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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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Kosuke Ono

Abstract

This study extends Bayesian model averaging (BMA) to a form suitable for time series forecasts. BMA is applied to a three-member ensemble for temperature forecasts with a 1-h interval time series at specific stations. The results of such an application typically have problematic characteristics. BMA weights assigned to ensemble members fluctuate widely within a few hours because BMA optimizations are independent at each lead time, which is incompatible with the spatiotemporal continuity of meteorological phenomena. To ameliorate this issue, a degree of correlation among different lead times is introduced by the extension of latent variables to lead times adjacent to the target lead time for the calculation of BMA weights and variances. This extension approach stabilizes the BMA weights, improving the performance of deterministic and probabilistic forecasts. Also, an investigation of the effects of this extension technique on the shapes of forecasted probability density functions showed that the extension approach offers advantages in bimodal cases. This extension technique may show promise in other applications to improve the performance of forecasts by BMA.

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Callie McNicholas and Clifford F. Mass

Abstract

With over a billion smartphones capable of measuring atmospheric pressure, a global mesoscale surface pressure network based on smartphone pressure sensors may be possible if key technical issues are solved, including collection technology, privacy, and bias correction. To overcome these challenges, a novel framework was developed for the anonymization and bias correction of smartphone pressure observations (SPOs) and was applied to billions of SPOs from the Weather Company (IBM). Bias correction using machine learning reduced the errors of anonymous (ANON) SPOs and uniquely identifiable (UID) SPOs by 43% and 57%, respectively. Applying multiresolution kriging, gridded analyses of bias-corrected smartphone pressure observations were made for an entire year (2018), using both anonymized (ANON) and nonanonymized (UID) observations. Pressure analyses were also generated using conventional Meteorological Assimilation Data Ingest System (MADIS) surface pressure networks. Relative to MADIS analyses, ANON and UID smartphone analyses reduced domain-average pressure errors by 21% and 31%, respectively. The performance of smartphone and MADIS pressure analyses was evaluated for two high-impact weather events: the landfall of Hurricane Michael and a long-lived mesoscale convective system. For these two events, both anonymized and nonanonymized smartphone pressure analyses better captured the spatial structure and temporal evolution of mesoscale pressure features than the MADIS analyses.

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Sean Ernst, Joe Ripberger, Makenzie J. Krocak, Hank Jenkins-Smith, and Carol Silva

Abstract

Although severe weather forecast products, such as the Storm Prediction Center (SPC) convective outlook, are much more accurate than climatology at day-to-week time scales, tornadoes and severe thunderstorms claim dozens of lives and cause billions of dollars in damage every year. While the accuracy of this outlook has been well documented, less work has been done to explore the comprehension of the product by nonexpert users like the general public. This study seeks to fill this key knowledge gap by collecting data from a representative survey of U.S. adults in the lower 48 states about their use and interpretation of the SPC convective outlook. Participants in this study were asked to rank the words and colors used in the outlook from least to greatest risk, and their answers were compared through visualizations and statistical tests across multiple demographics. Results show that the U.S. public ranks the outlook colors similarly to their ordering in the outlook but switches the positions of several of the outlook words as compared to the operational product. Logistic regression models also reveal that more numerate individuals more correctly rank the SPC outlook words and colors. These findings suggest that the words used in the convective outlook may confuse nonexpert users, and that future work should continue to use input from public surveys to test potential improvements in the choice of outlook words. Using more easily understood words may help to increase the outlook’s decision support value and potentially reduce the harm caused by severe weather events.

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Jinxiao Li, Qing Bao, Yimin Liu, Guoxiong Wu, Lei Wang, Bian He, Xiaocong Wang, Jing Yang, Xiaofei Wu, and Zili Shen

Abstract

There is a distinct gap between tropical cyclone (TC) prediction skill and the societal demand for accurate predictions, especially in the western Pacific (WP) and North Atlantic (NA) basins, where densely populated areas are frequently affected by intense TC events. In this study, seasonal prediction skill for TC activity in the WP and NA of the fully coupled FGOALS-f2 V1.0 dynamical prediction system is evaluated. In total, 36 years of monthly hindcasts from 1981 to 2016 were completed with 24 ensemble members. The FGOALS-f2 V1.0 system has been used for real-time predictions since June 2017 with 35 ensemble members, and has been operationally used in the two operational prediction centers of China. Our evaluation indicates that FGOALS-f2 V1.0 can reasonably reproduce the density of TC genesis locations and tracks in the WP and NA. The model shows significant skill in terms of the TC number correlation in the WP (0.60) and the NA (0.61) from 1981 to 2015; however, the model underestimates accumulated cyclone energy. When the number of ensemble members was increased from 2 to 24, the correlation coefficients clearly increased (from 0.21 to 0.60 in the WP, and from 0.18 to 0.61 in the NA). FGOALS-f2 V1.0 also successfully reproduces the genesis potential index pattern and the relationship between El Niño–Southern Oscillation and TC activity, which is one of the dominant contributors to TC seasonal prediction skill. However, the biases in large-scale factors are barriers to the improvement of the seasonal prediction skill, e.g., larger wind shear, higher relative humidity, and weaker potential intensity of TCs. For real-time predictions in the WP, FGOALS-f2 V1.0 demonstrates a skillful prediction for track density in terms of landfalling TCs, and the model successfully forecasts the correct sign of seasonal anomalies of landfalling TCs for various regions in China.

