Storm Parameters Research Articles

Sodium butyrate prevented mortality in Acute Respiratory Distress Syndrome through induction of anti-inflammatory response by regulation of epigenetic pathway

Abstract Acute Respiratory Distress Syndrome (ARDS) is a life-threatening disease that has been identified following pneumonia, inflammation, as well as accompained with COVID19 cases. In this study, we investigated the effect of Sodium Butyrate (BUT) in ARDS induced by Staphylococcus Enterotoxin B (SEB). To that end, C3H/HeJ mice were administered BUT through oral gavage with a dose of 200mg/kg and 30mins later followed by a dual dose of SEB, starting with an intranasal dose of 7ug (i.n.) and 2hrs later with another dose of 2ug intraperitoneally (i.p.). The adminstration of BUT continued as same daily and the sacrifice was 72 hrs after SEB exposure. Our data demonstrated that the mortality rate was 80% in SEB+VEH mice compared to 0% mortality in SEB+BUT mice. Plethysmography data indicated a significant improvement in the clinical pulomonary functions. ELISA results indicated that the cytokine storm parameters including IFNγ were significantly decreased after BUT treatment compared to SEB+VEH. Furthermore, flow cytometry data indicated that CD4+ T cells, Vβ8+ T cells and NK T cells were significantly decreased in SEB+BUT compared to SEB+VEH. Analysis of miRNA from MNCs of lungs indicated that anti-inflammatory genes related to T-regulatory cells (Tregs) and Myeloid Derived Suppressor Cells (MDSCs) were targeted by many downregulated miRNAs which were then validated by qRT-PCR. Furthermore, flowcytometry data indicated a significant increase in T regs and MDSCs. Together, our data indicated that BUT prevented mortality and improved Lungs clinical functions in ARDS through modulation of the miRNAs in MNCs in lungs leading to induction of anti-inflammatory genes and suppression of inflammatory T cell subsets as well as increase in Tregs and MDSCs. Supported by NIH P01AT003961, P20GM103641, R01AI129788, R01ES030144, R01AI160896 and R01AI123947

Accurate Storm Surge Prediction with a Parametric Cyclone and Neural Network Hybrid Model

Storm surges are one of the most devastating coastal disasters. Numerous efforts have continuously been made to achieve better prediction of storm surge variation. In this paper, we propose a parametric cyclone and neural network hybrid model for accurate, long lead-time storm surge prediction. The model was applied to the northeastern coastal region of Taiwan, i.e., Longdong station. A total of 14 historical typhoon events were used for model training and validation, and the results and questions associated with this hybrid approach carefully discussed. Overall, the proposed method reduced the complexity of network structure while retaining the important typhoon indicators. In particular, local pressure and winds estimated from the storm parameters through physically-based parametric cyclone models allow for inferring the possible future influence of a typhoon, unlike the simple collection and direct usage of observation data from local stations in earlier works. Meanwhile, the error-tolerance capability of the neural network alleviated some discrepancy in the model inputs and enabled good surge prediction. Further, the proposed method showed better and faster convergence thanks to the retention of storm information and the reduced dimensions of the search space. The hybrid model presented excellent performance or maintained reasonable capability for short lead-time and long lead-time storm surge prediction. Compared with the pure neural network model under the same network dimensions, the present model demonstrated great improvement in accuracy as the prediction lead time increased to 8 h, e.g., 33–40% (13–21%) and 32–37% (18–29%) RMSE and CE, respectively, in the training/validation phase.

On Correlation between Wind and Wave Storms

The paper is focused on the formulation of an adequate criterion for associating wave storm events to the generating wind storm ones, and on the study of correlation between their characteristic parameters. In this context, the sea storm definition commonly used for storm identification from significant wave height data is adapted for wind storm, by processing wind speed data. A sensitivity analysis is proposed as function of the storm thresholds aiming at identifying optimal combination of wind speed and significant wave height thresholds allowing the association of relatively large number of events ensuring high correlation between wind and wave storm parameters. The analysis is carried out using as input data wind speeds and significant wave heights from four meteorological (buoys and anemometers) stations of the National Data Buoy Center moored off the East Coast of the United States. Results reveal that an optimal threshold combination is achieved assuming both wind speed and significant wave height threshold as 1.5 time their respective averages.

