Individual Storms Research Articles

Пространственное распределение авроральных высыпаний и сбоев в работе железнодорожной автоматики на севере европейской части России

We study the relationship between space weather disturbances and spatial distribution of failures in railway automatics at segments of Northern and October railways in 2001–2006. During the most intensive magnetic storms that caused numerous failures, latitude distribution of auroral electron precipitation and local geomagnetic disturbance, determined as mean absolute value of time derivative of the geomagnetic field horizontal component |dBH/dt|, are examined. We show that in magnetic storm main and recovery phases the segments, where the failures were recorded, correspond to the region of intense auroral precipitation and |dBH/dt| exceeded 5 nT/s. The relationship between position of auroral oval equatorial boundary and spatial distribution of failures is analyzed for individual magnetic storms and statistically for five years of observations. Both individual cases and statistic tests show that southward displacement of the auroral oval equatorial boundary correlates with increase in the proportion of failures at lower latitude railway segments.

Spatial distribution of auroral precipitation and failures in railway automatics at the north of European Russia

We study the relationship between space weather disturbances and spatial distribution of failures in railway automatics at segments of Northern and October railways in 2001–2006. During the most intensive magnetic storms that caused numerous failures, latitude distribution of auroral electron precipitation and local geomagnetic disturbance, determined as mean absolute value of time derivative of the geomagnetic field horizontal component |dBH/dt|, are examined. We show that in magnetic storm main and recovery phases the segments, where the failures were recorded, correspond to the region of intense auroral precipitation and |dBH/dt| exceeded 5 nT/s. The relationship between position of auroral oval equatorial boundary and spatial distribution of failures is analyzed for individual magnetic storms and statistically for five years of observations. Both individual cases and statistic tests show that southward displacement of the auroral oval equatorial boundary correlates with increase in the proportion of failures at lower latitude railway segments.

Re-processing Datawell Directional Waverider MkIII heave data affected by breaking waves

Observations of nearshore waves are essential for coastal management, planning and operations. However, shallow water deployments often encounter breaking waves, causing anomalous measurements that are rejected during quality control. A new methodology for re-processing wave data containing breaking waves has been developed, utilising data from the National Network of Regional Coastal Monitoring Programmes of England which operates 37 Datawell Directional Waverider MkIII buoys. Heave data affected by breaking waves is reviewed, anomalous heaves removed, and the remaining data re-processed. Validation through a two-stage process confirms that re-processed wave parameters closely match those derived from the established Datawell processing and ensures strong agreement between parameters derived from shorter re-processed records and full-length data. Applying this new approach to all measured storm wave data, 7.3% (928.5 hours) of storm wave data is recovered across the 37 sites. At some sites, the retention of additional storm wave data has considerable impacts on significant wave height (Hm0) return periods. At five sites, the 1 in 100 year return period increased by over one meter. The enhanced dataset now includes more individual storm events and more complete records of storm events, providing a more robust foundation for coastal management and planning.

Dynamics of Barred Coast at Different Temporal Scales (by the Example of Vistula Spit in the Baltic Sea)

According to fundamental concepts, the morphodynamic system of an accumulative sandy coast with underwater bars exhibits cyclic behavior across various time scales. This raises the question: which factor is more significant for the dynamics of a given coast—individual storms or seasonal changes in wave activity? While observations and studies addressing this issue have primarily been conducted on oceanic coasts, there is a lack of comparable data for fetch-limited areas. Monitoring of the bottom topography along the west coast of Vistula Spit (Baltic Sea) revealed a cyclic behavior in morphology, transitioning from a straightened external bar to its connection with the shore. Analysis of field measurement results indicated that seasonal variations in wave intensity do not significantly impact coastal relief. Furthermore, it was found that the complete cycle of underwater bar evolution lasts approximately two years, during which the coast profile maintains a stable shape at the stage of the straightened external bar. The identification of the primary factor influencing coastal evolution can be characterized by the Dean number (Ω), which combines wave parameters (wave height and period) with sediment fall velocity. Utilizing ERA5 wave reanalysis data, we compared the variability of Ω values on both annual and monthly scales. The analysis revealed that for the section of the coast under consideration, there is no clearly dominant evolutionary factor; rather, the coast is influenced approximately equally by individual storm events and seasonal fluctuations in wave energy. Modeling storm-induced bed profile deformations using the CROSS-PB model demonstrated that the position of the external underwater bar remains nearly constant even during intense and prolonged storms. It is concluded that under specific conditions—determined by a combination of sediment size, coastal slope, and wave regime characteristics—the coast can remain stable, exhibiting minimal response to relatively strong storms and seasonal variations in wave energy. Such coasts are characterized by an absence of a dominant evolutionary factor as indicated by fluctuations in the Dean parameter, allowing their morphodynamic cycles to span several seasons. This type of morphodynamics in coastal accumulative relief appears to be typical for conditions found in fetch-limited areas, such as regional and semi-closed seas.