Open access
Charles R. Sampson, Efren A. Serra, John A. Knaff, and Joshua H. Cossuth

Abstract

The U.S. Navy is keenly interested in analyses and predictions of waves at sea due to their effects on important tasks such as shipping, base preparedness, and disaster relief. U.S. Tropical Cyclone (TC) Forecast Centers routinely disseminate wind probabilities consistent with official TC forecasts worldwide, but do not do the same for wave forecasts. These probabilities are especially important at longer leads where TC forecast accuracy diminishes. This work describes global wave probabilities consistent with both the official TC forecasts and their wind probabilities. Real-time runs for 84 TCs between May 2018 and March 2019, with probabilities generated for 12- and 18-ft significant wave heights are used to calculate verification statistics. This results in 347, 319, 261, 214, 155, and 112 verification cases at lead times of 1, 2, 3, 4, and 5 days where each verification case consists of a 20° × 20° latitude–longitude grid around the verifying TC position. When compared with wave probabilities generated solely by a global numerical weather prediction model, the wind probability–based algorithm demonstrates improved consistency with official forecasts and provides additional benefits. Those benefits include an improved capability to discriminate between 12- and 18-ft significant wave events and nonevents. The verification statistics also shows that the wind probability–based algorithm has a consistent high bias. How these biases can be reduced in future efforts is also discussed.

Open access
Makenzie J. Krocak, Matthew D. Flournoy, and Harold E. Brooks

Abstract

Increasing tornado warning skill in terms of the probability of detection and false-alarm ratio remains an important operational goal. Although many studies have examined tornado warning performance in a broad sense, less focus has been placed on warning performance within subdaily convective events. In this study, we use the NWS tornado verification database to examine tornado warning performance by order-of-tornado within each convective day. We combine this database with tornado reports to relate warning performance to environmental characteristics. On convective days with multiple tornadoes, the first tornado is warned significantly less often than the middle and last tornadoes. More favorable kinematic environmental characteristics, like increasing 0–1-km shear and storm-relative helicity, are associated with better warning performance related to the first tornado of the convective day. Thermodynamic and composite parameters are less correlated with warning performance. During tornadic events, over one-half of false alarms occur after the last tornado of the day decays, and false alarms are 2 times as likely to be issued during this time as before the first tornado forms. These results indicate that forecasters may be better “primed” (or more prepared) to issue warnings on middle and last tornadoes of the day and must overcome a higher threshold to warn on the first tornado of the day. To overcome this challenge, using kinematic environmental characteristics and intermediate products on the watch-to-warning scale may help.

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Matthew T. Bray, Steven M. Cavallo, and Howard B. Bluestein

Abstract

Midlatitude jet streaks are known to produce conditions broadly supportive of tornado outbreaks, including forcing for large-scale ascent, increased wind shear, and decreased static stability. Although many processes may initiate a jet streak, we focus here on the development of jet maxima by interactions between the polar jet and tropopause polar vortices (TPVs). Originating from the Arctic, TPVs are long-lived circulations on the tropopause, which can be advected into the midlatitudes. We hypothesize that when these vortices interact with the jet, they may contribute supplemental forcing for ascent and shear to tornado outbreaks, assuming other environmental conditions supportive of tornado development exist. Using a case set of significant tornado outbreak days from three states—Oklahoma, Illinois, and Alabama—we show that a vortex–jet streak structure is present (within 1250 km) in around two-thirds of tornado outbreaks. These vortices are commonly Arctic in origin (i.e., are TPVs) and are advected through a consistent path of entry into the midlatitudes in the week before the outbreak, moving across the northern Pacific and into the Gulf of Alaska before turning equatorward along the North American coast. These vortices are shown to be more intense and longer-lived than average. We further demonstrate that statistically significant patterns of wind shear, quasigeostrophic forcing for ascent, and low static stability are present over the outbreak regions on the synoptic scale. In addition, we find that TPVs associated with tornadic events occur most often in the spring and are associated with greater low-level moisture when compared to non-tornadic TPV cases.

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Yanshuang Xie, Shaoping Shang, Jinquan Chen, Feng Zhang, Zhigan He, Guomei Wei, Jingyu Wu, Benlu Zhu, and Yindong Zeng

Abstract

Accurate storm surge forecasts provided rapidly could support timely decision-making with consideration of tropical cyclone (TC) forecasting error. This study developed a fast storm surge ensemble prediction method based on TC track probability forecasting and searching optimization of a numerical scenario database (SONSD). In a case study of the Fujian Province coast (China), a storm surge scenario database was established using numerical simulations generated by 93 150 hypothetical TCs. In a GIS-based visualization system, a single surge forecast representing 2562 distinct typhoon tracks and the occurrence probability of overflow of seawalls along the coast could be achieved in 1–2 min. Application to the cases of Typhoon Soudelor (2015) and Typhoon Maria (2018) demonstrated that the proposed method is feasible and effective. Storm surge calculated by SONSD had excellent agreement with numerical model results (i.e., mean MAE and RMSE: 7.1 and 10.7 cm, respectively, correlation coefficient: >0.9). Tide prediction also performed well with MAE/RMSE of 9.7/11.6 cm versus the harmonic tide, and MAE/RMSE of phase prediction for all high waters of 0.25/0.31 h versus observations. The predicted high-water level was satisfactory (MAE of 10.8 cm versus observations) when the forecasted and actual positions of the typhoon were close. When the forecasted typhoon position error was large, the ensemble surge prediction effectively reduced prediction error (i.e., the negative bias of −58.5 cm reduced to −5.2 cm versus observations), which helped avoid missed alert warnings. The proposed method could be applied in other regions to provide rapid and accurate decision-making support for government departments.

Open access
Aaron J. Hill and Russ S. Schumacher

Abstract

Approximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting Model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding—including flash flood reports and 24-h average recurrence intervals (ARIs)—are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-yr ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and Southwest United States, in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill—–poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation.

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