Kimberly L. Elmore

Close Proximity Soundings within Supercell Environments Obtained from the Rapid Update Cycle

A sample of 413 soundings in close proximity to tornadic and nontornadic supercells is examined. The soundings were obtained from hourly analyses generated by the 40-km Rapid Update Cycle-2 (RUC-2) analysis and forecast system. A comparison of 149 observed soundings and collocated RUC-2 soundings in regional supercell environments reveals that the RUC-2 model analyses were reasonably accurate through much of the troposphere. The largest error tendencies were in temperatures and mixing ratios near the surface, primarily in 1-h forecast soundings immediately prior to the standard rawinsonde launches around 1200 and 0000 UTC. Overall, the RUC-2 analysis soundings appear to be a reasonable proxy for observed soundings in supercell environments. Thermodynamic and vertical wind shear parameters derived from RUC-2 proximity soundings are evaluated for the following supercell and storm subsets: significantly tornadic supercells (54 soundings), weakly tornadic supercells (144 soundings), nontornadic supercells (215 soundings), and discrete nonsupercell storms (75 soundings). Findings presented herein are then compared to results from previous and ongoing proximity soundings studies. Most significantly, proximity soundings presented here reinforce the findings of previous studies in that vertical shear and moisture within 1 km of the ground can discriminate between nontornadic supercells and supercells producing tornadoes with F2 or greater damage. Parameters that combine measures of buoyancy, vertical shear, and low-level moisture show the strongest ability to discriminate between supercell classes.

MPING: Crowd-Sourcing Weather Reports for Research

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near the Ground (mPING) was d...

Evaluation of WRF Model Output for Severe Weather Forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment

Abstract This study assesses forecasts of the preconvective and near-storm environments from the convection-allowing models run for the 2008 National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) spring experiment. Evaluating the performance of convection-allowing models (CAMs) is important for encouraging their appropriate use and development for both research and operations. Systematic errors in the CAM forecasts included a cold bias in mean 2-m and 850-hPa temperatures over most of the United States and smaller than observed vertical wind shear and 850-hPa moisture over the high plains. The placement of airmass boundaries was similar in forecasts from the CAMs and the operational North American Mesoscale (NAM) model that provided the initial and boundary conditions. This correspondence contributed to similar characteristics for spatial and temporal mean error patterns. However, substantial errors were found in the CAM forecasts away from airmass boundaries. The result is...

Assessing the Impacts of Proximity Sounding Criteria on the Climatology of Significant Tornado Environments

Abstract Proximity sounding studies typically seek to optimize several trade-offs that involve somewhat arbitrary definitions of how to define a “proximity sounding.” More restrictive proximity criteria, which presumably produce results that are more characteristic of the near-storm environment, typically result in smaller sample sizes that can reduce the statistical significance of the results. Conversely, the use of broad proximity criteria will typically increase the sample size and the apparent robustness of the statistical analysis, but the sounding data may not necessarily be representative of near-storm environments, given the presence of mesoscale variability in the atmosphere. Previous investigations have used a wide range of spatial and temporal proximity criteria to analyze severe storm environments. However, the sensitivity of storm environment climatologies to the proximity definition has not yet been rigorously examined. In this study, a very large set (∼1200) of proximity soundings associat...

Euclidean Distance as a Similarity Metric for Principal Component Analysis

Abstract Eigentechniques, in particular principal component analysis (PCA), have been widely used in meteorological analyses since the early 1950s. Traditionally, choices for the parent similarity matrix, which are diagonalized, have been limited to correlation, covariance, or, rarely, cross products. Whereas each matrix has unique characteristic benefits, all essentially identify parameters that vary together. Depending on what underlying structure the analyst wishes to reveal, similarity matrices can be employed, other than the aforementioned, to yield different results. In this work, a similarity matrix based upon Euclidean distance, commonly used in cluster analysis, is developed as a viable alternative. For PCA, Euclidean distance is converted into Euclidean similarity. Unlike the variance-based similarity matrices, a PCA performed using Euclidean similarity identifies parameters that are close to each other in a Euclidean distance sense. Rather than identifying parameters that change together, the r...

