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Damaging hydrological events can profoundly impact societies. This study reconstructs the longest flood history to date for the Orba River Basin (ORB) in northwestern Italy, from 1511 to 2021 CE. Using the Annual Flood Damage Index, we establish a continuous annual hydrological time-series. Our analysis, incorporating a seasonally-weighted function for interannual storm effects, uncovers anthropogenic influences on the ORB's fluvial landscape and flood characteristics. Specifically, a change-point emerged around the end of the Little Ice Age and the subsequent warming period (c. 1816), after which more intense storms prevailed. Remarkably, recent flood peaks align with heightened climate hazards, proving more extreme and unpredictable on a small scale in disaster-prone areas. Additionally, this study acknowledges competitive forcing factors on a larger scale, including landscape changes due to peasant civilisation expansion and 19th-century deforestation. Broader-scale factors, exemplified by the global impact of the 1815 eruption of Tambora in Indonesia, may have influenced the post-1816 climate conditions. These results emphasise the importance of considering both human-induced disturbances and precipitation occurrences in comprehending a territory's environmental history. [Display omitted] [ABSTRACT FROM AUTHOR]

With the projected escalation of extreme storm events, coastal ecosystems risk undergoing catastrophic shifts and losing essential ecosystem services. Subtidal soft‐bottom mussel beds, vital components of these ecosystems, are particularly vulnerable to hydrodynamically‐induced dislodgement (i.e., detachment of mussel clumps from the bed), especially during storms. However, the mechanisms underlying the resilience—comprising both resistance and recovery—of these beds to storms remain unclear, despite being essential for informed management.This study addresses this knowledge gap regarding subtidal soft‐bottom mussel beds by: (i) quantifying their dislodgement threshold (i.e., the hydrodynamics causing widespread dislodgement of mussel clumps) using novel in situ monitoring methodologies in a representative region, namely the Dutch Wadden Sea; (ii) unveiling the influence of prior life history (here, wave exposure extent) and storm durations on their dislodgement thresholds through a flume study; and (iii) assessing the impacts of repeated storms and prior life histories (here, wave exposure extent and substrate types) on their recovery (i.e. mussel re‐aggregation) through mesocosm experiments.Integrated experimental evidence indicates that: (i) hydrodynamic‐induced dislodgement is a sudden process characterized by distinct near‐bed orbital velocity thresholds, which were identified at our study site to be between 0.45 and 0.50 m s−1; (ii) peak storm intensity, rather than storm duration, primarily drives the dislodgement of subtidal soft‐bottom mussel beds, and prior wave exposure extent regulates the dislodgement threshold; (iii) repeated storms do not seem to affect the recovery of these beds following storm‐related disturbances when the conditions between storms are conducive to mussel re‐aggregation, whereas substrate type significantly impacts recovery.Synthesis and applications. Overall, concerns regarding subtidal soft‐bottom mussel beds degradation primarily stem from increasing storm intensity and their limited resistance to such events. The methodology we developed enables low‐cost quantification of mussel resistance thresholds across broad spatiotemporal scales, facilitating the pinpointing of vulnerable areas. Our findings inform strategic management by highlighting the influential role of prior life histories in shaping mussel bed resistance and the potential to accelerate mussel bed recovery through substrate modification (e.g., shell additions). Both our methodology and findings hold promise for application in comparable ecosystems, such as oyster and coral reefs. [ABSTRACT FROM AUTHOR]

Around midnight on 1/2 November 2023, Jersey (Channel Islands) was impacted by a supercell storm which produced both a tornado, rated T6/IF3, and very large hail. This article presents details of that remarkable hailstorm and places it into context by investigating severe hail events during the October–March cool season in the UK and Crown Dependencies of Jersey, Guernsey, and the Isle of Man. The storm is compared to previous ‘dual‐hazard’ events during the cool season, where both a tornado and severe hail were produced. [ABSTRACT FROM AUTHOR]

Storm Ciarán affected northern France, the Channel Islands and the United Kingdom as an intense North Atlantic depression on 1/2 November 2023, bringing damaging winds, large waves, heavy rain and an intense convective development spawning a tornado on Jersey. Amidst an extended period of stormy weather across Northwest Europe, Storm Ciarán was probably the most significant windstorm to affect the Channel Islands and northern France since the Great Storm of 1987 or Storm Lothar in 1999. Here we explore the storm's evolution, forecasting strategies and challenges and climatological context from the perspective of Met Office operational meteorologists. [ABSTRACT FROM AUTHOR]

Storm Ciarán was an extremely damaging windstorm which affected southern Britain, the Channel Islands and northern France on 1/2 November 2023. Storm force winds with gusts in excess of 90mph (81kn) widely affected Jersey, along with a severe thunderstorm bringing heavy precipitation, large hail and a severe tornado. Due the development of the storm, warnings were issued well in advance giving the island time to prepare. In many ways, Storm Ciarán was comparable to the Great Storm of 1987, both of which left severe damage across Jersey including widespread damage to homes and infrastructure across the island. [ABSTRACT FROM AUTHOR]

On 1‐2 November 2023, Storm Ciarán impacted the British Isles, causing widespread wind damage and significant disruption to the Channel Islands and parts of southern England. Three areas within this wider swathe ‐ Jersey (Channel Islands), Loders in Dorset and Sompting/Lancing in West Sussex ‐ suffered additional, more severe, localised wind damage. These areas were investigated by the Tornado and Storm Research Organisation (TORRO). Each of these sites was found to have been affected by a tornado. The most intense of these was in Jersey, where one of the strongest tornadoes in the UK and Channel Islands since 1954 occurred. [ABSTRACT FROM AUTHOR]

In July 2024, a sudden Southeastern pattern change brought cooler, wetter weather after a hot spell, while the Midwest received ample rain despite previous flooding. Hurricane Beryl became the earliest-ever Category 5 hurricane, causing flash flooding and power outages in Texas. The Western heat wave led to wildfires, while drought conditions fluctuated across the Lower 48 States. In August, Hurricane Debby brought heavy rain and tornadoes across the Southeast and mid-Atlantic, impacting various states with record-setting temperatures and rainfall. Late August saw Hurricane Hone near Hawaii, causing significant rainfall and wind impacts. Throughout the summer, the U.S. experienced above-average temperatures and precipitation, with notable weather events affecting different regions. [Extracted from the article]

The sinking of large particles (i.e., marine snow) has long been recognized as a key pathway for efficient particulate organic carbon (POC) export to the ocean interior during the decline of spring diatom blooms. Recent work has suggested that particles smaller than marine snow can also substantially contribute to POC export. However, a detailed characterization of small and large sinking particles at the end of blooms is missing. Here, we separately collected suspended and small and large sinking particles using Marine Snow Catchers and assessed their biogeochemical composition after the North Atlantic spring bloom in May 2021. During the 3 weeks of sampling, when four intense storms (maximum wind speeds 37–50 kt) created high turbulent kinetic energy dissipation rates and deepened the mixed layer, we observed two distinct sedimentation events. At first, sinking particles were dominated by small (diameter < 0.1 mm), slowly sinking (~$$ \sim $$ 18 m d−1), particles rich in silica that carried a moderate POC flux (< 6 mmol C m−2 d−1) to 500 m depth. Once the storms ceased, the volume of large (diameter > 0.1 mm), fast‐sinking (> 75 m d−1), carbon‐rich marine snow aggregates (not fecal pellets) increased exponentially and POC fluxes at 100 m depth were more than fourfold greater (30 ± 12 mmol C m−2 d−1) than those during the previous event. The aggregates consisted of a mixed post‐bloom plankton community. Our data suggest that the storms shaped the timing, type, and magnitude of POC flux at the end of this spring phytoplankton bloom. [ABSTRACT FROM AUTHOR]

An extreme rainstorm hit Fuyang District, Zhejiang Province, China, on 22 July 2023. A record-breaking 320 mm of rain fell in less than 48 h. In the early evening of 22 July, more than 145 shallow landslides emerged in a 20 km2 hilly area of Fuyang amid turbulent flash flooding and claimed five lives. Post-event field investigations and laboratory experiments were urgently carried out, targeting two geologically typical shallow landslides, to decipher the initiation mechanism and distributing features of the regionally distributed landslides. The main findings are summarized as follows: (1) The week-long rainfall preceding the event and the intense storm on 22 July are presumably primary triggers for the landslides. Besides rainfall, convergent topography of hollows may be another important factor influencing the initiation of landslides. (2) The dominant moso bamboo has an extensive and interconnected root system, which could significantly stabilize slopes by enhancing soil strength and restricting cracks. (3) The sedimentary Heshangzhen Group area is more susceptible to landslides than the igneous Shuangxiwu Group area. Bedrock lithologies affect landslide distribution through controlling soil structures and properties to shape the hillslope hydrology and also through influencing landscape morphology. This study offers new evidence and insights into the lithological control on landslide initiation and distribution. [ABSTRACT FROM AUTHOR]

