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Ryan A. Sobash and John S. Kain

Abstract

Eight years of daily, experimental, deterministic, convection-allowing model (CAM) forecasts, produced by the National Severe Storms Laboratory, were evaluated to assess their ability at predicting severe weather hazards over a diverse collection of seasons, regions, and environments. To do so, forecasts of severe weather hazards were produced and verified as in previous studies using CAM output, namely by thresholding the updraft helicity (UH) field, smoothing the resulting binary field to create surrogate severe probability forecasts (SSPFs), and verifying the SSPFs against observed storm reports. SSPFs were most skillful during the spring and fall, with a relative minimum in skill observed during the summer. SSPF skill during the winter months was more variable than during other seasons, partly due to the limited sample size of events, but was often less than that during the warm season. The seasonal behavior of SSPF skill was partly driven by the relationship between the UH threshold and the likelihood of obtaining severe storm reports. Varying UH thresholds by season and region produced SSPFs that were more skillful than using a fixed UH threshold to identify severe convection. Accounting for this variability was most important during the cool season, when a lower UH threshold produced larger SSPF skill compared to warm-season events, and during the summer, when large differences in skill occurred within different parts of the continental United States (CONUS), depending on the choice of UH threshold. This relationship between UH threshold and SSPF skill is discussed within the larger scope of generating skillful CAM-based guidance for hazardous convective weather and verifying CAM predictions.

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Ryan A. Sobash and David J. Stensrud

Abstract

Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

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Ryan A. Sobash and Louis J. Wicker

Abstract

Storm-scale ensemble Kalman filter (EnKF) studies routinely use methods to accelerate the spinup of convective structures when assimilating convective-scale radar observations. This typically involves adding coherent perturbations into analyses at regular intervals in regions where radar observations indicate convection is ongoing. Significant uncertainty remains as to the most effective use of these perturbations, including appropriate perturbation magnitudes, spatial scales, fields, and smoothing kernels, as well as flexible strategies that can be applied across a spectrum of convective events with negligible a priori tuning. Here, several idealized experiments were performed to elucidate the impact and sensitivity of adding coherent perturbations into storm-scale analyses of convection. Through the use of toy experiments, it is demonstrated that various factors exhibit substantial influence on the postsmoothed perturbation magnitudes, making tuning challenging. Several OSSEs were performed to document the impact of these perturbations on the analyses, particularly thermodynamic analyses within convection. The repeated addition of coherent perturbations produced temperature and moisture biases that are most pronounced in analyses of the surface cold pool and aloft near the tropopause, and eventually lead to biases in the dynamic fields. In an attempt to reduce these biases and make the noise procedure more adaptive, reflectivity innovations were used to restrict the addition of noise to areas where these innovations are large. This produced analyses with reduced thermodynamic biases and RMSE values comparable to the best-performing experiment where the noise magnitudes were manually adjusted. The impact of these findings on previous and future convective-scale EnKF analyses and forecasts are discussed.

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Craig S. Schwartz and Ryan A. Sobash

Abstract

Hourly accumulated precipitation forecasts from deterministic convection-allowing numerical weather prediction models with 3- and 1-km horizontal grid spacing were evaluated over 497 forecasts between 2010 and 2017 over the central and eastern conterminous United States (CONUS). While precipitation biases varied geographically and seasonally, 1-km model climatologies of precipitation generally aligned better with those observed than 3-km climatologies. Additionally, during the cool season and spring, when large-scale forcing was strong and precipitation entities were large, 1-km forecasts were more skillful than 3-km forecasts, particularly over southern portions of the CONUS where instability was greatest. Conversely, during summertime, when synoptic-scale forcing was weak and precipitation entities were small, 3- and 1-km forecasts had similar skill. These collective results differ substantially from previous work finding 4-km forecasts had comparable springtime precipitation forecast skill as 1- or 2-km forecasts over the central–eastern CONUS. Additional analyses and experiments suggest the greater benefits of 1-km forecasts documented here could be related to higher-quality initial conditions than in prior studies. However, further research is needed to confirm this hypothesis.