DOES BLOOD TYPE HAVE AN EFFECT ON THE COURSE OF COVID-19?

ObjectivePredictive parameters that can affect the course of this infection have been the main topic of research since the beginning of the COVID-19 (Coronavirus disease 2019) pandemic. Since the discovery of blood groups, the effect of these on infectious diseases has always been of interestMethodologyTo analyze the effect of ABO blood group on mortality, hospitalization duration and hematological and cytokine storm parameters in patients with COVID-19. This retrospective study was conducted on 140 patients diagnosed with COVID-19. Demographic characteristics, laboratory parameters including ABO blood group, complete blood count (CBC) parameters, biochemical tests, cytokine storm parameters, duration of hospitalization, and final status (discharge or death) were recorded.ResultsThe 140 patients included in the analysis comprised 72 (51.4%) males and 68 (48.6%) females with a mean age of 66.3±14.0 years. . Age and gender, hospitalization duration and mortality rates were similar in all blood group types. Only D-dimer values were found to be higher in blood group A compared with other blood groups.ConclusionAlthough no difference in mortality was determined between groups, the D-dimer level was statistically significantly higher in COVID-19 patients with A blood group. Larger studies are needed to reflect D-dimer levels on the clinical course of infection, and thus on daily practice

The Efficacy of Antioxidant Oral Supplements on the Progression of COVID-19 in Non-Critically Ill Patients: A Randomized Controlled Trial.

Modulation of cytokine production using immunonutrition is a relatively novel concept to improve outcomes among patients with SARS-CoV-2 infection and is now hypothesized to help manage COVID-19, however, clinical evidence is lacking. This prospective, double-blinded, randomized parallel-controlled interventional clinical trial investigated the effect of antioxidant supplements on inflammatory cytokines and disease progression in non-critically ill patients. A total of 87 hospitalized COVID-19 patients were randomized using computer-generated-randomization into the supplement group (n = 18) and the placebo group (n = 16) for 10 days. Baseline and final nutritional screening via nutrition risk screening (NRS-2002) and subjective global assessment (SGA), as well as the recording of anthropometric, clinical, biochemical, and functional parameters, were done. Serum ferritin level, cytokine storm parameters such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), monocyte chemoattractant protein 1(MCP-1), C-reactive protein, total leukocyte count, lymphocytic count, and neutrophil-to-lymphocyte ratio were measured. Anthropometric and clinical parameters showed nonsignificant differences between groups. The hematology profile showed improvement in lymphocyte count in the supplement group. However, levels of alkaline phosphatase, IL-6, TNF-α, and MCP-1 were significantly lower in the supplement group. In conclusion, antioxidant oral supplementation significantly reduced the cytokine storm and led to partial improvements in clinical parameters among patients with non-critical COVID-19.

Intraregional Comparisons of the Near‐Storm Environments of Storms Dominated by Frequent Positive Versus Negative Cloud‐to‐Ground Flashes

AbstractWe gridded 11 years of cloud‐to‐ground (CG) flashes detected by the U.S. National Lightning Detection Network during the warm season in 15 km × 15 km × 15 min grid cells to identify storms with substantial CG flash rates clearly dominated by flashes lowering one polarity of charge to the ground or the other (+CG flashes vs. −CG flashes). Previous studies in the central United States had found that the gross charge distribution of storms dominated by +CG flashes included a large upper negative charge over a large middle level positive charge, a reversal of the usual polarities. In each of seven regions spanning the contiguous United States (CONUS), we compared 17 environmental parameters of storms dominated by +CG flashes with those of storms dominated by –CG flashes. These parameters were chosen based on their expected roles in modulating supercooled liquid water content (SLWC) in the updraft because laboratory experiments have shown that SLWC affects the polarity of charge exchanged during rebounding collisions between riming graupel and small ice particles in the mixed phase region. This, in turn, would affect the vertical polarity of a storm's charge distribution and the dominant polarity of CG flashes. Our results suggest that the combination of parameters conducive to dominant +CG flash activity and, by inference, to anomalous storm charge structure varies widely from region to region, the lack of a favorable value of any particular parameter in a given region being offset by favorable values of one or more other parameters.

