NOAA Storm Prediction Center

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.

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.

An estimate is made of the probability of an occurrence of a tornado day near any location in the contiguous 48 states for any time during the year. Gaussian smoothers in space and time have been applied to the observed record of tornado days from 1980 to 1999 to produce daily maps and annual cycles at any point on an 80 km 80 km grid. Many aspects of this climatological estimate have been identified in previous work, but the method allows one to consider the record in several new ways. The two regions of maximum tornado days in the United States are northeastern Colorado and peninsular Florida, but there is a large region between the Appalachian and Rocky Mountains that has at least 1 day on which a tornado touches down on the grid. The annual cycle of tornado days is of particular interest. The southeastern United States, outside of Florida, faces its maximum threat in April. Farther west and north, the threat is later in the year, with the northern United States and New England facing its maximum threat in July. In addition, the repeatability of the annual cycle is much greater in the plains than farther east. By combining the region of greatest threat with the region of highest repeatability of the season, an objective definition of Tornado Alley as a region that extends from the southern Texas Panhandle through Nebraska and northeastward into eastern North Dakota and Minnesota can be provided.

A physically based, shear-relative, and Galilean invariant method for predicting supercell motion using a hodograph is presented. It is founded on numerous observational and modeling studies since the 1940s, which suggest a consistent pattern to supercell motion exists. Two components are assumed to be largely responsible for supercell motion: (i) advection of the storm by a representative mean wind, and (ii) propagation away from the mean wind either toward the right or toward the left of the vertical wind shear—due to internal supercell dynamics. Using 290 supercell hodographs, this new method is shown to be statistically superior to existing methods in predicting supercell motion for both right- and left-moving storms. Other external factors such as interaction with atmospheric boundaries and orography can have a pronounced effect on supercell motion, but these are difficult to quantify prior to storm development using only a hodograph.

Abstract Radar-based convective modes were assigned to a sample of tornadoes and significant severe thunderstorms reported in the contiguous United States (CONUS) during 2003–11. The significant hail (≥2-in. diameter), significant wind (≥65-kt thunderstorm gusts), and tornadoes were filtered by the maximum event magnitude per hour on a 40-km Rapid Update Cycle model horizontal grid. The filtering process produced 22 901 tornado and significant severe thunderstorm events, representing 78.5% of all such reports in the CONUS during the sample period. The convective mode scheme presented herein begins with three radar-based storm categories: 1) discrete cells, 2) clusters of cells, and 3) quasi-linear convective systems (QLCSs). Volumetric radar data were examined for right-moving supercell (RM) and left-moving supercell characteristics within the three radar reflectivity designations. Additional categories included storms with marginal supercell characteristics and linear hybrids with a mix of supercell and QLCS structures. Smoothed kernel density estimates of events per decade revealed clear geographic and seasonal patterns of convective modes with tornadoes. Discrete and cluster RMs are the favored convective mode with southern Great Plains tornadoes during the spring, while the Deep South displayed the greatest variability in tornadic convective modes in the fall, winter, and spring. The Ohio Valley favored a higher frequency of QLCS tornadoes and a lower frequency of RM compared to the Deep South and the Great Plains. Tornadoes with nonsupercellular/non-QLCS storms were more common across Florida and the high plains in the summer. Significant hail events were dominated by Great Plains supercells, while variations in convective modes were largest for significant wind events.

Abstract Moisture flux convergence (MFC) is a term in the conservation of water vapor equation and was first calculated in the 1950s and 1960s as a vertically integrated quantity to predict rainfall associated with synoptic-scale systems. Vertically integrated MFC was also incorporated into the Kuo cumulus parameterization scheme for the Tropics. MFC was eventually suggested for use in forecasting convective initiation in the midlatitudes in 1970, but practical MFC usage quickly evolved to include only surface data, owing to the higher spatial and temporal resolution of surface observations. Since then, surface MFC has been widely applied as a short-term (0–3 h) prognostic quantity for forecasting convective initiation, with an emphasis on determining the favorable spatial location(s) for such development. A scale analysis shows that surface MFC is directly proportional to the horizontal mass convergence field, allowing MFC to be highly effective in highlighting mesoscale boundaries between different air masses near the earth’s surface that can be resolved by surface data and appropriate grid spacing in gridded analyses and numerical models. However, the effectiveness of boundaries in generating deep moist convection is influenced by many factors, including the depth of the vertical circulation along the boundary and the presence of convective available potential energy (CAPE) and convective inhibition (CIN) near the boundary. Moreover, lower- and upper-tropospheric jets, frontogenesis, and other forcing mechanisms may produce horizontal mass convergence above the surface, providing the necessary lift to bring elevated parcels to their level of free convection without connection to the boundary layer. Case examples elucidate these points as a context for applying horizontal mass convergence for convective initiation. Because horizontal mass convergence is a more appropriate diagnostic in an ingredients-based methodology for forecasting convective initiation, its use is recommended over MFC.

