Weather Disturbances Research Articles

Extended drag-based model for better predicting the evolution of coronal mass ejections

Coronal mass ejections (CMEs) are one of the primary drivers of space weather disturbances, affecting both space-based and terrestrial technologies. The accurate prediction of CME trajectories and their arrival times at Earth is crucial for mitigating potential impacts. In this work, we introduce an extended drag-based model (EDBM) that incorporates an additional acceleration term to better capture the complex dynamics of CMEs as they propagate through the heliosphere. Preliminary results suggest that the EDBM can improve upon the classical drag-based model by providing more reliable estimates of CME travel times, particularly in cases where the CME experiences residual acceleration. However, further validation is required to fully assess the operational potential of the model for space weather forecasting. This study lays the groundwork for future investigations and applications, with the aim of enhancing the accuracy of CME prediction models.

Autonomous Quality Control of High Spatiotemporal Resolution Automatic Weather Station Precipitation Data

How to prevent the influence of precipitation’s localized and sudden characteristics is the most formidable challenge in the quality control (QC) of precipitation observations. However, with sufficiently high spatiotemporal resolution in observational data, nuanced information can aid us in accurately distinguishing between intense, localized precipitation events, and anomalies in precipitation data. China has deployed over 70,000 automatic weather stations (AWSs) that provide high spatiotemporal resolution surface meteorological observations. This study developed a new method for performing QC of precipitation data based on the high spatiotemporal resolution characteristics of observations from surface AWSs in China. The proposed QC algorithm uses the cumulative average method to standardize the probability distribution characteristics of precipitation data and further uses the empirical orthogonal function (EOF) decomposition method to effectively identify the small-scale spatial structure of precipitation data. Leveraging the spatial correlation characteristics of precipitation, partitioned EOF detection with a 0.5° spatial coverage effectively minimizes the influence of local precipitation on quality control. Analysis of precipitation probability distribution reveals that reconstruction based on the first three EOF modes can accurately capture the organized structural features of precipitation within the detection area. Thereby, based on the randomness characteristics of the residuals, when the residual of a certain observation is greater than 2.5 times the standard deviation calculated from all residuals in the region, it can be determined that the data are erroneous. Although the quality control is primarily aimed at accumulated precipitation, the randomness of erroneous data indicates that 84 continuous instances of error data in accumulated precipitation can effectively trace back to erroneous hourly precipitation observations. This ultimately enables the QC of hourly precipitation data from surface AWSs. Analysis of the QC of precipitation data from 2530 AWSs in Jiangxi Province (China) revealed that the new method can effectively identify incorrect precipitation data under the conditions of extreme weather and complex terrain, with an average rejection rate of about 5%. The EOF-based QC method can accurately detect strong precipitation events resulting from small-scale weather disturbances, thereby preventing local heavy rainfall from being incorrectly classified as erroneous data. Comparison with the quality control results in the Tianqing System, an operational QC system of the China Meteorological Administration, revealed that the proposed method has advantages in handling extreme and scattered outliers, and that the precipitation observation data, following quality control procedures, exhibits enhanced similarity with the CMAPS merged precipitation data. The novel quality control approach not only elevates the average spatial correlation coefficient between the two datasets by 0.01 but also diminishes the root mean square error by 1 mm.

Resilience in Function, Microbial Community Structure, and Nitrifier Composition of Bench-Scale Biofilm Reactors during Wet Weather Disturbances

Resilience in Function, Microbial Community Structure, and Nitrifier Composition of Bench-Scale Biofilm Reactors during Wet Weather Disturbances

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.

