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Timothy A. Coleman
,
Richard L. Thompson
, and
Gregory S. Forbes

Abstract

Recent articles have shown that the long-portrayed “tornado alley” in the central plains is not an accurate portrayal of current tornado frequency over the United States. The greatest tornado threat now covers parts of the eastern United States. This paper shows that there has been a true spatial shift in tornado frequency, dispelling any misconceptions caused by the better visibility of tornadoes in the Great Plains versus the eastern United States. Using F/EF1+ tornadoes (the dataset least affected by increasing awareness of tornado locations or by changing rating methods), a 1° × 1° grid, and data for the two 35-yr periods 1951–85 and 1986–2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado pathlength), tornado activity has shifted away from the Great Plains and toward the Midwest and Southeast United States. In addition, tornadoes have trended away from the warm season, especially the summer, and toward the cold season since 1951. Annual trends in tornadoes by season (winter, spring, summer, and autumn) confirm this. All of the increase in F/EF1+ tornadoes in the eastern United States is due to an increase in cold season tornadoes. Tornadoes in the western United States decreased 25% (from 8451 during 1951–85 to 6307 during 1986–2020), while tornadoes in the eastern United States. increased 12% (from 9469 during 1951–85 to 10 595 during 1986–2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined.

Significance Statement

This paper quantifies in many ways (tornadoes, tornado days, and pathlength) the geographical shift in tornadoes from the central to the eastern United States and from the warm season to the cold season, since 1951. Where and when tornadoes most frequently occur is significant not only for the research and operational meteorology communities but also for public perception and risk awareness. Some research studies have shown that tornado casualties are more likely in the eastern United States and the cold season because of preconceived notions of a “tornado alley” in the Great Plains and a “tornado season” in the spring. Publication of the results of this research might help ameliorate this problem.

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Mohammad Hadavi
and
Djordje Romanic

Abstract

Thunderstorms are recognized as one of the most disastrous weather threats in Canada because of their power to cause substantial damage to human-made structures and even result in fatalities. It is therefore essential for operational forecasting to diagnose thunderstorms that generate damaging downdrafts of negatively buoyant air, known as downbursts. This study develops several machine learning models to identify environments supportive of downbursts in Canada. The models are trained and evaluated using 38 convective parameters calculated based on ERA5 reanalysis vertical profiles prior to thunderstorms with (306 cases) and without (19 132 cases) downbursts across Canada. Various resampling techniques are implemented to adjust data imbalance. An increase in the performance of the random forest (RF) model is observed when the support vector machine synthetic minority oversampling technique is utilized. The RF model outperforms other tested models, as indicated by model performance metrics and calibration. Several model interpretability methods highlight that the RF model has learned physical trends and patterns from the input variables. Moreover, the thermodynamic parameters are deemed to have higher impacts on the model outcomes compared to parcel, kinematic, and composite variables. For example, a considerable rise in the downburst probability is detected with an increase in cold pool strength. This study serves as one of the earliest attempts toward the fledgling field of machine learning applications in weather forecasting systems in Canada. The findings suggest that the developed model has the potential to enhance the effectiveness of issuing severe thunderstorm warnings in Canada, although further assessment with operational meteorologists is needed to validate its practical application.

Significance Statement

Severe thunderstorms demand particular attention in forecasting as their outflow can pose a serious threat to both structures and human life. This study uses machine learning techniques to predict whether or not a thunderstorm generates a damaging outflow in Canada. Atmospheric conditions that could trigger a severe thunderstorm are identified and discussed. Results show that the models have the potential to assist forecasters in better analyzing and predicting thunderstorms that generate destructive winds. Consequently, taking advantage of promising machine learning tools can yield more reliable forecasts of damaging thunderstorms, thereby mitigating the economic and societal burdens of these storms on Canadian communities.

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Carl G. Schmitt
,
Dragos Vas
,
Martin Schnaiter
,
Emma Järvinen
,
Lea Hartl
,
Telayna Wong
,
Victor Cassella
, and
Martin Stuefer

Abstract

A three-winter study has been conducted to better understand the relationship between atmospheric conditions and ice fog or diamond dust microphysics. Measurements were conducted east of downtown Fairbanks in interior Alaska during nonprecipitating conditions. Atmospheric conditions were measured with several weather stations around the Fairbanks region and two meteorological temperature profiler instruments (ATTEX MTP-5HE and MTP-5PE). Near-surface ice particle microphysical observations were conducted with the Particle Phase Discriminator mark 2, Karlsruhe edition (PPD-2K), instrument, which measures particles from 8 to 112 μm (sphere equivalent). Panoramic camera images were captured and saved every 10 min throughout the campaign for visual assessment of atmospheric conditions. Machine learning was used to classify both cloud particle microphysical characteristics from the PPD-2K data and to categorize boundary layer conditions using the panoramic camera images. For panoramic camera images, data were categorized as cloudy, clear, fog, snowing, and a nearby power plant plume. For the PPD-2K machine learning study, the scattering pattern images were used to identify rough surface, pristine, sublimating, and spherical particles. Three additional categories were used to identify indeterminant or saturated images. These categories and categories derived from weather station data (e.g., temperature ranges) are used to quantify ice microphysical properties under different conditions. For the complete microphysical dataset, pristine plates or columns accounted for 15.5%, 16.3% appeared to be sublimating particles, and 43.4% were complex particles with either rough surfaces or multiple branches. Although the temperature was as warm as −20°C during measurements, only 1.3% of the particles were classified as liquid.