Abstract A sample of 1185 Rapid Update Cycle (RUC) model analysis (0 h) proximity soundings, within 40 km and 30 min of radar-identified discrete storms, was categorized by several storm types: significantly tornadic supercells (F2 or greater damage), weakly tornadic supercells (F0–F1 damage), nontornadic supercells, elevated right-moving supercells, storms with marginal supercell characteristics, and nonsupercells. These proximity soundings served as the basis for calculations of storm-relative helicity and bulk shear intended to apply across a broad spectrum of thunderstorm types. An effective storm inflow layer was defined in terms of minimum constraints on lifted parcel CAPE and convective inhibition (CIN). Sixteen CAPE and CIN constraint combinations were examined, and the smallest CAPE (25 and 100 J kg−1) and largest CIN (−250 J kg−1) constraints provided the greatest probability of detecting an effective inflow layer within an 835-supercell subset of the proximity soundings. Effective storm-relative helicity (ESRH) calculations were based on the upper and lower bounds of the effective inflow layer. By confining the SRH calculation to the effective inflow layer, ESRH values can be compared consistently across a wide range of storm environments, including storms rooted above the ground. Similarly, the effective bulk shear (EBS) was defined in terms of the vertical shear through a percentage of the “storm depth,” as defined by the vertical distance from the effective inflow base to the equilibrium level associated with the most unstable parcel (maximum θe value) in the lowest 300 hPa. ESRH and EBS discriminate strongly between various storm types, and between supercells and nonsupercells, respectively.

Abstract A sample of 22 901 tornado and significant severe thunderstorm events, filtered on an hourly 40-km grid, was collected for the period 2003–11 across the contiguous United States (CONUS). Convective mode was assigned to each case via manual examination of full volumetric radar data (Part I of this study), and environmental information accompanied each grid-hour event from the hourly objective analyses calculated and archived at the Storm Prediction Center (SPC). Sounding-derived parameters related to supercells and tornadoes formed the basis of this investigation owing to the dominance of right-moving supercells in tornado production and the availability of supercell-related convective parameters in the SPC environmental archive. The tornado and significant severe thunderstorm events were stratified by convective mode and season. Measures of buoyancy discriminated most strongly between supercell and quasi-linear convective system (QLCS) tornado events during the winter, while bulk wind differences and storm-relative helicity were similar for both supercell and QLCS tornado environments within in each season. The larger values of the effective-layer supercell composite parameter (SCP) and the effective-layer significant tornado parameter (STP) favored right-moving supercells that produced significant tornadoes, as opposed to weak tornadoes or supercells that produced only significant hail or damaging winds. Additionally, mesocyclone strength tended to increase with increasing SCP for supercells, and STP tended to increase as tornado damage class ratings increased. The findings underscore the importance of convective mode (discrete or cluster supercells), mesocyclone strength, and near-storm environment (as represented by large values of STP) in consistent, real-time identification of intense tornadoes.

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.

The NOAA Hazardous Weather Testbed (HWT) conducts annual spring forecasting experiments organized by the Storm Prediction Center and National Severe Storms Laboratory to test and evaluate emerging scientific concepts and technologies for improved analysis and prediction of hazardous mesoscale weather. A primary goal is to accelerate the transfer of promising new scientific concepts and tools from research to operations through the use of intensive real-time experimental forecasting and evaluation activities conducted during the spring and early summer convective storm period. The 2010 NOAA/HWT Spring Forecasting Experiment (SE2010), conducted 17 May through 18 June, had a broad focus, with emphases on heavy rainfall and aviation weather, through collaboration with the Hydrometeorological Prediction Center (HPC) and the Aviation Weather Center (AWC), respectively. In addition, using the computing resources of the National Institute for Computational Sciences at the University of Tennessee, the Center for Analysis and Prediction of Storms at the University of Oklahoma provided unprecedented real-time conterminous United States (CONUS) forecasts from a multimodel Storm-Scale Ensemble Forecast (SSEF) system with 4-km grid spacing and 26 members and from a 1-km grid spacing configuration of the Weather Research and Forecasting model. Several other organizations provided additional experimental high-resolution model output. This article summarizes the activities, insights, and preliminary findings from SE2010, emphasizing the use of the SSEF system and the successful collaboration with the HPC and AWC. A supplement to this article is available online (DOI:10.1175/BAMS-D-11-00040.2)

Observed upper air soundings that occurred within 2 h and 167 km of derechos were collected and analyzed to document atmospheric stability and wind shear conditions associated with long-lived convective windstorms. Sixty-seven derechos, accompanied by 113 proximity soundings, were identified during the years 1983-93. Owing to the large variability of the synoptic-scale environments associated with derechos, each derecho was further divided into categories based on the strength of synoptic-scale forcing associated with each event.

