

The weather research and forecasting (WRF) model and the maximum likelihood ensemble filter (MLEF) data assimilation approach are used to examine the potential impact of observations from the future Geostationary Operational Environmental Satellite, generation R (GOES-R) on improving our knowledge about clouds. Synthetic radiances are assimilated from the 10.35 μm channel of the GOES-R advanced baseline imager (ABI) employing a ‘non-identical twins’ experimental setup. The experimental results are examined for an extratropical cyclone named Kyrill that produced unusually strong winds, widespread damage and fatalities in Western Europe in January 2007. The data assimilation problem is especially challenging for this case, as there is a large error in the model-simulated radiances resulting from incorrect cloud location. Although this problem is difficult to eliminate, data assimilation results indicate the potential of GOES-R data to significantly reduce these errors. [ABSTRACT FROM AUTHOR]

Numerical prediction of precipitation associated with five cool-season atmospheric river events in northern California was analyzed and compared to observations. The model simulations were performed by using the Advanced Research Weather Research and Forecasting Model (ARW-WRF) with four different microphysical parameterizations. This was done as a part of the 2005–06 field phase of the Hydrometeorological Test Bed project, for which special profilers, soundings, and surface observations were implemented. Using these unique datasets, the meteorology of atmospheric river events was described in terms of dynamical processes and the microphysical structure of the cloud systems that produced most of the surface precipitation. Events were categorized as “bright band” (BB) or “nonbright band” (NBB), the differences being the presence of significant amounts of ice aloft (or lack thereof) and a signature of higher reflectivity collocated with the melting layer produced by frozen precipitating particles descending through the 0°C isotherm. The model was reasonably successful at predicting the timing of surface fronts, the development and evolution of low-level jets associated with latent heating processes and terrain interaction, and wind flow signatures consistent with deep-layer thermal advection. However, the model showed the tendency to overestimate the duration and intensity of the impinging low-level winds. In general, all model configurations overestimated precipitation, especially in the case of BB events. Nonetheless, large differences in precipitation distribution and cloud structure among model runs using various microphysical parameterization schemes were noted. [ABSTRACT FROM AUTHOR]

To assist in optimizing a mixed-physics ensemble for warm season mesoscale convective system rainfall forecasting, the impact of various physical schemes as well as their interactions on rainfall when different initializations were used has been investigated. For this purpose, high-resolution Weather Research and Forecasting (WRF) model simulations of eight International H2O Project events were performed. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer (PBL) schemes were used. All cases were initialized with both Local Analyses and Prediction System (LAPS) “hot” start analyses and 40-km Eta Model analyses. To evaluate the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall under the two different initial conditions, the factor separation method was used. The sensitivity to the use of various physical schemes and their interactions was found to be dependent on the initialization dataset. Runs initialized with Eta analyses appeared to be influenced by the use of the Betts–Miller–Janjić scheme in that model’s assimilation system, which tended to reduce the WRF’s sensitivity to changes in the microphysical scheme compared with that present when LAPS analyses were used for initialization. In addition, differences in initialized thermodynamics resulted in changes in sensitivity to PBL and convective schemes. With both initialization datasets, the greatest sensitivity to the simulated rain rate was due to changes in the convective scheme. However, for rain volume, substantial sensitivity was present due to changes in both the physical parameterizations and the initial datasets. [ABSTRACT FROM AUTHOR]