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Ryan A. Sobash and David J. Stensrud

Abstract

Surface data assimilation (DA) has the potential to improve forecasts of convection initiation (CI) and short-term forecasts of convective evolution. Since the processes driving CI occur on scales inadequately observed by conventional observation networks, mesoscale surface networks could be especially beneficial given their higher temporal and spatial resolution. This work aims to assess the impact of high-frequency assimilation of mesonet surface DA on ensemble forecasts of CI initialized with ensemble Kalman filter (EnKF) analyses of the 29 May 2012 convective event over the southern Great Plains.

Mesonet and conventional surface observations were assimilated every 5 min for 3 h from 1800 to 2100 UTC and 3-h ensemble forecasts were produced. Forecasts of CI timing and location were improved by assimilating the surface datasets in comparison to experiments where mesonet data were withheld. This primarily occurred due to a more accurate representation of the boundary layer moisture profile across the domain, especially in the vicinity of a dryline and stationary boundary. Ensemble forecasts produced by assimilating surface observations at hourly intervals, instead of every 5 min, showed only minor improvements in CI.

The 5-min assimilation of mesonet data improved forecasts of the placement and timing of CI for this particular event due to the ability of mesonet data to capture rapidly evolving mesoscale features and to constrain model biases, particularly surface moisture errors, during the cycling period.

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Craig S. Schwartz and Ryan A. Sobash

Abstract

“Neighborhood approaches” have been used in two primary ways to postprocess and verify high-resolution ensemble output. While the two methods appear deceptively similar, they define events over different spatial scales and yield fields with different interpretations: the first produces probabilities interpreted as likelihood of event occurrence at the grid scale, while the second produces probabilities of event occurrence over spatial scales larger than the grid scale. Unfortunately, some studies have confused the two methods, while others did not acknowledge multiple possibilities of neighborhood approach application and simply stated, “a neighborhood approach was applied” without supporting details. Thus, this paper reviews applications of neighborhood approaches to convection-allowing ensembles in hopes of clarifying the two methods and their different event definitions. Then, using real data, it is demonstrated how the two approaches can yield statistically significantly different objective conclusions about model performance, underscoring the critical need for thorough descriptions of how neighborhood approaches are implemented and events are defined. The authors conclude by providing some recommendations for application of neighborhood approaches to convection-allowing ensembles.

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Jonathan Poterjoy, Ryan A. Sobash, and Jeffrey L. Anderson

Abstract

Particle filters (PFs) are Monte Carlo data assimilation techniques that operate with no parametric assumptions for prior and posterior errors. A data assimilation method introduced recently, called the local PF, approximates the PF solution within neighborhoods of observations, thus allowing for its use in high-dimensional systems. The current study explores the potential of the local PF for atmospheric data assimilation through cloud-permitting numerical experiments performed for an idealized squall line. Using only 100 ensemble members, experiments using the local PF to assimilate simulated radar measurements demonstrate that the method provides accurate analyses at a cost comparable to ensemble filters currently used in weather models. Comparisons between the local PF and an ensemble Kalman filter demonstrate benefits of the local PF for producing probabilistic analyses of non-Gaussian variables, such as hydrometeor mixing ratios. The local PF also provides more accurate forecasts than the ensemble Kalman filter, despite yielding higher posterior root-mean-square errors. A major advantage of the local PF comes from its ability to produce more physically consistent posterior members than the ensemble Kalman filter, which leads to fewer spurious model adjustments during forecasts. This manuscript presents the first successful application of the local PF in a weather prediction model and discusses implications for real applications where nonlinear measurement operators and nonlinear model processes limit the effectiveness of current Gaussian data assimilation techniques.

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Ryan A. Sobash, Glen S. Romine, and Craig S. Schwartz

