Occurrence Of Severe Weather Research Articles

Mesoscale weather influenced by auroral gravity waves contributing to conditional symmetric instability release?

Abstract. We consider possible influence on severe weather occurrence in the context of solar wind coupling to the magnetosphere–ionosphere–atmosphere system, mediated by aurorally excited atmospheric gravity waves. Solar wind high-speed streams from coronal holes cause intensifications of ionospheric currents at high latitudes launching gravity waves propagating in the upper and lower atmosphere. While these gravity waves reach the troposphere with much attenuated amplitudes, they can contribute to conditional symmetric instability release and intensification of storms. Severe weather events, including winter storms and heavy rainfall causing floods and flash floods, show a tendency to follow arrivals of solar wind high-speed streams from coronal holes. The ERA5 re-analysis is used to evaluate slantwise convective available potential energy and vertically integrated extent of realizable symmetric instability to assess the likelihood of slantwise convection in frontal zones of extratropical cyclones during severe snowstorms and flash floods. The observed low-level southerly winds and high wind shears in these regions are favorable conditions for over-reflection of down-going aurorally excited gravity waves potentially contributing to conditional symmetric instability release leading to slantwise convection and high-rate precipitation.

Mitochondrial Protein-Coding Gene Expression in the Lizard Sphenomorphus incognitus (Squamata:Scincidae) Responding to Different Temperature Stresses.

In the context of global warming, the frequency of severe weather occurrences, such as unexpected cold spells and heat waves, will grow, as well as the intensity of these natural disasters. Lizards, as a large group of reptiles, are ectothermic. Their body temperatures are predominantly regulated by their environment and temperature variations directly impact their behavior and physiological activities. Frequent cold periods and heat waves can affect their biochemistry and physiology, and often their ability to maintain their body temperature. Mitochondria, as the center of energy metabolism, are crucial for maintaining body temperature, regulating metabolic rate, and preventing cellular oxidative damage. Here, we used RT-qPCR technology to investigate the expression patterns and their differences for the 13 mitochondrial PCGs in Sphenomorphus incognitus (Squamata:Scincidae), also known as the brown forest skink, under extreme temperature stress at 4 °C, 8 °C, 34 °C, and 38 °C for 24 h, compared to the control group at 25 °C. In southern China, for lizards, 4 °C is close to lethal, and 8 °C induces hibernation, while 34/38 °C is considered hot and environmentally realistic. Results showed that at a low temperature of 4 °C for 24 h, transcript levels of ATP8, ND1, ND4, COI, and ND4L significantly decreased, to values of 0.52 ± 0.08, 0.65 ± 0.04, 0.68 ± 0.10, 0.28 ± 0.02, and 0.35 ± 0.02, respectively, compared with controls. By contrast, transcript levels of COIII exhibited a significant increase, with a mean value of 1.86 ± 0.21. However, exposure to 8 °C for 24 h did not lead to an increase in transcript levels. Indeed, transcript levels of ATP6, ATP8, ND1, ND3, and ND4 were significantly downregulated, to 0.48 ± 0.11, 0.68 ± 0.07, 0.41 ± 0.08, 0.54 ± 0.10, and 0.52 ± 0.07, respectively, as compared with controls. Exposure to a hot environment of 34 °C for 24 h led to an increase in transcript levels of COI, COII, COIII, ND3, ND5, CYTB, and ATP6, with values that were 3.3 ± 0.24, 2.0 ± 0.2, 2.70 ± 1.06, 1.57 ± 0,08, 1.47 ± 0.13, 1.39 ± 0.56, and 1.86 ± 0.12, respectively, over controls. By contrast, ND4L exhibited a significant decrease (to 0.31 ± 0.01) compared with controls. When exposed to 38 °C, the transcript levels of the 13 PCGs significantly increased, ranging from a 2.04 ± 0.23 increase in ND1 to a 6.30 ± 0.96 rise in ND6. Under two different levels of cold and heat stress, the expression patterns of mitochondrial genes in S. incognitus vary, possibly associated with different strategies employed by this species in response to low and high temperatures, allowing for rapid compensatory adjustments in mitochondrial electron transport chain proteins in response to temperature changes. Furthermore, this underscores once again the significant role of mitochondrial function in determining thermal plasticity in reptiles.

UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations

AbstractThe frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

Impact of Surface Dew Point Temperature, Precipitable Water and the Lifted Index on the Precipitation over Hyderabad (India).

In this manuscript, we analyze some significant atmospheric parameters relating to precipitation over Hyderabad. For that purpose, we study the relationships between dew point temperature, precipitable water, lifted index with precipitation and the correlation between these parameters. Our analysis includes interpolation, correlation, and regression. For the ten-year average of monthly mean data, the correlation coefficient of dew point temperature with precipitable water is 0.95, the lifted index with precipitable water is (-0.82), the precipitable water with precipitation is 0.96, the lifted index with precipitation is (-0.69). For complete time series monthly mean data, the correlation of dew point temperature with precipitable water is 0.83, the lifted index with precipitable water is (-0.74), the precipitable water with precipitation is 0.78, lifted index with precipitation is (-0.51). Thus, there is positive correlation between some parameters, while negative correlation between others. We have also noticed more precipitation when the precipitable water values range from 40 mm to 60 mm and when the LI is more negative. The degree of relationship between dew point temperature and natural logarithm of Precipitable Water was excellent over Hyderabad and a regression equation relating the two parameters was computed. The probability of precipitation and severe weather occurrence could be identified along with Td, PW and LI. Thus, there is a possibility that LI, Td and PW can be potential parameters to measure the occurrence of precipitation events.

Revisiting the environmental Kuznets curve: assessing the impact of climate policy uncertainty in the Belt and Road Initiative.

Climate change repercussions such as temperature shifts and more severe weather occurrences are felt globally. It contributes to larger-scale challenges, such as climate change and biodiversity loss in food production. As a result, the purpose of this research is to develop strategies to grow the economy without harming the environment. Therefore, we revisit the environmental Kuznets curve (EKC) hypothesis, considering the impact of climate policy uncertainty along with other control variables. We investigated yearly panel data from 47 Belt and Road Initiative (BRI) nations from 1998 to 2021. Pooled regression, fixed effect, and the generalized method of moment (GMM) findings all confirmed the presence of inverted U-shaped EKC in BRI counties. Findings from this paper provide policymakers with actionable ideas, outlining a framework for bringing trade and climate agendas into harmony in BRI countries. The best way to promote economic growth and reduce carbon dioxide emissions is to push for trade and climate policies to be coordinated. Moreover, improving institutional quality is essential for strong environmental governance, as it facilitates the adoption of environmentally friendly industrialization techniques and the efficient administration of climate policy uncertainties.

Severe Convective Storms in Limited Instability Organized by Pattern and Distribution

Abstract 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 (θe) 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. Significance Statement 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.

Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model

AbstractThis paper first applies a prediction model based on self‐attention memory ConvLSTM (SAM‐ConvLSTM) to predict the global ionospheric total electron content (TEC) maps with up to 1 day of lead time. We choose the global ionospheric TEC maps released by the Center for Orbit Determination in Europe (CODE) as the training data set covering the period from 1999 to 2022. Besides that, we put several space environment data as additional multivariate‐features into the framework of the prediction model to enhance its forecasting ability. In order to confirm the efficiency of the proposed model, the other two prediction models based on convolutional long short‐term memory (LSTM) are used for comparison. The three models are trained and evaluated on the same data set. Results show that the proposed SAM‐ConvLSTM prediction model performs more accurately than the other two models, and more stably under space weather events. In order to assess the generalization capabilities of the proposed model amidst severe space weather occurrences, we selected the period of 22–25 April 2023, characterized by a potent geomagnetic storm, for experimental validation. Subsequently, we employed the 1‐day predicted global TEC products from the Center for Operational Products and Services (COPG) and the SAM‐ConvLSTM model to evaluate their respective forecasting prowess. The results show that the SAM‐ConvLSTM prediction model achieves lower prediction error. In one word, the ionospheric TEC prediction model proposed in this paper can establish the ionosphere TEC of spatio‐temporal data association for a long time, and realize high precision of prediction performance.