Forecasting Trends in Relative Humidity in Iraq (Study in Climate Geography)

The research aims to predict and determine the trend in relative humidity rates (%) in Iraq, for four months (October, January, April, July), as the percentage of water in the atmosphere does not exceed (0.01%) of the hydrosphere at any moment. Despite this, it is an important element in the weather and plays a major role in the formation of clouds, fog, and dew. He is responsible for all major weather events, such as hurricanes and other weather disturbances. To understand the nature of the behavior of relative humidity, its trend, fluctuations, and future changes in time and space, statistical models were designedm(SARIMA) based on the Box–Jenkins method in the time series forecasting method for the period (2023-2037), for the following stations (Kirkuk - Baghdad - Al-Hayy - Samawah - Nasiriyah - Basra) and relying on the base years from (1970 - 2022), and the research adopted the use of The algorithm drawn up by two researchers (BJ 1976), used the autoregressive model and the seasonal integration moving average, which is symbolized by the symbol (p, d, q) (P, D, Q) SARIMA, which is called the seasonal multiplier model.

МГД-волны в области предфронта межпланетных ударных волн 6 и 7 сентября 2017 г.

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

MHD waves at the pre-front of interplanetary shocks on September 6 and 7, 2017

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

Bayesian clustering of spatially distributed compositional data with application to the Great Barrier Reef.

The relative abundance of groups of species is often used in ecological surveys to estimate community composition, a metric that reflects patterns of commonness and rarity of biological assemblages. The focus of this paper is measurements of the abundances of four benthic groups (that live on the seafloor) at several reefs on Australia's Great Barrier Reef (GBR) gathered between 2012 and 2017. In this paper we develop a statistical model to find clusters of locations with similar composition. We examine the changes in clusters during a period impacted by an unprecedented sequence of extreme environmental disturbances. To achieve this, we propose a model that incorporates the geographical location of the data, accounting for the possibility that nearby reefs are similar in composition. This is accomplished with a Dirichlet mixture model and a Potts distribution on the cluster assignments. Non-availability of the normalised Potts distribution makes Bayesian inference a doubly-intractable task. To circumvent this additional inferential challenge, an approximate exchange algorithm is specified. The analysis of the 2012 data, collected before the weather disturbances, reveals four clusters. The four groups highlight the primary habitat patterns in the 2012 GBR, each with distinct ecological characteristics: (1) areas with above-average soft coral abundance, (2) sand-dominated regions commonly found in the central part, (3) southern reefs with a more balanced distribution of species, and (4) habitats dominated by algae and hard corals. Compared to subsequent surveys conducted after disturbances, there is evidence of a decline in the number of clusters and a simplification of reef composition at the regional scale.

Transient upstream mesoscale structures: drivers of solar-quiet space weather

In recent years, it has become increasingly clear that space weather disturbances can be triggered by transient upstream mesoscale structures (TUMS), independently of the occurrence of large-scale solar wind (SW) structures, such as interplanetary coronal mass ejections and stream interaction regions. Different types of magnetospheric pulsations, transient perturbations of the geomagnetic field and auroral structures are often observed during times when SW monitors indicate quiet conditions, and have been found to be associated to TUMS. In this mini-review we describe the space weather phenomena that have been associated with four of the largest-scale and the most energetic TUMS, namely, hot flow anomalies, foreshock bubbles, travelling foreshocks and foreshock compressional boundaries. The space weather phenomena associated with TUMS tend to be more localized and less intense compared to geomagnetic storms. However, the quiet time space weather may occur more often since, especially during solar minima, quiet SW periods prevail over the perturbed times.

Understanding hurricane effects on forestlands: Land cover changes and salvage logging

