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  Subjects -> METEOROLOGY (Total: 106 journals)
Showing 1 - 36 of 36 Journals sorted by number of followers
Nature Climate Change     Full-text available via subscription   (Followers: 201)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 182)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 84)
Climatic Change     Open Access   (Followers: 72)
Atmospheric Environment     Hybrid Journal   (Followers: 71)
Atmospheric Research     Hybrid Journal   (Followers: 71)
Bulletin of the American Meteorological Society     Open Access   (Followers: 64)
Advances in Climate Change Research     Open Access   (Followers: 61)
Journal of Climate     Hybrid Journal   (Followers: 60)
Climate Policy     Hybrid Journal   (Followers: 60)
Climate Change Economics     Hybrid Journal   (Followers: 52)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 50)
Climate Dynamics     Hybrid Journal   (Followers: 46)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 43)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 42)
Weather and Forecasting     Hybrid Journal   (Followers: 41)
American Journal of Climate Change     Open Access   (Followers: 41)
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 40)
Atmospheric Science Letters     Open Access   (Followers: 40)
Journal of Hydrology and Meteorology     Open Access   (Followers: 40)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 35)
Climate Resilience and Sustainability     Open Access   (Followers: 35)
Atmosphere     Open Access   (Followers: 33)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 32)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 32)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 32)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 31)
Monthly Weather Review     Hybrid Journal   (Followers: 30)
International Journal of Climatology     Hybrid Journal   (Followers: 29)
Journal of Space Weather and Space Climate     Open Access   (Followers: 29)
Climate Change Responses     Open Access   (Followers: 29)
Journal of Climate Change     Full-text available via subscription   (Followers: 29)
Space Weather     Full-text available via subscription   (Followers: 28)
International Journal of Environment and Climate Change     Open Access   (Followers: 28)
International Journal of Atmospheric Sciences     Open Access   (Followers: 26)
Current Climate Change Reports     Hybrid Journal   (Followers: 26)
Energy & Environment     Hybrid Journal   (Followers: 25)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 25)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 24)
Advances in Meteorology     Open Access   (Followers: 24)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 23)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 20)
Tellus A     Open Access   (Followers: 20)
Tellus B     Open Access   (Followers: 20)
Journal of Economic Literature     Hybrid Journal   (Followers: 19)
Meteorology     Open Access   (Followers: 19)
Weatherwise     Hybrid Journal   (Followers: 18)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 18)
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 18)
Global Meteorology     Open Access   (Followers: 17)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 16)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 16)
Weather and Climate Extremes     Open Access   (Followers: 16)
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 15)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 13)
The Cryosphere (TC)     Open Access   (Followers: 13)
Climate Risk Management     Open Access   (Followers: 12)
Climate and Energy     Full-text available via subscription   (Followers: 12)
Climate Change Research Letters     Open Access   (Followers: 11)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 10)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 9)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 9)
Journal of Climate Change and Health     Open Access   (Followers: 9)
Climate of the Past (CP)     Open Access   (Followers: 8)
Climate     Open Access   (Followers: 8)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 8)
Aeolian Research     Hybrid Journal   (Followers: 7)
Climate Law     Hybrid Journal   (Followers: 7)
Climate Research     Hybrid Journal   (Followers: 7)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 7)
Open Atmospheric Science Journal     Open Access   (Followers: 7)
Oxford Open Climate Change     Open Access   (Followers: 7)
Carbon Balance and Management     Open Access   (Followers: 6)
Open Journal of Modern Hydrology     Open Access   (Followers: 6)
Climate Services     Open Access   (Followers: 6)
npj Climate and Atmospheric Science     Open Access   (Followers: 6)
Meteorological Applications     Open Access   (Followers: 5)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 5)
Meteorological Monographs     Hybrid Journal   (Followers: 5)
Frontiers in Climate     Open Access   (Followers: 5)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 5)
International Journal of Biometeorology     Hybrid Journal   (Followers: 4)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 4)
Journal of Climatology     Open Access   (Followers: 4)
Urban Climate     Hybrid Journal   (Followers: 4)
Weather and Climate Dynamics     Open Access   (Followers: 4)
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Environmental and Climate Technologies     Open Access   (Followers: 3)
Atmospheric Environment : X     Open Access   (Followers: 3)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 3)
Meteorologica     Open Access   (Followers: 2)
Atmósfera     Open Access   (Followers: 2)
Journal of Weather Modification     Full-text available via subscription   (Followers: 2)
气候与环境研究     Full-text available via subscription   (Followers: 2)
Mediterranean Marine Science     Open Access   (Followers: 2)
GeoHazards     Open Access   (Followers: 2)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Climate of the Past Discussions (CPD)     Open Access   (Followers: 1)
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Earth Perspectives - Transdisciplinarity Enabled     Open Access   (Followers: 1)
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access   (Followers: 1)
Nīvār     Open Access   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
Ciencia, Ambiente y Clima     Open Access   (Followers: 1)
Journal of Agricultural Meteorology     Open Access  
Mètode Science Studies Journal : Annual Review     Open Access  

