Storm-Scale Ensemble Forecast Research Articles

Hierarchical Cluster Analysis of a Convection-Allowing Ensemble during the Hazardous Weather Testbed 2009 Spring Experiment. Part II: Ensemble Clustering over the Whole Experiment Period

Abstract Twenty-member real-time convection-allowing storm-scale ensemble forecasts with perturbations to model physics, dynamics, initial conditions (IC), and lateral boundary conditions (LBC) during the NOAA Hazardous Weather Testbed Spring Experiment provide a unique opportunity to study the relative impact of different sources of perturbation on convection-allowing ensemble diversity. In Part II of this two-part study, systematic similarity/dissimilarity of hourly precipitation forecasts among ensemble members from the spring season of 2009 are identified using hierarchical cluster analysis (HCA) with a fuzzy object-based threat score (OTS), developed in Part I. In addition to precipitation, HCA is also performed on ensemble forecasts using the traditional Euclidean distance for wind speed at 10 m and 850 hPa, and temperature at 500 hPa. At early lead times (3 h, valid at 0300 UTC) precipitation forecasts cluster primarily by data assimilation and model dynamic core, indicating a dominating impact of models, with secondary clustering by microphysics. There is an increasing impact of the planetary boundary layer (PBL) scheme on clustering relative to the microphysics scheme at later lead times. Forecasts of 10-m wind speed cluster primarily by the PBL scheme at early lead times, with an increasing impact of LBC at later lead times. Forecasts of midtropospheric variables cluster primarily by IC at early lead times and LBC at later lead times. The radar and Mesonet data assimilation (DA) show its impact, with members without DA in a distinct cluster, through the 12-h lead time (valid at 1200 UTC) for both precipitation and nonprecipitation variables. The implication for optimal ensemble design for storm-scale forecasts is also discussed.

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.