The Relative Importance of Geomagnetic Storm Signatures on the Total Electron Content Perturbations Over the Continental US

AbstractA study of 37 large solar storms occurring in the equinox periods from 2000 to 2018 is presented to quantify their effects on total electron content (TEC) variations in the ionosphere over the North American sector. The dependence of storm‐related TEC enhancements and depletions is studied as a function of various storm parameters using machine‐learning and statistical algorithms to quantify their relative importance. The variables under consideration include the onset time and duration of each storm, as well as characteristic scalar values derived from ring current variations detected by a network of ground‐based magnetometers. The TEC data used in the study are taken from the Continuously Operating Reference Station network of ground‐based GPS measurements, archived in the Madrigal database. The agreement and disagreement of the results across the different methods studied, along with the relative uncertainties in model prediction capability of the machine‐learning study, show that the geo‐effectiveness of large storms in the equinox periods is more closely related to the storm onset time and to ionospheric preconditioning than to any other parameters used in characterizing the storm. The effects in the higher latitude regions are shown to be very different from the lower latitude regions over the North American sector. This behavior can possibly be explained through storm‐induced changes in the atmospheric chemistry and the related temperature‐dependent production and loss processes at the higher latitudes.

Evaluating different lightning parameterization schemes to simulate lightning flash counts over Maharashtra, India

Thunderstorms source of lightning discharge is a major hazard to humans. In India, the loss of human life due to thunderstorms is high because of frequent lightning during pre-monsoon season (March-May). Therefore, simulation of lightning flash counts based on various lightning parameterization schemes and Lightning Potential Index (LPI) in Weather Research and Forecasting (WRF) model are evaluated over Maharashtra, India. The offline diagnostic methods for calculating lightning flashes are also evaluated from the model generated storm parameters. The observation from Maharashtra Lightning Detection Network (LDN) is used to validate the simulated total lightning flash for four events. The lightning flashes calculations by following Price and Rind (1992) based on cloud top height (PR92CTH) and vertically integrated ice water path (IWP) simulated the spatial pattern comparably well. The lightning parameterization based on cloud top height defined by the radar reflectivity factor threshold of 20 dBZ (DLP2) has performed better as compared to observation. The results show (i) better spatial pattern and frequency distribution of lightning flashes, (ii) accumulated rainfall, maximum reflectivity and time evolutions are in good agreement with flashes, (iii) correlation between simulated flash and hydrometeors are higher, (iv) the number of matching grid boxes due to randomness is also higher, 74.9%, 56.5%, 68.1% and 82.7% of matching grid boxes for the four cases and (v) the results based on LPI are also in consistent with the results of DLP2. The study highlights the robustness of DLP2 and indicates a promising future for the operational forecast of lightning prediction.

Evaluation of Different Storm Parameters as the Proxies for Gridded Total Lightning Flash Rates: A Convection-Allowing Model Study