Anthropogenic Climate Change Will Intensify European Explosive Storms Analogous to Alex, Eunice, and Xynthia

Abstract 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.

Dynamic and Thermodynamic Environmental Modulation of Tropical Congestus and Cumulonimbus in Maritime Tropical Regions

Abstract We have quantified the impacts of varying thermodynamic environments on tropical congestus and cumulonimbus clouds (CCCs) within maritime tropical regions. To elucidate this relationship, we employed the Regional Atmospheric Modeling System (RAMS) to conduct high-resolution (1 km) simulations of convection over the Philippine Archipelago for a month-long period in 2019. We subsequently performed a cloud-object-based analysis, identifying and tracking hundreds of thousands of individual CCCs using the Tracking and Object-Based Analysis of Clouds (tobac) tracking library. Using this object-oriented dataset of tracked cells, we examined differences in individual storm strength, organization, and morphology due to the storm’s initial environment. We found that storm strength, defined here as maximum midlevel updraft velocity, was controlled primarily by convective available potential energy (CAPE) and precipitable water (PW); high CAPE (>2500 J kg−1) and high (approximately 63 mm) PW were both required for midlevel CCC updraft velocities to reach at least 10 m s−1. Of the CCCs with the most vigorous updrafts, 80.9% were also in the upper tercile of precipitation rates, with the strongest precipitation rates requiring even higher PW. Further, we found that vertical wind shear was the primary differentiator between organized and isolated convective storms. Within the set of organized storms, linearly oriented CCC systems have significantly weaker vertical wind shear than nonlinear CCCs in low- (0–1, 0–3 km) and midlevels (0–5, 2–7 km). Overall, these results provide new insights into the environmental conditions determining the CCC properties in maritime tropical regions.

The Timing, Magnitude, and Relative Composition of Extreme Total Water Levels Vary Seasonally Along the U.S. Atlantic Coast

AbstractThe annual maximum (AM) method, which subsamples time series to retain the maximum event per year, and the peak‐over‐threshold (POT) method, which extracts values exceeding a threshold to define extremes, have long histories in determining flood event frequency. In practice, extreme value distributions applied to AM and POT events often assume that the data comes from the same statistical population. Locations across the world, like the United States (U.S.) Atlantic coastline, however, experience high coastal water levels driven by various individual processes and storms with different driving mechanisms during different seasons. This research investigates when extreme total water levels (TWLs) occur during the year along the U.S. Atlantic coast and whether individual components, like waves, tides, and storm surge, contributing to TWLs vary across regions and during the year. From 1980 to 2020, extreme TWLs occurred during the extratropical and tropical seasons, with the relative proportion of extreme TWLs occurring during the extratropical season increasing northward. Still water levels drive spatial variability in extreme TWL magnitude, while wave climate drives differences in extreme TWL magnitude between extratropical and tropical seasons. Month‐to‐month variability in the composition of extreme TWLs varies more than spatial variability, highlighting the importance of understanding the components driving extremes at different times of the year. Variations across storm seasons in the processes contributing to extreme TWLs may have implications for how large‐scale changes to the climate impact hazards along open sandy coastlines and influence the robustness of extrapolating rare events from models fit to a single population.