Fast upper-level jet stream winds get faster under climate change

Earth’s upper-level jet streams influence the speed and direction of travel of weather systems and commercial aircraft, and are linked to severe weather occurrence. Climate change is projected to accelerate the average upper-level jet stream winds. However, little is known about how fast (>99th percentile) upper-level jet stream winds will change. Here we show that fast upper-level jet stream winds get faster under climate change using daily data from climate model projections across a hierarchy of physical complexity. Fast winds also increase ~2.5 times more than the average wind response. We show that the multiplicative increase underlying the fast-get-faster response follows from the nonlinear Clausius–Clapeyron relation (moist-get-moister response). The signal is projected to emerge in both hemispheres by 2050 when considering scenario uncertainty. The results can be used to explain projected changes in commercial flight times, record-breaking winds, clear-air turbulence and a potential increase in severe weather occurrence under climate change.

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

Investigating Spatial Variations of Compound Heat–Precipitation Events in Guangdong, China through a Convection-Permitting Model

Compound heat–precipitation events exert significant impacts on severe weather occurrences. Intense vertical air movement, driving vigorous convection, primarily contributes to the formation of extreme precipitation. Nevertheless, such compound events’ temporal and spatial variation patterns at convection-permitting resolutions remain inadequately explored. This study assesses the performance of the Convection-Permitting Model (CPM) against a model of convection parameterization while investigating the spatial dynamics of compound heat–precipitation events in Guangdong, China. Our findings indicate that the CPM exhibits heightened reliability and precision in simulating temperature and precipitation patterns, especially in extreme precipitation simulation, which would be highly underestimated without a convection-permitting process. Projections from the CPM reveal that, across historical and future periods, the occurrence frequency and fraction of T-P events (instances of extreme heat followed by extreme precipitation) surpass those of P-T events (occurrences of extreme precipitation followed by extreme heat). For T-P events, the CPM exhibits better capability in capturing high-frequency occurrence areas, whereas the results of the relatively low-resolution model show less distinct spatial variations. Both types of events exhibit noticeable upward trends yearly within each period. By the close of this century, the provincial average frequency of P-T events is anticipated to decrease from 20.32 times to 14.55 times. In contrast, the frequency of T-P events is projected to increase from 87.7 times to 101.38 times. These projected changes underscore the shifting dynamics of compound heat–precipitation events in the study region.

Health effects of climate change in Africa: A call for an improved implementation of prevention measures

The world’s climate, particularly in Africa, has changed substantially during the past few decades, contributed by several human activities. Africa is one of the continents that is most vulnerable to climate change globally. Since the beginning of 2022, extreme weather events in Africa have affected about 19 million people and killed at least 4,000 individuals. Cyclones, floods, heatwaves, wildfires, droughts, and famine were among the severe weather occurrences. Natural disasters and extreme weather events brought on by climate change may compromise access to clean water, sanitation systems, and healthcare facilities, making people more vulnerable to a number of illnesses. Floods and drought can lead to both communicable and non-communicable diseases. The African population is more likely to experience more mental health disorders than before because of natural disasters, which result in the loss of property and sometimes loss of lives more frequently. We, therefore, call for an improved implementation of strategies to prevent the health effects of climate change so that the health of the people in Africa can be maintained.

Կլիմայի փոփոխության ազդեցությունը համաշխարհային տնտեսության վրա / NAVIGATING CLIMATE'S IMPACT ON THE WORLD ECONOMY

Climate change, a global phenomenon of paramount concern, has profound implications for the world economy. This scholarly paper thoroughly examines the many effects of climate change on the worldwide economic environment, utilizing a wide range of research and analysis. This study examines the direct economic impacts that result from rising temperatures and severe weather occurrences. The text thoroughly analyzes the impact of climate policies, such as carbon pricing and renewable energy subsidies, by evaluating their respective expenses and advantages. Furthermore, the paper examines the economic prospects that arise when the global economy shifts towards a low-carbon model, with a particular focus on the expansion of environmentally-friendly industries and the promotion of innovation. This paper aims to offer a comprehensive understanding of the complex correlation between climate change and the global economy. This emphasizes the immediate requirement for synchronized worldwide efforts to reduce the negative effects of climate change, while also emphasizing the economic prospects that come with moving to a more environmentally friendly global economy. The primary objective of this study is to enhance the overall understanding of different viewpoints. One important discovery of the research is that policymakers, corporations, and individuals need to effectively handle the difficulties and possibilities arising from the changing climate conditions.