Lightning simulation is important for a variety of applications, including lightning forecast, atmospheric chemical simulation, and lightning data assimilation. In this study, the potential of five storm parameters (graupel volume, precipitation ice mass, radar echo volume, maximum updraft, and updraft volume) to be used as the proxy for the diagnosis of gridded total lightning flash rates has been investigated in a convection-allowing model. A mesoscale convective system occurred in the Guangdong province of China was selected as the test case. Radar data assimilation was used to improve the simulation accuracy of the convective clouds, hence providing strong instantaneous correlations between observed and simulated storm signatures. The areal coverage and magnitude of the simulated lightning flash rates were evaluated by comparing to those of the total lightning observations. Subjective and the Fractions Skill Score (FSS) evaluations suggest that all the five proxies tested in this study are useful to indicate general tendencies for the occurrence, region, and time of lightning at convection-allowing scale (FSS statistics for the threshold of 1 flash per 9 km2 per hour were around 0.7 for each scheme). The FSS values were decreasing as the lightning flash rate thresholds used for FSS computation increased for all the lightning diagnostic schemes with different proxies. For thresholds from 1 to 3 and 16 to 20 flashes per 9 km2 per hour, the graupel contents related schemes achieved higher FSS values compared to the other three schemes. For thresholds from 5 to 15 flashes per 9 km2 per hour, the updraft volume related scheme yielded the largest FSS. When the thresholds of lightning flash rates were greater than 13 flashes per 9 km2 per hour, the FSS values were below 0.5 for all the lightning diagnostic schemes with different proxies.

Effect of waves, tides and storm parameters on water level rise through numerical simulation

In this study, the foot water is calculated by the factors of collecting parameters and tides, calculated waves are analyzed by the tidal integrated numerical model, Toring sea and storm surge (SuWAT - Surge, Wave and Tide). The SuWAT model has been applied to calculate storm surge in a number of storms that have made landfall on the northern coast (such as storms Franki-96, Washi-05, Xangxane-06, Kalmeagy-14) with some calculation options different. The results show that the influence of the tide on the rising water level is negligible in places with low tidal amplitude and with storm landing at high tide, the water will be lower than when the storm makes landfall at low tide; The water surge due to atmospheric pressure is only significant in the offshore area when the storm intensity is still strong. Meanwhile, in the area, shoreline rise due to wind stress and wave stress accounted for the majority of the total water level rise during storms. With storm parameters, the storm surge at Hon Dau station reached the maximum value with the case where the storm's movement speed was 1 hour slower than the actual one. The magnitude of the water decreased as the pressure in the storm increased, but the magnitude increased at three different stations. Water level rise at 3 stations Cua Ong, Hon Dau, and Hon Ngu increased with wind speed, however, Hon Ngu station had a higher rate of increase. The magnitude at Cua Ong station increases slowly. The water surge at Cua Ong station is dominated by wind speed more than at Hon Ngu station. The research results will be very useful in warning and forecasting storm surge in the area.

Matched Filter Cyclone Maximum Wind Retrievals Using CYGNSS: Progress Update and Error Analysis

This article describes progress relating to a previously reported matched filter retrieval approach for the estimation of hurricane maximum winds using delay Doppler map (DDM) measurements of the Cyclone Global Navigation Satellite System (CYGNSS) mission. The retrievals presented are based on comparisons of these measured DDMs, and their simulated counterparts as a set of storm parameters are varied. The analysis presented examines the dependencies of retrieval performance on the synthetic storm model used as part of the forward modeling process using a set of CYGNSS storm-observing full DDM downlinks containing 68 tracks and spanning 18 storms over the period 2017-2020. Based on the combined use of multiple parametric storm models, retrieved hurricane maximum wind speed estimates showed correlations of 92%, root-mean-square error (RMSE) of 6.05 m/s, unbiased RMSE of 6.05 m/s, mean difference of 4.83 m/s, and a bias of 0.09 m/s relative to reference data. Mean retrieval error relative to storm maximum wind is 11.11%. The dependence of retrieval error on measurement maximum delay extent is also analyzed using CYGNSS Raw I/F downlinks, from which a significant near-monotonic decrease in retrieval errors is observed as the delay extent of the measurement is increased. The analysis presented in this work highlights the potential for using matched filter retrieval methodologies for cyclone wind speed estimation in spaceborne Global Navigation Satellite Systems Reflectometry systems.

