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The National Airspace System (NAS) is a sophisticated network of air traffic control, navigation, and communication systems that play a critical role in ensuring the safe and efficient flow of air traffic across the United States. However, the occurrence of severe weather conditions, particularly hailstorms, poses a significant threat to flight safety within the NAS. To mitigate the risks associated with hail, aviation organizations have implemented a range of safety measures. This study utilized Esri's ArcGIS as a mapping software to conduct a geospatial analysis of the impact of severe weather, particularly hail, on the NAS. The Hail Awareness Spatial Analysis Toolkit (HASAT), developed as part of this research, leveraged Machine Learning (ML) as a forecasting method to predict the occurrence of severe hail events. The results of the analysis revealed that states such as Texas, Oklahoma, Kansas, and Nebraska emerged as the epicenter of these hailstorms. The Hail Awareness Spatial Analysis Toolkit (HASAT) possessed an additional capability to provide localized hail data to pilots, empowering aviation operators with critical information for flight safety. By incorporating this tool into existing systems, pilots can access real-time, location-specific hail data, enabling them to make informed choices regarding flight routes and potential hazards associated with hailstorms. [ABSTRACT FROM AUTHOR]

Climate change and extreme weather events have been shown to increase incidences of gender-based violence (GBV). Numerous organizations have devoted significant time, resources, and effort to the design and implementation of interventions aimed at reducing GBV in Africa. Some interventions effectively reduce violence, but GBV persists and remains pervasive. The United Nations has called for GBV interventions that consider the impact of climate change on violence. This review aims to determine whether public health interventions intended to reduce GBV in Africa take into account the effects of climate change on the region and the population. PubMed, PsychArticles, and CINAHL databases were searched systematically in February 2023 for interventions conducted in Africa published between 2010 and 2023. There were a total of 86 articles in the final review that described 40 distinct interventions. The intervention designs included empowerment and participatory approaches (microfinance, microfinance plus, community education, and community engagement), changing social and cultural norms (community education, community engagement, and media), and school-based programs. None of the 40 interventions mentioned climate, weather, or climate change as a component of the intervention. There are several opportunities to improve existing, successful GBV interventions in order to increase their efficacy. GBV interventions could incorporate economic independence programs that do not rely on agriculture and include climate change education. These findings could facilitate the integration of two previously distinct research disciplines—climate change and GBV prevention—to inform future research and develop more effective and cost-efficient interventions. [ABSTRACT FROM AUTHOR]

This protocol lays the groundwork for a community-centered investigation into the intersection between climate change and violence against women and girls. Providing a standardized framework, this paper will enable researchers to collect and analyze data on severe weather patterns and their potential impact on violence within communities. The interlinkage between violence and climate is an emerging field of research. There is a need for a comprehensive understanding of the intersection of these two issues focusing on the manifestations of violence, specific vulnerabilities, and coping strategies. This protocol outlines a qualitative research approach employed in Kilifi County, Kenya. The study will include key informant interviews with community leaders and policymakers, along with focus group discussions with women, adolescent girls, and men. Community engagement is a crucial component of this work as it will ensure that the research is conducted ethically and respectfully and ensure that the findings are relevant and applicable to the community being studied. This study will contribute to a deeper understanding of the complex dynamics between violence against women and girls and climate change, informing policies, interventions, and advocacy efforts and elevating the voices of women and girls to promote gender equity in the face of climate change challenges.

This paper assesses the extended‐range forecast skills of the extreme heat events (EHEs) over South China based on three subseasonal‐to‐seasonal models (European Centre for Medium‐Range Weather Forecasts [ECMWF], National Centers for Environmental Prediction [NCEP], and China Meteorological Administration [CMA]). Overall, ECMWF has the best skill, NCEP the second and CMA the poorest. The predicting skills of EHEs depend on the predicting skills of relevant circulation. Cases studies (June 4–6, 1999, August 19–29, 2009, and August 3–5, 2010) show that the three models generally predict circulation anomalies weaker than observation, leading to the misses of some extreme heat days (EHDs). In these cases, ECMWF is able to well predict the influence of tropical circulation, capture the major characteristics of mid‐latitude circulation but with a slower propagating speed. NCEP could capture the main signals of tropical (mid‐latitude) circulation, but with slower propagating speed (slower propagating speed, deviated direction or more northward location). CMA might produce some EHDs but is derived from the circulation anomaly with the wrong origin or location. Therefore, ECMWF could predict the EHEs most accurately, NCEP could reasonably predict the formation of EHEs and tend to have more delayed predictions, while CMA has the poorest skill due to the false origins of anomalies. These results suggest potential ways to improve the current models' extended‐range forecast skills. [ABSTRACT FROM AUTHOR]

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms. [ABSTRACT FROM AUTHOR]

