This study uses the WRF ARW to investigate how different atmospheric temperature environments impact the size and structure development of a simulated tropical cyclone (TC). In each simulation, the entire vertical virtual temperature profile is either warmed or cooled in 18C increments from an initial specified state while the initial relative humidity profile and sea surface temperature are held constant. This alters the initial amount of convective available potential energy (CAPE), specific humidity, and air-sea temperature difference such that, when the simulated atmosphere is cooled (warmed), the initial specific humidity and CAPE decrease (increase), but the surface energy fluxes from the ocean increase (decrease).It is found that the TCs that form in an initially cooler environment develop larger wind and precipitation fields with more active outer-core rainband formation. Consistent with previous studies, outer-core rainband formation is associated with high surface energy fluxes, which leads to increases in the outer-core wind field. A larger convective field develops despite initializing in a low CAPE environment, and the dynamics are linked to a wider field of surface radial inflow. As the TC matures and radial inflow expands, large imports of relative angular momentum in the boundary layer continue to drive expansion of the TC's overall size.

This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training data set that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article, Stovern et al. 2022).

The second part of this series presents results from verifying a precipitation forecast calibration method discussed in part 1, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12) reforecasts were used in this study. The method was validated with pre-operational GEFSv12 forecasts from the between December 2017 and November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. Part 1 described adaptations to the methodology to leverage the ~ 20-year GEFSv12 reforecast data. As shown in this part 2, when compared to probabilistic quantitative precipitation forecasts (PQPFs) from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., < 5 days) and for forecasts of events from light precipitation to > 10 mm 6 h−1. Cool-season events in the western US were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.