A study of photovoltaic degradation modes due to dust interaction on Mars

We explored the utility of ground-based highly accelerated life testing (HALT) on triple-junction coverglass interconnected cells (CICs) after exposure to Mars dust storm conditions. The CICs comprised the triple junction solar cells, a silicone encapsulant bonded to ceria-doped coverglass with an antireflection coating. These CICs were placed in an enclosure and sandblasted with Mars dust simulant to replicate dust impingement. We compared the apparatus properties with measured Martian dust storm parameters from Viking landers. We found that simulated Martian dust storms often result in increased reflectance and decreased absorption at visible wavelengths and subsequent reduced short circuit current (JSC). Reduction in the open circuit voltage (VOC) is observed in select cases, caused by structural damage after CIC fracture. We found that even if there is no observable VOC losses, minority carrier lifetime is reduced. The JSC tends to rebound upon cleaning, suggesting current losses are reversible and unrelated to permanent damage such as cell cracking and subsequent isolation of regions of the cell due to broken busbars. This observation indicates a quantifiable difference between recoverable and non-recoverable power loss, suggesting a permanent degradation could be determined. We compared experimental data to mined field data from the Mars Exploration Rover, Opportunity and extracted a degradation rate. We found exceptional agreement between the irreversible degradation in our laboratory experiments and 4.9 Martian years of mined field data, 9.7% and 9.4% respectively, demonstrating that the laboratory method well represents the physical reality of solar cells’ exposure to the Martian environment.

Statistical analysis of Mediterranean coastal storms

Coastal storms as extreme hydrometeorological events have severe impacts on the coasts and consequently affect the coastal communities, attracting considerable research interest nowadays. Attempting to understand the risk of these extreme events, a coastal storm analysis is accomplished by studying the parameters which define a coastal storm and their properties, such as the wave height, the wave period, the duration, the calm period, and the storm energy. The frequency of occurrence of coastal storms, the thresholds of storm parameters and the way they are interrelating with each other draw a rough outline of wave climate during coastal storm events for a specific location. This information is valuable afterwards for the design of coastal structures and the coastal zone management. In this work, buoy datasets from 30 locations in the Mediterranean Sea are analysed for describing coastal storm activity. A sample of 4008 coastal storms is identified. Each location faces around 10–14 coastal storms per year, with most of them to occur in winter months and their characteristics to be site-dependent. Their average duration is lower than 30 hours, and 25% of them are consecutive events which hit the same location in less than a day. Furthermore, the wave period and the main direction present no remarkable fluctuations during a coastal storm. With this analysis, a deeper understanding of coastal storm severity is pursued, gaining knowledge about their past activity, in order to be prepared in the future and to protect the coastal areas.

Copula-Based Vulnerability Analysis of Civil Infrastructure Subjected to Hurricanes

Coastal civil infrastructure can be susceptible to damages caused by hurricanes throughout its service life. Vulnerability assessment is a key component in hazard risk management of civil infrastructure systems. Previously, most studies assume storm parameters are independent when computing the vulnerability of infrastructure to hurricanes. Due to the complicated interactive effects between storm parameters during hurricanes, the independent model may mis-specify such intercorrelation, thus resulting in inaccurate estimation of the probability of failure. This paper proposes a copula-based vulnerability assessment framework to investigate the impact of dependent storm parameters on the vulnerability of civil infrastructure subjected to hurricanes. The developed framework is applied to a typical simply supported bridge to compute the probability with respect to deck unseating failure under hurricane hazards. The copula approach provides superior efficiency in modeling dependency between the maximum wave height and peak water level, by separately considering marginal distributions and the joint effects. Probabilistic wave-induced load acting on the bridge deck is computed using a three-dimensional finite element model, incorporating uncertainties associated with hazard parameters. The effect of correlated storm parameters and the tail dependence characteristics on the bridge vulnerability is investigated by using different copula models, including Clayton, Gaussian, and Husler-Reiss copula functions. By incorporating dependent hazard parameters, the failure probability of bridge may be significantly increased for non-major hurricanes (e.g., with a 50-year return period), while the failure probability under major hurricanes (e.g., with a 500-year return period) may not be severely affected.