Field significance revisited: Spatial bias errors in forecasts as applied to the Eta Model

Abstract The spatial structure of bias errors in numerical model output is valuable to both model developers and operational forecasters, especially if the field containing the structure itself has statistical significance in the face of naturally occurring spatial correlation. A semiparametric Monte Carlo method, along with a moving blocks bootstrap method is used to determine the field significance of spatial bias errors within spatially correlated error fields. This process can be completely automated, making it an attractive addition to the verification tools already in use. The process demonstrated here results in statistically significant spatial bias error fields at any arbitrary significance level. To demonstrate the technique, 0000 and 1200 UTC runs of the operational Eta Model and the operational Eta Model using the Kain–Fritsch convective parameterization scheme are examined. The resulting fields for forecast errors for geopotential heights and winds at 850, 700, 500, and 250 hPa over a period ...

Alternatives to the Chi-Square Test for Evaluating Rank Histograms from Ensemble Forecasts

Rank histograms are a commonly used tool for evaluating an ensemble forecasting system’s performance. Because the sample size is finite, the rank histogram is subject to statistical fluctuations, so a goodness-of-fit (GOF) test is employed to determine if the rank histogram is uniform to within some statistical certainty. Most often, the 2 test is used to test whether the rank histogram is indistinguishable from a discrete uniform distribution. However, the 2 test is insensitive to order and so suffers from troubling deficiencies that may render it unsuitable for rank histogram evaluation. As shown by examples in this paper, more powerful tests, suitable for small sample sizes, and very sensitive to the particular deficiencies that appear in rank histograms are available from the order-dependent Cramer–von Mises family of statistics, in particular, the Watson and Anderson–Darling statistics.

Explicit Cloud-Scale Models for Operational Forecasts: A Note of Caution

Abstract As computational capacity has increased, cloud-scale numerical models are slowly being modified from pure research tools to forecast tools. Previous studies that used cloud-scale models as explicit forecast tools, in much the same way as a mesoscale model might be used, have met with limited success. Results presented in this paper suggest that this is due, at least in part, to the nature of cloud-scale models themselves. Results from over 700 cloud-scale model runs indicate that, in some cases, differences in the initial soundings that are smaller than can be measured by the current observing system result in unexpected differences in storm longevity. In other cases, easily measurable differences in the initial soundings do not result in significant differences in storm longevity. There unfortunately appears to be no set of parameters that can be used to determine whether the initial sounding is near some part of the cloud-model parameter space that displays this sensitivity. Because different c...

Ensemble Cloud Model Applications to Forecasting Thunderstorms

Abstract A cloud model ensemble forecasting approach is developed to create forecasts that describe the range and distribution of thunderstorm lifetimes that may be expected to occur on a particular day. Such forecasts are crucial for anticipating severe weather, because long-lasting storms tend to produce more significant weather and have a greater impact on public safety than do storms with brief lifetimes. Eighteen days distributed over two warm seasons with 1481 observed thunderstorms are used to assess the ensemble approach. Forecast soundings valid at 1800, 2100, and 0000 UTC provided by the 0300 UTC run of the operational Meso Eta Model from the National Centers for Environmental Prediction are used to provide horizontally homogeneous initial conditions for a cloud model ensemble made up from separate runs of the fully three-dimensional Collaborative Model for Mesoscale Atmospheric Simulation. These soundings are acquired from a 160 km × 160 km square centered over the location of interest; they ar...

Radar Reflectivity–Derived Thunderstorm Parameters Applied to Storm Longevity Forecasting

Abstract In order for the Federal Aviation Administration (FAA) to use airspace more efficiently during thunderstorm events, accurate storm longevity forecasts are needed. Relationships between 16 radar reflectivity–derived storm characteristics and storm longevity are examined to determine which, if any, of the storm characteristics are strongly related to storm lifetime. Such relationships are potentially useful for the development of storm longevity forecasts. The study includes 879 storms that formed over the Memphis, Tennessee, area during 15 late spring and summer convective days. Statistical analyses comparing all 16 storm characteristics to the observed remaining lifetime show that these storm characteristics are not good predictors for storm remaining lifetime.