Weather Disturbances Research Articles

Analysis of Time Efficiency with CCPM Method and BIM in Construction Projects Construction of High-Rise Residential Building Basement

Development in Indonesia is experiencing a rapid increase, a lot of vacant land is used by investors to construct buildings. The implementation of the construction must pay attention to the project schedule, work delays often occur. The factors that cause work delays are usually from weather disturbances, delivery and supplies of materials and tools that are not in accordance with the plan, changes in design and other external, internal factors. For this reason, this study will discuss about controlling project time using the critical chain project management (CCPM) method and 4D building information modeling in high-rise residential building projects, especially in 5-layer basement work. The critical chain project management method eliminates safe time and replaces buffer time. Adding buffer time using the cut and paste method, namely adding a project buffer half the duration of the critical chain (critical chain) at the end of the chain and placing the feeding buffer with half the activity duration to activities on the non-critical chain. Delays in the construction of a 5-layer basement usually include changes in the level of soil surface conditions, the stability of the excavated soil during the secant pile excavation period as a retaining wall as deep as -17,350 m below the ground level. The results of this study can optimize the duration of the implementation with the final completion time of the structural work faster than 432 working days to 293 work days of implementation so that the time efficiency is 32,17%.

Implementation of Lean Six Sigma Method in High-Rise Residential Building Projects

Fueled by the growth of residential needs, high rise building projects has become a major contributing segment that increases the competition in the world of construction project services [1]. In the implementation of high-rise building construction projects, scheduling and good quality control plays a very important role in the timeliness, cost, and quality of the overall project completion. Project delays often occur in the construction project implementation process. It results in poor quality that is not in accordance with the technical specifications. In the end, it causes loss to a project [2]. Project delays may occur because of internal factors, external factors, weather disturbances, changes in images, etc. Quality failures are usually caused by poor communication between parties in a project. Project development must fulfill the 3 most important things, namely cost, quality, and time. [3]. Cost overrun is one of the issues confronting the construction industry. Most of the construction projects in Malaysia have experienced a cost overrun of 5 to 10% of the total contract [4]. To control the cost, quality, and time, the researchers used the Lean Six Sigma method. Lean Six Sigma is a combination of lean and six sigma which can be defined as a business philosophy, systemic and systematic approach to identifying and eliminating waste or activities that are not value-added through the continuous increase in productivity to reach level Six Sigma, and consists of 5 stages of improvement commonly called DMAIC consisting of Define, Measure, Analyze, Improve, and Control [5]. This research focuses on basement construction, especially bore pile secant pile and excavation work. By applying the Lean Six Sigma method, we can get a cost efficiency of 6,85% and a time efficiency of 9,60 %. Therefore, this research is very useful.

Development of IoT Based Smart Irrigation System with Programmable Logic Controller

Smart irrigation system is an automatic irrigation and monitoring system on agricultural land with a sensor, automation, and control technology based on the Internet of Things (IoT). This system can reduce the agricultural activities that were previously performed manually into an automatic system with a reduced human supervision. Smart Irrigation systems that are widely developed used Arduino as the controller. Arduino still lacks in response, low durability, and sensitivity to temperature change, hence requiring frequent maintenance to avoid weather disturbances, insects, and others. This paper presents a development of a smart irrigation system using a Programmable Logic Controller (PLC) as the controller and a soil moisture sensor as a humidity condition measurement tool. The advantage of using PLC as a controller is more stable and has sensor compatibility with higher accuracy. Hence the results are more consistent and accurate. The PLC system is expandable, allowing for the inclusion of more channels for sensors and other measurement instruments. The developed system can collect data on soil moisture conditions, trigger valves, and perform auto irrigation using sprinklers, reducing or even eliminating the need for human intervention. The IoT collects data from sensors and updates the data into a database system, allowing users to monitor the land conditions in real-time. The developed system was predicted to save manpower (20%) and water usage (42.47%) compared to the conventional method. Keywords : Smart Irrigation; IoT; PLC; Moisture Sensor; Sprinkler

Indications of stellar coronal mass ejections through coronal dimmings

Coronal mass ejections (CMEs) are huge expulsions of magnetized matter from the Sun and stars, traversing space with speeds of millions of kilometers per hour. Solar CMEs can cause severe space weather disturbances and consumer power outages on Earth, whereas stellar CMEs may even pose a hazard to the habitability of exoplanets. While CMEs ejected by our Sun can be directly imaged by white-light coronagraphs, for stars this is not possible. So far, only a few candidates for stellar CME detections are reported. Here we demonstrate a different approach, based on sudden dimmings in the extreme-ultraviolet (EUV) and X-ray emission caused by the CME mass loss. We report dimming detections associated with flares on cool stars, indicative of stellar CMEs and benchmarked by Sun-as-a-star EUV measurements. This study paves the way for comprehensive detections and characterizations of CMEs on stars, important for planetary habitability and stellar evolution.

