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Ryan D. Torn and Glen S. Romine

Abstract

The role of upstream subsynoptic forecast errors on forecasts of two different central Oklahoma severe convection events (19 and 31 May 2013) characterized by strong synoptic forcing during the Mesoscale Predictability Experiment (MPEX) are evaluated by applying the ensemble-based sensitivity technique to WRF ensemble forecasts with explicit convection. During both cases, the forecast of the timing and intensity of convection over central Oklahoma is modulated by the southward extent of upstream midtropospheric potential vorticity anomalies that are moving through the base of a larger-scale upstream trough but pass by central Oklahoma prior to convective initiation. In addition, the convection forecasts are also sensitive to the position of lower-tropospheric boundaries, such that moving the boundaries in a manner that would lead to increased equivalent potential temperature over central Oklahoma prior to convective initiation leads to more precipitation. Statistical PV inversion and correlation calculations suggest that the midtropospheric PV and near-surface boundary sensitivities are not independent; the winds associated with the PV error can modulate the position of the lower-tropospheric boundary through advection in a manner consistent with the implied sensitivity. As a consequence, it appears that reducing the uncertainty in specific upstream subsynoptic features prior to convective initiation could improve subsequent forecasts of severe convection.

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Glen S. Romine and Robert B. Wilhelmson

Abstract

One of the most recognizable features associated with a well-organized tropical system are spiral rainbands. These quasi-stationary rainbands often extend hundreds of kilometers from the storm center and have been well described in the literature. Observational studies have since identified additional banding structures, including outward-propagating small-scale spiral bands. These rainbands may have considerable implications for “core type” tornadoes, local wind maxima associated with downburst damage swaths, as well as a role in overall hurricane dynamics. As such, here a numerical simulation of Hurricane Opal (1995) is examined with unprecedented resolution necessary to capture these small-scale spiral bands. Opal was an intense landfalling hurricane that demonstrated small-scale spiral banding features analogous to those observational studies. The scale and characteristics of the simulated bands are consistent with observed small-scale spiral banding of intense hurricanes. A varietal of Kelvin–Helmholtz instability combined with boundary layer shear is offered as the most plausible dynamical mechanism for the generation and maintenance of these propagating bands outward of the eyewall region.

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

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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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Craig S. Schwartz, Glen S. Romine, and David C. Dowell

Abstract

Using the Weather Research and Forecasting model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly configured EnKF analyses with 15-km horizontal grid spacing were also produced. At 0000 UTC, 15- and 3-km EnKF analyses initialized 36-h, 3-km, 10-member ensemble forecasts that were verified with a focus on precipitation. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental “blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter.

The EnKFs had stable climates with generally small biases, and precipitation forecasts initialized from 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best performance considering all times: blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF analyses and ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts.

This work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS, and results suggest blending high-resolution EnKF analyses with low-resolution global fields can potentially unify short-term and next-day convection-allowing ensemble forecast systems under a common framework.

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Glen S. Romine, Donald W. Burgess, and Robert B. Wilhelmson

Abstract

On 8 May 2003, a tornadic supercell tracked through portions of the Oklahoma City, Oklahoma, metropolitan area and produced violent damage along portions of its path. This storm passed through the dense in situ radar network in central Oklahoma and provided close-range operational, prototype polarimetric and terminal Doppler weather radar observations of the storm as it made the transition into the tornadic phase. The time-evolving polarimetric features were scrutinized with regard to storm morphology, particularly as related to the development of a rear-flank downdraft pulse within the storm immediately preceding the long-track tornado event. Two new polarimetric terms are introduced, the Z dr shield and K dp foot, along with a discussion of the orientation of the Z dr and K dp columns relative to midlevel rotation signatures. Storm downdraft and gust front characteristics are discussed relative to polarimetric fields and background environment characteristics. Highlighted for this event are a “warm” forward-flank downdraft and a particularly cold rear-flank downdraft. Emphasis is also placed on demonstrating key polarimetric field characteristics relative to traditional features at low and midlevels defined in familiar conceptual models of severe storms.

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Ryan D. Torn, Glen S. Romine, and Thomas J. Galarneau Jr.

Abstract

The sensitivity of convective forecasts along the Texas dryline to upstream forecast fields at earlier lead times is evaluated for two consecutive days (27–28 May) characterized by no clear synoptic forcing for convection initiation (CI) during the 2013 Mesoscale Predictability Experiment (MPEX) by applying the ensemble-based sensitivity technique to convection-allowing WRF ensemble forecasts. For both cases, the members with stronger convection are characterized by higher water vapor just above the top of the boundary layer, which is associated with lower convective inhibition (CIN) at the time of CI. Forecast convection is sensitive to the lower-tropospheric water vapor and zonal wind at earlier lead times farther south along the dryline, such that increasing the water vapor and/or making the wind more easterly is associated with more convection. For 28 May, the water vapor along the dryline is also sensitive to the convection that occurs around 0600 UTC, which leads to cold pool–induced surface divergence that subsequently shifts the dryline north or south. Ensemble members that correctly have decreased convection in the Texas Panhandle on 28 May have more accurate forecasts of water vapor and meridional wind with respect to dropwindsondes in the sensitive region 9 h prior to CI compared to members with more extensive convection. Reducing the 0-h water vapor within the sensitive region can suppress convection in members with extensive convection; however, increasing the 0-h water vapor does not lead to more convection in members without convection.