Hurricanes can cause catastrophic damage to forestlands, prompting forest owners to engage in salvage logging to mitigate losses and resulting in a sudden and pronounced impact on the timber supply. At the same time, salvaging efforts have profound ecological repercussions, reshaping forest dynamics and affecting vital ecosystem processes. Given the relevant impact of salvage logging to the timber market and its potential ecological effects, evaluating the main factors motivating or limiting its occurrence and the common spatial patterns of salvage sites could offer valuable support for post-storm management efforts following future hurricane events.This study evaluates the land cover change (LCC) of forested areas following hurricanes, recognizing that the change to land cover classification could be derived from storm damage or additional modifications, such as salvage logging activities. We used a set of predictive machine learning models to investigate the primary factors influencing these modifications and determining a possible connection to salvage operations. To serve this purpose, we selected variables that relate to both forest’s vulnerability to hurricane disturbance and salvage site selection criteria. This research was centered primarily on the category-five Hurricane Michael, using three additional hurricanes of different intensities to compare results. Random Forest was the bestperforming model for predicting LCC in all cases, having wind speed and distance to nearest wood-consuming mills as the most important features when trained on Hurricane Michael data, emphasizing the relevance of market- and operation-related variables alongside weather disturbance aspects. The findings of our study lead us to infer that the observed modifications in land cover following hurricanes likely represent an additional transformation induced by the removal of damaged timber material, potentially serving as a proxy for salvage logging activities.

China's Ground‐Based Space Environment Monitoring Network—Chinese Meridian Project (CMP)

AbstractMonitoring and investigation of the solar‐terrestrial space environment is a huge challenge for humans in space age. To this end, China has established the Ground‐based Space Environment Monitoring Network, namely Chinese Meridian Project (CMP). The project comprises three major systems: the Space Environment Monitoring System, Data and Communication System, and Scientific Application System. The Space Environment Monitoring System adopts a well‐designed monitoring architecture, known as “One Chain, Three Networks, and Four Focuses,” to achieve stereoscopic and comprehensive monitoring of the entire solar‐terrestrial space. The “One‐Chain” component utilizes optical, radio, interplanetary scintillation, cosmic ray instruments to cover the causal chain of space weather disturbances from the solar surface to near‐Earth space. For the ionosphere, middle and upper atmosphere, and magnetic field, instruments are deployed along longitudes of 120° and 100°E, and latitudes of 30° and 40°N, forming the “Three Networks.” Furthermore, more powerful monitoring facilities or large‐scale instruments have been deployed in four key regions: the high‐latitude polar region, mid‐latitude region in northern China, low‐latitude region at Hainan Island, and the Tibet region. These four regions are crucial for disturbances propagation and evolution, or possess unique geographical and topographical characteristics. The Data and Communication System and Scientific Application System are designed for data collecting, processing, storage, mining, and providing user service based on data acquired by the Space Environment Monitoring System. The data obtained by CMP will be shared with the global scientific community, facilitating enhanced collaboration on space weather and space physics research.

Challenges in Forecasting the Evolution of a Distorted CME Observed During the First Close Solar Orbiter Perihelion

Coronal Mass Ejections (CMEs), drivers of the most severe Space Weather disturbances, are often assumed to evolve self-similarly during their propagation. However, open magnetic field structures in the corona, leading to higher-speed streams in the ambient solar wind, can be source of strong distortions of the CME front. In this paper, we investigate a distorted and Earth-directed CME observed on 2022 March 25 combining three remote sensing with three in situ observatories at different heliocentric distances (from 0.5 to 1 au). Near quadrature observations by Solar Orbiter and the Solar Terrestrial Relations Observatory revealed a distortion of the CME front in both latitude and longitude, with Solar Orbiter observations showing an Earth-directed latitudinal distortion as low as ≈6 R ⊙. Near-Earth extreme-ultraviolet observations indicated the distortion was caused by interaction with faster wind from a nearby equatorial coronal hole. To evaluate the effect of the distortion on the CME's propagation, we adopted a three-point-of-view graduated cylindrical shell (GCS) fitting approach. For the first time, the GCS results are combined with an additional heliospheric single-viewpoint that looks further out in the heliosphere, revealing a deceleration in the CME before reaching ≈100 R ⊙. The CME geometry and velocity determined by this enhanced GCS are used to initialize a drag-based model and a WSA-Enlil MHD model. The estimated times of arrival are compared with in situ data at different heliocentric distances and, despite the complexity of the event, the error in the arrival times at each spacecraft results much lower (≈4 hr error) than the typical errors in literature (≈8–10 hr).