Troposphere’s asymmetry can introduce errors ranging from centimeters to decimeters at low elevation angles, which cannot be ignored in high-precision positioning technology and meteorological research. The traditional two-axis gradient model, which strongly relies on an open-sky environment of the receiver, exhibits misfits at low elevation angles due to their simplistic nature. In response, we propose a directional mapping function based on cyclic B-splines named B-spline mapping function (BMF). This model replaces the conventional approach, which is based on estimating Zenith Wet Delay and gradient parameters, by estimating only four parameters which enable a continuous characterization of the troposphere delay across any directions. A simulation test, based on a numerical weather model, was conducted to validate the superiority of cyclic B-spline functions in representing tropospheric asymmetry. Based on an extensive analysis, the performance of BMF was assessed within precise point positioning using data from 45 International GNSS Service stations across Europe and Africa. It is revealed that BMF improves the coordinate repeatability by approximately 10 % horizontally and about 5 % vertically. Such improvements are particularly pronounced under heavy rainfall conditions, where the improvement of 3-dimensional root mean square error reaches up to 13 % . [ABSTRACT FROM AUTHOR]

Operational Meteorologists (OMs) in the Met Office have a perception that elevated convection is not well represented in kilometre‐scale models, which are generally associated with an improved representation of convection. Here, we consider why there may be a problem with representing elevated convection and consider how OMs judge the model to be poor so often. Three OMs have subjectively scored and classified observed elevated convection cases over the UK from 2017 to 2020. Continental plumes (warm, moist, air coming from the near continent or Africa) account for 73% of the cases. The most frequent errors are associated with (i) location, (ii) organisation, (iii) timing and (iv) intensity of the convection. Thus, OMs perceive that the biggest problem with predicting elevated convection is constraining the location of the convective events. The location errors are particularly prevalent for events coming to the UK from the near continent. The location errors are most frequently identified for flow regimes coming from the near continent in weakly forced synoptic conditions. The identification of this problem enables the specific targeting of research into continental plumes (for UK elevated convection) but also raises questions around the role of lateral boundary conditions in the forecasts of elevated convection. [ABSTRACT FROM AUTHOR]

This protocol lays the groundwork for a community-centered investigation into the intersection between climate change and violence against women and girls. Providing a standardized framework, this paper will enable researchers to collect and analyze data on severe weather patterns and their potential impact on violence within communities. The interlinkage between violence and climate is an emerging field of research. There is a need for a comprehensive understanding of the intersection of these two issues focusing on the manifestations of violence, specific vulnerabilities, and coping strategies. This protocol outlines a qualitative research approach employed in Kilifi County, Kenya. The study will include key informant interviews with community leaders and policymakers, along with focus group discussions with women, adolescent girls, and men. Community engagement is a crucial component of this work as it will ensure that the research is conducted ethically and respectfully and ensure that the findings are relevant and applicable to the community being studied. This study will contribute to a deeper understanding of the complex dynamics between violence against women and girls and climate change, informing policies, interventions, and advocacy efforts and elevating the voices of women and girls to promote gender equity in the face of climate change challenges. [ABSTRACT FROM AUTHOR]

In this work, we introduce a formalism to highlight the role of decision‐making implicit in the setup of early warning systems (EWSs) and its consequences with respect to loss avoidance for end users. The formalism, a close relative of the cost/loss approach, combines EWS verification scores with traditional expressions of risk from the point of view of the user. This formalism articulates in mathematical format many well‐known issues surrounding EWS usage, offering a conceptual anchor for concepts that otherwise may seem to wobble among the multidisciplinary perspectives participating in the EWS chain. This decision model is visually represented in a variation of the popular "performance diagram" used in forecast and warning verification. Our diagram adds to this the perspective of a generic user, in an effort to gain insight into how choices made regarding EWS settings may determine which users benefit from warnings and which do not. Although these results are based on a conceptual model, they are useful to better understand the actual benefits experienced by users and to highlight aspects that may temper unrealistic expectations on EWSs. The recent United Nations initiative to extend EWSs for natural hazards to all nations within 5 years will make EWSs more common and more public. The approach proposed here can be a tool to promote greater transparency and improve the necessary dialog between warning issuers and users in order to reduce loss. [ABSTRACT FROM AUTHOR]

Analyzing traffic accident data is crucial for pinpointing contributing factors, forecasting accident patterns, and informing effective safety measures. This insight leads to enhanced road safety, decreased fatalities, and better resource allocation in transportation planning. Severe weather conditions, including rain, fog, and sandstorms, can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly, collect traffic accident data under adverse weather conditions and complete attribute reduction, denoising, and normalization processing. Then, by improving the decision tree algorithm, the attribute selection criteria of the decision tree algorithm were enhanced; Finally, the improved decision tree algorithm is applied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%, an accuracy rate of over 94.3%, a recall rate of over 92.7%, and a stable F1 value of around 0.8, demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision support for traffic management departments. [ABSTRACT FROM AUTHOR]

Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013–2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode. [ABSTRACT FROM AUTHOR]

This paper describes a dataset of convective systems (CSs) associated with hailstorms over Brazil tracked using GOES-16 Advanced Baseline Imager (ABI) measurements and the Tracking and Analysis of Thunderstorms (TATHU) tool. The dataset spans from June 5, 2018, to September 30, 2023, providing five-year period of storm activity. CSs were detected and tracked using the ABI's clean IR window brightness temperature at 10.3 µm, projected on a 2 km x 2 km Lat-Lon WGS84 grid. Systems were identified using a brightness temperature (BT) threshold of 235 K, conducive to detecting convective clusters with larger area and excluding smaller or non-convective cells such as groups of thin Cirrus clouds. Each detected CS was treated as an object, containing geographic boundaries and raster statistics such as BT's mean, minimum, standard deviation, and count of data points within the CS polygon, which serves as proxy for size estimates. The life cycle of each system was tracked based on a 10 % overlap area criterion, ensuring continuity, unless disrupted by dissociative or associative events. Then, the tracked CSs were filtered for intersections in space and time with verified ground reports of hail, from the Prevots group. The matches were then exported to a database with SpatiaLite enabled data format to facilitate spatial data queries and analyses. This database is structured to support advanced research in severe weather events, in particular hailfall. This setting allows for extensive temporal and spatial analyses of convective systems, making it useful for meteorologists, climate scientists, and researchers in related fields . The inclusion of detailed tracking information and raster statistics offers potential for diverse applications, including climate model validation, weather prediction enhancements, and studies on the climatological impact of severe weather phenomena in Brazil.

Polar lows (PLs) are intense maritime mesoscale weather systems that develop during marine cold air outbreaks at high latitudes. The objective of this review is to describe the advances in polar low research since the last literature review--published 3 years ago--, indicate the knowledge gaps that remain, and suggest promising courses of research. Among the breakthroughs identified here are the first climatology of PLs obtained with a global atmospheric model, and increased evidence showing that baroclinic instability is the main mechanism leading to PL development. Despite these advances, many challenges persist such as the lack of conventional observations of PLs and the need to better understand coupled atmosphere-ocean processes involved in PL development. With the rapid advances in deep learning, this method has the potential to be used for PL forecasting. [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds. [ABSTRACT FROM AUTHOR]

The spatial and temporal changes in social vulnerability to natural hazards in Mexico are analyzed. To this end, using census data from 2000, 2010, and 2020, and a statistical method, different indices were computed, and with a GIS-based approach, patterns of social vulnerability are examined. In addition, a risk assessment test for severe weather (thunderstorms, hailstorms, and tornadoes) is made out. The results show different common social vulnerability driving factors in the 3 analyzed years, with root causes that have not been addressed since the beginning of the century. Likewise, a wider gap between Mexico's most and least vulnerable populations is identified. The changes in spatial patterns respond to different historical situations, such as migration, urbanization, and increased population. Also, poverty, ethnicity, and marginalization factors located in very particular regions in Mexico have remained relatively the same in the last 20 twenty years. These situations have strongly influenced the spatial–temporal distribution of vulnerability in the country. The role of social vulnerability in the disaster risk to extreme events such as thunderstorms, hailstorms, and tornadoes in Mexico is fundamental to understanding changes in disaster distribution at the national level, and it is the first step to generating improvements in integrated risk management. [ABSTRACT FROM AUTHOR]

In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future. Plain Language Summary: Satellite observations are the backbone of modern weather forecast operations, especially for severe weather monitoring and prediction. However, they are also severely underutilized by computer weather models. Many satellite observations impacted by clouds and precipitation are not used in these models, despite their ability to characterize important features of ongoing severe weather events. This study focuses on satellite observations at microwave (MW) frequencies that are impacted by clouds and precipitation. We explore their potential benefits by incorporating them into a computer weather model using data assimilation and examining their impact on severe weather forecasts. We utilize the 10 August 2020 Midwest derecho that produced extensive wind damage as a case study. Because these MW observations contain contributions from precipitating particles within the derecho, assimilating these observations yields better depictions of the derecho structure in the computer model. Consequently, more accurate forecasts of surface gusts are produced. The results of this study suggest a promising avenue for improving severe weather forecasts worldwide in the future, especially for regions that lack the resources and infrastructure to support high‐spatiotemporal‐resolution weather observations. Key Points: Microwave (MW) all‐sky radiances are impacted by hydrometeors within clouds, complementing cloud‐top dominated infrared all‐sky radiancesAssimilating MW all‐sky radiances leads to improved storm structure in the analyses of the 10 August 2020 Midwest derechoWith improved depiction of the derecho's structure, accurate gust forecasts are obtained 2 hr earlier [ABSTRACT FROM AUTHOR]