Extratropical storms, particularly explosive storms or "weather bombs" with exceptionally high deepening rates, present substantial risks and are susceptible to climate change. Individual storms may exhibit a complex and hardly detectable response to human-driven climate change because of the atmosphere's chaotic nature and variability at the regional level. It is thus essential to understand changes in specific storms for building local resilience and advancing our overall comprehension of storm trends. To address this challenge, this study compares analogs—storms with a similar backward track until making landfall—in two climates of three explosive storms impacting different European locations: Alex (October 2020), Eunice (January 2022), and Xynthia (February 2010). We use a large ensemble dataset of 105 members from the Community Earth System Model, version 1 (CESM1). These analogs are identified in two periods: the present-day climate (1991–2001) and a future climate scenario characterized by high anthropogenic greenhouse gas emissions [representative concentration pathway 8.5 (RCP8.5), 2091–2101]. We evaluate future changes in the frequency of occurrence of the storms and intensity, as well as in meteorological hazards and the underlying dynamics. For all storms, our analysis reveals an increase in precipitation and wind severity over land associated with the explosive analogs in the future climate. These findings underscore the potential consequences of explosive storms modified by climate change and their subsequent hazards in various regions of Europe, offering evidence that can be used to prepare and enhance adaptation processes. Significance Statement: This study investigates the impact of climate change on explosive storms, or weather bombs, and their potential consequences for European regions. We project future scenarios of three specific storms, Alex, Eunice, and Xynthia, using a state-of-the-art climate model. Our findings reveal an increase in precipitation and wind over land associated with these storms, emphasizing the heightened risks associated with climate change. The significance lies in understanding the local implications of explosive storms, aiding in the development of resilient strategies and adaptation measures. [ABSTRACT FROM AUTHOR]

AbstractSevere thunderstorms cause substantial economic and human losses in the United States. Simultaneous high values of convective available potential energy (CAPE) and storm relative helicity (SRH) are favorable to severe weather, and both they and the composite variable PROD=CAPE×SRH can be used as indicators of severe thunderstorm activity. Their extremal spatial dependence exhibits temporal non-stationarity due to seasonality and large-scale atmospheric signals such as El Niño-Southern Oscillation (ENSO). In order to investigate this, we introduce a space-time model based on a max-stable, Brown–Resnick, field whose range depends on ENSO and on time through a tensor product spline. We also propose a max-stability test based on empirical likelihood and the bootstrap. The marginal and dependence parameters must be estimated separately owing to the complexity of the model, and we develop a bootstrap-based model selection criterion that accounts for the marginal uncertainty when choosing the dependence model. In the case study, the out-sample performance of our model is good. We find that extremes of PROD, CAPE and SRH are generally more localized in summer and, in some regions, less localized during El Niño and La Niña events, and give meteorological interpretations of these phenomena. [ABSTRACT FROM AUTHOR]

Skill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales. Plain Language Summary: Early warning of severe weather is particularly important in Africa, where resilience to storm hazards such as flash flooding is weak. Given large‐scale atmospheric conditions favorable for convective activity, understanding where storms will occur is challenging for conventional weather prediction models. In semi‐arid regions such as the Sahel, the spatial distribution of SM provides additional predictability of convective rain, via its impact on heating and moistening of the atmosphere. Given that convection is favored over drier soils and that storms create new SM patterns every few days during the wet season, the extent to which knowledge of today's SM aids rainfall prediction in future days is unclear. Here we use 17 years of satellite observations to document how surface properties evolve over 20 days after a storm, and how the surface influences subsequent rainfall patterns. We find that even in regions of West Africa where storms are frequent, the suppression of rain over recently‐wetted soils is evident out to 2 days. In climatologically drier regions, this predictability extends out to 8 days. Overall, the feedback between SM and rainfall enhances rainfall predictability in the short‐term (days), but effectively degrades the skill of longer‐term (weeks) forecasts. Key Points: Satellite observations over the Sahel reveal how the land surface evolves in the 20 days after a Mesoscale Convective System (MCS)After an MCS, rainfall is suppressed over wet soils for 2 days in humid regions and up to 8 days in drier areasInitially soil moisture enhances rainfall predictability, but the strong land feedback degrades skill at longer lead times [ABSTRACT FROM AUTHOR]

Atmospheric energetics is frequently used to diagnose how different atmospheric processes contribute to the development of transient storm track activity. Okajima et al. (2024), https://doi.org/10.1029/2023gl106932 developed an ad hoc method to separate the contributions of cyclones and anticyclones to the energetics using the value of the curvature of the instantaneous local wind. Here, using simple examples in which the physics is exactly known, it is shown that cyclones embedded within a constant zonal flow exhibit large regions with anticyclonic curvature despite the absence of any real anticyclones. Using the method of Okajima et al., a large fraction of the eddy kinetic energy is erroneously attributed to being associated with anticyclones. Furthermore, the fraction that is misattributed varies substantially with changes in the background wind speed. It is concluded that using the curvature to separate energetics contributions from cyclones and anticyclones is not likely to be physically meaningful. Plain Language Summary: How different physical processes contribute to the development of cyclones and anticyclones is frequently diagnosed using the energy budget. Previous studies examined the combined contributions of cyclones and anticyclones to the budget terms. A recent study (Okajima et al., 2024, https://doi.org/10.1029/2023gl106932) developed an ad hoc method to use the value of the curvature of the instantaneous local wind to separate the contributions of cyclones from those of anticyclones. In this study, using idealized examples in which the physics is exactly known, it is shown that cyclones embedded within a constant zonal flow exhibit large regions with anticyclonic curvature despite the absence of any real anticyclones within the domain. In these examples, large fractions of the eddy energy can be misattributed as contributions from anticyclones based on the method of Okajima et al. In addition, the fraction that is misattributed varies substantially with changes in the background wind. It is concluded that it may not be physically meaningful to separate the energetics into contributions from cyclones and anticyclones separately. Key Points: The use of the curvature of the local wind to identify cyclones and anticyclones can lead to significant misattributionThe magnitude of the error varies substantially with changes in the background wind speed [ABSTRACT FROM AUTHOR]

ABSTRACT Coastal zones are susceptible to increasing pressures from urban development and natural hazards, such as storm events, climate change, and rising sea levels. The GIS‐based enhanced bathtub model (eBTM) enables the identification of areas at risk of flooding as a baseline for disaster management and coastal adaptation. This study aims to establish the methodological robustness of the eBTM for coastal flood modeling, by analyzing eight sites flooded during a recent storm event in Table Bay, Cape Town by comparing eBTM outputs with observed flood extent data collected after the storm. The validation showed that for 74% of the 332 validation points the spatial modeling error was < 6 m and for 56% below 3 m. The root mean square error for the model was 4.88 m, indicating an acceptable level of accuracy of the eBTM outputs for coastal risk assessments where more sophisticated models are unavailable. [ABSTRACT FROM AUTHOR]

Global Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales. Plain Language Summary: This study explores the use of advanced weather and climate models, known as Next‐generation Global Storm Resolving Models (GSRMs), to improve our understanding of weather and climate events at different scales. This work is based on a cutting‐edge modeling framework called CAM‐MPAS, which is based on the open‐source Community Earth System Model (CESM2), to study tropical convection features over the Maritime Continent region. This study focused on examining storm‐scale features with a high‐resolution of 3 km, targeting specific numerical experiments during the active convection period in the winter of 2018. The study investigated the accuracy of representing convective systems, precipitation patterns, and tropical cyclone behaviors. Results indicate that the regional‐refined experiments provided more precise precipitation distributions, captured the diurnal cycle more accurately, and demonstrated better agreement with observations regarding tropical cyclone intensity and strength statistics. The findings suggest that refining the regional scale in storm‐resolving models can enhance precipitation predictions across scales. This work contributes to advancing the development and application of storm‐resolving models for better‐informed climate impact assessments. Key Points: The mean state and convective features, centered over the Maritime Continent, are examined at coarse and convection‐permitting scalesCoarse and refined meshes have different precipitation statistics and probability distributionsPrecipitation statistics, diurnal cycles, and tropical cyclone features are improved with regional refinement [ABSTRACT FROM AUTHOR]

Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification framework on a multivariate time-series data set covering solar cycles 22, 23, and 24. We implement ensemble modeling that merges the results from three proton channels (E ≥ 10 MeV, 50 MeV, and 100 MeV) and the long-band X-ray flux (1–8 Å) channel from the Geostationary Operational Environmental Satellite missions. Our task is binary classification, such that the aim of the model is to distinguish strong SEP events from nonevents. Here, strong SEP events are those crossing the Space Weather Prediction Center's "S1" threshold of solar radiation storm and proton fluxes below that threshold are weak SEP events. In addition, we consider periods of nonoccurrence of SEPs following a flare with magnitudes ≥C6.0 to maintain a natural imbalance of sample distribution. In our data set, there are 244 strong SEP events comprising the positive class. There are 189 weak events and 2460 "SEP-quiet" periods for the negative class. We experiment with summary statistic, one-nearest neighbor, and supervised time-series forest (STSF) classifiers and compare their performance to validate our methods for prediction windows from 5 minutes up to 60 minutes. We find the STSF model to perform better under all circumstances. For an optimal classification threshold of ≈0.3 and a 60 minutes prediction window, we obtain a true skill statistic TSS = 0.850, Heidke skill score HSS = 0.878, and Gilbert skill score GSS = 0.783. [ABSTRACT FROM AUTHOR]

In the Southern Hemisphere, Earth system models project an intensification of winter storm tracks by the end of the twenty-first century. Previous studies using idealized models showed that storm track intensity saturates with increasing temperatures, suggesting that the intensification of the winter storm tracks might not continue further with increasing greenhouse gases. Here, we examine the response of midlatitude winter storm tracks in the Southern Hemisphere to increasing CO2 from two to eight times preindustrial concentrations in more realistic Earth system models. We find that at high CO2 levels (beyond 4×CO2), winter storm tracks no longer exhibit an intensification across the extratropics. Instead, they shift poleward, weakening the storm tracks at lower midlatitudes and strengthening at higher midlatitudes. By analyzing the eddy kinetic energy (EKE) budget, the nonlinear storm-track response to an increase in CO2 levels in the lower midlatitudes is found to stem from a scale-dependent conversion of eddy available potential energy to EKE. Specifically, in the lower midlatitudes, this energy conversion acts to oppositely change the EKE of long and short scales at low CO2 levels, but at high CO2 levels, it mostly reduces the EKE of shorter scales, resulting in a poleward shift of the storms. Furthermore, we identify a "tug of war" between the upper and lower temperature changes as the primary driver of the nonlinear-scale-dependent EKE response in the lower midlatitudes. Our results suggest that in the highest emission scenarios beyond the twenty-first century, the storm tracks' response may differ in magnitude and latitudinal distribution from projected changes by 2100. Significance Statement: The Southern Hemisphere winter storm track is projected to intensify by the end of the century, with the most significant intensification occurring in the higher midlatitudes. However, we show that the intensification is not a linear function of the radiative forcing associated with increasing CO2 levels. In fact, our study shows a poleward shift at very high CO2 levels, with the storm track moving southward. This suggests that the Southern Hemisphere winter storm track may require time-sensitive adaptation strategies, as the impacts of global warming on the storm track may not be a linear function of CO2 concentration in the atmosphere. [ABSTRACT FROM AUTHOR]

This research work focuses on the development and application of a storm-tracking algorithm for identifying and tracking storm cells. The algorithm first identifies storm cells on the basis of reflectivity thresholds and then matches the cells in the tracking procedure on the basis of their geometrical characteristics and the distance within the weather radar image. A sensitivity analysis was performed to evaluate the preferable thresholds for each case and test the algorithm's ability to perform in different time step resolutions. Following this, we applied the algorithm to 54 rainfall events recorded by the National Technical University X-Band weather radar, the rainscanner system, from 2018 to 2023 in the Attica region of Greece. Testing of the algorithm demonstrated its efficiency in tracking storm cells over various time intervals and reflecting changes such as merging or dissipation. The results reveal the predominant southwest-to-east storm directions in 40% of cases examined, followed by northwest-to-east and south-to-north patterns. Additionally, stratiform storms showed slower north-to-west trajectories, while convective storms exhibited faster west-to-east movement. These findings provide valuable insights into storm behavior in Athens and highlight the algorithm's potential for integration into nowcasting systems, particularly for flood early warning systems. [ABSTRACT FROM AUTHOR]

The spatial distribution of precipitation in Xinjiang, China, is extremely heterogeneous due to its complex subsurface, and microphysical studies of cloud precipitation over Xinjiang are severely constrained by observational means. The Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR) provides improved three‐dimensional observation data of strong and weak precipitation and performs effective observations in alpine mountain regions. Therefore, this study used GPM/DPR Level‐3 data from January 2015 to December 2022 to analyze the convective and stratiform precipitation characteristics over Xinjiang. The results showed that (1) the range of maximum precipitation reflectivity factors (PRF_max) near the ground and at different heights were larger in summer and maximum PRF_max values appeared in areas along the Altai, Tianshan, and Kunlun Mountains and plains on the respective mountain slopes. In summer, the PRF_max of stratiform (convective) precipitation nearsurface ranged from 24 to 34 dBZ (28–40 dBZ). (2) The two types of precipitation exhibited the highest storm top heights in summer, with convective precipitation having higher values than stratiform precipitation and mountainous areas having higher values than the surrounding basins due to the effects of topographic uplift. (3) The value domains of the PRF, particle mass‐weighted diameter (Dm), and particle number concentration for stratiform (convective) precipitation were more concentrated (wider), that is, PRF: 14–30 dBZ (12–40 dBZ), Dm: 0.9–1.4 mm (0.7–2.1 mm), and the particle concentration: 29–35 (24–39). The height of the center of the probability density of each variable was higher in the three major mountain ranges than in the two major basins owing to the influence of topography. PRF increased and then decreased with height. Convective raindrops were in an active state of high concentration and exhibited a large scale in the lower layers, whereas the particle spectral parameters of stratiform precipitation showed a more concentrated and symmetric distribution and insignificant changes with height. [ABSTRACT FROM AUTHOR]

In the ocean, the performance of active sonar systems depends on the acoustic properties of the seafloor. Daily to monthly variations in near-bottom hydrodynamics and benthic biological activity may affect seafloor properties which then influence the acoustic response of the seafloor. The dependence of seafloor scatter on evolving environmental parameters was investigated using high-frequency active acoustic systems. Seafloor scattering measurements were analyzed in a series of experiments (two weeks to five months in duration) from downward-looking sonars oriented at 20° grazing angle with respect to the seafloor. Data were obtained in two shallow water locations near Portsmouth, New Hampshire, USA: a wave-dominated site and a site dominated by tidal currents. The bottom type for both sites was gravelly sand. The experimental set-up consisted of a tripod placed on the seafloor equipped with three transducers operating at 38, 70, and 200 kHz, a wave-sensing CTD, and underwater cameras. Scattering strength time series were obtained taking into account the local seafloor slope. Results show that there is variability in scattering strength (both in mean levels and distributions). Large variations often coincided with storm events, suggesting that this variability may be driven by changes in bottom roughness caused by storm-related hydrodynamics. [ABSTRACT FROM AUTHOR]

A method for forecasting geomagnetic storms based on deep learning neural networks using digital time series processing for matrix observations of the URAGAN muon hodoscope and scalar Dst-indices has been developed. A scheme of computational operations and extrapolation formulas for matrix observations are proposed. The a variant of the neural network software module and its parameters are chosen. A decision-making rule is formed to forecast and assess the probabilities of correct and false forecasts of geomagnetic storms. An experimental study of estimates of the probabilistic characteristics and forecasting intervals of geomagnetic storms has confirmed the efficiency of the method. The obtained forecasting results are oriented towards solving a number of solar–terrestrial physics and national economic problems. [ABSTRACT FROM AUTHOR]