Significance Statement

Boundary layer ice particles are frequently present in the near-surface atmosphere when surface temperatures drop below −20°C. Substantial human impacts can occur due to visibility degradation and deposition of particles on surfaces. Understanding particle shape, size, and phase (liquid or solid) is important for understanding those impacts. This study presents the results of a 3-yr measurement campaign in Fairbanks, Alaska, in which we relate ice particle characteristics to lower atmospheric conditions. Results should improve weather forecasting and hazard prediction.

Open access
Michael T. Kiefer
,
Shiyuan Zhong
,
Joseph J. Charney
,
Xindi Bian
,
Warren E. Heilman
, and
Joseph Seitz

Abstract

Broadly speaking, prediction of the negative impacts of prescribed fire on air quality is limited by gaps in our understanding of the underlying fire, fuels, and atmospheric processes. These knowledge gaps hinder our ability to accurately predict smoke concentration distributions, leading to unintended smoke intrusions into nearby communities and subsequent threats to public health and safety. In this study, numerical simulations are performed using the Flexible Particle Weather Research and Forecasting (FLEXPART-WRF) Model, a Lagrangian particle dispersion model, with particle motion driven by output from a full-physics atmospheric model with a forest canopy submodel and 10-m horizontal grid spacing [Advanced Regional Prediction System (ARPS)-CANOPY], to address two research questions. First, what is the relationship between near-fire (within ∼50–150 m of fire) smoke concentration distribution and (i) vertical canopy structure and (ii) fire heat source strength? Second, what roles do mean transport (i.e., transport by the mean wind) and turbulent diffusion play in shaping the near-fire smoke concentration distribution? To address these questions, simulations are run with 25 combinations of plant area density profile and fire sensible heat flux magnitude, and smoke is represented by particles with diameters ≤ 2.5 μm (PM2.5). Results show that near-fire PM2.5 concentration distribution is primarily controlled by vertical canopy structure, with fire heat source strength primarily controlling the PM2.5 concentration magnitude. Analysis of the underlying ARPS-CANOPY variables driving the FLEXPART-WRF particle dispersion helps elucidate the roles of mean transport and turbulent diffusion. In total, the study findings suggest that the vertical distribution of canopy vegetation and fire heat source strength are important factors influencing PM2.5 dispersion and concentration distribution near low-intensity fires.

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Jangho Lee

Abstract

This study utilizes hourly land surface temperature (LST) data from the Geostationary Operational Environmental Satellite (GOES) to analyze the seasonal and diurnal characteristics of surface urban heat island intensity (SUHII) across 120 largest U.S. cities and their surroundings. Distinct patterns emerge in the classification of seasonal daytime SUHII and nighttime SUHII. Specifically, the enhanced vegetation index (EVI) and albedo (ALB) play pivotal roles in influencing these temperature variations. The diurnal cycle of SUHII further reveals different trends, suggesting that climate conditions, urban and nonurban land covers, and anthropogenic activities during nighttime hours affect SUHII peaks. Exploring intracity LST dynamics, the study reveals a significant correlation between urban intensity (UI) and LST, with LST rising as UI increases. Notably, populations identified as more vulnerable by the social vulnerability index (SVI) are found in high UI regions. This results in discernible LST inequality, where the more vulnerable communities are under higher LST conditions, possibly leading to higher heat exposure. This comprehensive study accentuates the significance of tailoring city-specific climate change mitigation strategies, illuminating LST variations and their intertwined societal implications.

Open access
Hong Wang
,
Liang Gao
,
Lei Zhu
,
Lulu Zhang
, and
Jiahao Wu

Abstract

Accurately assessing cyclone intensity changes due to global warming is crucial for predicting and mitigating sequential hazards. This study develops a high-resolution, fully coupled air–sea model to investigate the impact of global warming on Supertyphoon Mangkhut (2018). A numerical sensitivity analysis is conducted using the pseudo–global warming (PGW) technique based on multiple global climate models (GCMs) from phase 6 of Coupled Model Intercomparison Project (CMIP6). Under ocean warming scenarios, the increasing average sea surface temperature (SST) by 2.26°, 2.44°, 3.45°, and 4.53°C results in reductions in the minimum sea level pressure by 9.2, 10.6, 15.7, and 19.4 hPa, respectively, compared to the original state of Typhoon Mangkhut. Rising SST increases the turbulent heat flux; to be specific, an average SST increase of 2.26°–4.53°C changes the turbulent heat flux into 177%–272% of the original value. Besides, stronger winds enhance SST cooling, including upwelling and entrainment, leading to an increase in the mixed layer depth (MLD). Tropical cyclone heat potential (TCHP) tends to be enhanced under the combined influences as the SST rises. An average increase in the SST of 2.26°, 2.44°, 3.45°, and 4.53°C leads to an increase in the TCHP of 9.94%, 9.85%, 14.67%, and 15.30%, respectively. However, future changes in atmospheric temperature and humidity will moderate typhoon intensification induced by ocean warming. Considering atmospheric conditions, the maximum wind speed decreases by approximately 10% compared to only considering ocean warming. Nevertheless, typhoon intensity is projected to strengthen under future climate change.

Significance Statement

This study examines the role of global warming in typhoon intensity and the response of typhoon events to changes in the oceanic thermal structure. Sensitivity experiments considering future warming climates are conducted using a fully coupled air–sea numerical model. An average increase in sea surface temperature (SST) by 4.53°C can lead to a reduction in the minimum sea level pressure by 19.4 hPa. Ocean warming enhances oceanic mixing, potentially increasing the availability of heat energy for typhoon’s development (i.e., an average increase in SST by 4.53°C leads to a 15.30% increase in heat energy). However, future changes in atmospheric temperature and humidity will moderate the intensification of typhoons induced by ocean warming. These results are expected to provide information for assessing the future changes in typhoon intensity under a warming climate, which is important for predicting and reducing sequential risks.

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