Climate change: A Global Crisis

Climate change is affecting people's lives and health in many different ways. Clean air, safe drinking water, a plentiful supply of nutrient-rich food, and a safe place to live - key elements of good well-being are all at risk, and it may undo decades of progress in world health. Climate change is predicted to result in an increase in yearly mortality from starvation, malaria, diarrhea, and heat stress. The areas least equipped and with limited health infrastructure, such as developing nations like Pakistan, will be less able to deal with this crisis. The mining and burning of fossil fuels produces greenhouse gas emissions, which play a significant role in both climate change and air pollution. Numerous programmes and individual decisions about how to utilize energy, food, and transportation have the potential to cut greenhouse gas emissions and have a significant positive impact on people's health, especially by reducing air pollution. Examples of strategies that might reduce carbon emissions and ease the burden of indoor and outdoor air pollution include the phase-out of polluting energy systems or the promotion of active lifestyles and public transportation. The increase in death and illness from increasing extreme weather effects, food-, water-, and vector-borne illnesses, as well as mental health problems are all significant effects of climate change on health. Extreme weather events like heatwaves, storms, and floods are also becoming more often. In addition, many of the social factors that influence health, such as access to healthcare, equality, and livelihoods, are being deteriorated by climate change. The most vulnerable are people, such as women, children, ethnic minorities, impoverished communities, migrants or displaced people, elderly populations, and those with underlying health issues, as they are disproportionately affected by these climate-sensitive health concerns.
 Climate change has had a significant impact on Pakistan, as seen by the recent devastating floods that claimed thousands of lives and left millions homeless. People who were impacted by flood waters contracted several illnesses, including malaria and dengue. Recent severe weather occurrences in Pakistan include heat waves that melt glaciers and cause devastating floods, smog, and air pollution posing serious health problems to people. If necessary steps are not taken, these effects of climate change might worsen. To address this crisis, significant decisions must be made, including coordinating reviews of the scientific evidence on the relationships between climate change and health; determining a nation's readiness for and needs in the face of climate change; enhancing national capacities and enhancing the resilience and adaptive capacity of health systems to address the negative health effects of climate change; developing expertise on the links between climate change and human health; and reducing the risk of global warming.

Using the daily change in the Southern Oscillation Index to develop analogues and the relationship to severe weather outbreaks

AbstractThe occurrence of severe weather is an annual problem for much of the United States and North America and maximizes from March through June. With the increased interest in subseasonal weather forecasting, there have been attempts to anticipate the occurrence of anomalous weather on the timescale of one to 4 weeks including the occurrence of severe weather. Previous research has shown that teleconnection indices, associated with long period Rossby wave activity, or persistent large‐scale flow regimes have been useful tools in this endeavour. Here, abrupt changes over a 24–72‐hr period (10 or more units per day or 20 or more units over 3 days) in the Southern Oscillation Index (SOI) time series will be used to demonstrate that these changes can be associated with the possible occurrence of major severe weather event‐days defined as; 20 or more tornado, 155 or more wind speed events >25.9 m·s−1, or 135 or more hail diameter larger than 25.4 mm, reports over the United States one to 3 weeks in advance, especially during the March through June period. The severe weather events obtained from the archive at the Storm Prediction Center (SPC) from 1991 through 2020 were used. The results here demonstrate that more than 7 in 10 major severe weather occurrences were associated with abrupt positive and negative changes in the daily SOI when using signal detection methods.