Synthetic downscaling of tropical cyclones (TCs) is critically important to estimate the long‐term hazard of rare high‐impact storm events. Existing downscaling approaches rely on statistical or statistical‐deterministic models that are capable of generating large samples of synthetic storms with characteristics similar to observed storms. However, these models do not capture the complex two‐way interactions between a storm and its environment. In addition, these approaches either necessitate a separate TC size model to simulate storm size or involve post‐processing to capture the asymmetries in the simulated surface wind. In this study, we present an innovative data‐driven approach for TC synthetic downscaling. Using a machine learning‐based high‐resolution global weather model (ML‐GWM), our approach can simulate the full life cycle of a storm with asymmetric surface wind that accounts for the two‐way interactions between the storm and its environment. This approach consists of multiple components: a data‐driven model for generating synthetic TC seeds, a blending method that seamlessly integrates storm seeds into the surrounding while maintaining the seed structure, and a model based on a recurrent neural network to correct for biases in storm intensity. Compared to observations and synthetic storms simulated using existing statistical‐deterministic and statistical downscaling approaches, our method shows the ability to effectively capture many aspects of TC statistics, including track density, landfall frequency, landfall intensity, and outermost wind extent. Leveraging the computational efficiency of ML‐GWM, our approach shows substantial potential for TC regional hazard and risk assessment. Plain Language Summary: Tropical cyclones (TCs) cause significant destruction each year. It is crucial to accurately assess the risks they present, but this is challenging due to a scarcity of historical data. A commonly used approach involves creating a large number of synthetic TCs that share key characteristics with real storms. However, traditional synthetic TC generation approaches do not capture the complex interactions between storms and their larger‐scale environment. Furthermore, these approaches do not adequately represent the asymmetric structure of TCs. Recently, advances in machine learning‐based global weather model (ML‐GWM) have provided highly accurate and efficient high‐resolution global weather forecasts that surpass conventional numerical weather forecasting. Here, we introduce a novel synthetic TC generation approach, which we call the synthetic TC‐GENerative Model (or "TC‐GEN"), leveraging the state‐of‐the‐art ML‐GWM. We show that TC‐GEN can generate a large number of synthetic storms that allow the interaction between the storm and its environment. We evaluate the performance of TC‐GEN in various aspects, including several landfall characteristics, which are of the most importance for local TC risk analysis. Our study also serves as a compelling example of the transformative impact of machine learning and data science in revolutionizing climate studies during the era of artificial intelligence. Key Points: We introduce a novel approach to tropical cyclone (TC) downscaling using a machine learning based global weather model, named "TC‐GEN"We generate and integrate synthetic TC seeds into the surrounding environment using a data‐driven approachTC‐GEN establishes a framework that opens the possibility of modeling the two‐way interactions between storms and the environment [ABSTRACT FROM AUTHOR]

Landslide is the most common natural risk in mountainous regions on all five continents and they can pose a serious threat in these areas. Strong earthquakes, unusual weather events such as storms and eruptions of volcanoes, and human-caused events such as creating roadways that crossed the slopes are the main causes of landslides and they cause significant dangers to residential properties and society as a whole. The Landslide4sense dataset is used for identifying landslides, which contains 3799 training samples and 245 testing samples. These image patches are taken from the Sentinel-2 sensor, while the slope and Digital Elevation Model (DEM) are from the ALOS PALSAR sensor. Data was gathered from four distinct geographical areas namely Kodagu, Iburi, Taiwan, and Gorkha. We use Deep Learning (DL) models such as ResNet18, U-Net, and VGG16 to predict the landslide. By comparing the above models with the evaluation metrics like loss, precision, recall, F1 score and accuracy, ResNet18 model is selected as the best model for landslide identification. [ABSTRACT FROM AUTHOR]

Sea level monitoring is of great significance to the life safety and daily production activities of coastal residents. In recent years, GNSS interferometric reflectometry (GNSS-IR) has gradually developed into a powerful complementary technique for sea level monitoring, with the advantages of wide signal spatial coverage and lower maintenance cost. However, GNSS-IR-retrieved sea level estimates suffer from a prominent error source, referred to as the dynamic height error due to the nonstationary sea level. In this study, the tidal analysis method, least squares method and cubic spline fitting method are used to correct the dynamic height error, and their performances are analyzed. These three methods are applied to multi-system and multi-frequency data from three coastal GNSS stations, MAYG, SC02 and TPW2, for three years, and the retrievals are compared and analyzed with the in situ measurements from co-located tide gauges to explore the applicability of the three methods. The results show that the three correction methods can effectively correct the sea level dynamic height error and improve the accuracy and reliability of the GNSS-IR sea level retrievals. The tidal analysis method shows the best correction performance, with an average reduction of 39.3% (10.7 cm) and 37.6% (6.7 cm) in RMSE at the MAYG and TPW2 stations, respectively. At station SC02, the cubic spline fitting method performs the best, with the RMSE reduced by an average of 39.3% (5.5 cm) after correction. Furthermore, the iterative process of the tidal analysis method is analyzed for the first time. We found the tidal analysis method could significantly remove the outliers and correct the dynamic height error through iterations, generally superior to the other two correction methods. With the dense preliminary GNSS-IR sea level retrievals, the smaller window length of the least squares method can yield more corrected retrievals and better correction performance. The least squares method and cubic spline fitting method, especially the former, are highly dependent on the amount of daily GNSS-IR sea level retrievals, but they are more suitable for dynamic height correction in storm events than the tidal analysis method. [ABSTRACT FROM AUTHOR]

An introduction is presented in which editor discusses various articles within the issue on topics including disasters that occurred between the years 1802 and 1812 in South America; publication of effects of the "storm" in newpapers and knowledge of meteorological science.

Quasi-linear convective systems (QLCSs) are responsible for approximately a quarter of all tornado events in the United States, but no field campaigns have focused specifically on collecting data to understand QLCS tornadogenesis. The Propagation, Evolution, and Rotation in Linear Storms (PERiLS) project was the first observational study of tornadoes associated with QLCSs ever undertaken. Participants were drawn from more than 10 universities, laboratories, and institutes, with over 100 students participating in field activities. The PERiLS field phases spanned 2 years, late winters and early springs of 2022 and 2023, to increase the probability of intercepting significant tornadic QLCS events in a range of large-scale and local environments. The field phases of PERiLS collected data in nine tornadic and nontornadic QLCSs with unprecedented detail and diversity of measurements. The design and execution of the PERiLS field phase and preliminary data and ongoing analyses are shown. [ABSTRACT FROM AUTHOR]

Hailstones in the clouds develop under various environmental conditions (temperature, humidity, wind, etc.), which impact the size and texture of the ice composing the hail. The stratified structures within a hailstone provide direct insight into the stages it underwent during its growth. In each region, the grains grow with numerous bubbles both inside them and along the grain boundaries (GBs). As hail takes shape, its grain size gradually increases while within the storm cloud and this growth continues even after it falls to the ground. Grain growth is a thermally activated process that modifies the structure of hail, influenced by the presence of bubbles and atmospheric contaminants accumulated within the cloud. This study examines the evolution of grain size in a cylindrical hailstone containing bubbles, employing theoretical equations and a Monte Carlo-based growth model. The computational results contribute to a better understanding of the interaction between GBs and pre-existing bubbles, thereby elucidating the grain size evolution in hailstone containing bubbles near the melting point. Additionally, the study assesses the significance of bubbles in retaining information about a storm within hailstones. [ABSTRACT FROM AUTHOR]

The spatial atmospheric pressure field due to Storm Ciarán is investigated in the south‐east of the UK at high temporal and spatial resolution using data from roadside sites. During Ciarán's passage, a reduction in the boiling point of water was measured at Reading, from which a wider effect on the boiling point of water associated with Ciarán was calculated for the populated regions of the southeast and London. For many people in this region on 2 November 2023, the boiling point of water at breakfast time would have been below the recommended temperature range for optimal brewing of tea. [ABSTRACT FROM AUTHOR]

The storms over the Bay of Bengal (BoB) often combine with the weather systems such as the South Branch Trough (SBT) and the West Pacific Subtropical High (WPSH) to transport plenty of moisture inducing extreme precipitation on the Tibetan Plateau (TP). Determining the fine moisture structures of storms helps understand mechanism of this kind of extreme precipitation. An extreme precipitation occurred on the TP influenced by storm Rashmi (2008). A Lagrangian approach is scrutinized the forward and backward moisture transport trajectories of Rashmi and the TP, respectively. The moisture source of this extreme precipitation is relatively clear, which comes from the collaborative influence of Rashmi with the southwest jet generated by the SBT and the WPSH. Utilizing a three‐dimensional K‐means clustering method devised in this study, the Rashmi's forward trajectories are classified into three categories, the particles ascending with the northward movement of Rashmi (45%), consistently below 1 km (37.5%), and rapidly ascending into the southwest jet stream (17.5%). Notably, 97.5%, 1.2%, and 91% of these categories impact the TP, respectively. The moisture transport structure of storm is verified by backward tracking of moisture over the TP. In addition, the three‐dimensional moisture trajectories classification method is recommended when trajectories suffer rapid altitude changes. [ABSTRACT FROM AUTHOR]

Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity. [ABSTRACT FROM AUTHOR]