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

Electrical power projects: the role of risk management and reliability

The management of power outages caused by severe weather disasters such as earthquakes, tornadoes, and weather disturbances is critical in power transmission and distribution systems. The transmission system is the key to any power system and can trigger severe or significant effects if it fails. Different causes of fires, extreme weather conditions, ageing components, poor maintenance and malfunctions, human error, mal-operative procedures, and high operating network variables may cause transmission components to fail. System reliability is the probability that, despite component failures, a system remains available or functional. Risk management of the failures describes it. There are several methods to predict the failure rate, including exponential and Bayesian distributions. In this study, Poisson distribution forecasted the outage patterns for transmission and distribution networks for one and five years. A probability distribution function generated this pattern. Moreover, previous data were obtained from the National Electric Power Regulatory Authority (NEPRA). On comparing the acquired results with previous outage data, it was reported that the average outages in the transmission system were 567. The average outages in the distribution system stood at 37 for five years with a 100% confidence level. However, the probability of getting 620 transmission outages and 50 distribution outages was the highest in the probability distribution function for five years. Poisson distribution proved to be a useful tool to assess the transmission and distribution system reliability by estimating the failure rate over the years. It would allow the risk professionals to schedule, reduce, and track the systems’ risks. It would also help to improve the system’s reliability.

Quantifying hurricane impacts on United States Virgin Islands reef fishes using a catchability invariant approach to compare uncalibrated survey indices

The United States Virgin Island's (USVI) coral reefs support many economically and ecologically important fish species. Located in the Caribbean Sea, they are subject to frequent severe weather disturbances, including two category 5 hurricanes, Irma and Maria, in 2017. The overarching goal of this study was to identify reef fish community impacts following these extreme events using discontinuous survey indices. Long-term (2001–2021), in situ, fishery-independent survey data using two methods were standardized to the extent possible in two regions of the USVI, St. Thomas/St. John (STT/STJ) and St. Croix (STX). Comparable data were assessed, 73 species collected on hard- bottom habitat, to identify the number of these species with significant changes in density and/or mean length between 2-yr survey intervals over the historic baseline (2001–2015), disturbance (2017–2019), and postdisturbance (2019–2021) periods. The results varied by region: STT/STJ had no disturbance impact and STX had a significant disturbance impact. In STX, 20 species had significant changes in density in the disturbance period compared to an average of 9.7 (SD 3.8) species for the baseline period. The proportion of species with significant density increases and decreases were similar suggesting that overall disturbance impacts are nuanced. Mean length observations were less informative, likely due to survey method and sample size changes. However, in combination with density they provided useful insights into the possible causes of population change. The successful use of discontinuous survey indices to obtain meaningful biological insights has broader applications to ecosystem and fishery datasets with similar limitations.

Quasi-planar ICME sheath: A cause of the first two-step extreme geomagnetic storm of the 25th solar cycle observed on 23 April 2023

Interplanetary Coronal Mass Ejections (ICMEs) are prominent drivers of space weather disturbances. The reported studies suggested that the planar ICME sheath and planar magnetic clouds (MCs) cause extreme geomagnetic storms. Here, we investigated the first two-step extreme geomagnetic storm (Sym-H∼-231 nT) of 25th solar cycle. Our analysis demonstrates the transformation of ICME sheath into quasi-planar magnetic structures (PMS). The study corroborates our earlier reported finding that the less adiabatic expansion in possible quasi-PMS transformed ICME enhanced the strength of the southward magnetic field component. It contributes to the efficient transfer of plasma and energy in the Earth’s magnetosphere to cause the observed extreme storm. We found that the magnetosphere stand-off distance reduced to <6RE (where RE stands for the Earth’s radius), and it impacts geosynchronous satellites moving close to 6RE.