Data-driven modelling of coastal storm erosion for real-time forecasting at a wave-dominated embayed beach

Emergency managers have an increasing need for tools to enhance preparedness to extreme coastal storms and support disaster risk reduction measures. With the emergence of Early Warning Systems (EWSs) for coastal storm hazards, a fundamental challenge is the accurate prediction of sandy beach erosion at lead times of days to weeks corresponding to an approaching storm event. This work presents a data-driven modelling approach to predict storm-driven beach erosion (shoreline change) using a large dataset of 276 individual storm events at Narrabeen-Collaroy Beach, SE Australia. Correlation analysis between individual storm characteristics and shoreline response at three locations along the embayment with varying exposure to the prevailing waves indicates that cumulative storm wave energy is the dominant driver of storm erosion at this site. This is followed by the pre-storm beach width, storm wave direction and to a minimal extent, storm wave period and water levels. A multi-linear regression model of storm erosion is developed and found to accurately predict shoreline change due to individual storm events (RMSE = 3.7 m–6.4 m). This work highlights the value of high-frequency shoreline data for storm erosion forecasting and provides a framework for real-time forecasting applications.

Leading role of Saharan dust on tropical cyclone rainfall in the Atlantic Basin.

Tropical cyclone rainfall (TCR) extensively affects coastal communities, primarily through inland flooding. The impact of global climate changes on TCR is complex and debatable. This study uses an XGBoost machine learning model with 19-year meteorological data and hourly satellite precipitation observations to predict TCR for individual storms. The model identifies dust optical depth (DOD) as a key predictor that enhances performance evidently. The model also uncovers a nonlinear and boomerang-shape relationship between Saharan dust and TCR, with a TCR peak at 0.06 DOD and a sharp decrease thereafter. This indicates a shift from microphysical enhancement to radiative suppression at high dust concentrations. The model also highlights meaningful correlations between TCR and meteorological factors like sea surface temperature and equivalent potential temperature near storm cores. These findings illustrate the effectiveness of machine learning in predicting TCR and understanding its driving factors and physical mechanisms.

Understanding compound extreme precipitations preconditioned by heatwaves over China under climate change

Compared with individual heatwaves or storm events, the compound extreme precipitations preconditioned by heatwaves (CHEPs) usually amplify their adverse repercussions on both ecosystems and society. However, little is known about the physical mechanisms of generations, especially considering precipitation types triggered by various factors and synoptic patterns. By classifying extreme precipitations based on duration, we conduct an event-based analysis and comprehensively assess CHEPs using the machine learning-constrained framework and binning scaling methods over China. We find the fraction of CHEPs to total extreme short-duration/long-duration precipitations (ESDPs/ELDPs) has substantially increased by 18%/15% from 1979 to 2021, when using dry-bulb temperature to identify heatwaves. More notably, the hotspots of CHEPs are generally consistent with those of ESDPs. The ESDPs play a dominant role in shaping CHEPs episodes, which are governed by enhancing atmospheric instability due to preconditioned heatwaves. The horizontal moisture advection and transient vertical dynamic motion of moisture, which are paramount to LDPs, is not significantly enhanced by the overheating atmosphere, leading to a small fraction of LDPs to CHEPs. In addition, the intensity of ESDPs tends to increase with air temperature at higher rates than that of ELDPs. As short-duration storms may trigger severe flash floods, ample attention should be paid to the escalating risks of CHEPs under climate change.

Characterizing Spatial and Temporal Trends in Net Sediment Accumulation in Seagrass Meadows

Seagrass meadows are known as hot spots for carbon accumulation, but there is limited field data on the variability of sediment accumulation across time and space. We developed a method to assess spatial and temporal heterogeneity in net sediment accumulation in seagrass meadows using small, inexpensive samplers, allowing for over 200 unique measurements across multiple transects within our study site. Using this method, we assessed sediment accumulation across seagrass meadow edges, and in varying weather conditions. We found the greatest accumulation of sediment 5 m outside of seagrass meadow edges, with sediment accumulation rates averaging just under 100 g m−2 day−1, though rates were highly variable. Carbon accumulation from settled sediment was generally greater outside of seagrass meadow edges than within the bed, especially at sites undergoing recent expansion. Measurements made during tropical storms showed both scouring of sediment away from sites, and increased accumulation, depending on site properties as well as individual tropical storm characteristics. In the storm that had a measurable storm surge, scouring of sediment was a more dominant mechanism, whereas deposition dominated in the storm that had high winds but no associated storm surge. Our data demonstrate the necessity of including measurements that characterize both spatial and meteorological variability to develop a more holistic understanding of the movement of sediment and particulate organic carbon associated with seagrass meadows, especially as meadow area becomes increasingly fragmented with human activity and global change.

