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Evan S. Bentley, Richard L. Thompson, Barry R. Bowers, Justin G. Gibbs, and Steven E. Nelson

Abstract

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

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Sebastian S. Harkema, Emily B. Berndt, John R. Mecikalski, and Alana Cordak

Abstract

Using gridded and interpolated Derived Motion Winds from the Advanced Baseline Imager (ABI), a Lagrangian cloud-feature tracking technique was developed to create, and document trajectories associated with electrified snowfall and changes in cloud characteristics leading up to the initiation of lightning, respectively. This study implemented the thundersnow detection algorithm and defined thundersnow initiation (TSI) as the first group in a flash detected by the Geostationary Lightning Mapper when snow was occurring. Ten ABI channels and four multispectral (e.g., red-green-blue–RGB) composites were analyzed to investigate characteristics that lead up to TSI for 16,644 thundersnow (TSSN) flashes. From the 10.3 μm channel, TSI trajectories were associated with a median decrease of 12.2 K in brightness temperature (TB) one hour prior to TSI. Decreases in the reflectance component of the 3.9 μm channel indicated that TSI trajectories were associated with ice crystal collisions and/or particle settling at cloud top. The Nighttime Microphysics, Day Cloud Phase Distinction, Differential Water Vapor, and Airmass RGBs were examined to evaluate the microphysical and environmental changes prior to TSI. For daytime TSI trajectories, the predominant colors associated with the Day Cloud Phase Distinction RGB transitioned from cyan to yellow/green, physically representing cloud growth and glaciation at cloud top. Gold/orange hues in the Differential Water Vapor RGB indicated that some trajectories were associated with dry upper-level air masses prior to TSI. The analysis of ABI characteristics prior to TSI, and subsequently relating those characteristics to physical processes, inherently increases the fundamental understanding and ability to forecast TSI; thus, providing additional lead-time into changes in surface conditions (i.e., snowfall rates).

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Qian Zhou, Lei Chen, Wansuo Duan, Xu Wang, Ziqing Zu, Xiang Li, Shouwen Zhang, and Yunfei Zhang

Abstract

Using the latest operational version of the ENSO forecast system from the National Marine Environmental Forecasting Center (NMEFC) of China, ensemble forecasting experiments are performed for El Niño-Southern Oscillation (ENSO) events that occurred from 1997 to 2017 by generating initial perturbations of the conditional nonlinear optimal perturbation (CNOP) and Climatically relevant Singular Vector (CSV) structures. It is shown that when the initial perturbation of the leading CSV structure in the ensemble forecast of the CSVs-scheme is replaced by those of the CNOP structure, the resulted ensemble ENSO forecasts of the CNOP+CSVs-scheme tend to possess a larger spread than the forecasts obtained with the CSVs-scheme alone, leading to a better match between the root mean square error and the ensemble spread, a more reasonable Talagrand diagram and an improved Brier skill score (BSS). All these results indicate that the ensemble forecasts generated by the CNOP+CSVs-scheme can improve both the accuracy of ENSO forecasting and the reliability of the ensemble forecasting system. Therefore, ENSO ensemble forecasting should consider the effect of nonlinearity on the ensemble initial perturbations to achieve a much higher skill. It is expected that fully nonlinear ensemble initial perturbations can be sufficiently yielded to produce ensemble forecasts for ENSO, finally improving the ENSO forecast skill to the greatest possible extent. The CNOP will be a useful method to yield fully nonlinear optimal initial perturbations for ensemble forecasting.

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Rachel Prudden, Niall Robinson, Peter Challenor, and Richard Everson

Abstract

Downscaling aims to link the behaviour of the atmosphere at fine scales to properties measurable at coarser scales, and has the potential to provide high resolution information at a lower computational and storage cost than numerical simulation alone. This is especially appealing for targeting convective scales, which are at the edge of what is possible to simulate operationally. Since convective scale weather has a high degree of independence from larger scales, a generative approach is essential. We here propose a statistical method for downscaling moist variables to convective scales using conditional Gaussian random fields, with an application to wet bulb potential temperature (WBPT) data over the UK. Our model uses an adaptive covariance estimation to capture the variable spatial properties at convective scales. We further propose a method for the validation, which has historically been a challenge for generative models.

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SINCLAIR CHINYOKA, THIERRY HEDDE, and GERT-JAN STEENEVELD

Abstract

This study improves surface wind predictions in an unresolved valley using an artificial neural network (ANN). Forecasting winds in complex terrain with a mesoscale model is challenging. This study assesses the quality of 3-km wind forecasts by the Weather Research and Forecasting (WRF) model and the potential of post-processing by an ANN within the 1-2 km wide Cadarache Valley in southeast France. Operational wind forecasts for 110m above ground level and the near-surface vertical potential temperature gradient with a lead time of 24-48h were used as ANN input. Observed horizontal wind components at 10m within the valley were used as targets during ANN training. We use the Directional ACCuracy (DACC 45, wind direction error ≤ 45°) and mean absolute error to evaluate the WRF direct model output and the ANN results. By post-processing, the score for DACC 45 improves from 56% in the WRF direct model output to 79% after applying the ANN. Furthermore, the ANN performed well during the day and night, but poorly during the morning and afternoon transitions. The ANN improves the DACC 45 at 10m even for poor WRF forecasts (direction bias ≥ 45°) from 42% to 72%. A shorter lead time and finer grid spacing (1 km) showed negligible impact which suggests that a 3 km grid spacing and a 24-48h lead time is effective and relatively cheap to apply. We find that WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. The ANN post-treatment consistently improves the wind forecast for all stability classes to a DACC 45 of about 80%. The study demonstrates the ability to improve Cadarache valley wind forecasts using an ANN as post-processing for WRF daily forecasts.