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Similar Journals
Journal Cover
Modeling Earth Systems and Environment
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2363-6203 - ISSN (Online) 2363-6211
Published by Springer-Verlag Homepage  [2468 journals]
  • Incremental–decremental data transformation based ensemble deep learning
           model (IDT-eDL) for temperature prediction

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      Abstract: Abstract Human life heavily depends on weather conditions, which affect the necessary operations like agriculture, aviation, tourism, industries, etc., where the temperature plays a vital role in deciding the weather conditions along with other meteorological variables. Therefore, temperature forecasting has drawn considerable attention from researchers because of its significant effect on daily life activities and the ever-challenging forecasting task. These research objectives are to investigate the transformation of data based on incremental and decremental approaches and to find the practical ensemble approach over proposed models for effective temperature prediction, where the proposed model is called the Incremental–Decremental Data Transformation-Based Ensemble Deep Learning Model (IDT-eDL). The temperature dataset from Delhi, India, has been utilized to compare proposed and traditional deep learning models over various performance measures. The proposed IDT-eDL with BiLSTM deep learning model (i.e., IDT-eDL_BiLSTM ) has performed the best among the proposed models and traditional deep learning model and achieved Performance over measures MSE: 1.36, RMSE: 1.16, MAE: 0.89, MAPE: 4.13 and \(R^2\) :0.999. Additionally, non-parametric statistical analysis of Friedman ranking is also performed to validate the effectiveness of the proposed IDT-eDL model, which also shows a higher ranking of the proposed model than the traditional deep learning models.
      PubDate: 2024-02-26
       
  • Quantifying flood risk using InVEST-UFRM model and mitigation strategies:
           the case of Adama City, Ethiopia

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      Abstract: Abstract Adama, the second largest city in Ethiopia, faces regular flash floods. These floods severely impact the city dwellers’ livelihoods due to unplanned urbanization in flood-prone low-lying areas surrounding deforested mountains and ridges. To address this issue, the current study employed the Integrated Valuation of Ecosystem Services and Tradeoffs-Urban Flood Risk Mitigation (InVEST-UFRM) model as a tool to quantify adaptation-planning strategies for the study area. The InVEST-UFRM model effectively analyzed urban watersheds and spatially represented flooding, providing a comprehensive understanding of the flood risk scenario in the city. The model was run four times with different rainfall scenarios based on an intensity–duration–frequency curve specific to the study area to assess the impact of varying rainfall depths. These scenarios produced significant findings in terms of increased runoff retention and highlighting the varying capacities of micro-catchments in the study area for retaining runoff and generating floods. The study also emphasized the suitability of the InVEST-UFRM model in quantifying trade-offs associated with structural and non-structural flood mitigation measures. It underscored the importance of integrating multidisciplinary fields such as hydrology, remote sensing, geographic information systems, and natural capital assessment tools in urban planning strategies to achieve flood resilience and sustainable urban development. Overall, the InVEST-UFRM model proved to be a valuable tool for urban planners and policymakers for adaptive strategies to cope with urban flood events.
      PubDate: 2024-02-26
       
  • Predictability of the extreme precipitation days in central Eastern Africa
           during january to may period