Effects of Background Synoptic Environment in Controlling South China Sea Tropical Cyclone Intensity and Size Changes in Pseudo–Global Warming Experiments

Abstract Assessing how global warming affects tropical cyclones (TC) is immensely important for climate change adaptation and hazard mitigation. However, projected intensity and size change can vary greatly among different individual storms, even under the same forcing in pseudo–global warming (PGW) experiments. Here we hypothesize that these variations are related to the historical environment in which each TC was embedded. Twenty-five TCs in the South China Sea (SCS) region were simulated using the Weather Research and Forecasting (WRF) Model. Their changes in the near (2036–65) and far future (2075–99) following the representative concentration pathways 8.5 (RCP8.5) and 4.5 (RCP4.5) under phase 5 of the Coupled Model Intercomparison Project (CMIP5) were investigated by the PGW technique. The mean changes in TC intensity and gale-force wind radius (R17) in the SCS were +6.4% and +1.5% for a 2°C warming, respectively. Multiple linear regression and stepwise regression analysis revealed that storm intensity variations were positively correlated with historical sea surface temperature and negatively with outer (i.e., outside the TC’s R17) atmospheric instability, while the R17 variations correlated positively with outer midtropospheric relative humidity (RH) and surface outer wind speed (OWS). Ertel potential vorticity (EPV) diagnostics further showed a moister SCS background can cause stronger diabatic heating and EPV production at the spiral rainbands under PGW, which increases R17. Additionally, stronger background absolute angular momentum (AAM) promoted stronger AAM influx, leading to larger R17. Implications were drawn to explain the uncertainties in projected TC intensity and size due to natural variability. Significance Statement Changes in the intensity and size of individual tropical cyclones in future climates are highly variable. This study aims to understand the environmental factors influencing these variations. We selected 25 storms that entered the South China Sea region and modeled them in both present and future climates. The mean intensity and size were projected to increase by 6.4% and 1.5% for a 2°C warming, respectively. We found that tropical cyclones embedded in historically warmer oceans and more stable atmospheres intensified more than the others, while historically wetter and windier environments promoted larger size growth. Our results suggest that certain tropical cyclones can exhibit a greater increase in intensity and size in a warmer climate under favorable environmental conditions.

Modeling the potential impact of storm surge and sea level rise on coastal archaeological heritage: A case study from Georgia.

Climate change poses great risks to archaeological heritage, especially in coastal regions. Preparing to mitigate these challenges requires detailed and accurate assessments of how coastal heritage sites will be impacted by sea level rise (SLR) and storm surge, driven by increasingly severe storms in a warmer climate. However, inconsistency between modeled impacts of coastal erosion on archaeological sites and observed effects has thus far hindered our ability to accurately assess the vulnerability of sites. Modeling of coastal impacts has largely focused on medium-to-long term SLR, while observations of damage to sites have almost exclusively focused on the results of individual storm events. There is therefore a great need for desk-based modeling of the potential impacts of individual storm events to equip cultural heritage managers with the information they need to plan for and mitigate the impacts of storm surge in various future sea level scenarios. Here, we apply the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model to estimate the risks that storm surge events pose to archaeological sites along the coast of the US State of Georgia in four different SLR scenarios. Our results, shared with cultural heritage managers in the Georgia Historic Preservation Division to facilitate prioritization, documentation, and mitigation efforts, demonstrate that over 4200 archaeological sites in Georgia alone are at risk of inundation and erosion from hurricanes, more than ten times the number of sites that were previously estimated to be at risk by 2100 accounting for SLR alone. We hope that this work encourages necessary action toward conserving coastal physical cultural heritage in Georgia and beyond.

Radar and environment-based hail damage estimates using machine learning

Abstract. Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias.