Weather radar is a useful tool for monitoring and forecasting severe weather but has limited coverage due to beam blockage from mountainous terrain or other factors. To overcome this issue, an intelligent technology called "Echo Reconstruction UNet (ER-UNet)" is proposed in this study. It reconstructs radar composite reflectivity (CREF) using observations from Fengyun-4A geostationary satellites with broad coverage. In general, ER-UNet outperforms UNet in terms of root mean square error (RMSE), mean absolute error (MAE), structural similarity index (SSIM), probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and Heidke skill score (HSS). Additionally, ER-UNet provides the better reconstruction of CREF compared to the UNet model in terms of the intensity, location, and details of radar echoes (particularly, strong echoes). ER-UNet can effectively reconstruct strong echoes and provide crucial decision-making information for early warning of severe weather. [ABSTRACT FROM AUTHOR]

The bora is a well‐known downslope windstorm that blows at the eastern coast of the Adriatic Sea from inland towards the sea, mainly from the northeasterly direction. One of the notable bora characteristics is the quasi‐periodic gust pulsations. We address their characteristics during strong to severe bora events by applying rotational spectral analysis on 9 months of continuous high‐frequency sonic anemometer measurements in the town of Senj, Croatia. The analysis shows that the orientation of the pulsations' plane of oscillation does not correspond to the measured near‐ground wind direction. In particular, these two directions seem to be negatively correlated. Although the structure of the pulsations is mostly rectilinear, the positive rotational component was larger than the negative one in all except one bora event. The coincidence of the wind shear direction within the lee‐side jet with the orientation of the plane of oscillation leads to a tentative conclusion that its orientation was governed by the Kelvin–Helmholtz instability, at least in the cases presented. [ABSTRACT FROM AUTHOR]

Flash flooding from intense rainfall frequently results in major damage and loss of life across Africa. In the Sahel, automatic prediction and warning systems for these events, driven by Mesoscale Convective Systems (MCSs), are limited, and Numerical Weather Prediction (NWP) forecasts continue to have little skill. The ground observation network is also sparse, and very few operational meteorological radars exist to facilitate conventional nowcasting approaches. Focusing on the western Sahel, we present a novel approach for producing probabilistic nowcasts of convective activity out to six hours ahead, using the current location of observed convection. Convective parts of the MCS, associated with extreme and heavy precipitation, are identified from 16 years of Meteosat Second Generation thermal–infrared cloud‐top temperature data, and an offline database of location‐conditioned probabilities calculated. From this database, real‐time nowcasts can be quickly produced with minimal calculation. The nowcasts give the probability of convection occurring within a square neighbourhood surrounding each grid point, accounting for the inherent unpredictability of convection at small scales. Compared to a climatological reference, formal verification approaches show the nowcasts to be skilful at predicting convective activity over the study region, for all times of day and out to the six‐hour lead time considered. The nowcasts are also skilful at capturing extreme 24‐hour rain gauge accumulations over Dakar, Senegal. The nowcast skill peaks in the afternoon, with a minimum in the evening. We find that the optimum neighbourhood size varies with lead time, from 10 km at the nowcast origin to around 100 km at a six‐hour lead time. This simple and skilful nowcasting method could be highly valuable for operational warnings across West Africa and other regions with long‐lived thunderstorms, and help to reduce the impacts from heavy rainfall and flooding. [ABSTRACT FROM AUTHOR]

Abstract Operational Meteorologists (OMs) in the Met Office have a perception that elevated convection is not well represented in kilometre‐scale models, which are generally associated with an improved representation of convection. Here, we consider why there may be a problem with representing elevated convection and consider how OMs judge the model to be poor so often. Three OMs have subjectively scored and classified observed elevated convection cases over the UK from 2017 to 2020. Continental plumes (warm, moist, air coming from the near continent or Africa) account for 73% of the cases. The most frequent errors are associated with (i) location, (ii) organisation, (iii) timing and (iv) intensity of the convection. Thus, OMs perceive that the biggest problem with predicting elevated convection is constraining the location of the convective events. The location errors are particularly prevalent for events coming to the UK from the near continent. The location errors are most frequently identified for flow regimes coming from the near continent in weakly forced synoptic conditions. The identification of this problem enables the specific targeting of research into continental plumes (for UK elevated convection) but also raises questions around the role of lateral boundary conditions in the forecasts of elevated convection.