Rainstorm and strong wind weathers largely increase greenhouse gases flux in shallow ponds

Shallow-water ponds represent the hotspots of greenhouse gas (GHG) emissions. Most current studies focus on the temporal dynamics for GHGs in water, with little consideration given to the effects of weather changes. In this study, we measured and compared the concentrations and fluxes of CO2, CH4, and N2O from a pond in Northeast China under different meteorological conditions. Results showed that the rates of CO2, CH4, and N2O emissions from pond into the atmosphere during strong winds were 85.85 ± 7.55 mmol m−2 d−1, 22.05 ± 6.80 mmol m−2 d−1, and 10.87 ± 0.72 μmol m−2 d−1, respectively, significantly higher than those measured during non-rain weather. Among which, over 88 % of CH4 emissions were contributed by ebullition. Meanwhile, the CO2 and N2O flux were also significantly higher during heavy rainfall, reaching 100.05 ± 19.76 mmol m−2 d−1 and 5.90 ± 1.03 μmol m−2 d−1, respectively. Strong winds and precipitation induced sediment disturbances, high gas transport coefficients, reduced photosynthesis and oxygen greatly promoted the GHGs escape evasion. Wind speed, air pressure, solar radiation, and dissolved oxygen in water were important influencing factors. Our results emphasize the importance of capturing short-term weather disturbance events, especially rainstorm and strong winds, to accurately assess the annual GHG budget from these shallow water ecosystems.

Predicting the Emergence of Solar Active Regions Using Machine Learning

AbstractTo create early warning capabilities for upcoming Space Weather disturbances, we have selected a dataset of 61 emerging active regions, which allows us to identify characteristic features in the evolution of acoustic power density to predict continuum intensity emergence. For our study, we have utilized Doppler shift and continuum intensity observations from the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO). The local tracking of 30.66 × 30.66-degree patches in the vicinity of active regions allowed us to trace the evolution of active regions starting from the pre-emergence state. We have developed a machine learning model to capture the acoustic power flux density variations associated with upcoming magnetic flux emergence. The trained Long Short-Term Memory (LSTM) model is able to predict 5 hours ahead whether, in a given area of the solar surface, continuum intensity values will decrease. The performed study allows us to investigate the potential of the machine learning approach to predict the emergence of active regions using acoustic power maps as input.

Three principal components describe the spatiotemporal development of mesoscale ionospheric equivalent currents around substorm onsets

Abstract. Substorms are a commonly occurring but insufficiently understood form of dynamics in the coupled magnetosphere–ionosphere system, associated with space weather disturbances and auroras. We have used principal component analysis (PCA) to characterize the spatiotemporal development of ionospheric equivalent currents as observed by the International Monitor for Auroral Geomagnetic Effects (IMAGE) magnetometers during 28 substorm onsets identified by Frey et al. (2004). Auroral observations were provided by all-sky cameras. We found that the equivalent currents can typically be described by three components: a channel of poleward equivalent current (wedgelet), a westward electrojet (WEJ) associated with an auroral arc, and a vortex. The WEJ and vortex are located at the equatorward end of the channel, which has been associated with bursty bulk flows (BBFs) by previous studies. Depending on its polarity, the vortex either indents the WEJ and arc equatorward or bulges the WEJ poleward while winding the arc into an auroral spiral. In addition, there may be a background current system associated with the large-scale convection. The dynamics of the WEJ, vortex, and channel can describe up to 95 % of the variance of the time derivative of the equivalent currents during the examined 20 min interval. Rapid geomagnetic variations at the substorm onset location, which can drive geomagnetically induced currents (GICs) in technological conductor networks, are mainly associated with the oscillations of the WEJ, which may be driven by oscillations of the transition region between dipolar and tail-like field lines in the magnetotail due to the BBF impact. The results contribute to the understanding of substorm physics and to the understanding of processes that drive intense GICs.