Typical Storm Research Articles

A Comprehensive Classification and Analysis of Geomagnetic Storms Over Solar Cycle 24

A geomagnetic storm is a global disturbance of Earth's magnetosphere, occurring as a result of the interaction with magnetic plasma ejected from the Sun. Despite considerable research, a comprehensive classification of storms for a complete solar cycle has not yet been fully developed, as most previous studies have been limited to specific storm types. This study, therefore, attempted to present complete statistics for solar cycle 24, detailing the occurrence of geomagnetic storm events and classifying them by type of intensity (moderate, intense, and severe), type of complete interval (normal or complex), duration of the recovery phase (rapid or long), and the number of steps in the storm's development. The analysis was applied to data from ground-based magnetometers, which measured the Dst index as provided by the World Data Center for Geomagnetism, Kyoto, Japan. This study identified 211 storm events, comprising moderate (177 events), intense (33 events), and severe (1 event) types. About 36% of ICMEs and 23% of CIRs are found to be geoeffective, which caused geomagnetic storms. Up to four-step development of geomagnetic storms was exhibited during the main phase for this solar cycle. Analysis showed the geomagnetic storms developed one or more steps in the main phase, which were probably related to the driver that triggered the geomagnetic storms. A case study was additionally conducted to observe the variations of the ionospheric disturbance dynamo (Ddyn) phenomenon that resulted from the geomagnetic storm event of 2015 July 13. The attenuation of the Ddyn in the equatorial region was analyzed using the H component of geomagnetic field data from stations in the Asian sector (Malaysia and India). The variations in the Ddyn signatures were observed at both stations, with the TIR station (India) showing higher intensity than the LKW station (Malaysia).

Sedimentary characteristics and model of Cambrian storm in Xuzhou area

The storm deposition types within the Dabeiwang area of Xuzhou City, Jiangsu Province, China, showcase a wide range of diversity and distinctly apparent characteristics. Based on measured sections and thin-section observations, we have studied the typical characteristics of storm deposition in this area, divided the sedimentary sequence, and summarized a comprehensive model of storm deposition. Various rock types such as thick-bedded dolomitic limestone, oolitic limestone, and calcirudite are developed in the study area. The calcirudite exhibits bamboo leaf-like morphology and various distribution forms. Typical storm sedimentary structures such as scour surfaces and mound cross-bedding were identified in the study area. Based on the distance of the gravel layer from the storm center and the depositional environment, the storm sedimentary sequence in the study area can be divided into allopatric-proximal-lagoon facies storm deposit, allopatric-distal-oolitic shoal facies storm deposit, autochthonous-supratidal facies storm deposit, allopatric-distal-interbeach marine storm deposit, sub-allopatric-tide zone facies storm deposit, and autochthonous-tide zone facies storm deposit. We summarize the comprehensive model of Cambrian storm deposition in the Dabeiwang area of Xuzhou, which has enriched the research results of Cambrian storm deposition in Xuzhou and provides support for the construction of a lithofacies paleogeographic pattern.

The role of wave setup on extreme water levels around Australia

Estimates of extreme sea levels have mainly been derived using atmospheric and tidal forcing only, but surface gravity waves are a key process that can cause a substantial elevation of mean sea level at the coast. In this paper a coupled wave-circulation model was used to simulate storm surge and wave setup across a broad range of coastal topographies and storm types around Australia. The main aim was a practical assessment of the benefits and limitations of using a coupled wave-surge model to determine wave effects for extreme sea level studies on the continental scale. The simulations included: tropical cyclones Yasi (2011) and George (2007) on the northeast and northwest coasts of Australia; Cyclone Alby (1978) that underwent extratropical transition in southwest Australia; and a large extratropical winter storm in the Southern Ocean (2016). Predicted wave setup was highly variable both spatially and temporally, with values up to 0.45 m, 35% of maximum storm surge height, and 9% of incident significant wave heights. In general, where storm surge heights were smaller, the relative importance of wave setup increased in the presence of large waves. The model best resolved wave setup in shallow gently sloping coastal areas, whilst unrealistically low values were predicted for steep coastlines. Predictions improved when wave setup was included for most sites and events, however, these wave effects were often secondary to other assumptions implicit in modeling extreme events. This was especially true for tropical cyclones where reliable wind forcing was not available. The results showed similar contributions from wave setup to other studies but highlighted the high temporal and spatial variability of wave setup across a wider variety of storms and coastal morphologies. The results also revealed that the maximum wave setup did not always coincide with the maximum storm surge. Overall, the coupled wave-surge model presented a useful tool to predict extreme water levels including wave setup when accurate depth and atmospheric data are available, however, significant challenges still exist to model this process at the continental scale.