Tornadoes are a co-occurring extreme that can be produced by landfalling tropical cyclones (TCs). These tornadoes can exacerbate the loss of life and property damage caused by the TC from which they were spawned. It is uncertain how the severe weather environments of landfalling TCs may change in a future climate and how this could impact tornado activity from TCs. In this study, we investigated four TCs that made landfall in the U.S. and produced large tornado outbreaks. We performed four-member ensembles of convective-allowing (4-km resolution) regional climate model simulations representing each TC in the historical climate and a mid-twenty-first century future climate. To identify potentially tornadic storms, or TC-tornado (TCT) surrogates, we used thresholds for three-hourly maximum updraft helicity and radar reflectivity, as tornadoes are not resolved in the model. We found that the ensemble-mean number of TCT-surrogates increased substantially (56–299%) in the future, supported by increases in most-unstable convective available potential energy, surface-to-700-hPa bulk wind shear, and 0–1-km storm-relative helicity in the tornado-producing region of the TCs. On the other hand, future changes in most-unstable convective inhibition had minimal influence on future TCT-surrogates. This provides robust evidence that tornado activity from TCs may increase in the future. Furthermore, TCT-surrogate frequency between 00Z and 09Z increased for three of the four cases, suggesting enhanced tornado activity at night, when people are asleep and more likely to miss warnings. All of these factors indicate that TC-tornadoes may become more frequent and a greater hazard in the future, compounding impacts from future increases in TC winds and precipitation.

This work investigates the meteorological mechanisms forming a classical frontal system on 26 August 2020 in the northeast and eastern parts of Afghanistan. The weather system caused heavy rainfall and led to severe flash floods. Flooding, affected by torrential rain showers, struck mostly the city of Charikar in Parvan province early in the morning day, while most people were asleep. This caused 150 deaths, and nearly 500 houses were destroyed. This research explores atmospheric processes by examining the National Centers for Environmental Prediction dataset and MERRA Model database. The calculation of the convective available potential energy (CAPE) and Showalter index extracted from the Skew-T log-pressure diagram shows a high value of the CAPE at around 2,632 J/kg and −6.6 for the Showalter index, respectively. This presents a very extreme instability in the study area during the time of the flood. The study reveals that the triggering of this system was mostly by thermodynamical aspects, low-level deep convergence, and local topographical aspects rather than the PV streamer. However, the anomaly climate analysis for different atmospheric elements with a comparison of the climate normal values shows the importance of climate change in the weather system into a stronger frontal activity associated with stronger baroclinicity over the study area. HIGHLIGHTS Investigation of the anomalies in different meteorological parameters and comparison with the normal mean climatological values.; Climate change's role in the brutal Charikar flash floods during late August 2020.;

The Madden–Julian Oscillation (MJO) is the leading intraseasonal mode of tropical variability and has been connected to influences in extratropical weather. Recently, the MJO has been gaining traction as a useful teleconnection for extending severe weather outbreak forecast lead time. However, the MJO's intraseasonal, planetary‐scale is far from the meso‐ or micro‐scale severe weather phenomena, making identifying links between the two more difficult. In our study, we use severe weather outbreaks comprising all three main hazards (wind, hail and tornadoes), which are typically linked to a synoptic‐scale system. Outbreaks are devised using kernel density estimation and were used to determine spatial shifts for each of the eight phases of the MJO in each month. A total of 15 months/phases indicated a significant spatial shift. In the spring and summer months (March–August), southerly shifts are more likely to occur in MJO phases 7–8 and 1–3, while northerly shifts are more likely in phases 4–6. In the cool season (September–February), only 3 month/phase groups indicated significant shifts, two shifting west and one north. Composite analysis showed the likely link between the MJO and these outbreak shifts was rooted in Rossby wave breaking and generation of a Rossby wave source induced by the barotropic vorticity advection from the MJO convection. These resulting wave breaks and wave trains led to changes in the storm track (amplitude and location) that resulted in a more favourable synoptic‐scale pattern for severe weather over the United States that deviated from the climatologically favourable region. [ABSTRACT FROM AUTHOR]

The study of atmospheric models and climate systems has been hampered by the restricted availability of wind profile measurements. By recording wind profiles in near real time and giving useful information for Numerical Weather Prediction (NWP) models, the Doppler wind lidar technology used in the European Space Agency's Aeolus mission is expected to unravel this problem. By analyzing the precipitation from IANOS medicane, which occurred over the Thessaly plain in September 2020, a case study utilizing the COSMO NWP model illustrates the potential usefulness of Aeolus data in strengthening NWP models. Run in hindcast mode and forced by analyses with and without Aeolus, the model assimilated data that were produced at the European Centre for Medium-Range Forecasts (ECMWF). Evaluation against observations from Greek synoptic stations showed that the model precipitation using the Aeolus-assimilated data preponderated over the model results without their inclusion. This work aims to demonstrate the additional value of the Aeolus project towards the expansion of our knowledge of Earth's atmosphere, particularly the improvement of our capacity to estimate severe weather events via the use of Aeolus with NWP models. [ABSTRACT FROM AUTHOR]