Longshore sediment transport (LST) is essential for shaping sandy shorelines. Many shorelines are complex and indented, containing headlands, offshore islands and tombolos. Tombolos often form between islands and the mainland; however, the conditions for LST across tombolos are unclear. This question is important because tombolos are often reinforced with anthropogenic infrastructure, potentially causing sediment starvation of downdrift beaches. Along many shorelines, the return to a tombolo's natural condition has been proposed to promote sediment connectivity and counteract erosion. Nevertheless, the implications of such restorations remain uncertain. In this study, we employ the Delft3D wave‐current model to investigate hydrodynamics and sediment dynamics across a tombolo, examining its role as a connector between adjacent beaches. Contrary to expectations, our simulations show only diminutive longshore currents from the updrift beach across the tombolo unless offshore wave heights exceed 8 m. Instead, predominant currents crossing the tombolo originate from offshore of the island, driven by storm‐induced water level differences and circulation cells on both sides of the tombolo. The offshore island shelters the downdrift domain, resulting in higher wave energy and dissipation updrift of the tombolo. Further, increasing wave height or wave approach angle not only intensifies water level differences but also relocates circulation cells, enhancing total sediment transport from the updrift beach across the tombolo. However, in general, the deposition of sediment from the updrift side of the domain does not compensate for the sediment loss on the downdrift beach. We conclude that LST across tombolos is limited and occurs only under extreme wave conditions. Plain Language Summary: Longshore transport, the movement of sand and fine gravel along the shoreline driven by waves and tides, plays a crucial role in building and maintaining stable and healthy beaches. Here, we focus on the potential impact of the removal of a road that obstructs water flow and sediment transport across a tombolo. Contrary to our expectations, removal of the road does not result in longshore currents across the tombolo unless the wave heights are very large. Instead, we observe water movement from the offshore of the island's updrift side toward the downdrift beach. This unexpected pattern is attributed to storm‐driven water level differences bet side of the island. Additionally, storms generate circulating current patterns on both sides of the island, influencing the direction of water movement. Further investigation reveals that larger waves, particularly those moving more parallel to the beach, amplify the water level difference and alter the location of the rotating currents. Extremely large waves produce longshore currents across the tombolo, which increase the amount of sediment transferred from the updrift to the downdrift domain. However, this addition of sediment from the updrift domain does not fully compensate for sediment loss from the downdrift beach during storms. Key Points: The position, magnitude, and size of circulation cells on both sides of a tombolo vary with storm surge, wave height, and wave directionCirculation cells and storm‐driven water level differences determine sediment transport across the tomboloLongshore sediment transport across the tombolo is negligible except during extreme storms [ABSTRACT FROM AUTHOR]

The most detrimental geomagnetically induced currents (GICs) documented to date have all taken place during geomagnetic storms. Yet, the probability of GICs throughout geomagnetic storms driven by different solar wind transients, such as high‐speed streams/stream interaction regions (HSS/SIR) or interplanetary coronal mass ejection (ICME) sheaths and magnetic clouds (MC), is poorly understood. We present an algorithm to detect geomagnetic storms and storm phases, resulting in a catalog of 755 geomagnetic storms from January 1996 to June 2023 with the solar wind drivers. Using these storms and the IMAGE magnetometer network, we study the temporal and spatial evolution of spikes in the time derivative of the horizontal component of the external magnetic field, |dHext/dt| $\vert \mathrm{d}{\boldsymbol{H}}_{\mathrm{e}\mathrm{x}\mathrm{t}}/\mathrm{d}t\vert $, greater than 0.5 nT/s during geomagnetic storms driven by HSS/SIR, sheaths and MCs. Spikes occur more often toward the end of the storm main phase for HSS/SIR and MC‐driven storms, while sheaths have spikes throughout the entire main phase. During the main phase most spikes occur in the morning sector around 05 magnetic local time (MLT) and the extent in MLT is narrowest for MCs and widest for sheaths. However, spikes in the pre‐midnight sector during the main and recovery phases are most prominent for HSS/SIR‐driven storms. During the storm sudden commencement (SSC), three MLT hotspots exist, the post‐midnight at 04 MLT, pre‐noon at 09 MLT and afternoon at 15 MLT. The pre‐noon hotspot has the highest probability of spikes and the widest extent in magnetic latitude. Plain Language Summary: Geomagnetic storms can have damaging implications on technological infrastructures such as power grids, pipelines or railway systems. Those implications arise due to geomagnetically induced currents (GICs) caused by rapid changes in the Earth's magnetic field. In this paper, we present a way to automatically detect geomagnetic storms as well as the key periods of the storm's activity. The primary solar wind structures that cause geomagnetic storms are high‐speed streams and their stream interaction regions (HSS/SIR), sheaths ahead of interplanetary coronal mass ejections and magnetic clouds (MC). Using the detected geomagnetic storms we study spikes in the magnetic field changes in Fennoscandia to map the GIC activity throughout the geomagnetic storms caused by HSS/SIR, sheaths and MCs. We report on several similarities and differences in the evolution and extent of spikes during geomagnetic storms for the different solar wind structures. One such difference is for example, in the likelihood of spikes throughout the storm. For those storms caused by HSS/SIR and MCs spikes are more likely close to the time of maximum geomagnetic storm disturbance, while for storms caused by sheaths spikes are likely at any time from the beginning of the storm to the maximum storm disturbance. Key Points: This paper presents a catalog of geomagnetic storms, their phases and interplanetary drivers from 1996 to 2023During the storm sudden commencement three hotspots of spikes in |dHext/dt| exist, at 04, 09 and 15 magnetic local timeSheath storms have |dHext/dt| spikes occurring during the entire main phase, while for MC and HSS/SIR peak toward the end of the main phase [ABSTRACT FROM AUTHOR]

A variety of magnetosphere‐ionosphere current systems and waves have been linked to geomagnetic disturbance (GMD) and geomagnetically induced currents (GIC). However, since many location‐specific factors control GMD and GIC intensity, it is often unclear what mechanisms generate the largest GMD and GIC in different locations. We address this challenge through analysis of multi‐satellite measurements and globally distributed magnetometer and GIC measurements. We find embedded within the magnetic cloud of the 23–24 April 2023 coronal mass ejection (CME) storm there was a global scale density pulse lasting for 10–20 min with compression ratio of ∼10 ${\sim} 10$. It caused substantial dayside displacements of the bow shock and magnetopause, changes of 6RE $6{R}_{E}$ and 1.3−2RE $1.3-2{R}_{E}$, respectively, which in turn caused large amplitude GMD in the magnetosphere and on the ground across a wide local time range. At the time this global GMD was observed, GIC measured in New Zealand, Finland, Canada, and the United States were observed. The GIC were comparable (within factors of 2–2.5) to the largest ever recorded during ≥ ${\ge} $14 year monitoring intervals in New Zealand and Finland and represented ∼ ${\sim} $2‐year maxima in the United States during a period with several Kp≥ ${\ge} $7 geomagnetic storms. Additionally, the GIC measurements in the USA and other mid‐latitude locations exhibited wave‐like fluctuations with 1–2 min period. This work suggests that large density pulses in CME should be considered an important driver of large amplitude, global GMD and among the largest GIC at mid‐latitude locations, and that sampling intervals ≤10s ${\le} 10s$ are required to capture these GMD/GIC. Plain Language Summary: We explore how disturbances in the Earth's magnetic field, known as geomagnetic disturbances (GMD), and the resulting geomagnetically induced currents (GIC) in power systems are influenced by different electrical currents and waves in near‐Earth space. One challenge is the lack of easily accessible data on GIC over long periods, which makes it hard to figure out what factors are most responsible for changes in GIC in different places. Also, there is limited research combining data from satellites with data collected on the ground to figure out exactly how GMD and GIC are generated. To tackle these issues, we looked at data collected by multiple satellites in different parts of near‐Earth space along with data from ground magnetometers and GIC measurements distributed around the world. Our results suggest that density pulses from coronal mass ejections, a particular type of structure in the solar wind, are important in causing significant disturbances in the Earth's magnetic field globally and contribute to some of the largest GIC seen in the mid‐latitude region of United States. We emphasize the importance of taking measurements with high sampling rates (≤10s) $(\le 10s)$ to accurately capture these disturbances and the resulting GIC. Key Points: A density pulse embedded in a coronal mass ejection drives global geomagnetic disturbances (GMD) and geomagnetically induced currents (GIC)Measured GIC's comparable to or exceed reference values in several regions, including 58.1 A in the mid‐latitude region of United StatesLarge‐amplitude density pulses are an important driver of GIC and GMD for mid‐latitude regions with large populations [ABSTRACT FROM AUTHOR]