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Timothy Olander, Anthony Wimmers, Christopher Velden, and James P. Kossin

Abstract

Several simple and computationally inexpensive machine learning models are explored that can use Advanced Dvorak Technique (ADT) retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (Version 9.0) TC analysis parameters and operational TC forecast center Best Track data sets from 2005-2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 and 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced Advanced Dvorak Technique (AiDT). The 2017 and 2018 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt, respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 data set in the North Atlantic, East/Central Pacific, Northwest Pacific, South Pacific/Indian, and North Indian ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 data set. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017 and 2018 data sets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

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Vittorio A. Gensini, Cody Converse, Walker S. Ashley, and Mateusz Taszarek

Abstract

Previous studies have identified environmental characteristics that skillfully discriminate between severe and significant-severe weather events, but they have largely been limited by sample size and/or population of predictor variables. Given the heightened societal impacts of significant-severe weather, this topic was revisited using over 150 000 ERA5 reanalysis-derived vertical profiles extracted at the grid-point nearest—and just prior to—tornado and hail reports during the period 1996–2019. Profiles were quality-controlled and used to calculate 84 variables. Several machine learning classification algorithms were trained, tested, and cross-validated on these data to assess skill in predicting severe or significant-severe reports for tornadoes and hail. Random forest classification outperformed all tested methods as measured by cross-validated critical success index scores and area under the receiver operating characteristic curve values. In addition, random forest classification was found to be more reliable than other methods and exhibited negligible frequency bias. The top three most important random forest classification variables for tornadoes were wind speed at 500 hPa, wind speed at 850 hPa, and 0–500-m storm-relative helicity. For hail, storm-relative helicity in the 3–6 km and -10 to -30 °C layers, along with 0–6-km bulk wind shear, were found to be most important. A game theoretic approach was used to help explain the output of the random forest classifiers and establish critical feature thresholds for operational nowcasting and forecasting. A use case of spatial applicability of the random forest model is also presented, demonstrating the potential utility for operational forecasting. Overall, this research supports a growing number of weather and climate studies finding admirable skill in random forest classification applications.

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Rochelle P. Worsnop, Michael Scheuerer, Francesca Di Giuseppe, Christopher Barnard, Thomas M. Hamill, and Claudia Vitolo

Abstract

Wildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

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Michael Maier-Gerber, Andreas H. Fink, Michael Riemer, Elmar Schoemer, Christoph Fischer, and Benedikt Schulz

Abstract

While previous research on sub-seasonal tropical cyclone (TC) occurrence has mostly focused on either the validation of numerical weather prediction (NWP) models, or the development of statistical models trained on past data, the present study combines both approaches to a statistical–dynamical model for probabilistic forecasts in the North Atlantic basin. Although state-of-the-art NWP models have been shown to lack predictive skill with respect to sub-seasonal weekly TC occurrence, they may predict the environmental conditions sufficiently well to generate predictors for a statistical model. Therefore, an extensive predictor set was generated, including predictor groups representing the climatological seasonal cycle (CSC), oceanic, and tropical conditions, tropical wave modes, as well as extratropical influences, respectively. The developed hybrid forecast model is systematically validated for the Gulf of Mexico and Central Main Development Region (MDR) for lead times up to five weeks. Moreover, its performance is compared against a statistical approach trained on past data, as well as against different climatological and NWP benchmarks. For sub-seasonal lead times, the CSC models are found to outperform the NWP models, which quickly loose skill within the first two forecast weeks, even in case of recalibration. The statistical models trained on past data increase skill over the CSC models, whereas even greater improvements in skill are gained by the hybrid approach out to week five. The vast majority of the additional sub-seasonal skill in the hybrid model, relative to the CSC model, could be attributed to the tropical (oceanic) conditions in the Gulf of Mexico (Central MDR).

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Makenzie J. Krocak, Jinan N. Allan, Joseph T. Ripberger, Carol L. Silva, and Hank C. Jenkins-Smith

Abstract

Nocturnal tornadoes are challenging to forecast and even more challenging to communicate. Numerous studies have evaluated the forecasting challenges, but fewer have investigated when and where these events pose the greatest communication challenges. This study seeks to evaluate variation in confidence among U.S. residents in receiving and responding to tornado warnings by hour of day. Survey experiment data come from the Severe Weather and Society Survey, an annual survey of U.S. adults. Results indicate that respondents are less confident about receiving warnings overnight, specifically in the early morning hours [from 12:00 AM to 4:00 AM local time (0000–0400 LT)]. We then use the survey results to inform an analysis of hourly tornado climatology data. We evaluate where nocturnal tornadoes are most likely to occur during the time frame when residents are least confident in their ability to receive tornado warnings. Results show that the Southeast experiences the highest number of nocturnal tornadoes during the time period of lowest confidence, as well as the largest proportion of tornadoes in that time frame. Finally, we estimate and assess two multiple linear regression models to identify individual characteristics that may influence a respondent’s confidence in receiving a tornado between 12:00 AM and 4:00 AM. These results indicate that age, race, weather awareness, weather sources, and the proportion of nocturnal tornadoes in the local area relate to warning reception confidence. The results of this study should help inform policymakers and practitioners about the populations at greatest risk for challenges associated with nocturnal tornadoes. Discussion focuses on developing more effective communication strategies, particularly for diverse and vulnerable populations.

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