Cold temperature extremes, causing damage to industry, agriculture and human health, are often associated with midlatitude cyclones. Here, we quantify the relation between cyclones and cold extremes by highlighting two cyclone‐associated dynamical features that may, differently, induce cold extremes. Namely, slantwise‐descending dry intrusions (DIs) advect cold air equatorward into the cyclone's cold sector, and upper‐tropospheric troughs, diagnosed as cyclonic potential vorticity (PV) anomalies, induce cold anomalies directly below. By objectively‐identified DI outflows and cyclonic PV anomalies for 1979–2019 in ERA5 reanalysis, their climatological association with the 5% lowest 2‐m temperature is globally quantified. We find that in midlatitudes upper PV anomalies dominate cold extremes, while DIs dominate extremes in the subtropics and even the tropics. The rare overlap between the two features can potentially act as a strong predictor for cold extremes, accompanying ∼30% of extremes at 30° latitude with local hotspots of 80%–100%. Plain Language Summary: Cold extremes, here defined as 5% of the coldest deviations from the long‐term near‐surface temperatures, can lead to major damage to industry, agriculture and human health. We take a global view to better understand the local and non‐local drivers for cold extremes. We associate the cold extremes with two features related to midlatitude cyclones. The first is slantwise‐descending dry intrusion airstreams transporting cold air toward the equator in the cold sector of the cyclones, which are mostly associated with cold extremes in the subtropics. The second is upper‐tropospheric troughs in the midlatitude jet stream that induce cold anomalies below them, closer to the cyclone center in midlatitudes. The overlap of these features, though rare, was found to be a potential predictor for cold extremes. Understanding these mechanisms is essential for predicting cold extremes and improving preparedness. Key Points: Dry intrusions (DIs) and troughs associated with midlatitude cyclones translate upper‐level patterns to surface cold extremesThe non‐local influence of midlatitude cyclones extends all the way to the subtropics and tropicsThe horizontal overlap of DI outflows and troughs is a strong predictor for near‐surface cold extremes [ABSTRACT FROM AUTHOR]

Previous studies demonstrated that increased soil moisture can affect the water vapor in the overlaying atmospheric boundary layer (ABL). This can increase the surface buoyancy which can be more conducive for the occurrence of severe weather. This research will demonstrate that soil moisture can be an indicator for increased tornado, large hail, and severe wind activity within the state of Missouri for the period from 1980–2018. Then, using data obtained from the Storm Prediction Center in Norman, OK, the April–June soil moisture anomalies were used to determine if there is a correlation to severe weather occurrence during those months. The correlation of soil moisture anomalies for the months of January–March and September through February before the April–June severe weather season was examined next. These experiments will use the Pearson correlation coefficient and a Poisson regression to test for significance. Only significant results from the Pearson correlation method will be shown for brevity. Then, synoptic maps, in conjunction with the ENSO phenomena, were examined to understand what other mechanisms were contributing to the increase/decrease in storm reports within the state. A spectral analysis using Fourier transforms was conducted to examine the interannual variability of severe weather occurrence with respect to soil moisture. Results showed a significant positive relationship between April–June soil moisture with April–June tornado and severe wind activity. Fewer significant positive and negative relationships were found relating the January–March soil moisture with April–June severe weather reports. However, no statistical significance was found between September–February soil moisture and the ensuing April–June severe weather reports. Most of the statistically significant correlations were noted in south central Missouri. Some variability was observed with ENSO years and tornado activity, indicating that the synoptic setup may play more of a role than soil moisture. La Niña was also found to produce a greater number of tornadic systems, while El Niño produces more potent systems. [ABSTRACT FROM AUTHOR]

Polar lows (PLs) are intense maritime mesoscale weather systems that develop during marine cold air outbreaks at high latitudes. The objective of this review is to describe the advances in polar low research since the last literature review—published 3 years ago—, indicate the knowledge gaps that remain, and suggest promising courses of research. Among the breakthroughs identified here are the first climatology of PLs obtained with a global atmospheric model, and increased evidence showing that baroclinic instability is the main mechanism leading to PL development. Despite these advances, many challenges persist such as the lack of conventional observations of PLs and the need to better understand coupled atmosphere-ocean processes involved in PL development. With the rapid advances in deep learning, this method has the potential to be used for PL forecasting.

Abstract In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future.

There is growing concern that severe weather events are negatively affecting solar photovoltaic (PV) systems and pose ongoing and unrecognized risks. Clearly research is needed to quantify and characterize the risks so that properly calibrated improvements can be implemented. To begin this process, this report is a collection of information of severe weather impacts on solar PV available today taken from research, insurance data, and field inspections. While it is not comprehensive enough to draw conclusions, this information provides a baseline for action and indicates an urgent need to investigate further.