Convective Phenomenes in the Context of Meso-β-scale Convective Structures

This review article presents the results of radar studies of convective phenomena in Moldavia and the North Caucasus using Eulerian (ECS) and Lagrangian (LCS) coordinate systems. Application of the Lagrangian approach allowed us to exclude the influence of the tropospheric displacement and to obtain integral grid patterns of thunderstorm-hail processes. These structures are especially well manifested at small wind shears (up to 1 m/sec/km). At large shears, the mesh structures are transformed predominantly into linear structures. The methodology for obtaining integral pictures of radio echoes of thunderstorm processes is described. Intersections of linear elements, which we call faces, occur at nodes. The latter plays a particularly important role in the dynamics and kinematics of convective storms. The latter play a particularly important role in the dynamics and kinematics of convective storms. The development of storms occurs along the facets and at the nodes of meso-β-scale convective structures (MMCS), which explains the mechanisms of splitting and merging of storms: in the first case the facets diverge, in the second case they converge. The relations of motion vectors for different types of storms are obtained. It is shown that the direction of the radio echo canopy coincides with the storm motion trajectory; the evolution vector (propagation) for the most powerful storms deviates from the storm displacement direction by 80°–135°. The structure of the updated band within which the Flanking Line is formed for supercells and multicells is studied. Mnemonic rules have been derived that allow one to infer from instantaneous patterns of anvil orientation and the mutual location of storms whether they are converging or diverging, and to identify left- or right-moving storms. A hypothesis on the internal structure of the cold front of the 2nd kind is stated. The main conclusion of the work is that the evolution of storms is determined by the configuration of meso-β-scale convective structures. This explains various convective phenomena from unified positions. The results are applicable in works on modification of convective cloudiness, for ultra-short-term forecasts of dangerous phenomena, storm warnings of the population, rescue services, etc.

Assessment of Storm‐Associated Precipitation and Its Extremes Using Observational Data Sets and Climate Model Short‐Range Hindcasts

AbstractHeavy precipitation, often associated with weather phenomena such as tropical cyclones, extratropical cyclones (ETCs), atmospheric rivers (ARs), and mesoscale convective systems (MCSs), can cause significant socio‐economic loss. In this study, we apply atmospheric feature trackers to quantify the contributions of these storm types in observational data sets and climate model short‐range hindcasts. We generate a global hourly storm data set at 0.25° spatial resolution covering 2006–2020, based on the tracking results from TempestExtremes and Python FLEXible object TRacKeR. Our analyses show that these four storm types account for 67% of global annual mean precipitation and 82% of top 1% precipitation extremes, with MCSs mainly over the tropics, and ARs and ETCs over the midlatitudes. The percentage of precipitation contributions from these storms also show strong seasonality over many geographical locations. We further apply the tracking results to the Energy Exascale Earth System Model (E3SM) short‐range hindcasts and evaluate how well these storms are simulated. The evaluation show that E3SM, with ∼1° resolution, significantly underestimates storm‐associated precipitation totals and extremes, especially for MCSs in the tropics. Our analysis also suggests that model fails to capture the correct mean diurnal phases and amplitude of MCS precipitation. This phenomenon‐based approach provides a better understanding of precipitation characteristics and can lead to enhanced model evaluation by revealing underlying problems in model physics related to precipitation processes associated with the heavy‐precipitating storms.