Precipitating Cloud Analysis during Extreme Rain Events over the Chao Phraya River Basin Based on the Developed Radar Mosaic Products

During rainy season, Tropical Cyclones (TCs) regularly provide heavy rain over Indochina. In addition, the monsoon trough also produces rainfall to support agricultural activities across the area. However, the extreme rainfall events usually occur during the seasonal monsoon march. Understanding the precipitating cloud characteristics produced by the extreme rainfall events could alleviate the destruction of natural resources over Indochina. The variation in precipitating clouds can be analyzed using fine spatio-temporal resolution data, such as ground-based radar data from multiple radar stations. Since the Chao Phraya river basin is the most important basin in Thailand, this study is based on radar images collected from the five installed radar stations of the Thai Meteorological Department over this basin. The analysis of the extreme rain events during rainy season in 2018 was carried out using the developed mosaic method. Python and Open Source Computer Vision Library (OpenCV) were used in the development of mosaic products to focus on characteristics of precipitating clouds. Beam-blockage fraction was analyzed for each radar station and surrounding terrain using an open source library for processing weather radar data (wradlib) (Heistermann et al., 2013) with our developed beam-blockage analysis program. The severity of blocked beam is found over the northern mountain region of study area. The precipitating clouds were detected and their parameters extracted using the ellipse fitting technique. The analysis of these clouds revealed that, among other cases, Sontihn TC was a severely destructive event based on the storm parameters of both stratiform and convective clouds. The number and length of the convective storms during Sontihn TC was the most severe when compared to other events. It has been found that the diurnal cycle of the convective cloud shows their patterns by following solar heating surface over the study area for all of the selected events.

A Rapid Forecasting and Mapping System of Storm Surge and Coastal Flooding

AbstractA prototype of an efficient and accurate rapid forecasting and mapping system (RFMS) of storm surge is presented. Given a storm advisory from the National Hurricane Center, the RFMS can generate a coastal inundation map on a high-resolution grid in 1 min (reference system Intel Core i7–3770K). The foundation of the RFMS is a storm surge database consisting of high-resolution simulations of 490 optimal storms generated by a robust storm surge modeling system, Curvilinear-Grid Hydrodynamics in 3D (CH3D-SSMS). The RFMS uses an efficient quick kriging interpolation scheme to interpolate the surge response from the storm surge database, which considers tens of thousands of combinations of five landfall parameters of storms: central pressure deficit, radius to maximum wind, forward speed, heading direction, and landfall location. The RFMS is applied to southwest Florida using data from Hurricane Charley in 2004 and Hurricane Irma in 2017, and to the Florida Panhandle using data from Hurricane Michael in 2018 and validated with observed high water mark data. The RFMS results agree well with observation and direct simulation of the high-resolution CH3D-SSMS. The RFMS can be used for real-time forecasting during a hurricane or “what-if” scenarios for mitigation planning and preparedness training, or to produce a probabilistic flood map. The RFMS can provide more accurate surge prediction with uncertainties if NHC can provide more accurate storm forecasts in the future. By incorporating storms for future climate and sea level rise, the RFMS could be used to generate future flood maps for coastal resilience and adaptation planning.

Idealized Simulations of City-Storm Interactions in a Two-Dimensional Framework

Numerous studies have identified spatial variability in convective parameters such as rainfall totals and lightning flashes in the vicinity of large urban areas, yet many questions remain regarding the storm-scale processes that are altered during interaction with a city as well as which urban features are most responsible for storm modification. This study uses an idealized, two-dimensional cloud model to investigate structural and evolutionary changes in a squall line as it passes over a simplified representation of a large city. A parameter space exploration is done in which the parameters of the city—surface temperature and surface roughness length—are systemically increased relative to the region surrounding the idealized city. The resultant suite of simulations demonstrates that storm parameters such as vertical velocity, hydrometeor mass, upward mass flux, and buoyant accelerations are enhanced when the storm passes over the idealized city. No such enhancement occurs in the control simulation without an idealized city.