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Severe weather often occurs in the areas where different cold-warm and dry-wet air masses converge orthogonally. The orthogonal convergence of two-adjacent air parcels can be observed from planetary-scale to synoptic-scale and meso-scale circulations. This study pointed out that an orthogonal convergence of two-adjacent horizontal air parcels can produce a significant non-zero shear stress with its modulus as anomalous energy density and form vertical motions of new airflows, while tailgating and head-on horizontal convergences cannot. Therefore, the orthogonal convergence can induce strong vertical motions and result in severe weather. Continuous convergence of airflows can lead to unusual climatic events. Storm cases of single tornado, tornado swarms, a strong tropical cyclone, and extratropical cyclones associated with heavy rainfall or heavy airborne dust show that the shear stress modulus and total kinetic energy of anomalous winds can better explain extreme weather events than other commonly used dynamical parameters such as divergence and vorticity. Therefore, the shear stress modulus can be used in the forecasting of extreme weather events in operation.

In 2019, Hurricane Dorian affected Atlantic Canada with widespread impacts across the region. In the days preceding landfall, there was a great deal of discussion about the storm and its potential impacts. This discussion also extended onto Twitter, which provided a platform for users to engage with storm‐related information. In this research, we disseminated a questionnaire to residents of Atlantic Canada from late September to late October through Qualtrics, an online survey provider. The questionnaire explored how Twitter influenced respondents' (n = 1218) self‐reported informational behaviors (i.e., searching, sharing, and processing) and behavioral responses before, during, and after the storm. The results demonstrate that users' informational needs and preferences were closely related to their online behaviors. For example, conduits (i.e., those who both searched for and shared information) were highly proactive users who disseminated information about evacuations, recommended protective actions, and other official guidance more so than others. Conduits were also the most likely to heed official guidance in terms of their own preparedness and response. Amplifiers (i.e., those who only share information) and consumers (i.e., those who only search for information) were also motivated to take action by information they saw online, albeit at lower rates than conduits. Lastly, the results demonstrate that users can be positively influenced by information they see online even if they do not actively engage with it. Taken together, the results of this study suggest that Twitter users may interact with storm‐related information in more nuanced and complex ways than previously understood. [ABSTRACT FROM AUTHOR]

Severe weather conditions are low-probability, high-impact events that affect grid operations. The majority of power outages are caused by severe weather conditions. Grid resiliency to weather events can be enhanced by decreasing the reliance on its affected sections. One way to do this is to reduce the power flow through lines vulnerable to severe weather. If a line is disconnected, its initial power flow is distributed through the neighbor lines, which may cause congestion in the grid. FACTS devices can be used to control the power flow of lines that have a higher chance of power outages. Most previous works do not consider weather events in power flow control. In this work, a linearized optimal power flow (OPF)–based algorithm is developed to minimize the real power flow of vulnerable lines considering the thermal limits of lines to prevent infeasible solutions; the simulation is fast, making it suitable for large-scale systems. The proposed optimization problem is presented as a mixed-integer linear program (MILP), making it capable of using short-term load forecasting due to its high solution speed. The proposed optimization problem considers multiple lines with different outage probabilities and the uncertainties of the weather forecast. Moreover, it estimates the power reduction in vulnerable lines due to changes in the series FACTS devices. The performance of the proposed optimization problem is tested on IEEE 14-, 30-, and 118-bus systems for several scenarios. The results are validated with the AC power flow results from MATPOWER. [ABSTRACT FROM AUTHOR]

The phenomenon of sudden drought after pluvial (or heavy pluvial after drought) is known as the Dry–Wet Abrupt Alternation (DWAA). This kind of disaster has been evaluated globally based on various DWAA indices. However, few validations have been performed to compare the various DWAA indices. Given that directly validating the identified DWAA events remains difficult because of the limited historical records of DWAA events, in this study, we developed a simple weighting analysis method to cross‐validate the detected DWAA events using three widely‐used indices. The weighting analysis method can be used to estimate the effectiveness of the DWAA indices by calculating the ratio of the synchronous DWAA events (the DWAA events could be synchronously detected by different indices) to the total number of the detected DWAA events. Results suggest that the standard weighted average of precipitation (SWAP) index has better performance for detecting DWAA events in the Pearl River Basin (PRB) compared to other indices. The soil moisture synchronization, meteorological concentration and real DWAA records further testify that the SWAP is more capable of detecting the DWAA events in the PRB. Based on the validation, the SWAP‐derived DWAA events show that both dry–wet (D–W) events and wet–dry events are severe in the upstream sub‐basins in the PRB, while D–W events are more frequent in the downstream PRB. Our study provides a simple but effective method to validate the detected DWAA. The finding underscores the need to validate DWAA detection generated from multiple indicators before further research, and it could help develop adaptation strategies to improve resilience to combined extremes under changing climate. [ABSTRACT FROM AUTHOR]