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning— an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting.The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

Advanced forecasting and disturbance modelling for model predictive control of smart energy systems

We describe a method for embedding advanced weather disturbance models in model predictive control (MPC) of energy consumption and climate management in buildings. The performance of certainty-equivalent controllers such as conventional MPC for smart energy systems depends critically on accurate disturbance forecasts. Commonly, meteorological forecasts are used to supply weather predictions. However, these are generally not well suited for short-term forecasts. We show that an advanced physical and statistical description of the disturbances can provide useful short-term disturbance forecasts. We investigate the case of controlling the indoor air temperature of a simulated building using stochastic differential equations (SDEs) and certainty-equivalent MPC using the novel short-term forecasting method. A Lamperti transformation of the data and the models is an important contribution in making this SDE-based approach work. Simulation-based studies suggest that significant improvements are available for the performance of certainty-equivalent MPC based on short-term forecasts generated by the advanced disturbance model: Electricity savings of 5%–10% while at the same time improving the indoor climate by reducing comfort violations by up to over 90%.

Changes in spring vegetation greenness over Siberia associated with weather disturbances during 1982–2015

AbstractAs the global climate warms rapidly, terrestrial vegetation experiences significant changes. The impact of high‐frequency variability, especially for variations related to weather disturbances (i.e., day‐to‐day weather fluctuations), however, is less studied and remains poorly understood. In the present study, we quantitatively investigated the linkages between the normalized difference vegetation index (NDVI) and weather disturbances in spring (April and May) over Siberia for the period 1982–2015. Identified by empirical orthogonal function analysis, the inherent large‐scale spatial structure of weather disturbances shows synchronous variations over Siberia. It was found that weather disturbances are significantly related to the synchronous NDVI at a large spatial scale. The regional mean (48–67°N and 50–105°E) time series of the NDVI and weather variance, a measure of weather disturbance, is correlated at −0.32 (significant at the 0.05 level), suggesting that in springs with larger weather fluctuations, the concurrent NDVI tends to clearly decrease. Changes of frost events in association with weather disturbances are investigated to help understand the vegetation greenness response. We identified springs with the largest and smallest weather variances. Then, their differences were utilized to estimate the possible maximum changes for extremely cold temperatures. The results show that corresponding to the largest variance increase, for Siberia as a whole, the number of frost days has increased by 0.9 days (5%) and the accumulated adverse temperature has undergone a substantial increase of 18.9°C·day (15%). These results suggest that when there are active weather disturbances, the simultaneous increase in extremely cold temperatures plays a dominant role in adversely impacting vegetation activity. Our study demonstrates that, in addition to the increased mean temperature, the long‐term decrease in weather disturbances over Siberia, as observed in recent decades, should also contribute significantly to vegetation greening. This finding promotes a further understanding of the impacts of climate change on regional vegetation activity.

Methods for Monitoring Strong Space Weather Disturbances to Support International Air Navigation

Methods for Monitoring Strong Space Weather Disturbances to Support International Air Navigation

Meteorological Drivers of Permian Basin Methane Anomalies Derived from TROPOMI

The launch of the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S-5P) satellite has revolutionized pollution observations from space. The purpose of this study was to link spatiotemporal variations in TROPOMI methane (CH4) columns to meteorological flow patterns over the Permian Basin, the largest oil and second-largest natural gas producing region in the United States. Over a two-year period (1 December 2018–1 December 2020), the largest average CH4 enhancements were observed near and to the north and west of the primary emission regions. Four case study periods—two with moderate westerly winds associated with passing weather disturbances (8–15 March 2019 and 1 April–10 May 2019) and two other periods dominated by high pressure and low wind speeds (16–23 March 2019 and 24 September–9 October 2020)—were analyzed to better understand meteorological drivers of the variability in CH4. Meteorological observations and analyses combined with TROPOMI observations suggest that weakened transport out of the Basin during low wind speed periods contributes to CH4 enhancements throughout the Basin, while valley and slope flows may explain the observed western expansion of the Permian Basin CH4 anomaly.