The present study investigates mid‐ and low‐latitude ground magnetic disturbances observed following the arrival of three interplanetary (IP) shocks during the super‐geomagnetic storms of February 1958 and July 1959. One may expect that after IP shocks, the H (northward) magnetic component increases globally but especially on the dayside. However, in each event, the H component was depressed sharply for 1–2 hr in the dawn‐to‐noon sector, whereas it increased in other local time (LT) sectors. Observed magnetic deflections suggest that there existed field‐aligned currents (FACs) flowing into and out of the auroral zone around the western and eastern edges of the LT sector of the dayside H depression. These features strongly suggests that the observed H depression was a remote effect of a R1‐sense FAC system. It was previously reported that similar ground magnetic disturbances were observed after the SSC of the 2003 Halloween storm, which reveals striking similarities to the well‐known H depression observed at Colaba during the 1859 Carrington storm. It is therefore suggested that the external driving behind IP shocks, especially those associated with major storms, is most optimum for the sharp reduction of the dayside H component through the formation and intensification of the dayside FAC system. Associated magnetic disturbances are considered to be larger in magnitude with increasing magnetic latitude, and oriented azimuthally as well as meridionally. Such magnetic disturbances in dayside midlatitudes may not be discussed very often as a target of space weather, but their potential impacts on ground infrastructures probably require closer attention. Plain Language Summary: Geomagnetic storms of February 1958 and July 1959 were historically intense. After the arrival of interplanetary (IP) shocks during these storms, the horizontal component of midlatitude magnetic field was sharply depressed at prenoon, but at other local times (LTs) it enhanced significantly as generally expected from solar wind compression. This LT distribution of magnetic disturbances can be explained by a dayside wedge‐shaped current circuit, which connects the magnetosphere with the ionosphere. Similar ground magnetic disturbances were reported previously for the October 2003 (Halloween) storm, and the extraordinary H depression observed at Colaba during the 1859 Carrington storm was also observed when Colaba was at prenoon. It is therefore suggested that the driver of this dayside current system, probably southward interplanetary magnetic field, often becomes extraordinarily large behind IP shocks, especially those of major storms, and intensifies this current system to unusual levels. The associated magnetic disturbance is expected to be larger at higher latitudes, and oriented in any direction. These features exhibit a distinct contrast to magnetic disturbances caused by other magnetospheric current systems (e.g., the ring current). Dayside ground magnetic disturbances are rarely discussed as a potential cause of hazardous space weather events, but probably deserve closer attention. Key Points: The H magnetic component was depressed sharply in dayside mid‐ and low latitudes following IP shock arrivals during the 1958 and 1959 stormsThe H depression was confined from dawn to noon, and the direction of magnetic deflection depended on local time as expected for an R1 field‐aligned current (FAC) systemFor extreme solar wind driving, this dayside R1 FAC system can be a potential cause of geomagnetic hazards especially in the subauroral zone [ABSTRACT FROM AUTHOR]

In 2020, Colorado experienced the most severe wildfire season in recorded history, with wildfires burning 625 357 acres across the state. Two of the largest fires burned parts of Rocky Mountain National Park (RMNP), and a study was initiated to address concerns about potential effects on drinking water quality from mobilization of ash and sediment. The study took advantage of a wealth of pre‐fire data from adjacent burned and unburned basins in western RMNP. Pre‐ and post‐fire data collection included discrete sample collection and high‐frequency water‐quality measurements using in‐stream sensors. Kruskal–Wallis tests on discrete data indicated that specific conductance, base cations, sulphate, chloride, nitrate, and total dissolved nitrogen concentrations increased post‐fire, whereas silica and dissolved organic carbon (DOC) did not (p ≤ 0.05). In‐stream sensors captured large spikes in concentrations of nutrients, turbidity, and DOC in the burned basin that were missed by discrete sampling. Sensor data indicated nitrate and turbidity increased by up to one and two orders of magnitude, respectively, from pre‐event concentrations during storms, and DOC increased up to 3.5×. Empirical regression equations were developed using pre‐fire data and applied to the post‐fire period to estimate expected stream chemistry in the absence of fire (a 'no‐fire' scenario). Overlays of actual post‐fire chemistry showed the timing and magnitude of differences between observed and 'estimated' chemistry. For most solutes, observed post‐fire concentrations were notably greater than expected under the 'no‐fire' scenario, and differences were greatest during storm events. Comparison of data from the burned and unburned basins indicated DOC concentrations were affected by climate as well as fire. Results from this study demonstrate the importance of both pre‐fire data and high‐frequency data for characterizing dynamic hydrochemical responses in wildfire‐affected areas. [ABSTRACT FROM AUTHOR]

This study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use. [ABSTRACT FROM AUTHOR]

Water column removal in streams is a nitrogen (N) cycling pathway that has been historically overlooked. Studies filling this knowledge gap have focused on the role of water column N removal in mid‐to‐large‐order rivers with consistently high suspended sediment concentrations. However, smaller streams may provide comparable suspended sediment concentrations during and after storm events, creating favorable conditions for water column N removal. To assess the presence, magnitude, and control of water column N removal during storms in low‐order watersheds, we measured water column denitrification and heterotrophic assimilatory N uptake rates at three locations in a Mid‐Atlantic watershed during five storm events of different magnitude, sediment loads, and nutrient availability. We found large variations in water column denitrification (0–5.56 mg N g−1 d−1) and assimilatory uptake (0.003–1.67 mg N g−1 d−1). Higher rates of N removal occurred during flow recession, with a correlation between suspended sediment organic matter content and denitrification. On average, denitrification rates in the water column were higher when flashy responses to storm events occurred. In contrast to denitrification, water column N removal rates (as both denitrification and heterotrophic assimilation) during storm events were comparable to those measured at baseflow in larger rivers. However, water column denitrification could only account for less than 10% of potential reach‐scale N removal during most of the storm events. Our findings provide insight into the ecological relevance of small stream water columns and suggest that more research is needed to understand the magnitude of stream water column processing on watershed‐scale N removal. Plain Language Summary: In freshwater ecosystems, microorganisms facilitate the transfer of nutrients from the surrounding environment to other organisms. Through a process known as nitrogen removal, microbes capture and modify nitrogen—an essential element for life—into forms that other organisms can use. In streams, microbes that perform nitrogen removal can live on the streambed, or on the surface of small sediment particles suspended in the water column. During storms, the amount of suspended particles in a stream increases dramatically, potentially providing more surface area where nitrogen removal can occur. Our study measured how much nitrogen removal takes place in the water column of streams during storms. We also determined whether other environmental factors influence the rates of removal. We found that higher rates of nitrogen removal occur after the peak of a storm, when the stream water level is decreasing. Additionally, we found that the rate of nitrogen removal increases with the amount of organic matter present in the suspended sediment. Overall, the rates of uptake we measured were comparable to those observed in much larger rivers, suggesting that nitrogen uptake in the water column during storms may be an important but overlooked component of the nitrogen cycle in small streams. Key Points: More flashy storms showed higher rates of water column denitrificationWater column removal rates were higher during flow recession than during rising flowsDenitrification was positively related to organic matter content in suspended sediment, whereas N uptake decreased with suspended chlorophyll [ABSTRACT FROM AUTHOR]

Weather forecasts at subseasonal-to-seasonal (S2S) timescales have little forecast skill in the troposphere: individual ensemble members are mostly uncorrelated and span a range of atmospheric evolutions that are possible for the given set of external forcings. The uncertainty of such a probabilistic forecast is then determined by this range of possible evolutions – often quantified in terms of ensemble spread. Various dynamical processes can affect the ensemble spread within a given region, including extreme events simulated in individual members. For forecasts of geopotential height at 1000 hPa (Z1000) over Europe, such extremes are mainly comprised of synoptic storms propagating along the North Atlantic storm track. We use ECMWF reforecasts from the S2S database to investigate the connection between different storm characteristics and ensemble spread in more detail. We find that the presence of storms in individual ensemble members at S2S timescales contributes about 20 % to the total Z1000 forecast uncertainty over northern Europe. Furthermore, certain atmospheric conditions associated with substantial anomalies in the North Atlantic storm track show reduced Z1000 ensemble spread over northern Europe. For example, during periods with a weak stratospheric polar vortex, the genesis frequency of Euro-Atlantic storms is reduced and their tracks are shifted equatorwards. As a result, we find weaker storm magnitudes and lower storm counts, and hence anomalously low subseasonal ensemble spread, over northern Europe. [ABSTRACT FROM AUTHOR]

Influenced by the Northeast China cold vortex, Jiangsu Province experienced strong thunderstorm and gale weather on April 30, 2021, which caused 17 deaths and direct economic losses of 164 million yuan. In particular, a rare extreme gale of 47.9 m s− 1 occurred in the Tongzhou Bay area. To gain insights into the mechanisms of the discharge and lightning activity in the thunderstorm, the lightning characteristics during the evolution of the storm and their relationship with microphysical parameters are analyzed using the satellite-based and ground-based lightning detection data and dual-polarization Doppler weather radar data. The results show that the extreme gale occurred during the weakening stage of lightning activity and was located between the bow echo and gust front, where the lightning activity was scarce. The intracloud (IC) lightning flashes always played a major role throughout the development, maturation and weakening of the storm, with an average percentage of approximately 80.0%. During the 30 min before the extreme gale, the average percentage of the IC lightning flashes was 83.6%. The proportions of the positive total lightning flashes and positive IC lightning flashes were large, whereas the proportion of the positive cloud-to-ground (CG) lightning flashes remained small (5.5% on average). Additionally, four lightning jumps passing the 2σ threshold test occurred during this strong storm. The total lightning jump appeared approximately 34 min before the extreme gale. When lightning jumps are considered as a strong weather warning indicator, the total lightning jump has advantages over the CG and IC flashes in terms of accuracy and timeliness. The Linear fitting indicates that the volume of ice-phase precipitation particles at or above the altitude of the − 10 °C isotherm, with radar reflectivity above 35dBZ, is the best indicator of the CG and total lightning activities, and the correlation coefficients of it with the CG and total flash rates are 0.77 and 0.67, respectively. [ABSTRACT FROM AUTHOR]