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      Abstract: Abstract Extreme precipitation events significantly affect both human activities and the natural environment. Despite their severe impacts, accurately predicting these extreme precipitation events remains a substantial challenge.The study explores the predictability of extreme precipitation events in Central Eastern Africa (CEA) from January to May, aiming to improve forecasting and provide insights for policymakers and meteorological agencies. The study uses data from Rwanda, Burundi, Uganda, Kenya, and Tanzania and employs a physics-based empirical (P-E) model to identify relevant predictors for extreme precipitation events. The research uses observatin data from Climate prediction center (CPC), Monthly mean sea level pressure (MSLP) Temperature at 2 m, geopotential height and winds, and employs a physics-based empirical (P-E) model to identify relevant potential predictors and precursors for extreme precipitation events. The findings indicated that the P-E models show improved predictability of extreme precipitation days over CEA, with positive correlations for Central East Africa Predictor (EAP1 &3) for Mean sea level pressure and Temperature at 2 m height, but negative correlation for EAP2 for sea serfuce temperature. EPD3 is associated with the temperature at 2-meter height, where positive temperature values result in positive EPDs across the entire region. The percentile-based extreme precipitation index, which is consistent with earlier studies, takes regional variances into account. If the daily precipitation exceeds the 90th percentile of all rainfall records (daily rainfall > 0.1 mm) for the entire 40 years (1981–2020), it is considered an extreme precipitation event on this study. Utilizing the identified predictors, a series of Physics-based Empirical models is developed. These correlations serve as a robust indication of the temporal predictability for Extreme Precipitation Days (EPDs) during the March-April-May (MAM) season over Central East Africa (CEA). The research emphasizes the influence of equatorial Indo-Pacific Sea Surface Temperature (SST) anomalies on Extreme Precipitation Days (EPDs) in Central East Africa (CEA). It elucidates the complex interplay between large-scale anomalies related to regional EPD indices, particularly during El Niño. The study highlights that the prolonged presence of the anticyclonic anomaly, combined with a positive air-sea feedback mechanism, amplifies the likelihood of EPDs in CEA. This heightened probability is attributed to atmospheric changes induced by El Niño and the associated SST anomalies, fostering conditions conducive to increased precipitation events in the region.
      PubDate: 2024-02-19
       
  • Experimental and modeling of CO2 absorption in a bubble column using a
           water-based nanofluid containing co-doped SiO2 nanoparticles

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      Abstract: Abstract This study tried to investigate the effect of Co/SiO2 NPs on CO2 absorption in a single raising bubble column (20 °C and 1 atm). Co-doped SiO2 nanoparticles were first synthesized through the chemical vapor deposition (CVD) method, then several nanofluids, including different weight percentages of the synthesized NPs (0.001, 0.01, 0.02, 0.05, and 0.1 wt%) were prepared. Comprehensive experimental studies examined the effect of NPs concentration and nanofluid volume on CO2 absorption rate. The stability of nanofluids, as an affecting factor on nanofluid efficiency, was investigated over 10 days. It was tried to obtain mass transfer parameters, including Sherwood (Sh), and Schmidt (Sc) numbers, incorporating the CO2 diffusivity into the Co/SiO2 nanofluid. Results showed that increasing NPs concentration from 0.001 to 0.02 caused the CO2 absorption rate to reach a maximum point followed by a downward trend. Increasing nanofluid volume was not beneficial for increasing gas absorption, which is attributed to the fact that the predominant mechanism of CO2 absorption was the Brownian motion of NPs. Results confirmed that the prepared nanofluids had acceptable stability over 10 days, and the nanofluid (80 mL), including 0.02 wt% of NPs, had the maximum CO2 absorption, which was 28% more than the base fluid. Findings indicated that the magnitude of the CO2 mass transfer coefficient in the nanofluid was 1.953 * 10− 4 (m.s− 1), which was 1.89 times more than that for the base fluid. Finally, a comprehensive correlation (R2 = 0.99) was introduced to predict the CO2 mass transfer coefficient in the Co/SiO2 nanofluid.
      PubDate: 2024-02-16
       
  • Stochastic Bayesian approach and CTSA based rainfall prediction in Indian
           states

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      Abstract: Abstract Recently the advancement of technology provided numerous ways for predicting the variations of weather from a specific location. In the agricultural field, the success and failure of crop harvesting mainly depend on the amount of rainfall. However, if excess rainfall flows it obtains a challenging role and is not able to predict the accurate rate of rainfall. Elsewhere the early rainfall prediction helped to balance the economic conditions in an agriculture-dominated country like India. Although there have been significant advancements in weather and climate adaptation in recent decades, traditional methods for rainfall prediction remain computationally expensive and complex due to high uncertainty and variability in weather patterns. So to perform an accurate as well as early rainfall prediction the stochastic Bayesian method is proposed that helped to predict the rainfall employed in Indian states such as Uttar Pradesh, Assam, Jharkhand, Tamil Nadu, Andhra Pradesh, etc. Also, the exploration stage is enhanced by tunicate swarm optimization (TSA). The outcomes demonstrate that the scalable stochastic Bayesian approach method was more useful than the existing methods and the accuracy of the rainfall prediction is enhanced by utilizing the crossover-based tunicate swarm algorithm (CTSA). The proposed model is compared in times of MER (%), RMSE (mm), MAPE (%), and MAE (mm). The presented stochastic Bayesian with CTSA achieves 16.23 MER, 4.45 MAPE, 17.96 RMSE, and 13.78 MAE. According to the outcomes, the CTSA algorithm has lower training loss and it proves that the suggested method stochastic Bayesian with CTSA predicts rainfall efficiently.
      PubDate: 2024-02-15
       