Comparison of Methods for Imputing Non-Wetting Storm Surge to Improve Hazard Characterization

Joint probability methods for characterizing storm surge hazards involve the use of a collection of hydrodynamic storm simulations to fit a response surface function describing the relationship between storm surge and storm parameters. However, in areas with a sufficiently low probability of flooding, few storms in the simulated storm suite may produce surge, resulting in a paucity of information for training the response surface fit. Previous approaches have replaced surge elevations for non-wetting storms with a constant value or truncated them from the response surface fitting procedure altogether. The former induces bias in predicted estimates of surge from wetting storms, and the latter can cause the model to be non-identifiable. This study compares these approaches and improves upon current methodology by introducing the concept of “pseudo-surge,” with the intent to describe how close a storm comes to producing surge at a given location. Optimal pseudo-surge values are those which produce the greatest improvement to storm surge predictions when they are used to train a response surface. We identify these values for a storm suite used to characterize surge hazard in coastal Louisiana and compare their performance to the two other methods for adjusting training data. Pseudo-surge shows potential for improving hazard characterization, particularly at locations where less than half of training storms produce surge. We also find that the three methods show only small differences in locations where more than half of training storms wet.

Evaluation of storms through the lens of erosion potential along the New Jersey, USA coast

Coastal erosion is driven by both a storm's erosion potential and by an area's vulnerability. Therefore, the problem of estimating impacts can be approached in two-step. The first includes an assessment of erosion potential based on readily available storm parameters, while the second combines this information with highly localized parameters, describing vulnerability, to more directly predict local impacts. The work presented in this paper focuses on this first step, where a storm erosion potential climatology is developed by analyzing historical storms and is then utilized to identify historic patterns. Specifically, storms which have impacted the New Jersey coast over the past several decades are reevaluated using the Storm Erosion Index (SEI), developed by Miller and Livermont (2008), which considers the three primary storm-related drivers of coastal erosion (wave height, water level, and storm duration). These storms are assessed at thirteen shoreline segments defined along New Jersey's Atlantic coast from a 34 year-period (1980–2013) when concurrent wave and water level data is available. Approximately 130 unique storms are identified with the top three being the December 1992 nor'easter, the November 2009 Veteran's Day Storm, and Hurricane Sandy in October 2012, each having an estimated return period of greater than 15 years. The resulting climatology is found to exhibit several interesting spatial and temporal trends. Both portions of the state as well as months of the year that have historically experienced more storms and/or higher cumulative SEI (over the 34 years) are identified. While analysis of the climatology has also identified periods of reduced storm activity and those of intensified conditions, future monitoring is suggested to assess whether these patterns are persistent or related to climatic variations with cycles longer than can be captured by the thirty years included in this study.

Capability of Geomagnetic Storm Parameters to Identify Severe Space Weather

Abstract The paper investigates the capability of geomagnetic storm parameters in the disturbance storm-time (Dst), Kp, and AE indices to distinguish between severe space weather (SvSW) that causes the reported electric power outages and/or telecommunication failures and normal space weather (NSW) that does not cause these severe effects in a 50 yr period (1958–2007). The parameters include the storm intensities DstMin (minimum Dst during the main phase, MP, of the storm), (dDst/dt)MPmax, Kpmax, and AEmax. In addition, the impulsive parameter is derived for the storms that are automatically identified in the Kyoto Dst and USGS is the integral of the modulus of the Dst from onset of the MP (MPO) to the DstMin. T MP is the MP duration from MPO to DstMin. The corresponding mean values and are also calculated. Regardless of the significant differences in the storm parameters between the two Dst indices, the IpsDst in both indices seems to identify four of the five SvSW events (and the Carrington event) in more than 750 NSW events that have been reported to have occurred in 1958–2007, while all other parameters separate one or two SvSWs from the NSWs. Using the Kyoto IpsDst threshold of −250 nT, we demonstrate a 100% true SvSW identification rate with only one false NSW. Using the false NSW event (1972 August 4), we investigate whether using a higher resolution Dst might result in a more accurate identification of SvSWs. The mechanism of the impulsive action leading to large IpsDst and SvSW involves the coincidence that the fast interplanetary coronal mass ejection velocity V contains its shock (or front) velocity and large interplanetary magnetic field Bz southward covering .