Severe weather conditions and urban intensification are key factors affecting the response of combined sewer systems, especially during storm events. In this regard, the capacity assessment of combined sewer networks under the impact of rainfall storm events of different return periods was the focus of this work. The selected case study area was a mixed-use catchment in the city centre of Thessaloniki, Greece. The hydraulic performance of the examined sewer network was assessed using an InfoWorks ICM model. The results indicated that mitigation strategies, such as the application of nature-based solutions (NBSs) or low-impact developments (LIDs) are considered essential for controlling combined sewer overflows. A multicriteria analysis was conducted to select the most appropriate NBSs/LIDs to be located in the study area to enhance the system's capacity. The results of this multicriteria analysis were used to propose a combined sewer overflow mitigation scenario, based on the installation of green roofs, as the most highly ranked solution in the analysis performed. Incorporating the proposed NBS/LID in the hydrologic-hydraulic model significantly increased the performance of the studied combined sewer network. [ABSTRACT FROM AUTHOR]

Since the 1970s, comprehensive control measures on soil erosion in the headwater region of the Loess Plateau have been carried out. Quantitative evaluation of the benefits of soil and water conservation measures during extreme rainstorms is of great significance for the comprehensive management of the catchments. In this study, a systematic modeling methodology for evaluating the effects of soil and water conservation measures on sediment reduction was developed based on a distributed soil erosion model (DSEM). Taking the Chabagou basin in the Loess Plateau as the study area, the tested DSEM was used to simulate soil erosion and sediment yield during an extreme rainstorm under two scenarios, the uncontrolled condition and the controlled condition with soil and water conservation measures implemented. The results showed that DSEM could successfully simulate soil and water losses and evaluate the effects of soil and water conservation measures during extreme storm events. The evaluated results showed that each soil and water conservation measure had the specific function of sediment reduction. And under rainstorm conditions, the effect of engineering measures on sediment reduction was greater than that of forest and grass measures. [ABSTRACT FROM AUTHOR]

The downshear‐left enhancement of tropical cyclone rainfall has been demonstrated previously, but the radial dependence of this effect was not analyzed in detail. This study quantifies the progressive upwind shift of the wavenumber‐1 maximum rain position with radius relative to the vertical wind shear direction. This shift is visualized as a distinctive upwind spiral of the maximum. It is shown that this spiral pattern is generally observed across various storm intensities, shear strength, and ocean basins. Detailed examination revealed that the maximum downwind deflection angle of the wavenumber‐1 rain maximum relative to the shear direction is smaller for tropical storms than hurricanes, but insensitive to hurricane intensity. It is proposed that the spirality is produced by a continuous decline in angular advection of air parcels with radius. The stability of the deflection angle in hurricanes may be accounted for by a corresponding increase in vertical ascent under strengthening angular flow. Plain Language Summary: Tropical cyclones (TCs) can produce torrential rainfall that generates floods, causing significant socio‐economic losses. Understanding the spatial structure of the TC rain field is crucial for improving disaster preparedness. The TC rain field can be thought of as the combination of a symmetric and an asymmetric part. By using a technique called Fourier decomposition, we can break down the asymmetric part into individual wavenumber components. The first component, wavenumber‐1 (WN‐1), is dominant and tends to be larger in the downshear quadrants under vertical wind shear. Using 21 years of global WN‐1 rain fields, we produced composite images aligned with the shear direction. We discovered that the positions at which the WN‐1 maximum occurs progressively shift upwind with increasing distance from the TC center, forming a spiral. We provided the first quantification of the observed spirals and showed that this is a general pattern that exists across different TC intensities, shear strength, and ocean basins. We also identified detailed changes in the pattern with storm intensity and introduced simple models as a first attempt to comprehend these changes. The findings can improve weather forecasts and risk predictions, making us better prepared for hazards associated with TC rainfall. Key Points: There exists a general and progressive upwind shift in the wavenumber‐1 maximum with radius in global shear‐relative rainfall compositesMaximum downwind deflection of the wavenumber‐1 maxima rises with storm intensity up to Category 1 on the Saffir–Simpson Hurricane ScaleLinearity between the angular velocity and the vertical velocity of the storm may stabilize the downwind deflection beyond Category 1 [ABSTRACT FROM AUTHOR]

This study explores the mechanisms contributing to heavy precipitation associated with landfalling atmospheric rivers (ARs) and orography, focusing on a high‐profile case study of an extratropical cyclone, storm Bronagh, which caused flooding and travel disruption in the UK. While prior research has established the connection between ARs, orography, and precipitation, the specific mechanisms leading to intense rainfall remain unclear. A novel methodology is introduced that quantifies the relative contribution to the total precipitation from cyclone‐induced processes and orographic processes using modifications to the Met Office Unified Model orography and microphysics parameterization scheme. Results show that the majority (63%) $(63\%)$ of storm Bronagh 's rainfall over land is attributed to cyclone‐related processes (frontal ascent and embedded convection). However, interaction of the AR associated with storm Bronagh and orography focuses more of the rain over the hills, enhancing precipitation in this region by a further 50% $50\%$. Further analysis reveals that the seeder‐feeder mechanism plays a predominant role in orographically enhanced rainfall. Accretion and riming processes occur as rain from the cyclone induced (seeder) cloud falls through the orographically induced (feeder) cloud enhancing precipitation rates. The feeder cloud formation is related to forced ascent of air within the AR over the orography. In conclusion, this study sheds light on the intricate interplay between ARs, orography, and cyclone precipitation during the landfall of extratropical cyclones. By separating the mechanisms behind heavy rainfall in the context of storm Bronagh, we quantify for the first time the effect of the seeder‐feeder effect at different heights over orography. Plain Language Summary: This study investigates why we experience heavy rainfall during storms in the UK. We focus on storm Bronagh which reached the national news because of the flooding and travel disruption it caused. While there is an established statistical link between atmospheric rivers, mountains, and rain, the details of how atmospheric rivers interact with mountains leading to intense rainfall were not well‐understood. In this research, a new approach which made adjustments to a weather model is used to determine these complex interactions. The results showed that most of the heavy rain during storm Bronagh came from the storm itself, but that it was enhanced via interaction with the mountains. The moisture in an atmospheric river turns into cloud droplets as it is forced to rise over the mountains. A specific process, allowing small raindrops to grow rapidly as they fall through orographic cloud, caused the localized rainfall enhancement. Understanding these processes helps us predict and prepare for extreme weather events better, enhancing our ability to respond to their impacts effectively. Key Points: The largest contribution to observed rainfall totals (63%) was associated with frontal ascent and convection in storm BronaghInteraction of storm Bronagh with orography enhanced rainfall totals by a further 50%Orographic rainfall enhancement in storm Bronagh is dominated by the seeder‐feeder process [ABSTRACT FROM AUTHOR]

Climate projections like UKCP18 predict that the UK will move towards a wetter and warmer climate with a consequent increased risk from surface water flooding (SWF). SWF is typically caused by localized convective rainfall, which is difficult to predict and requires high spatial and temporal resolution observations. The likelihood of SWF is also affected by the microtopographic configuration near buildings and the presence of resilience and resistance measures. To date, most research on SWF has focused on modelling and prediction, but these models have been limited to 2 m resolution for England to avoid excessive computational burdens. The lead time for predicting convective rainfall responsible for SWF can be as little as 30 minutes for a 1 km x 1 km part of the storm. Therefore, it is useful to identify the locations most vulnerable to SWF based on past rainfall data and microtopography to provide better risk management measures for properties. In this study, we present a framework that uses long-term gridded rainfall data to quantify SWF hazard at the 1 km x 1 km pixel level, thereby identifying localized areas vulnerable to SWF. We also use high-resolution photographic (10 cm) and LiDAR (25 cm) DEMs, as well as a property flood resistance and resilience (PFR) database, to quantify SWF exposure at property level. By adopting this methodology, locations and properties vulnerable to SWF can be identified, and appropriate SWF management strategies can be developed, such as installing PFR features for the properties at highest risk from SWF. [ABSTRACT FROM AUTHOR]