  • Modelling landslide susceptibility along major transportation corridor in
           Darjeeling Himalayas using GIS-based MCDA approaches

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      Abstract: Abstract Landslide is one of the most destructive geomorphic hazards that usually occur in monsoon in the Darjeeling Himalayas. To minimize the landslide-induced causalities present study aims to assess the landslide susceptibility along a major transportation corridor, i.e., National Highway 110 (NH-110)—Darjeeling Himalayan Railway (DHR) in Darjeeling Himalayas. Geographic Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) approaches, i.e., Analytic Hierarchy Process (AHP) and Multi-influencing Factor (MIF) have been used for this purpose. Ten geo-environmental factors; elevation, slope, aspect, curvature, lithology, lineament density, rainfall, drainage density, topographic wetness index (TWI) and land use were identified as landslide causative factors. All the topographical and hydrological factors were extracted from a 12 m resolution TanDEM-X Digital Elevation Model (DEM). Lithology, lineament, and land use maps were prepared after interpretation of Landsat 8 OLI (2021) satellite image in conjunction with Google Earth image and intensive field survey. Landslide inventory was prepared from multi-temporal satellite images (2015–2021). The AHP-based landslide susceptibility map showed that 7.19% of the area comes under very high, 79.04% as high, 8.98% as moderate, and 4.79% as low susceptible zone. Whereas, in the MIF-based approach it is 11.38%, 81.43%, 1.80%, and 5.39%, respectively. In this study slope was identified as the most determining factor of landslide, followed by lithology, rainfall, and land use. The Receiver Operating Characteristics (ROC) curve shows that the AHP-based approach is comparatively better (0.776) than the MIF (0.757). Moreover, it can be concluded that both approaches performing well in the Himalayan terrain.
      PubDate: 2024-02-11
       
  • Evaluation of best management practices (BMPS) and their impact on
           environmental flow through SWAT+ model

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      Abstract: Abstract This study addresses the critical issue of land degradation due to soil erosion in elevated agricultural lands, emphasizing the imminent threat to crop viability. Best Management Practices (BMPs) appear as a crucial strategy for mitigating soil deterioration and safeguarding rainwater resources. In the Kinaye sub-watershed, characterized by minimal concentration time, crops face withering within a week after rainfall ceases, prompting the implementation of BMPs. The research focuses on evaluating the implemented BMPs and their impact on environmental flow using the SWAT+ model. By employing a Digital Elevation Model (DEM), the drainage region is subdivided into 52 sub-basins. Land usage and land cover (LULC) data are sourced from a Bhuvan Panchayat webpage, while soil classification details are extracted from a digital soil map prepared by the National Remote Sensing Centre (NRSC). Utilizing 24 years of daily meteorological data for SWAT+ simulation, model calibration, and validation rely on the flow record from 1996 to 2016. Parameter sensitivity analysis and model calibration, facilitated by the SWAT+ toolbox, reveal the efficacy of BMPs. Comparative analysis of runoff and sediment output with and without BMP implementation highlights a significant decrease in average monthly runoff (22.58%) and sediment yield (36.59%). The study additionally explores the reduction in annual flood event frequency, noting a decrease from 11 to 42 occurrences to 7 to 39. Despite the positive impact of combined BMPs on runoff reduction, opportunities for further runoff retention are identified, underscoring the ongoing need for sustainable land management practices.
      PubDate: 2024-02-09
       
  • Impact of climate change on the habitat range and distribution of Cordyla
           pinnata, Faidherbia albida and Balanites aegyptiaca in Senegal

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      Abstract: Abstract The survival of agroforestry species in Senegal is threatened by climate change. Hence, it has become important to identify the most endangered species for prioritising conservation efforts. This study aimed to assess the impact of climate change on the distribution of three key agroforestry species in Senegal: Faidherbia albida, Balanites aegyptiaca, and Cordyla pinnata. To predict their habitat suitability, we evaluated the performance of five algorithms: Random Forest, Boosted Regression Trees, Generalised Linear Models, Maximum Entropy, and Support Vector Machines. Two shared socio-economic pathways (SSP2-4.5 and SSP5-8.5) were used over four time slices (2041–2060, 2061–2080, and 2081–2100). The dataset included 19 bioclimatic and 12 soil variables, with 5342 species occurrences collected from fieldwork and the Global Biodiversity Information Facility website. The main variables influencing the species distribution were selected based on collinearity, jackknife tests, and their contribution. The results revealed that the Random Forest outperformed other models in terms of performance, with an area under the curve (AUC) greater than 0.95 for all species. The distribution of F. albida and C. pinnata was mainly influenced by precipitation related variables, while that of B. aegyptiaca was mainly influenced by both precipitation and temperature related variables. The current habitat range of F. albida, C. pinnata, and B. aegyptiaca represents 45% (88,515.63 km2), 14.43% (28,381.55 km2) and 29.73% (58,479.53 km2) of the total area of Senegal, respectively. However, the models showed a decrease in the habitat range of C. pinnata, regardless of the scenario or time horizon. The distribution of F. albida is predicted to increase under SSP2-4.5 and decrease under SSP5-8.5. In contrast, B. aegyptiaca is predicted to be more resilient to climate change as its habitat suitability would expand under both scenarios. Hence, conservation efforts should prioritise C. pinnata and, to a lesser extent, F. albida.
      PubDate: 2024-02-08
       