Global Precipitation Experiment - A New World Climate Research Programme Lighthouse Activity

Abstract The future state of the global water cycle and prediction of freshwater availability for humans around the world remain among the challenges of climate research and are relevant to several United Nations Sustainable Development Goals. The Global Precipitation EXperiment (GPEX) takes on the challenge of improving the prediction of precipitation quantity, phase, timing and intensity, characteristics that are products of a complex integrated system. It will achieve this by leveraging existing World Climate Research Programme (WCRP) activities and community capabilities in satellite, surface-based, and airborne observations, modeling and experimental research, and by conducting new and focused activities. It was launched in October 2023 as a WCRP Lighthouse Activity. Here we present an overview of the GPEX Science Plan that articulates the primary science questions related to precipitation measurements, process understanding, model performance and improvements, and plans for capacity development. The central phase of GPEX is the WCRP Years of Precipitation for 2-3 years with coordinated global field campaigns focusing on different storm types (atmospheric rivers, mesoscale convective systems, monsoons, and tropical cyclones, among others) over different regions and seasons. Activities are planned over the three phases (before, during, and after the Years of Precipitation) spanning a decade. These include gridded data evaluation and development, advanced modeling, enhanced understanding of processes critical to precipitation, multi-scale prediction of precipitation events across scales, and capacity development. These activities will be further developed as part of the GPEX Implementation Plan.

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

AbstractExisting stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

Effects of major storms over a spit-tidal flat interaction system (Punta Rasa-Samborombón, Argentina)

Sandy coastal areas are very dynamic systems in which morphological changes occur over different time scales from hours to decades. However, it has been widely reported that major storms are the main responsible of the most significant changes in short to medium time scales. Major storms have been defined using a variety of environmental variables, but they are normally associated with high values of wave height, duration, return period and direction.Here, we aim to characterize types of major storms and to categorize associated morphological impacts over a complex coastal system. The study site, known as Punta Rasa, is located in the Samborombón bay in the outer part of the Río de La Plata estuary (Argentina) and corresponds to a zone of interaction between a large sandy spit and a backwash tidal flat system. Methods combine statistics of wave climate time-series, analysis of wave energy using nearshore numerical modelling (SWAN) and comparison of pre- and post-storm morphological changes by means of shoreline change detection and satellite images derived indexes (CoastSat Toolkit and NDWI index respectively).Results allowed to characterize four types of major storms impacting the study area: High-Energy Storms (HES), defined by an average storm wave below the 1 % exceedance (>2.6 m), Long-Lived Storms (LLS) represented by an exceedance of the 1 % of Du (>60 h), Storm Groups (SG) in which storm return period is <6 days and Northeastern moderate storms (NMS) defined by their eastern, onshore oriented direction. Under HES and NMS storms erosional areas are dominant over depositional, causing shoreline retreat, a growth of the end-spit and the increase on sand deposition on the back-barrier areas. Under LLS and SG storms, the morphological impact varies alongshore, with multiple erosional hotspots found along the shoreline accompanied by a general flattens of the end-spit system.

Atmospheric conditions conducive to thunderstorms with downbursts in Canada and a downburst precursor parameter

Thunderstorm winds are the costliest wind hazard in Canada, but they have not been well studied. This paper investigates atmospheric conditions prone to downbursts in Canada using a combination of reported downbursts from damage survey reports, ERA5 reanalysis data, lightning flash observations, radar imagery, and upper air measurements for the period of 2019–2023. We propose a novel Downburst Precursor Parameter (DPP) by integrating two low-correlated thermodynamic and kinematic parameters. DPP outperforms other existing instability indices in downburst diagnosis in Canada. Our spatial analysis reveals the absence of damaging downbursts west of the Rockies, while approximately half of all recorded damaging downbursts occurred in southeastern Canada. Squall lines and supercells are the most common types of downburst-generating storms in Canada. Furthermore, nearly two-thirds of storms are squall lines when a downburst is accompanied by lightning flashes, which occurred in ∼71% of all cases. The remaining ∼29% of downbursts without lightning are mostly dry downbursts, typically displaying a disorganized pattern of convective storms on a radar screen. The equivalent potential temperature difference between the surface and mid-level in the troposphere (∆θe) exceeds 13 K prior to 61% of damaging downbursts. This result indicates that ∆θe might be a useful downburst indicator in Canada despite lower values compared to many regions in the United States.