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Richard Rotunno, Glen S. Romine, and Howard B. Bluestein

Abstract

A recent study found that surface hodographs over the Great Plains of the United States turn in a counterclockwise direction with time. This observed turning is opposite of the clockwise turning observed (and expected, based on theory) at higher altitudes. Using a mesoscale forecast model, the same study shows that it has the same hodograph behavior as found in the observations. The study further shows that the reason for this anomalous counterclockwise turning is the decoupling of the surface layer from the boundary layer after sunset and its recoupling after sunrise. The present paper presents a simple model for this behavior by extending a recent analytical model for the diurnal oscillation to include the surface-layer effect. In addition, selected solution features are analyzed in terms of several of the nondimensional input parameters.

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Lili Lei, Jeffrey L. Anderson, and Glen S. Romine

Abstract

For ensemble-based data assimilation, localization is used to limit the impact of observations on physically distant state variables to reduce spurious error correlations caused by limited ensemble size. Traditionally, the localization value applied is spatially homogeneous. Yet there are potentially larger errors and different covariance length scales in precipitation systems, and that may justify the use of different localization functions for precipitating and nonprecipitating regions. Here this is examined using empirical localization functions (ELFs). Using output from an ensemble observing system simulation experiment (OSSE), ELFs provide estimates of horizontal and vertical localization for different observation types in regions with and without precipitation. For temperature and u- and υ-wind observations, the ELFs for precipitating regions are shown to have smaller horizontal localization scales than for nonprecipitating regions. However, the ELFs for precipitating regions generally have larger vertical localization scales than for nonprecipitating regions. The ELFs are smoothed and then applied in three additional OSSEs. Spatially homogeneous ELFs are found to improve performance relative to a commonly used localization function with compact support. When different ELFs are applied in precipitating and nonprecipitating regions, performance is further improved, but varying ELFs by observation type was not found to be as important. Imbalance in initial states caused by use of different localization functions is diagnosed by the domain-averaged surface pressure tendency. Forecasts from analyses with ELFs have smaller surface pressure tendencies than the standard localization, indicating improved initial balance with ELFs.

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Glen S. Romine, Craig S. Schwartz, Ryan D. Torn, and Morris L. Weisman

Abstract

Over the central Great Plains, mid- to upper-tropospheric weather disturbances often modulate severe storm development. These disturbances frequently pass over the Intermountain West region of the United States during the early morning hours preceding severe weather events. This region has fewer in situ observations of the atmospheric state compared with most other areas of the United States, contributing toward greater uncertainty in forecast initial conditions. Assimilation of supplemental observations is hypothesized to reduce initial condition uncertainty and improve forecasts of high-impact weather.

During the spring of 2013, the Mesoscale Predictability Experiment (MPEX) leveraged ensemble-based targeting methods to key in on regions where enhanced observations might reduce mesoscale forecast uncertainty. Observations were obtained with dropsondes released from the NSF/NCAR Gulfstream-V aircraft during the early morning hours preceding 15 severe weather events over areas upstream from anticipated convection. Retrospective data-denial experiments are conducted to evaluate the value of dropsonde observations in improving convection-permitting ensemble forecasts. Results show considerable variation in forecast performance from assimilating dropsonde observations, with a modest but statistically significant improvement, akin to prior targeted observation studies that focused on synoptic-scale prediction. The change in forecast skill with dropsonde information was not sensitive to the skill of the control forecast. Events with large positive impact sampled both the disturbance and adjacent flow, akin to results from past synoptic-scale targeting studies, suggesting that sampling both the disturbance and adjacent flow is necessary regardless of the horizontal scale of the feature of interest.

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Craig S. Schwartz, Glen S. Romine, Kathryn R. Fossell, Ryan A. Sobash, and Morris L. Weisman

Abstract

Precipitation forecasts from convection-allowing ensembles with 3- and 1-km horizontal grid spacing were evaluated between 15 May and 15 June 2013 over central and eastern portions of the United States. Probabilistic forecasts produced from 10- and 30-member, 3-km ensembles were consistently better than forecasts from individual 1-km ensemble members. However, 10-member, 1-km probabilistic forecasts usually were best, especially over the first 12 h and at rainfall rates ≥ 5.0 mm h−1 at later times. Further object-based investigation revealed that better 1-km forecasts at heavier rainfall rates were associated with more accurate placement of mesoscale convective systems compared to 3-km forecasts. The collective results indicate promise for 1-km ensembles once computational resources can support their operational implementation.

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