The Utilization of Weather Research Forecasting (WRF) Model of 3DVar (Three Dimensional Variational) and Himawari-8 Satellite Imagery to the Heavy Rain in Palangkaraya (Case Study : April 27, 2018)

<p><em>On April 27, 2018 heavy rain was occurred in Palangkaraya. Based on surface data observations at Tjilik Riwut Meteorological Station, the peak of rain occurred between 18-21 UTC, which 54 mm within 3 hours. As a result, the flood inundated on the following day. This research purposed to discover the cause of heavy rain used the WRF model of 3DVar technique that assimilated with AMSU-A satellite which used the tropical physic suite parameterization scheme and Himawari-8 Satellite (IR-1 data), processed by Python Programming. Based on the results, the WRF of the 3DVar model is not representative enough in total rainfall results. However, several weather disturbances show the potency for severe weather occurrence from WRF 3DVar modeling. These are indicated by the shear line and eddy circulation at 18 and 21 UTC, and the time series of air pressure decreases with a 0.5 Mb tendency between 15 to 18 UTC. Moreover, the cloud top temperature graph from Himawari-8 Satellite data shows a drastic reduction in temperature to -61.4323 at 18.20 UTC, which supports the heavy rain process. The weather analysis above show that WRF 3DVar is not representative enough for total rainfall result, but appropriate for other weather aspects (shear line, eddy, and air pressure). Therefore, the heavy rain is caused by shear line and eddy condition, air pressure and low temperature of the cloud top.</em><em></em></p>

Future Global Convective Environments in CMIP6 Models

AbstractThe response of severe convective storms to a warming climate is poorly understood outside of a few well studied regions. Here, projections from seven global climate models from the CMIP6 archive, for both historical and future scenarios, are used to explore the global response in variables that describe favorability of conditions for the development of severe storms. The variables include convective available potential energy (CAPE), convection inhibition (CIN), 0–6 km vertical wind shear (S06), storm relative helicity (SRH), and covariate indices (i.e., severe weather proxies) that combine them. To better quantify uncertainty, understand variable sensitivity to increasing temperature, and present results independent from a specific scenario, we consider changes in convective variables as a function of global average temperature increase across each ensemble member. Increases to favorable convective environments show an overall frequency increases on the order of 5%–20% per °C of global temperature increase, but are not regionally uniform, with higher latitudes, particularly in the Northern Hemisphere, showing much larger relative changes. The driving mechanism of these changes is a strong increase in CAPE that is not offset by factors that either resist convection (CIN), or modify the likelihood of storm organization (S06, SRH). Severe weather proxies are not the same as severe weather events. Hence, their projected increases will not necessarily translate to severe weather occurrences, but they allow us to quantify how increases in global temperature will affect the occurrence of conditions favorable to severe weather.

Evolution of Radar and Lightning Variables in Convective Events in Barcelona and Surroundings for the Period 2006–2020

The Metropolitan Area of Barcelona is a densely populated region in the North-East of the Iberian Peninsula. Infrastructures in this area play a significant role in the economy of this part of Europe. The combination of the Mediterranean Sea and the complex topography is responsible for the occurrence of severe weather events in this location and the surrounding areas. The use of remote sensing data in an hourly resolution allows the identification and characterization of those severe episodes, helping in determining the future trends of the adverse weather. This fact is crucial in the development of new engineering projects, as well as in the maintenance of the current ones. Weather radar and lightning observations have enabled the monitoring of an increase in severe weather occurrence and, in addition, the prime characteristics of the thunderstorms responsible for producing them. Deepening vertical developments, the presence of hail, and the decrease of the rainfall efficiency are some of the characteristics that must be taken into account in the near future.