Region-wide characterization of structural diversity of the U.S. Outer Coastal Plain Mixed Forests Province

Forest structural diversity is related to stability, biodiversity, adaptability and resilience, as well as productivity of forest ecosystems. In this study, we characterized structural diversity of the diverse forest type groups in the southeastern Outer Coastal Plains Mixed Forest Province of the United States. We utilized data from 13,658 Forest Inventory and Analysis (FIA) plots across the region and classified them on the basis of forest type group (i.e., longleaf-slash pine, loblolly-shortleaf pine, oak-pine, oak-hickory, oak-gum-cypress, or other), disturbance type (disturbed by animal, fire, insect and disease, weather, other disturbances, or no disturbance), ownership (public or private and Native American), site condition (xeric, mesic, or hydric), stand origin (natural or planted), and age structure (one or two age classes). The structural diversity was estimated as Shannon index of tree size diversity. Our findings indicate a range of 0.00 to 2.40 for Shannon index values across the forest type groups and the ecoregion. All forest attributes (i.e., forest type groups, disturbance type, ownership, site condition, stand origin, and age structure) significantly affected structural diversity at α = 0.05. Oak-gum-cypress was structurally the most diverse forest type group. Overall, plots that were publicly owned, naturally regenerated, two-aged, and affected by weather disturbances exhibited higher structural diversity. We also created region-wide structural diversity distribution and hot spot maps. The maps identified southern Mississippi, northern and eastern Virginia, northern North Carolina, eastern South Carolina, and the border between the states of Texas and Louisiana as the structural diversity hot spots. The middle part of Outer Coastal Plain consisting of southcentral Georgia, northcentral Florida, and southwest Alabama exhibited the lowest structural diversity.

Electric Mode Excitation in the Atmosphere by Magnetospheric Impulses and ULF Waves

Variations of vertical atmospheric electric field Ez have been attributed mainly to meteorological processes. On the other hand, the theory of electromagnetic waves in the atmosphere, between the bottom ionosphere and earth’s surface, predicts two modes, magnetic H (TE) and electric E (TH) modes, where the E-mode has a vertical electric field component, Ez. Past attempts to find signatures of ULF (periods from fractions to tens of minutes) disturbances in Ez gave contradictory results. Recently, study of ULF disturbances of atmospheric electric field became feasible thanks to project GLOCAEM, which united stations with 1 sec measurements of potential gradient. These data enable us to address the long-standing problem of the coupling between atmospheric electricity and space weather disturbances at ULF time scales. Also, we have reexamined results of earlier balloon-born electric field and ground magnetic field measurements in Antarctica. Transmission of storm sudden commencement (SSC) impulses to lower latitudes was often interpreted as excitation of the electric TH0 mode, instantly propagating along the ionosphere–ground waveguide. According to this theoretical estimate, even a weak magnetic signature of the E-mode ∼1 nT must be accompanied by a burst of Ez well exceeding the atmospheric potential gradient. We have examined simultaneous records of magnetometers and electric field-mills during >50 SSC events in 2007–2019 in search for signatures of E-mode. However, the observed Ez disturbance never exceeded background fluctuations ∼10 V/m, much less than expected for the TH0 mode. We constructed a model of the electromagnetic ULF response to an oscillating magnetospheric field-aligned current incident onto the realistic ionosphere and atmosphere. The model is based on numerical solution of the full-wave equations in the atmospheric-ionospheric collisional plasma, using parameters that were reconstructed using the IRI model. We have calculated the vertical and horizontal distributions of magnetic and electric fields of both H- and E-modes excited by magnetospheric field-aligned currents. The model predicts that the excitation rate of the E-mode by magnetospheric disturbances is low, so only a weak Ez response with a magnitude of ∼several V/m will be produced by ∼100 nT geomagnetic disturbance. However, at balloon heights (∼30 km), electric field of the E-mode becomes dominating. Predicted amplitudes of horizontal electric field in the atmosphere induced by Pc5 pulsations and travelling convection vortices, about tens of mV/m, are in good agreement with balloon electric field and ground magnetometer observations.

Development of space environment customized risk estimation for satellites (SECURES)

Plasma variations in the geospace environment driven by solar wind–magnetosphere interactions are one of the major causes of satellite anomaly. To mitigate the effect of satellite anomaly, the risk of space weather disturbances predicted by space weather forecasting needs to be known in advance. However, the risk of satellite anomaly owing to space weather disturbances is not the same for all satellites, because the risk depends not only on the space environment itself but also on the design and materials of individual satellites. From the viewpoint of satellite operators, it is difficult to apply a general alert level of the space environment to the risk of individual satellites. To provide tailored space weather information, we have developed SECURES (Space Environment Customized Risk Estimation for Satellites) by combining models of the space environment and those of spacecraft charging. In SECURES, we focus on the risk of spacecraft charging (surface/internal) for geosynchronous satellites. For the risk estimation of surface charging, we have combined the global magnetosphere magnetohydrodynamics (MHD) model with the satellite surface charging models. For the risk estimation of internal charging, we have combined the radiation belt models with the satellite internal charging models. We have developed prototype products for both types of charging/electrostatic discharge (ESD). The development of SECURES and our achievements are introduced in this paper.