Impact of precipitation mass sinks on midlatitude storms in idealized simulations across a wide range of climates

Abstract. The combination of precipitation formation and fallout affects atmospheric flows through the release of latent heat and through the removal of mass from the atmosphere, but because the mass of water vapor is only a small fraction of the total mass of Earth's atmosphere, precipitation mass sinks are often neglected in theory and models. However, a small number of modeling studies suggest that water mass sources and sinks can intensify heavily precipitating weather systems. These studies point to a need to more systematically verify the impact of neglecting precipitation mass sinks, particularly for warmer and moister climates in which precipitation rates can be much higher. In this paper, we add precipitation mass sources and sinks to an idealized general circulation model and examine their effects on steady-state midlatitude storm track statistics. The model has several idealizations, including that all condensates immediately fall out of the atmosphere, and is run across a wide range of climates, including very warm climates. We find that modifying the model to include mass sources and sinks has no detectable effect on midlatitude variability or extremes, even in climates much warmer and moister than the modern. However, we find that a 10-fold exaggeration of mass sources and sinks is sufficient to produce more intense midlatitude weather extremes and increase surface pressure variance. This result is consistent with theoretical potential vorticity analysis, which suggests that the dynamical effects of mass sources and sinks are much smaller than the dynamical effects of accompanying latent heating unless mass sinks are artificially amplified by at least a factor of 10. Finally, we use simulations of “tropical cyclone worlds” to attempt to reconcile our results with earlier work showing stronger deepening in a simulation of a tropical cyclone case study when precipitation mass sinks were included. We demonstrate that abruptly “turning on” mass sources and sinks can lead to stronger transient deepening in some individual storms (consistent with results of past work) but weaker transient deepening in other storms, without modifying the steady-state statistics of storms in equilibrium with the large-scale environment (consistent with our other results). Our results provide a firmer foundation for using general circulation models that neglect moist mass sources and sinks in climate simulations, even in climates much warmer than today, while leaving open the possibility that their inclusion might lead to short-term improvements in forecast skill.

Assessing RRFS versus HRRR in Predicting Widespread Convective Systems over the Eastern CONUS

Abstract This study provides a comparison of the operational HRRR version 4 and its eventual successor, the experimental Rapid Refresh Forecast System (RRFS) model (summer 2022 version), at predicting the evolution of convective storm characteristics during widespread convective events that occurred primarily over the eastern United States during summer 2022. In total 32 widespread convective events were selected using observations from the MRMS composite reflectivity, which includes an equal number of MCSs, quasi-linear convective systems (QLCSs), clusters, and cellular convection. Each storm system was assessed on four primary characteristics: total storm area, total storm count, storm area ratio (an indicator of mean storm size), and storm size distributions. It was found that the HRRR predictions of total storm area were comparable to MRMS, while the RRFS overpredicted total storm area by 40%–60% depending on forecast lead time. Both models tended to underpredict storm counts particularly during the storm initiation and growth period. This bias in storm counts originates early in the model runs (forecast hour 1) and propagates through the simulation in both models indicating that both miss storm initiation events and/or merge individual storm objects too quickly. Thus, both models end up with mean storm sizes that are much larger than observed (RRFS more so than HRRR). Additional analyses revealed that the storm area and individual storm biases were largest for the clusters and cellular convective modes. These results can serve as a benchmark for assessing future versions of RRFS and will aid model users in interpreting forecast guidance.

Rainfall frequency analysis using event-maximum rainfalls – An event-based mixture distribution modeling approach

Rainfall frequency analysis, an essential work for water resources management, is often conducted by using the annual maximum rainfall series. For rainfall stations with short record lengths and outliers presence, the use of annual maximum series for rainfall frequency analysis may yield design rainfall estimates of higher uncertainties. Moreover, for regions with cyclostationary climate patterns, the annual maximum rainfalls may be caused by different prevalent storm types, which differ in terms of their occurrence frequency and storm rainfall characteristics. In this study, we propose a novel event-maximum-rainfall-based mixture distribution modeling approach for rainfall frequency analysis. By considering the event-maximum rainfalls of individual storm events, the sample size for parameter estimation increases, and the uncertainty of design rainfall estimates reduces. Mixture distribution modeling enables a thorough investigation of the contributing probabilities of different storm types to the annual maximum rainfall. Through rigorous stochastic simulation, we demonstrated the superiority of the proposed approach over the conventional annual maximum rainfall approach. The proposed approach was applied to four representative rainfall stations in Taiwan, and the results revealed that the proposed approach is more robust than the conventional annual maximum rainfall approach. The results provide insights into the contributions of individual storm types to the annual maximum rainfall.

