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Severe convection occurring in high-shear, low-CAPE (HSLC) environments is a common cool-season threat in the southeastern United States. Previous studies of HSLC convection document the increased operational challenges that these environments present compared to their high-CAPE counterparts, corresponding to higher false-alarm ratios and lower probability of detection for severe watches and warnings. These environments can exhibit rapid destabilization in the hours prior to convection, sometimes associated with the release of potential instability. Here, we use self-organizing maps (SOMs) to objectively identify environmental patterns accompanying HSLC cool-season severe events and associate them with variations in severe weather frequency and distribution. Large-scale patterns exhibit modest variation within the HSLC subclass, featuring strong surface cyclones accompanied by vigorous upper-tropospheric troughs and northward-extending regions of instability, consistent with prior studies. In most patterns, severe weather occurs immediately ahead of a cold front. Other convective ingredients, such as lower-tropospheric vertical wind shear, near-surface equivalent potential temperature (θ_e) advection, and the release of potential instability, varied more significantly across patterns. No single variable used to train SOMs consistently demonstrated differences in the distribution of severe weather occurrence across patterns. Comparison of SOMs based on upper and lower quartiles of severe occurrence demonstrated that the release of potential instability was most consistently associated with higher-impact events in comparison to other convective ingredients. Overall, we find that previously developed HSLC composite parameters reasonably identify high-impact HSLC events.

Even when atmospheric instability is not optimal for severe convective storms, in some situations they can still occur, presenting increased challenges to forecasters. These marginal environments may occur at night or during the cool season, when people are less attuned to severe weather threats. Here, we use a sorting algorithm to classify different weather patterns accompanying such storms, and we distinguish which specific patterns and weather system features are most strongly associated with severe storms. Our goals are to increase situational awareness for forecasters and to improve understanding of the processes leading to severe convection in marginal environments.

 

Abstract Recently, the use of machine learning in meteorology has increased greatly. While many machine learning methods are not new, university classes on machine learning are largely unavailable to meteorology students and are not required to become a meteorologist. The lack of formal instruction has contributed to perception that machine learning methods are “black boxes” and thus end-users are hesitant to apply the machine learning methods in their everyday workflow. To reduce the opaqueness of machine learning methods and lower hesitancy toward machine learning in meteorology, this paper provides a survey of some of the most common machine learning methods. A familiar meteorological example is used to contextualize the machine learning methods while also discussing machine learning topics using plain language. The following machine learning methods are demonstrated: linear regression, logistic regression, decision trees, random forest, gradient boosted decision trees, naïve Bayes, and support vector machines. Beyond discussing the different methods, the paper also contains discussions on the general machine learning process as well as best practices to enable readers to apply machine learning to their own datasets. Furthermore, all code (in the form of Jupyter notebooks and Google Colaboratory notebooks) used to make the examples in the paper is provided in an effort to catalyze the use of machine learning in meteorology. 

Extreme heat is investigated in a series of high‐resolution time‐slice global simulations comparing the current and late‐21st century climates. An increase in climate‐relative extreme heat is found in the region surrounding the Black Sea. Similarities between the synoptic‐scale flows in current and future heat events combined with a decrease in future summer precipitation suggests that the increased future severity stems from strengthened land‐atmosphere feedbacks driven primarily by the changes in precipitation. The resulting intensification of heat events beyond the mean warming driven by climate change could generate significant future heat hazards in vulnerable regions. Given the continental cool bias in the present‐day simulations, the resulting estimates of future extreme heat are likely to be conservative.

 

Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. To fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plain language format that is targeted to the operational meteorological community. This is the second paper in a pair that aim to serve as a machine learning resource for meteorologists. While the first paper focused on traditional machine learning methods (e.g., random forest), here a broad spectrum of neural networks and deep learning methods is discussed. Specifically, this paper covers perceptrons, artificial neural networks, convolutional neural networks, and U-networks. Like the Part I paper, this manuscript discusses the terms associated with neural networks and their training. Then the manuscript provides some intuition behind every method and concludes by showing each method used in a meteorological example of diagnosing thunderstorms from satellite images (e.g., lightning flashes). This paper is accompanied with an open-source code repository to allow readers to explore neural networks using either the dataset provided (which is used in the paper) or as a template for alternate datasets.

 

Tropical cyclones (TCs) undergoing extratropical transition (ET) can develop into intense cyclonic systems accompanied by high-impact weather in areas far removed from the original TC. This study presents an analysis of multiseasonal global simulations representative of present-day and projected future climates using the Model for Prediction Across Scales–Atmosphere (MPAS-A), with high resolution (15-km grid) throughout the Northern Hemisphere. TCs are tracked as minima in sea level pressure (SLP) accompanied by a warm core, and TC tracks are extended into the extratropical phase based on local minima in SLP and use of a cyclone phase space method. The present-day simulations adequately represent observed ET characteristics such as frequency, location, and seasonal cycles throughout the Northern Hemisphere. The most significant changes in future ET events occur in the North Atlantic (NATL) basin. Here, a more favorable background environment, a shift toward stronger TC warm cores in the lower troposphere, and a significant poleward shift in TC location lead to a ~40% increase in the number of NATL ET events and a ~6% increase in the fraction of TCs undergoing ET. This equates to approximately 1–2 additional ET events per year in this region. In the future simulations, ET in the NATL occurs markedly farther north by ~4°–5°N, and the resultant extratropical cyclones are stronger by ~6 hPa. These changes hold potentially important implications for areas directly affected by ET events, such as eastern North America, as well as for regions indirectly impacted by downstream effects, including western Europe.