Addressing the meteotsunami risk in the united states

Meteotsunamis are created by transitory weather disturbances moving over water, have a long history of impacting the United States (U.S.) and have resulted in loss of life and property. Many of these events have been historically mischaracterized as seiches, anomalous weather-related waves, or ignored altogether. In this paper, we review meteotsunami generation mechanisms common in the U.S. and highlight several classic historical cases of U.S. meteotsunami formation and impact. We then describe recent advances in sensing and understanding that led to the establishment of initial, rudimentary alerting capabilities for the U.S. Great Lakes and U.S. East Coast. Finally, we describe the major challenges and gaps that must be overcome to move the U.S. toward a comprehensive meteotsunami forecast and warning capability. We also discuss how we envision the various relevant offices of the national oceanic and atmospheric administration (NOAA) working together to achieve this vision. These offices include the NOAA research laboratories, national weather service (NWS) Weather Forecast Offices and National Centers, National Ocean Service Center for Operational Oceanographic Products and Services and NWS Tsunami Warning Centers.

Short‐Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion

Short‐term traffic flow prediction is an effective means for intelligent transportation system (ITS) to mitigate traffic congestion. However, traffic flow data with temporal features and periodic characteristics are vulnerable to weather effects, making short‐term traffic flow prediction a challenging issue. However, the existing models do not consider the influence of weather changes on traffic flow, leading to poor performance under some extreme conditions. In view of the rich features of traffic data and the characteristic of being vulnerable to external weather conditions, the prediction model based on traffic data has certain limitations, so it is necessary to conduct research studies on traffic flow prediction driven by both the traffic data and weather data. This paper proposes a combined framework of stacked autoencoder (SAE) and radial basis function (RBF) neural network to predict traffic flow, which can effectively capture the temporal correlation and periodicity of traffic flow data and disturbance of weather factors. Firstly, SAE is used to process the traffic flow data in multiple time slices to acquire a preliminary prediction. Then, RBF is used to capture the relation between weather disturbance and periodicity of traffic flow so as to gain another prediction. Finally, another RBF is used for the fusion of the above two predictions on decision level, obtaining a reconstructed prediction with higher accuracy. The effectiveness and robustness of the proposed model are verified by experiments.

Radiation Data Portal: Integration of Radiation Measurements at the Aviation Altitudes and Solar‐Terrestrial Environment Observations

AbstractThe impact of radiation dramatically increases at high altitudes in the Earth's atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires convenient access to multisource radiation environment data and enhancement of visualization and search capabilities. The Radiation Data Portal represents an interactive web‐based application for search and visualization of in‐flight radiation measurements. The portal enhances the exploration capabilities of various properties of the radiation environment and provides measurements of dose rates along with information on space weather‐related conditions. The Radiation Data Portal back‐end is a MySQL relational database that contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS) device and the soft X‐ray and proton flux measurements from the Geostationary Operational Environmental Satellite. The implemented application programming interface and Python routines allow a user to retrieve the database records without interaction with the web interface. As a use case of the Radiation Data Portal, we examine ARMAS measurements during an enhancement of the solar proton (SP) fluxes, known as solar proton events, and compare them to measurements during SP‐quiet periods.

Методы мониторинга сильных возмущений космической погоды в интересах международной аэронавигации

The object of research is the methods for monitoring and forecasting strong space weather disturbances affecting the radiation environment and radio communication during air travels. The monitoring techniques used by the existing space weather centers are analyzed: the U.S. Center, the PECASUS consortium (Great Britain, Finland, Germany, Poland, Austria, Italy, the Netherlands, Belgium, Cyprus, and South Africa), the AJCF consortium (Australia, Japan, Canada, France), and the Russian-Chinese space weather consortium.

Genetic structure as a response to anthropogenic and extreme weather disturbances of a coastal dune dwelling spider, Arctosa sanctaerosae.