Thunderstorm and hailstorm environments from 1981 to 2018 are analysed using the proximity soundings approach based on the European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5) reanalysis dataset. Given the nature of the severe weather dataset, four proximity soundings 0000, 0600, 1200, and 1800 local time were retrieved per day with hail activity or thunderstorms. After quality control, a total of 30,760 vertical profiles were used and grouped seasonally and spatially to consider the local variation of the convective environments. Diverse instability, kinematic, and composite parameters were calculated to characterize the environments for these two convective hazards. The results indicate differences mainly modulated by orographic features, continentality, regional climate, and general synoptic characteristics. Higher instability environments of thunderstorms and hailstorms are observed in the subregions near moisture sources such as oceans, principally during summer and autumn when tropical activity occurs. High wind shear environments are concentrated during winter and spring, mainly in central and northern regions of the country, associated with the passage of frontal systems. The covariates are reasonably accurate at predicting severe weather, but their prediction skill decreases during the warm season when severe weather is more common in Mexico. Increased knowledge of severe weather and associated convective hazards will improve weather forecasting and may be helpful in disaster risk reduction due to meteorological hazards in Mexico. [ABSTRACT FROM AUTHOR]

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios. [ABSTRACT FROM AUTHOR]

Abstract Cold temperature extremes, causing damage to industry, agriculture and human health, are often associated with midlatitude cyclones. Here, we quantify the relation between cyclones and cold extremes by highlighting two cyclone‐associated dynamical features that may, differently, induce cold extremes. Namely, slantwise‐descending dry intrusions (DIs) advect cold air equatorward into the cyclone's cold sector, and upper‐tropospheric troughs, diagnosed as cyclonic potential vorticity (PV) anomalies, induce cold anomalies directly below. By objectively‐identified DI outflows and cyclonic PV anomalies for 1979–2019 in ERA5 reanalysis, their climatological association with the 5% lowest 2‐m temperature is globally quantified. We find that in midlatitudes upper PV anomalies dominate cold extremes, while DIs dominate extremes in the subtropics and even the tropics. The rare overlap between the two features can potentially act as a strong predictor for cold extremes, accompanying ∼30% of extremes at 30° latitude with local hotspots of 80%–100%.

The increasing and extreme weather phenomena observed in the Mediterranean basin are only one aspect of the problem which has broader effects on population, structures and infrastructure. Each of these aspects is itself characterized by a wide variety of issues, which are increasingly leading studies toward a multidimensional assessment of impacts (economic, social and environmental). In this study, we focus on the impact related to the increase in extreme weather events in a specific area characterized by typical vernacular architecture: the "trabocchi" of the Italian Adriatic coast, whose identification as cultural heritage is the result of historical events and social dynamics closely linked to the collective imagination and for which inclusion as intangible cultural heritage in the UNESCO World heritage List has been requested. The weather event investigation was performed considering both long-term large-scale (using the ERA5 dataset) analysis and short-term small-scale (models and ground-based sensors) analysis. The results provide an overview of the event dynamics and enhanced understanding of the area's vulnerability factors to extreme weather phenomena, as well as emphasized the need, in order to protect the integrity of the asset, to study environment changes and to plan concrete actions aimed at conservation, including social actions, to mitigate the problem. [ABSTRACT FROM AUTHOR]

The study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall. [ABSTRACT FROM AUTHOR]

Thermodynamic and liquid water profiles can be retrieved by a ground-based microwave radiometer (MWR) in nearly all weather conditions, which is useful for detecting mesoscale phenomena. This paper reviews the advances in remote sensing of atmospheric profiles and cloud properties by MWR in central China. Comparative studies indicate that MWR retrieval accuracy is different under various skies, especially those that decay under precipitation. The off-zenith method is proven to be capable of reducing the impact of precipitation and snow on MWR retrieval accuracy. Application studies demonstrate that MWR retrievals are helpful for early warning of rainstorms, hailstorms, and thunderstorms. Moreover, MWR retrievals provide a way to study cloud properties. The temporal variations of cloud occurrence frequency (COF) and liquid water path (LWP) are different for low, middle, and high clouds, and the vertical distribution of COF is also different in autumn and other seasons. Note that MWR can infer valid retrievals over the eastern Tibetan Plateau due to the weak precipitation over there. Also, cloud properties over the eastern Tibetan Plateau present differences from those over central China, and this is related to the different characteristics of atmospheric water vapor between these two regions. To bring more benefits for mechanism study and early warning of severe weather and numerical weather prediction, the decayed accuracy of MWR zenith retrievals under precipitation should be resolved. And combining MWR with other instruments is necessary for MWR application in detecting multi-layer clouds and ice clouds.