A Deep-Learning Model for Automated Detection of Intense Midlatitude Convection Using Geostationary Satellite Images

AbstractIntense thunderstorms threaten life and property, impact aviation, and are a challenging forecast problem, particularly without precipitation-sensing radar data. Trained forecasters often look for features in geostationary satellite images such as rapid cloud growth, strong and persistent overshooting tops, U- or V-shaped patterns in storm-top temperature (and associated above-anvil cirrus plumes), thermal couplets, intricate texturing in cloud albedo (e.g., “bubbling” cloud tops), cloud-top divergence, spatial and temporal trends in lightning, and other nuances to identify intense thunderstorms. In this paper, a machine-learning algorithm was employed to automatically learn and extract salient features and patterns in geostationary satellite data for the prediction of intense convection. Namely, a convolutional neural network (CNN) was trained on 0.64-μm reflectance and 10.35-μm brightness temperature from the Advanced Baseline Imager (ABI) and flash-extent density (FED) from the Geostationary Lightning Mapper (GLM) on board GOES-16. Using a training dataset consisting of over 220 000 human-labeled satellite images, the CNN learned pertinent features that are known to be associated with intense convection and skillfully discriminated between intense and ordinary convection. The CNN also learned a more nuanced feature associated with intense convection—strong infrared brightness temperature gradients near cloud edges in the vicinity of the main updraft. A successive-permutation test ranked the most important predictors as follows: 1) ABI 10.35-μm brightness temperature, 2) ABI GLM flash-extent density, and 3) ABI 0.64-μm reflectance. The CNN model can provide forecasters with quantitative information that often foreshadows the occurrence of severe weather, day or night, over the full range of instrument-scan modes.

Distinctive Signals in 1‐min Observations of Overshooting Tops and Lightning Activity in a Severe Supercell Thunderstorm

AbstractThis work examines a severe weather event that took place over central Argentina on 11 December 2018. The evolution of the storm from its initiation, rapid organization into a supercell, and eventual decay was analyzed with high‐temporal resolution observations. This work provides insight into the spatio‐temporal co‐evolution of storm kinematics (updraft area and lifespan), cloud‐top cooling rates, and lightning production that led to severe weather. The analyzed storm presented two convective periods with associated severe weather. An overall decrease in cloud‐top local minima IR brightness temperature (MinIR) and lightning jump (LJ) preceded both periods. LJs provided the highest lead time to the occurrence of severe weather, with the ground‐based lightning networks providing the maximum warning time of around 30 min. Lightning flash counts from the Geostationary Lightning Mapper (GLM) were underestimated when compared to detections from ground‐based lightning networks. Among the possible reasons for GLM's lower detection efficiency were an optically dense medium located above lightning sources and the occurrence of flashes smaller than GLM's footprint. The minimum MinIR provided the shorter warning time to severe weather occurrence. However, the secondary minima in MinIR that preceded the absolute minima improved this warning time by more than 10 min. Trends in MinIR for time scales shorter than 6 min revealed shorter cycles of fast cooling and warming, which provided information about the lifecycle of updrafts within the storm. The advantages of using observations with high‐temporal resolution to analyze the evolution and intensity of convective storms are discussed.

Human–Environment Natural Disasters Interconnection in China: A Review

This study aimed to assess the interrelationship among extreme natural events and their impacts on environments and humans through a systematic and quantitative review based on the up-to-date scientific literature. Namely, the main goal was to add additional knowledge to the existing evidence of the impacts related to floods, droughts, and landslides on humans and the environment in China; this in order to identify knowledge gaps in research and practice to aid in improving the adaptation and mitigation measures against extreme natural events in China. In this study, 110 documents were analyzed in the evaluation of several impacts triggered by extreme events. Records were obtained from Scopus and Web of Science and examined with a text mining instrument to assess the pattern of publications over the years; the problems linked to extreme weather events were investigated, and the study gaps were discussed. This paper extends work by systematically reviewing recent evidence related to floods, droughts, and landslides in China. We listed the critical studies that focused on the impact of extreme events on both humans and the environment described in current reviews. The findings revealed that goods safety, social safety, and financial losses are of significant concern to the scientific community due to extreme natural events, which from our analysis resulted in being more frequent and intense. It is still underdeveloped to implement distant sensing and imaging methods to monitor and detect the impact of severe weather occurrences. There are still significant study gaps in the fields of the effects of extreme weather events. The analysis result shows that extreme events are increased during the time, so more in-depth investigation and efforts on adaptation, mitigation measures, and strategical governance plans are desperately required.