Approximately 11 years of reforecasts from NOAA’s Second-Generation Global Ensemble Forecast System Reforecast (GEFS/R) model are used to train a contiguous United States (CONUS)-wide gridded probabilistic prediction system for locally extreme precipitation. This system is developed primarily using the random forest (RF) algorithm. Locally extreme precipitation is quantified for 24-h precipitation accumulations in the framework of average recurrence intervals (ARIs), with two severity levels: 1- and 10-yr ARI exceedances. Forecasts are made from 0000 UTC forecast initializations for two 1200–1200 UTC periods: days 2 and 3, comprising, respectively, forecast hours 36–60 and 60–84. Separate models are trained for each of eight forecast regions and for each forecast lead time. GEFS/R predictors vary in space and time relative to the forecast point and include not only the quantitative precipitation forecast (QPF) output from the model, but also variables that characterize the meteorological regime, including winds, moisture, and instability. Numerous sensitivity experiments are performed to determine the effects of the inclusion or exclusion of different aspects of forecast information in the model predictors, the choice of statistical algorithm, and the effect of performing dimensionality reduction via principal component analysis as a preprocessing step. Overall, it is found that the machine learning (ML)-based forecasts add significant skill over exceedance forecasts produced from both the raw GEFS/R ensemble QPFs and from the European Centre for Medium-Range Weather Forecasts’ (ECMWF) global ensemble across almost all regions of the CONUS. ML-based forecasts are found to be underconfident, while raw ensemble forecasts are highly overconfident.
Eight years’ worth of day 1 and 4.5 years’ worth of day 2–3 probabilistic convective outlooks from the Storm Prediction Center (SPC) are converted to probability grids spanning the continental United States (CONUS). These results are then evaluated using standard probabilistic forecast metrics including the Brier skill score and reliability diagrams. Forecasts are gridded in two different ways: one with a high-resolution grid and interpolation between probability contours and another on an 80-km-spaced grid without interpolation. Overall, the highest skill is found for severe wind forecasts and the lowest skill is observed for tornadoes; for significant severe criteria, the opposite discrepancy is observed, with highest forecast skill for significant tornadoes and approximately no overall forecast skill for significant severe winds. Highest climatology-relative skill is generally observed over the central and northern Great Plains and Midwest, with the lowest—and often negative—skill seen in the West, southern Texas, and the Atlantic Southeast. No discernible year-to-year trend in skill was identified; seasonally, forecasts verified the best in the spring and late autumn and worst in the summer and early autumn. Forecasts are also evaluated in CAPE-versus-shear parameter space; forecasts struggle most in very low shear but also in high-shear, low-CAPE environments. In aggregate, forecasts for all variables verified more skillfully using interpolated probability grids, suggesting utility in interpreting forecasts as a continuous field. Forecast reliability results depend substantially on the interpretation of the forecast fields, but day 1 and day 2–3 tornado outlooks consistently exhibit an underforecast bias.
Three different statistical algorithms are applied to forecast locally extreme precipitation across the contiguous United States (CONUS) as quantified by 1- and 10-yr average recurrence interval (ARI) exceedances for 1200–1200 UTC forecasts spanning forecast hours 36–60 and 60–84, denoted, respectively, day 2 and day 3. Predictors come from nearly 11 years of reforecasts from NOAA’s Second-Generation Global Ensemble Forecast System Reforecast (GEFS/R) model and derive from a variety of thermodynamic and kinematic variables that characterize the meteorological regime in addition to the quantitative precipitation forecast (QPF) output from the ensemble. In addition to encompassing nine different atmospheric fields, predictors also vary in space and time relative to the forecast point. Distinct models are trained for eight different hydrometeorologically cohesive regions of the CONUS. One algorithm supplies the GEFS/R predictors directly to a random forest (RF) procedure to produce extreme precipitation forecasts; the second also employs RFs, but the predictors instead undergo principal component analysis (PCA), and extracted leading components are supplied to the RF. In the last algorithm, dimension-reduced predictors are supplied to a logistic regression (LR) algorithm instead of an RF. A companion paper investigated the quality of the forecasts produced by these models and other RF-based forecast models. This study is an extension of that work and explores the internals of these trained models and what physical and statistical insights they reveal about forecasting extreme precipitation from a global, convection-parameterized model.
While both tornadoes and flash floods individually present public hazards, when the two threats are both concurrent and collocated (referred to here as TORFF events), unique concerns arise. This study aims to evaluate the climatological and meteorological characteristics associated with TORFF events over the continental United States. Two separate datasets, one based on overlapping tornado and flash flood warnings and the other based on observations, were used to arrive at estimations of the instances when a TORFF event was deemed imminent and verified to have occurred, respectively. These datasets were then used to discern the geographical and meteorological characteristics of recent TORFF events. During 2008-14, TORFF events were found to be publicly communicated via overlapping warnings an average of 400 times per year, with a maximum frequency occurring in the lower Mississippi River valley. Additionally, 68 verified TORFF events between 2008 and 2013 were identified and subsequently classified based on synoptic characteristics and radar observations. In general, synoptic conditions associated with TORFF events were found to exhibit similar characteristics of typical tornadic environments, but the TORFF environment tended to be moister and have stronger synoptic-scale forcing for ascent. These results indicate that TORFF events occur with appreciable frequency and in complex meteorological scenarios. Furthermore, despite these identified differences, TORFF scenarios are not easily distinguishable from tornadic events that fail to produce collocated flash flooding, and present difficult challenges both from the perspective of forecasting and public communication.
During the Plains Elevated Convection at Night (PECAN) field campaign, 15 mesoscale convective system (MCS) environments were sampled by an array of instruments including radiosondes launched by three mobile sounding teams. Additional soundings were collected by fixed and mobile PECAN integrated sounding array (PISA) groups for a number of cases. Cluster analysis of observed vertical profiles established three primary preconvective categories: 1) those with an elevated maximum in equivalent potential temperature below a layer of potential instability; 2) those that maintain a daytime-like planetary boundary layer (PBL) and nearly potentially neutral low levels, sometimes even well after sunset despite the existence of a southerly low-level wind maximum; and 3) those that are potentially neutral at low levels, but have very weak or no southerly low-level winds. Profiles of equivalent potential temperature in elevated instability cases tend to evolve rapidly in time, while cases in the potentially neutral categories do not. Analysis of composite Rapid Refresh (RAP) environments indicate greater moisture content and moisture advection in an elevated layer in the elevated instability cases than in their potentially neutral counterparts. Postconvective soundings demonstrate significantly more variability, but cold pools were observed in nearly every PECAN MCS case. Following convection, perturbations range between −1.9 and −9.1 K over depths between 150 m and 4.35 km, but stronger, deeper stable layers lead to structures where the largest cold pool temperature perturbation is observed above the surface.
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