The continued increase in the number of tourists visiting the Northern Gulf Coast (NGC), USA, in the last century, and the resulting sprawl of large cities along the coast, has degraded and fragmented the available habitat of Arctosa sanctaerosae, a wolf spider endemic to the secondary dunes of the white sandy beaches of the NGC. In addition to anthropogenic disturbance to this coastal region, hurricanes are an additional and natural perturbation to the ecosystem. The data presented here explore the status of populations of this species spanning the entire known range and the factors influencing population demography including anthropogenic disturbance and severe tropical storms. Using microsatellite markers, we were able to document the genetic structure of A. sanctaerosae, including current and historic patterns of migration. These results combined with ecological and census data reveal the characteristics that have influenced population persistence: ecological variables affecting the recovery of the population clusters after severe tropical storms, genetic fragmentation due to anthropogenic disturbance, and their interaction. These findings demonstrate the significance that the high traffic beach communities of the NGC and their impact on the once intact contiguous dune ecosystem have on recovery after severe tropical storms. Contemporary modeling methods that compare current and historic levels of gene flow suggest A. sanctaerosae has experienced a single, contiguous population subdivision, and the isolates reduced in size since the onset of commercial development of the NGC. These results point to the need for monitoring of the species and increased protection for this endangered habitat.

The Conditionality of Outdoor Sports Events on Weather-Induced Impacts and Possible Solution

All over the globe, outdoor sports events are highly affected by weather conditions that frequently influence the comfort of the players and sports fans, the duration, or even the cancelation of the event. To verify the range of weather-induced impacts on selected Major League Baseball events, this interdisciplinary study investigated the causes of unexpected postponements of several Major League Baseball games in March and April 2018, according to official weather reports in the event host destinations. The impact was measured by a recurrent neural network with long short-term memory as an investigation model. Autocorrelation analysis was conducted between all input and target fields. The research focused on the explanation of the influence of weather risks on the event host destinations. Moreover, the study is based on projecting an effective instrument for managing weather risks and then evaluating the tourism services that the future climatic conditions will support. The long short-term memory analysis revealed that solar activity had the greatest influence on the temperature. On the other hand, humidity and air pressure depended very little on solar activity; this finding was confirmed by the zero values of the sensitivity. These values encourage further research on weather disturbances and their connections with outdoor sports and tourism activities.

Decentralized versus Clustered Microgrids: An Energy Systems Study for Reliable Off-Grid Electrification of Small Islands

Philippine off-grid islands are mostly electrified by diesel generators, resulting in costly electricity that is interrupted by fuel supply disruptions. The archipelagic nature of the country also impedes off-grid electrification due to the high capital cost of grid extension. Transitioning from diesel-only systems to hybrid renewable energy systems and interconnecting the island microgrids can solve these problems while promoting cleaner energy production. In this work, a comparative study on decentralized and clustered hybrid renewable energy system microgrids in the Polillo group of islands in the Philippines, using HOMER Pro, was performed. Microgrids comprising solar photovoltaics, lithium-ion battery energy storage, and diesel generators were designed on each island. Clustered systems encompassing multiple islands in the island group were simulated by also considering the least-cost interconnection paths. The techno-economics of each decentralized or clustered system and the four-island system were evaluated based on the levelized cost of electricity (LCOE). Reliability was assessed using the change in LCOE upon the failure of a component and during weather disturbances. Transitioning from diesel-only systems to hybrid systems reduces generation costs by an average of 42.01% and increases the renewable energy share to 80%. Interconnecting the hybrid systems results in an average increase of 2.34% in generation costs due to the cost of submarine cables but improves system reliability and reduces the optimum solar photovoltaic and lithium-ion storage installations by 6.66% and 8.71%, respectively. This research serves as a framework for the interconnection pre-feasibility analysis of other small off-grid islands.

Importance of a vertically tilting structure for energizing the North Atlantic Oscillation

The North Atlantic Oscillation (NAO) is a prominent mode of atmospheric variability that influences weather and climate, including the occurrence of extreme events, over a large part of Europe and Northeastern America. The NAO has been considered to be maintained primarily by migratory weather disturbances and to have a deep structure with no vertical tilt. A careful inspection nonetheless reveals that the associated anomalies do exhibit a subtle vertical tilt, but its dynamical implications are still unknown. Here we show that this vertical tilt is of vital dynamical significance for the wintertime NAO. We find, using atmospheric reanalysis data, that the tilted anomalies transport heat across the pronounced thermal gradient associated with a background westerly jetstream, advecting air from the cooler North America and Greenland to the warmer Atlantic, thereby acting to reinforce NAO’s thermal anomalies. The resultant conversion of potential energy from the background state is a larger energy source for maintaining the NAO than the feedback from migratory disturbances. Our findings thus uncover a fundamental mechanism of the NAO dynamics, with implications for the improvement of seasonal predictions for the Euro-Atlantic climate and the representation of the NAO variability in climate models.