Abstract

A feed-forward neural network (NN) was trained to produce gridded probabilistic convective hazard predictions over the contiguous United States. Input fields to the NN included 174 predictors, derived from 38 variables output by 497 convection-allowing model forecasts, with observed severe storm reports used for training and verification. These NN probability forecasts (NNPFs) were compared to surrogate-severe probability forecasts (SSPFs), generated by smoothing a field of surrogate reports derived with updraft helicity (UH). NNPFs and SSPFs were produced each forecast hour on an 80-km grid, with forecasts valid for the occurrence of any severe weather report within 40 or 120 km, and 2 h, of each 80-km grid box. NNPFs were superior to SSPFs, producing statistically significant improvements in forecast reliability and resolution. Additionally, NNPFs retained more large magnitude probabilities (>50%) compared to SSPFs since NNPFs did not use spatial smoothing, improving forecast sharpness. NNPFs were most skillful relative to SSPFs when predicting hazards on larger scales (e.g., 120 vs 40 km) and in situations where using UH was detrimental to forecast skill. These included model spinup, nocturnal periods, and regions and environments where supercells were less common, such as the western and eastern United States and high-shear, low-CAPE regimes. NNPFs trained with fewer predictors were more skillful than SSPFs, but not as skillful as the full-predictor NNPFs, with predictor importance being a function of forecast lead time. Placing NNPF skill in the context of existing baselines is a first step toward integrating machine learning–based forecasts into the operational forecasting process.

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Stanley B. Trier, Glen S. Romine, David A. Ahijevych, and Ryan A. Sobash

Abstract

A 50-member convection-allowing ensemble is used to examine effects of daytime PBL evolution and ambient flow interacting with modest terrain features on convection initiation (CI) in the lee of the Rocky Mountains. The examined case (4 June 2015) has isolated supercell storms that initiate during mid- to late afternoon along the northern portion of the Palmer Lake Divide, which is a ~0.5-km-deep zonally oriented terrain feature in east-central Colorado that extends eastward from the Rocky Mountains. To diagnose factors most crucial to storm development, two 10-member subensembles are constructed from the full 50-member ensemble. One subensemble (STRONG) has storm locations with mature storm intensities, and average timing of CI similar to that observed. The other subensemble (WEAK) has fewer storms, with generally weaker intensity, and delayed CI. Environmental composites constructed from these subensembles reveal a stronger surface horizontal convergence zone and moisture gradient in STRONG, resulting from 2–3.5 m s−1 stronger southerly winds on the south flank of the convergence zone. The stronger southerlies result from accelerated PBL growth and momentum mixing in the presence of strong low-to-midtropospheric vertical shear, which is facilitated by reduced above-PBL static stability in the composite STRONG initial condition. Stronger time-averaged low-to-midtropospheric upward motion coincides with the surface convergence zone in STRONG, and individual CI locations occur at the northeastern edge of the composite vertical motion maximum. Trajectory analysis with STRONG members confirms that the CI locations are consistent with large vertical displacements, and corresponding relative humidity increases leading to decreases in convective inhibition, as the southerly airstream ascends across the convergence zone.

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Stanley B. Trier, Glen S. Romine, David A. Ahijevych, Ryan A. Sobash, and Manda B. Chasteen

Abstract

A 50-member convection-allowing ensemble was used to examine environmental factors influencing afternoon convection initiation (CI) and subsequent severe weather on 5 April 2017 during intensive observing period (IOP) 3b of the Verification of the Origins of Rotation in Tornadoes Experiment in the Southeast (VORTEX-SE). This case produced several weak tornadoes (rated EF1 or less), and numerous reports of significant hail (diameter ≥ 2 in.; ≥~5 cm), ahead of an eastward-moving surface cold front over eastern Alabama and southern Tennessee. Both observed and simulated CI was facilitated by mesoscale lower-tropospheric ascent maximized several tens of kilometers ahead of the cold-frontal position, and the simulated mesoscale ascent was linked to surface frontogenesis in the ensemble mean. Simulated maximum 2–5 km AGL updraft helicity (UHmax) was used as a proxy for severe-weather-producing mesocyclones, and considerable variability in UHmax occurred among the ensemble members. Ensemble members with UHmax > 100 m2 s−2 had stronger mesoscale ascent than in members with UHmax < 75 m2 s−2, which facilitated timelier CI by producing greater adiabatic cooling and moisture increases above the PBL. After CI, storms in the larger UHmax members moved northeastward toward a mesoscale region with larger convective available potential energy (CAPE) than in smaller UHmax members. The CAPE differences among members were influenced by differences in the location of an antecedent mesoscale convective system, which had a thermodynamically stabilizing influence on the environment toward which storms were moving. Despite providing good overall guidance, the model ensemble overpredicted severe weather likelihoods in northeastern Alabama, where comparisons with VORTEX-SE soundings revealed a positive CAPE bias.

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