Joint Failure Probability of Dams Based on Probabilistic Flood Hazard Analysis

Probabilistic risk methods are becoming increasingly accepted as a means of carrying out risk-informed decision making regarding the design and operation policy of structures such as dams. Probabilistic risk calculations require the quantification of epistemic and aleatory uncertainties not investigated through deterministic methodologies. In this hydrological study, a stochastic sampling methodology is employed to investigate the joint failure probability of three dams in adjacent similarly sized watersheds within the same hydrologic unit code (HUC) 6 basin. A probabilistic flood hazard analysis (PFHA) framework is used to simulate the hydrologic loading of a range of extreme precipitation events across the combined watershed area of the three studied dams. Precipitation events are characterized by three distinct storm types influential in the Tennessee Valley region with implications for weather variability and climate change. The stochastic framework allows for the simulation of hundreds of thousands of spillway outflows that are used to produce empirical bivariate exceedance probabilities for spillway discharge pairs at selected dams. System response curves that indicate the probability of failure given spillway discharge are referenced for each dam and applied to generate empirical bivariate failure probability (joint failure probability) estimates. The stochastic simulation results indicate the range of spillway discharges for each pair of dams that pose the greatest risk of joint failure. The estimate of joint failure considering the dependence of spillway discharges between dams is shown to be three to four orders of magnitude more likely (7.42 × 102 to 5.68 × 103) than estimates that assume coincident failures are the result of independent hydrologic events.

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

The Influence of Typhoon Events on the Design Storm for the Shanghai Metropolitan Area in the Yangtze River Delta, China

Given the fact that the high frequency of extreme weather events globally, in particular typhoons, has more of an influence on flood forecasting, there is a great need to further understand the impact of typhoon events on design storms. The main objectives of this paper are to examine the magnitude, occurrence, and mechanism of typhoon events in southeast coastal China and their contribution to the design storm study. We take Shanghai, which is a typical metropolitan region in the Yangtze River Delta, China, as an example. The impact of typhoons on the rainfall frequency analysis is quantitatively evaluated using stochastic storm transposition (SST)-based intensity–duration–frequency (IDF) estimates with various temporal and spatial structures under different return periods. The results show that there is significant variability in the storm magnitude within the transposition domain across different durations, highlighting the spatiotemporal heterogeneity over the coastal area. Moreover, the probability of random storm transposition exhibits an uneven distribution. The frequency of typhoon rainfall events within the transposition domain is notably high, and there is considerable variability in the structure of rainfall. Typhoon rainfall amplifies the intensity of design storms, and its contribution increases with return periods. The variability in design storms increases accordingly. Based on the advantages of SST, which retains the spatiotemporal structure of the rainfall in the generated scenarios, the overall framework provides an effective way to examine the impact of diverse characteristics of typhoon rainfall on frequency analysis and facilitate a deeper exploration of the direct impact of various types of extreme storms on the intensity, spatial, and temporal distributions of design storms amidst evolving environmental conditions over this metropolitan region.

A 16-year meteotsunami climatology in the coastal areas of southern Asia-Pacific Ocean

This study presents the meteotsunami behavior in response to different storm types in the coasts of southern Asia-Pacific Ocean from 16 years water level records. Through the size- frequency analysis, the dangerous meteotsunami, wave height exceeding 0.3 m, can occur up to 44 events per year. Notably, during the extreme waves of the 2007 event, wave heights reached approximately 0.9-1.5 m in the Taiwan Strait and the western coastal areas of Taiwan. We have classified storms into six types by radar reflectivity images and satellite-derived precipitation. Findings indicate that predicted wave heights caused by bows and typhoons could reach hazardous magnitude of exceeding 2 m in a 100-yr interval. Spatial and temporal analysis reveals that meteotsunami occurrences are most frequent in the western regions during the winter to early spring months (December to April). Of all meteotsunami occurrences, cluster storms are identified as the most prevalent atmospheric forcing, accounting for 60% of meteostunamis. Typhoons have a 20% association with meteotsunamis along the east coasts of Taiwan during late summer to autumn. On the east coasts, typhoon type-induced events may be attributed to the combination effect of meteotsunamis and infra-gravity waves. Overall, this study provides the first comprehensive examination of meteotsunami-storm characteristics and their associated hazard risks in the coastal areas of the southern Asia-Pacific Ocean.