Accurate characterization of the movement of water through catchments, particularly during precipitation event response, is critical for hydrological efforts such as contaminant transport modeling or prediction of extreme flows. Abiotic hydrogeochemical tracers are commonly used to track sources and ages of surface waters but provide limited details about transit pathways or the spatial dynamics of water storage and release. Alternatively, biotic material in streams is derived from thousands of taxa originating from a variety of environments within watersheds, including groundwater, sediment, and upslope terrestrial environments, and this material can be characterized with genetic sequencing and bioinformatics. We analyzed the stable water isotopes (δ18O and δ2H) and microbiome composition (16S rRNA gene amplicon sequencing) of the Marys River of western Oregon, USA during an early season storm to describe the processes, storage, and flowpaths that shape surface water hydrology. Stable water isotopes (δ18O and δ2H) typified an event response in which stream water is composed largely of 'old' water introduced to the catchment before the storm, a common though not well understood phenomenon. In contrast, microbial biodiversity spiked during the storm, consisting of early- and late-event communities clearly distinguishable from pre-event communities. We applied concentration-discharge (cQ) analysis to individual microbial taxa and found that most Alphaproteobacteria sequences were positively correlated (i.e., were mobilized) with discharge, whereas most sequences from phyla Gammaproteobacteria and Bacteroidota were negatively correlated with discharge (i.e., were diluted). Source predictions using the prokaryote habitat preference database ProkAtlas found that freshwater-associated microbes composed a smaller fraction of the microbial community during the stream rise and a larger fraction during the recession, while soil and biofilm-associated microbes increased during the storm and remained high during recession. This suggests that the "old" water discharged during the storm was likely stored and released from, or passed through, soil- and biofilm-rich environments, demonstrating that this approach adds new, biologically derived tracer information about the hydrologic pathways active during and after this event. Overall, this study demonstrates an approach for integrating information-rich DNA into water resource investigations, incorporating tools from both hydrology and microbiology to demonstrate that microbial DNA is useful not only as an indicator of biodiversity but also functions as an innovative hydrologic tracer. [ABSTRACT FROM AUTHOR]

The eradication of recalcitrant bacterial biofilm infections necessitates the development of speedy diagnostics and prompt therapeutics. However, constructing a portable versatile platform that enables in situ monitoring of biofilm infections accompanied by potent antibiofilm activity remains challenging. To address this conundrum, a microneedle theranostic patch (Mn: C/G@MN) is devised by incorporating an innovative biophotonic probe (manganese‐doped carbon dots, Mn: CDs) into methacrylated gelatin for visual monitoring of biofilm infection and on‐demand photo‐therapy. The Mn: C/G@MN patch penetrates the physical barrier of biofilms to track their acidic microenvironment, exhibiting a visualized fluorescence color change (from yellow to turquoise) to enable naked‐eye monitoring of biofilm infection. Furthermore, the Mn: C/G@MN patch can drastically eradicate biofilm on‐demand through the synergy of local hyperthermia and hydroxyl radical (•OH) storm under 808 nm near‐infrared light illumination, enabling the damaging of extracellular polymeric substances (EPS) matrix to disperse biofilms and subsequently kill detached bacteria. Both in vitro and in vivo findings authenticate that biofilm infection monitoring‐and‐treating can be achieved. Moreover, the versatile Mn: C/G@MN patch is conducive to suppressing inflammatory responses, expediting collagen deposition, stimulating angiogenesis, and accelerating biofilm‐infected wound healing. As envisaged, this work highlights the potential of such a versatile platform for application in integrated theranostics for biofilm infection. [ABSTRACT FROM AUTHOR]

Antecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (δ7 ${\delta }^{7}$Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and δ7 ${\delta }^{7}$Li are negatively correlated in both events, but mean δ7 ${\delta }^{7}$Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream δ7 ${\delta }^{7}$Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream δ7 ${\delta }^{7}$Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm δ7 ${\delta }^{7}$Li‐discharge patterns distinguished by the antecedent conditions of the watershed. Plain Language Summary: Storm events often cause characteristic shifts in stream solute chemistry. Interpreting these signals offers insight into the water‐rock interactions occurring within watersheds. Here, we use lithium stable isotopes and reactive transport modeling to relate how long water spends in a catchment, or how deep water infiltrates through a catchment, to the extent of chemical weathering. We show that the first significant storm after a dry season exports more chemically evolved water, while a wet season storm releases less evolved, shallower, and younger water. Our results indicate that stream flow δ7 ${\delta }^{7}$Li in small watersheds offers a sensitive record of hydrological conditions prior to the storm, reflecting subtle shifts in the efficiency of the Critical Zone to generate, transport, and ultimately export solutes. Key Points: Stream lithium stable isotope ratios (δ7Li) recorded at high frequency over storm events are sensitive to antecedent conditionsA reactive transport model cannot produce observed shifts in stream chemistry through variations in flow rate aloneFlux‐weighting of model fluid outputs based on time‐varying fluid transit time distributions describes stream δ7Li over storm hydrographs [ABSTRACT FROM AUTHOR]

The physical processes governing a tropical cyclone's lifecycle are largely understood, but key processes occur at scales below those resolved by global climate models. Increased resolution may help simulate realistic tropical cyclone intensification. We examined fully coupled, global storm‐resolving models run at resolutions in the range 28–2.8 km in the atmosphere and 28–5 km in the ocean. Simulated tropical cyclone activity, peak intensity, intensification rate, and horizontal wind structure are all more realistic at a resolution of ∼5 km compared with coarser resolutions. Rapid intensification, which is absent at typical climate model resolutions, is also captured, and exhibits sensitivity to how, and if, deep convection is parameterized. Additionally, the observed decrease in inner‐core horizontal size with increasing intensification rate is captured at storm‐resolving resolution. These findings highlight the importance of global storm‐resolving models for quantifying risk and understanding the role of intense tropical cyclones in the climate system. Plain Language Summary: Simulating strong tropical storms (i.e., major hurricanes, super typhoons) with climate models is challenging because important processes that act to intensify a storm occur over spatial scales that are too small for global models to capture. Typical models lack sufficient resolution in the atmosphere and ocean, often constrained by computational resources. Recently, in a few models, resolution has increased to a point where each grid cell represents an area of just a few square kilometres, a significant leap of one or two orders of magnitude. We analyzed tropical storms simulated by these state‐of‐the‐art, so‐called storm‐resolving models and found that peak tropical storm intensity and the rate at which storms intensify are both more realistic. These models also simulate the rapid intensification of tropical storms and capture the small eye diameters often seen in the most intense storms. Our work provides evidence that storm‐resolving resolution may help us better understand the role of tropical storms in the climate system and predict their behavior in a warming climate. Key Points: Simulated tropical cyclone characteristics analyzed in two fully coupled global climate models at atmospheric resolutions of 28 to 2.8 kmTropical cyclone intensification rate is close to observations at resolutions of 5 km or finer, and rapid intensification is capturedStorm‐resolving models also capture the observed relationship between high intensification rate and small inner‐core size [ABSTRACT FROM AUTHOR]

Stream water chemistry at catchment outlets is commonly used to infer flow paths of water through catchments and to quantify the relative contributions of various flow paths and/or end-members, especially during storm events. For this purpose, the number and nature of these flow paths or end-members are commonly determined with principal component analysis based on all available conservative solute data in inverse end-member mixing analyses (EMMAs). However, apart from the selection of conservative solutes, little attention is paid to the number and choice of the solutes that are included in the analysis, despite the impact this choice can have on the interpretation of the results from an inverse EMMA. Here, we propose a methodology that tries to fill this gap. For a given pair of measured solutes, the proposed methodology determines the minimum number of required end-members, based on the synchronous variation of the solutes during storm events. This allows identification of solute pairs for which a simple two-end-member mixing model is sufficient to explain their variation during storm events and of solute pairs, which show a more complex pattern requiring a higher-order end-member mixing model. We analyse the concentration–concentration relationships of several major ion pairs on the storm-event scale, using multi-year, high-frequency (< 60 min) monitoring data from the outlet of two small (0.8 to 5 km 2) French catchments with contrasting land use, climate, and geology. A large number of storm events (56 % to 79 %) could be interpreted as being the result of a mixture of only two end-members, depending on the catchment and the ion pairs used. Even though some of these results could have been expected (e.g. a two-end-member model for the Na +/Cl- pair in a catchment close to the Atlantic coast), others were more surprising and in contrast to previous studies. These findings might help to revise or improve perceptual catchment understanding of flow path or end-member contributions and of biogeochemical processes. In addition, this methodology can identify which solute pairs are governed by identical hydro-biogeochemical processes and which solutes are modified by more complex and diverse processes. [ABSTRACT FROM AUTHOR]