NOAA Hazardous Weather Testbed Spring Research Articles

Improving Probabilistic Ensemble Forecasts of Convection through the Application of QPF–POP Relationships

Four new approaches of postprocessing quantitative precipitation forecasts (QPFs) from model ensemble output were used to generate probability of precipitation (POP) tables in order to develop a forecasting method that could outperform a traditional method that relies upon calibration of POP forecasts derived using equal weighting of ensemble members. Early warm season 10-member ensemble output from the NOAA Hazardous Weather Testbed Spring Experiments was used, with 29 cases serving as a training set to create the POP tables and 20 cases used as a test set. The new approaches use QPF–POP relationships based on two properties termed precipitation amount characteristic (PAC) and ensemble member agreement. Exploratory results are presented for 20-km grid spacing and selectively for 4-km grid spacing. In the first approach, POPs were based on a binned PAC and the number of ensemble members with 6-h precipitation accumulations greater than given thresholds. In a second approach, a neighborhood method was used to find the number of points in a given neighborhood area around each of the domain grid points with precipitation amounts greater than a given threshold, while also considering the binned PAC representative of the neighborhood. A third approach synthesized the previous methods and led to an increase in skill relative to the individual methods, and a fourth approach using a combination of methods produced forecasts with even greater skill. All of the forecasts from the four approaches were improved statistically significantly compared to the calibrated traditional method’s forecasts at 20-km grid spacing. The second approach on its own showed skill comparable to that obtained by a traditional calibrated 10-member ensemble, so adopting this approach alone could potentially save computer resources that could then be used for model refinements, only sacrificing the increased skill that could have been obtained by using the fourth approach.

Convection-Allowing and Convection-Parameterizing Ensemble Forecasts of a Mesoscale Convective Vortex and Associated Severe Weather Environment

Abstract An analysis of a regional severe weather outbreak that was related to a mesoscale convective vortex (MCV) is performed. The MCV-spawning mesoscale convection system (MCS) formed in northwest Kansas along the southern periphery of a large cutoff 500-hPa low centered over western South Dakota. As the MCS propagated into eastern Kansas during the early morning of 1 June 2007, an MCV that became evident from multiple data sources [e.g., Weather Surveillance Radar-1988 Doppler (WSR-88D) network, visible satellite imagery, wind-profiler data, Rapid Update Cycle 1-hourly analyses] tracked through northwest Missouri and central Iowa and manifested itself as a well-defined midlevel short-wave trough. Downstream of the MCV in southeast Iowa and northwest Illinois, southwesterly 500-hPa winds increased to around 25 m s−1 over an area with southeasterly surface winds and 500–1500 J kg−1 of surface-based convective available potential energy (CAPE), creating a favorable environment for severe weather. In the favorable region, multiple tornadoes occurred, including one rated as a category 3 storm on the enhanced Fujita scale (EF3) that caused considerable damage. In the analysis, emphasis is placed on the role of the MCV in leading to a favorable environment for severe weather. In addition, convection-allowing forecasts of the MCV and associated environmental conditions from the 10-member Storm-Scale Ensemble Forecast (SSEF) system produced for the 2007 NOAA Hazardous Weather Testbed Spring Experiment are compared to those from a similarly configured, but coarser, 30-member convection-parameterizing ensemble. It was found that forecasts of the MCV track and associated environmental conditions (e.g., midlevel winds, low-level wind shear, and instability) were much better in the convection-allowing ensemble. Errors in the MCV track from convection-parameterizing members likely resulted from westward displacement errors in the incipient MCS. Furthermore, poor depiction of MCV structure and maintenance in convection-parameterizing members, which was diagnosed through a vorticity budget analysis, likely led to the relatively poor forecasts of the associated environmental conditions. The results appear to be very encouraging for convection-allowing ensembles, especially when environmental conditions lead to a high degree of predictability for MCSs, which appeared to be the case for this particular event.