Characterization of extreme wave fields during Mediterranean tropical-like cyclones

The Mediterranean Sea is a primary source of food, ecosystem services and economic activities and one of the most active cyclogenetic regions in the world, where the influence of orographic and morphological features of the relatively small basin plays an important role. Together with the explosive cyclogenesis, tropical-like cyclones (also called Mediterranean Hurricanes or Medicanes) are among the strongest types of storms that can be found in the Mediterranean basin, occurring predominantly in the Ionian, Balearic and Tyrrhenian sub-basins. Similarly to tropical cyclones (Hurricanes or Typhoons), these cyclonic structures are characterized by strong rotating and translating wind fields, which often lead to a combination of remotely generated swell waves and locally generated wind waves, often referred to as crossing sea states. Despite the well-known potential of Medicanes to cause significant damage near islands and coastal zones, which is predicted to intensify as a result of climate change, to date the characterization of maximum individual waves generated during these events is still lacking. In this study, we carry out the first analysis of the large-scale geographical distribution of wave maxima within the wave fields generated during three recent Medicane events using the WAVEWATCH III® spectral wave model forced by ERA5 reanalysis winds, also investigating the influence of crossing sea states on the maximum wave amplitudes with novel statistical formulations developed for such conditions. Our results show that, as in the case of tropical cyclones, several regions of the cyclone field are characterized by crossing sea states, whose role in the formation of the maximum individual waves occurring near the eye of the storm was found to be confined. Furthermore, extreme wave predictions accounting for the local crossing conditions yield differences up to 5% compared to standard statistical distributions.

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.

COMPARATIVE ANALYSIS OF MAGNETIC STORMS IN THE ODESSA MAGNETIC ANOMALY REGION WITH PLANETARY AND IONOSPHERIC STORMS DURING THE MONITORING PROGRAM AT RT "URAN-4" (1987 – 2010)

This work is a continuation of the study of the response of the Odesa geomagnetic anomaly to the manifestations of solar and planetary geomagnetic activity. Not far from the territory of this magnetic anomaly there is the geomagnetic observatory "Odesa" and the decameter radio telescope "URAN-4" of the RI NASU. A catalogue of magnetic storms for the period 1987-1994 and 2000-2009 was compiled for the monitoring program of high-power space radio sources at RT "URAN-4" using the database of the geomagnetic observatory "Odesa". In this paper, a comparative analysis of the features of the manifestations of geomagnetic storms in the zone of the Odesa magnetic anomaly, planetary storms and ionospheric storms in the 22nd and 23rd cycles of solar activity is carried out. The main results are as follows. The magnetic anomaly amplifies the effects of regional manifestations of planetary magnetic storms. The number of different types of magnetic storms and the dynamics of their changes in the Odesa anomaly differ significantly in the 22nd and 23rd cycles of solar activity. Comparisons have shown that the duration of magnetic storms in the Odessa anomaly is longer than the duration of planetary magnetic and ionospheric storms. According to the data of the «URAN-4» radio telescope, geomagnetic storms form various manifestations of the effects of ionospheric flickers and flux changes for space radio sources depending on the time of their observations. For the region of the Odesa geomagnetic anomaly, such comprehensive studies have not been carried out before. The conducted studies will become an important basis for understanding the influence of magnetic anomalies on the dynamics of the ionosphere during periods of solar and geomagnetic activity.

Deriving Gridded Hourly Rainfall on O‘ahu by Combining Gauge and Radar Rainfall

Abstract High temporal and spatial resolution precipitation datasets are essential for hydrological and flood modeling to assist water resource management and emergency responses, particularly for small watersheds, such as those in Hawai‘i in the United States. Unfortunately, fine temporal (subdaily) and spatial (&lt;1 km) resolutions of rainfall datasets are not always readily available for applications. Radar provides indirect measurements of the rain rate over a large spatial extent with a reasonable temporal resolution, while rain gauges provide “ground truth.” There are potential advantages to combining the two, which have not been fully explored in tropical islands. In this study, we applied kriging with external drift (KED) to integrate hourly gauge and radar rainfall into a 250 m × 250 m gridded dataset for the tropical island of O‘ahu. The results were validated with leave-one-out cross validation for 18 severe storm events, including five different storm types (e.g., tropical cyclone, cold front, upper-level trough, kona low, and a mix of upper-level trough and kona low), and different rainfall structures (e.g., stratiform and convective). KED-merged rainfall estimates outperformed both the radar-only and gauge-only datasets by 1) reducing the error from radar rainfall and 2) improving the underestimation issues from gauge rainfall, especially during convective rainfall. We confirmed the KED method can be used to merge radar with gauge data to generate reliable rainfall estimates, particularly for storm events, on mountainous tropical islands. In addition, KED rainfall estimates were consistently more accurate in depicting spatial distribution and maximum rainfall value within various storm types and rainfall structures. Significance Statement The results of this study show the effectiveness of utilizing kriging with external drift (KED) in merging gauge and radar rainfall data to produce highly accurate, reliable rainfall estimates in mountainous tropical regions, such as O‘ahu. The validated KED dataset, with its high temporal and spatial resolutions, offers a valuable resource for various types of rainfall-related research, particularly for extreme weather response and rainfall intensity analyses in Hawai’i. Our findings improve the accuracy of rainfall estimates and contribute to a deeper understanding of the performance of various rainfall estimation methods under different storm types and rainfall structures in a mountainous tropical setting.