Abstract During the 2007 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma produced convection-allowing forecasts from a single deterministic 2-km model and a 10-member 4-km-resolution ensemble. In this study, the 2-km deterministic output was compared with forecasts from the 4-km ensemble control member. Other than the difference in horizontal resolution, the two sets of forecasts featured identical Advanced Research Weather Research and Forecasting model (ARW-WRF) configurations, including vertical resolution, forecast domain, initial and lateral boundary conditions, and physical parameterizations. Therefore, forecast disparities were attributed solely to differences in horizontal grid spacing. This study is a follow-up to similar work that was based on results from the 2005 Spring Experiment. Unlike the 2005 experiment, however, model configurations were more rigorously controlled in the present study, providing a more robust dataset and a cleaner isolation of the dependence on horizontal resolution. Additionally, in this study, the 2- and 4-km outputs were compared with 12-km forecasts from the North American Mesoscale (NAM) model. Model forecasts were analyzed using objective verification of mean hourly precipitation and visual comparison of individual events, primarily during the 21- to 33-h forecast period to examine the utility of the models as next-day guidance. On average, both the 2- and 4-km model forecasts showed substantial improvement over the 12-km NAM. However, although the 2-km forecasts produced more-detailed structures on the smallest resolvable scales, the patterns of convective initiation, evolution, and organization were remarkably similar to the 4-km output. Moreover, on average, metrics such as equitable threat score, frequency bias, and fractions skill score revealed no statistical improvement of the 2-km forecasts compared to the 4-km forecasts. These results, based on the 2007 dataset, corroborate previous findings, suggesting that decreasing horizontal grid spacing from 4 to 2 km provides little added value as next-day guidance for severe convective storm and heavy rain forecasters in the United States.

Abstract The probability of nontornadic severe weather event reports near any location in the United States for any day of the year has been estimated. Gaussian smoothers in space and time have been applied to the observed record of severe thunderstorm occurrence from 1980 to 1994 to produce daily maps and annual cycles at any point. Many aspects of this climatology have been identified in previous work, but the method allows for the consideration of the record in several new ways. A review of the raw data, broken down in various ways, reveals that numerous nonmeteorological artifacts are present in the raw data. These are predominantly associated with the marginal nontornadic severe thunderstorm events, including an enormous growth in the number of severe weather reports since the mid-1950s. Much of this growth may be associated with a drive to improve warning verification scores. The smoothed spatial and temporal distributions of the probability of nontornadic severe thunderstorm events are presented in several ways. The distribution of significant nontornadic severe thunderstorm reports (wind speeds ≥ 65 kt and/or hailstone diameters ≥ 2 in.) is consistent with the hypothesis that supercells are responsible for the majority of such reports.

Abstract Convection-allowing configurations of the Weather Research and Forecast (WRF) model were evaluated during the 2004 Storm Prediction Center–National Severe Storms Laboratory Spring Program in a simulated severe weather forecasting environment. The utility of the WRF forecasts was assessed in two different ways. First, WRF output was used in the preparation of daily experimental human forecasts for severe weather. These forecasts were compared with corresponding predictions made without access to WRF data to provide a measure of the impact of the experimental data on the human decision-making process. Second, WRF output was compared directly with output from current operational forecast models. Results indicate that human forecasts showed a small, but measurable, improvement when forecasters had access to the high-resolution WRF output and, in the mean, the WRF output received higher ratings than the operational Eta Model on subjective performance measures related to convective initiation, evolution, and mode. The results suggest that convection-allowing models have the potential to provide a value-added benefit to the traditional guidance package used by severe weather forecasters.

The primary factors that affect the direction of propagation and overall movement of surface-based mesoscale convective systems (MCSs) are discussed. It is shown that although propagation is indeed related to the strength and direction of the low-level jet as previous studies have shown, it is more specifically dependent upon the degree of cold-pool-relative flow and to the distribution of conditional instability present along a system's gust front. An updated technique that may be used to forecast the short-term (3-6 h) motion of MCS centroids based on these concepts is introduced. The procedure builds on the long-established observation that MCS motion is a function of 1) the advection of existing cells by the mean wind and 2) the propagation of new convection relative to existing storms. Observed wind and thermodynamic data, in conjunction with anticipated cold-pool motion and orientation, are used to assess the speed and direction of cell propagation, that is, whether propagation will be upwind, downwind, or some combination of the two. The technique ultimately yields an estimate of overall system movement and has application regardless of scale, season, or synoptic regime.