Growth of Spread in Convection-Allowing and Convection-Parameterizing Ensembles

Abstract During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, a 10-member 4-km grid-spacing Storm-Scale Ensemble Forecast (SSEF) system was run in real time to provide experimental severe weather forecasting guidance. Five SSEF system members used perturbed initial and lateral boundary conditions (ICs and LBCs) and mixed physics (ENS4), and five members used only mixed physics (ENS4phys). This ensemble configuration facilitates a comparison of ensemble spread generated by a combination of perturbed ICs/LBCs and mixed physics to that generated by only mixed physics, which is examined herein. In addition, spread growth and spread-error metrics for the two SSEF system configurations are compared to similarly configured 20-km grid-spacing convection-parameterizing ensembles (ENS20 and ENS20phys). Twelve forecast fields are examined for 20 cases. For most fields, ENS4 mean spread growth rates are higher than ENS20 for ensemble configurations with both sets of perturbations, which is expected as smaller scales of motion are resolved at higher resolution. However, when ensembles with only mixed physics are compared, mass-related fields (i.e., geopotential height and mean sea level pressure) in ENS20phys have slightly higher spread growth rates than ENS4phys, likely resulting from the additional physics uncertainty in ENS20phys from varied cumulus parameterizations that were not used at 4-km grid spacing. For 4- and 20-km configurations, the proportion of spread generated by mixed physics in ENS4 and ENS20 increased with increasing forecast lead time. In addition, low-level fields (e.g., 2-m temperature) had a higher proportion of spread generated by mixed physics than mass-related fields. Spread-error analyses revealed that ensemble variance from the current uncalibrated ensemble systems was not a reliable indicator of forecast uncertainty. Furthermore, ENS4 had better statistical consistency than ENS20 for some mass-related fields, wind-related fields, precipitation, and most unstable convective available potential energy (MUCAPE) with no noticeable differences for low-level temperature and dewpoint fields. The variety of results obtained for the different types of fields examined suggests that future ensemble design should give careful consideration to the specific types of forecasts desired by the user.

Toward Improved Convection-Allowing Ensembles: Model Physics Sensitivities and Optimizing Probabilistic Guidance with Small Ensemble Membership

Abstract During the 2007 NOAA Hazardous Weather Testbed Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced a daily 10-member 4-km horizontal resolution ensemble forecast covering approximately three-fourths of the continental United States. Each member used the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model core, which was initialized at 2100 UTC, ran for 33 h, and resolved convection explicitly. Different initial condition (IC), lateral boundary condition (LBC), and physics perturbations were introduced in 4 of the 10 ensemble members, while the remaining 6 members used identical ICs and LBCs, differing only in terms of microphysics (MP) and planetary boundary layer (PBL) parameterizations. This study focuses on precipitation forecasts from the ensemble. The ensemble forecasts reveal WRF-ARW sensitivity to MP and PBL schemes. For example, over the 7-week experiment, the Mellor–Yamada–Janjić PBL and Ferrier MP parameterizations were associated with relatively high precipitation totals, while members configured with the Thompson MP or Yonsei University PBL scheme produced comparatively less precipitation. Additionally, different approaches for generating probabilistic ensemble guidance are explored. Specifically, a “neighborhood” approach is described and shown to considerably enhance the skill of probabilistic forecasts for precipitation when combined with a traditional technique of producing ensemble probability fields.

Some Practical Considerations Regarding Horizontal Resolution in the First Generation of Operational Convection-Allowing NWP

Abstract During the 2005 NOAA Hazardous Weather Testbed Spring Experiment two different high-resolution configurations of the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) model were used to produce 30-h forecasts 5 days a week for a total of 7 weeks. These configurations used the same physical parameterizations and the same input dataset for the initial and boundary conditions, differing primarily in their spatial resolution. The first set of runs used 4-km horizontal grid spacing with 35 vertical levels while the second used 2-km grid spacing and 51 vertical levels. Output from these daily forecasts is analyzed to assess the numerical forecast sensitivity to spatial resolution in the upper end of the convection-allowing range of grid spacing. The focus is on the central United States and the time period 18–30 h after model initialization. The analysis is based on a combination of visual comparison, systematic subjective verification conducted during the Spring Experiment, and objective metrics based largely on the mean diurnal cycle of the simulated reflectivity and precipitation fields. Additional insight is gained by examining the size distributions of the individual reflectivity and precipitation entities, and by comparing forecasts of mesocyclone occurrence in the two sets of forecasts. In general, the 2-km forecasts provide more detailed presentations of convective activity, but there appears to be little, if any, forecast skill on the scales where the added details emerge. On the scales where both model configurations show higher levels of skill—the scale of mesoscale convective features—the numerical forecasts appear to provide comparable utility as guidance for severe weather forecasters. These results suggest that, for the geographical, phenomenological, and temporal parameters of this study, any added value provided by decreasing the grid increment from 4 to 2 km (with commensurate adjustments to the vertical resolution) may not be worth the considerable increases in computational expense.