Storm characteristics influence nitrogen removal in an urban estuarine environment

Abstract. Sustaining water quality is an important component of coastal resilience. Floodwaters deliver reactive nitrogen (including NOx) to sensitive aquatic systems and can diminish water quality. Coastal habitats in flooded areas can be effective at removing reactive nitrogen through denitrification (DNF). However, less is known about this biogeochemical process in urbanized environments. This study assessed the nitrogen removal capabilities of flooded habitats along an urban estuarine coastline in the upper Neuse River estuary, NC, USA, under two nitrate concentrations (16.8 and 52.3 µM NOx, respectively). We also determined how storm characteristics (e.g., precipitation and wind) affect water column NOx concentrations and consequently DNF by flooded habitats. Continuous flow sediment core incubation experiments quantified gas and nutrient fluxes across the sediment–water interface in marsh, swamp forest, undeveloped open space, stormwater pond, and shallow subtidal sediments. All habitats exhibited net DNF. Additionally, all habitats increased DNF rates under elevated nitrate conditions compared to low nitrate. Structured habitats with high-sediment organic matter had higher nitrogen removal capacity than unstructured, low-sediment organic matter habitats. High-precipitation–high-wind-storm events produced NOx concentrations significantly lower than other types of storms (e.g., low-precipitation–high-wind, high-wind–low-precipitation, low-wind–low-precipitation), which likely results in relatively low DNF rates by flooded habitats and low removal percentages of total dissolved nitrogen loads. These results demonstrate the importance of natural systems to water quality in urbanized coastal areas subject to flooding.

Community power outage prediction modeling for the Eastern United States

In the United States, weather-related power outages cost the economy tens of billions annually, and there has been an upward trend in billion-dollar disasters over the last two decades. Thus, it is of growing importance to be able to predict outages and understand local resilience. However, many outage prediction models rely on utility infrastructure and outage data, which can be difficult to obtain when a study domain covers many utility territories. This study demonstrates two gradient-boosting machine-learning models driven by utility-agnostic non-proprietary data, eliminating the need for utility-specific data, and allowing individuals or communities to build and use such models for emergency planning or vulnerability analysis. Further, the framework is novel for its ability to incorporate data from various ecoregions, utilize infrastructure proxy data, and provide outage predictions for a breadth of storm types over a large and scalable domain. In this study, vegetation, land cover, energy infrastructure proxy, and weather data are used as model inputs to evaluate 15,872 events across 17 states in the Eastern U.S., where an event is defined as a unique combination of geographic county and storm episode ID. The model predicting all storm types except thunderstorms was validated using 10-fold cross-validation where folds were split chronologically, and demonstrates an r-squared value between predicted and actual outages of 0.61. Similarly, the thunderstorm-only model demonstrates an r-squared of 0.31. For future work, the addition of flooding data may be considered as the r-squared for the various-storm-type model increases to 0.77 when data from New York and New Jersey for Hurricane Sandy are removed. Additionally, the framework demonstrated here can be used to create a real-time outage prediction forecasting tool for storm events, and can be used to analyze resilience at a county resolution under future climate scenarios.

Evaluating the Water Cycle Over CONUS at the Watershed Scale for the Energy Exascale Earth System Model Version 1 (E3SMv1) Across Resolutions

AbstractThe water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low‐resolution (roughly 110 km) and high‐resolution (roughly 25 km) configurations—as established by the High Resolution Model Intercomparison Project protocol—to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The warm season water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes with resolution to the water cycle come from reductions in precipitation and evapotranspiration. The reduction in evapotranspiration reduces the biases across nearly all of the CONUS. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.