Evaluating Mesoscale Convective Systems Over the US in Conventional and Multiscale Modeling Framework Configurations of E3SMv1

AbstractOrganized mesoscale convective systems (MCSs) contribute a significant amount of precipitation in the Central and Eastern US during spring and summer, which impacts the availability of freshwater and flooding events. However, current global Earth system models cannot capture MCSs well and misrepresent the statistics of precipitation in the region. In this study, we investigate the representation of MCSs in three configurations of the Energy Exascale Earth System Model (E3SMv1) by tracking individual storms based on outgoing longwave radiation using a new application of TempestExtremes. Our results indicate that conventional parameterizations of convection, implemented in both low (LR; ∼150 km) and high (HR; ∼25 km) resolution configurations, fail to capture almost all MCS‐like events, in‐part because they underestimate high‐level cloud ice associated with deep convection. On the other hand, the multiscale modeling framework (MMF; cloud‐resolving models embedded in each grid‐column of ∼150 km resolution E3SMv1) configuration represents MCSs and their annual cycle better. Nevertheless, relative to observations, the E3SMv1‐MMF spatial distribution of MCSs and associated precipitation is shifted eastward, and the diurnal timing is lagged. A comparison between the large‐scale environment in E3SMv1‐MMF and ERA5 reanalysis suggests that the biases during the summer in E3SMv1‐MMF are associated with biases in low‐level humidity and meridional moisture transport within the low‐level jet. The fact that conventional parameterizations of convection, even with high‐resolution, cannot capture MCSs over the US suggests that methods with explicit representation of kilometer‐scale convective organization, such as the MMF, may be necessary for improving the simulation of these convective systems.

A Model of Hourly Variations of the Near‐Earth Magnetic Field Generated in the Inner Magnetosphere and Its Induced Counterpart

AbstractWe present a new model of the near‐Earth magnetospheric field produced by electric currents in the inner magnetosphere and the associated induced magnetic field. The model is designed to track hourly variations of these fields and accounts for their local time asymmetries. It is built by applying spherical harmonic analysis to vector measurements from the ground observatory network at low and mid‐latitudes. The primary and induced fields are separated with an approach in the time domain that uses a a priori radially‐symmetric electric conductivity model of the Earth. The model coefficients are computed at one‐hour time steps between 1997 and 2022. This model is shown to be consistent to within a few nT with previously developed indices which track the magnetospheric ring current. It is also validated against data from the Swarm, CHAMP and Øersted satellites. The fit to satellite data is comparable to that of the CHAOS‐7.15 model for geomagnetically quiet times, and improved by up to 20% on some components for geomagnetically moderate and active times. We attribute these differences mostly to a better representation of local time asymmetries, both on average and during individual geomagnetic storms. This model can be used in various applications, such as investigating the properties of the magnetospheric field and its sources and separating the magnetospheric field from the fields of other sources in geomagnetic field modeling.

Occurrence of Large Geomagnetically Induced Currents Within the EPRI SUNBURST Monitoring Network

AbstractSpace weather, a natural hazard, can adversely impact human technological assets. High‐voltage electric power transmission grids constitute one of the most critical technological systems vulnerable to space weather driven geomagnetically induced currents (GICs). One of the major challenges pertaining to the study of GICs over the continental United States has been the availability of GIC measurements, which are critical for validation of geoelectric field and power flow models, for example. In this study, we analyze GIC measurements collected at 17 Electrical Power Research Institute (EPRI) SUNBURST transformer locations across the United States for which a GIC value of 10 A or greater was recorded. This data set includes 52 individual geomagnetic storms with Kp index 6 and above during the period from 2010 to 2021. The analysis confirms that there is a good correlation between the number of geomagnetic storms per year and the number of recorded GIC events. Our results also show that about 76% of the top 17 GIC events are associated with the storm main phase, while only 24% are attributed to storm sudden commencements. In addition, it is shown, for the first time, that mid‐latitude positive bays can cause large GICs over the continental United States. Finally, this study shows that the largest measured GIC event in the data set was associated with a localized intense dB/dt structure, which could be attributed to substorm activity.