During the early to middle 2000s, in response to demand for more detail in wind damage surveying and recordkeeping, a team of atmospheric scientists and wind engineers developed the enhanced Fujita (EF) scale. The EF scale, codified officially into National Weather Service (NWS) use in February 2007, offers wind speed estimates for a range of degrees of damage (DoDs) across each of 28 damage indicators (DIs). In practice, this has increased precision of damage surveys for tornado and thunderstorm-wind events. Still, concerns remain about both the representativeness of DoDs and the sufficiency of DIs, including the following: How dependable are the wind speed ranges for certain DoDs? What other DIs can be included? How can recent advances in mapping and documentation tools be integrated into the surveying process and the storm records? What changes should be made to the existing scale: why, how, and by whom? What alternative methods may be included or adapted for estimating tornado intensity? To begin coordinated discussion on these and related topics, interested scientists and engineers (including some involved in EF scale development) organized a national EF Scale Stakeholders' Meeting, held on 2–3 March 2010 in Norman, Oklahoma. This article presents more detailed background information, summarizes the meeting, presents possibilities for the future of the EF scale and damage surveys, and solicits ideas from the engineering and atmospheric science communities.

Abstract MetPy is an open-source, Python-based package for meteorology, providing domain-specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform. MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross sections, skew T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general road map for future planned development for MetPy is also discussed.

Two extreme wind-driven wildfire events impacted California in late 2017, leading to 46 fatalities and thousands of structures lost. This study characterizes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies their rarity over the observational record. Both events featured key fire-weather metrics that were unprecedented in the observational record that followed a sequence of climatic conditions that enhanced fine fuel abundance and fuel availability. The North Bay fires of October 2017 occurred coincident with strong downslope winds, with a majority of burned area occurring within the first 12 hours of ignition. By contrast, the southern California fires of December 2017 occurred during the longest Santa Ana wind event on record, resulting in the largest wildfire in California’s modern history. Both fire events occurred following an exceptionally wet winter that was preceded by a severe four-year drought. Fuels were further preconditioned by the warmest summer and autumn on record in northern and southern California, respectively. Finally, delayed onset of autumn precipitation allowed for critically low dead fuel moistures leading up to the wind events. Fire weather conditions were well forecast several days prior to the fire. However, the rarity of fire-weather conditions that occurred near populated regions, along with other societal factors such as limited evacuation protocols and limited wildfire preparedness in communities outside of the traditional wildland urban interface were key contributors to the widespread wildfire impacts.

Approximately 400 Automated Surface Observing System (ASOS) observations of convective cloud-base heights at 2300 UTC were collected from April through August of 2001. These observations were compared with lifting condensation level (LCL) heights above ground level determined by 0000 UTC rawinsonde soundings from collocated upper-air sites. The LCL heights were calculated using both surface-based parcels (SBLCL) and mean-layer parcels (MLLCL-using mean temperature and dewpoint in lowest 100 hPa). The results show that the mean error for the MLLCL heights was substantially less than for SBLCL heights, with SBLCL heights consistently lower than observed cloud bases. These findings suggest that the mean-layer parcel is likely more representative of the actual parcel associated with convective cloud development, which has implications for calculations of thermodynamic parameters such as convective available potential energy (CAPE) and convective inhibition. In addition, the median value of surface-based CAPE (SBCAPE) was more than 2 times that of the mean-layer CAPE (MLCAPE). Thus, caution is advised when considering surface-based thermodynamic indices, despite the assumed presence of a well-mixed afternoon boundary layer.

Abstract With a variety of programming languages and data formats available, widespread adoption of computing standards by the atmospheric science community is often difficult to achieve. The Sounding and Hodograph Analysis and Research Program in Python (SHARPpy) is an open-source, cross-platform, upper-air sounding analysis and visualization package. SHARPpy is based on the National Oceanic and Atmospheric Administration/Storm Prediction Center’s (NOAA/SPC) in-house analysis package, SHARP, and is the result of a collaborative effort between forecasters at the SPC and students at the University of Oklahoma’s School of Meteorology. The major aim of SHARPpy is to provide a consistent framework for sounding analysis that is available to all. Nearly all routines are written to be as consistent as possible with the methods researched, tested, and developed in the SPC, which sets this package apart from other sounding analysis tools. SHARPpy was initially demonstrated and released to the atmospheric community at the American Meteorological Society (AMS) Annual Meeting in 2012, and an updated and greatly expanded version was released at the AMS Annual Meeting in 2015. Since this release, SHARPpy has been adopted by a variety of operational and research meteorologists across the world. In addition, SHARPpy’s open-source nature enables collaborations between other developers, resulting in major additions to the program.