  • Modelling of insitu channel migration vis-à-vis bank stability of
           Brahmani River, Odisha

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      Abstract: Abstract The process of insitu migration of river channels involves the alteration of morphological parameters, which affects river morphodynamics at both temporal and spatial scales. Over the past 2 decades, the Brahmani River has been subjected to five significant flood events and the impact of Cyclone Phailin. In recent years, there have been numerous instances of embankment failure along the Brahmani River, leading to significant loss of life and property. The present study focused on understanding the mechanism of the insitu migration of river channels by analyzing the temporal scale change from 2000 to 2019. The entire course of the Brahmani River, spanning approximately 799 km, was examined using Landsat data and Bhuvan Cartosat DEM. Planform, erosion, accretion, and stability parameters were analyzed using Landsat data, whereas slope-forming parameters were derived using the Cartosat DEM. The factors of stability, erosion, accretion, sinuosity, and channel area exert considerable influence in directing the river toward insitu migration and embankment failure. The insitu migration zone model was derived by integrating planform, slope-forming, erosion, accretion, and stability parameters using the Analytical Hierarchy Process (AHP). The lower reaches of the Insitu migration model map exhibit numerous reported riverbank erosion locations, which are situated in areas of very high to high vulnerability zones. More vulnerable zones were also identified along the entire stretch of the river. Field and geotechnical investigations of vulnerable riverbanks were conducted along critical zones. Slope stability analysis in PLAXIS 2D using finite element modelling of vulnerable slopes was carried out to validate the Insitu migration model. The simulation of highly vulnerable slopes utilized data pertaining to river water levels to evaluate fluctuations that could result in slope failure. The slopes of the riverbanks in areas designated as very high vulnerability zones in the model are susceptible to erosion, as the Factor of Safety is less than 1, leading to slope failure. This research will prove highly beneficial in gaining a comprehensive understanding of the temporal evolution of rivers in relation to areas of Insitu migration, which will facilitate the formulation of effective strategies by governmental and non-governmental agencies for the management of hazard-prone zones and provision of emergency relief in the region.
      PubDate: 2024-02-08
       
  • Precision modeling of slope stability for optimal landslide risk
           mitigation in Ramban road cut slopes, Jammu and Kashmir (J&K) India

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      Abstract: Abstract The socioeconomic growth and development are extensively aided by the development of road networks in the Union Territory (UT): Jammu and Kashmir (J&K), a Himalayan state. However, due to complex geological factors, road cut slope instability is ubiquitous in the area, leading to frequent slope failures and interruption of transportation, loss of life, environmental deterioration and economic damage. To this direction, a comprehensive investigation was undertaken using field observations and variance in geological conditions at six different locations present along the routes linking Gool town of district Ramban with other neighboring villages in the area. The study carried out kinematic analysis and employs rock mass rating (RMR), geological strength index (GSI) and slope mass rating (SMR) techniques to characterize rockmass of the selected slopes for the stability assessment. A detailed structural analysis was carried out to understand the mechanism of slope failure in the area. The kinematic analysis reveals that joint planes cross at various angles, leading to diverse possible failure mechanisms with dominantly wedge failures, accompanied by a lesser occurrence of toppling and planar failures. Furthermore, the landslide possibility index (LPI) system was also used to assess the stability. The stability of the slopes has been identified to range from stable to entirely unstable. Based on the above data sets, a landslide susceptibility map was produced to demarcate the zones of high risk in the area. In addition to assisting with existing developmental projects in the region, the present study is well-positioned to aid in the mitigation of risks. The high-risk elements were also identified, and specific mitigation strategies are recommended for those elements.
      PubDate: 2024-02-08
       
  • Modelling monthly rainfall of India through transformer-based deep
           learning architecture

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      Abstract: Abstract In the realm of Earth systems modelling, the forecasting of rainfall holds crucial significance. The accurate prediction of monthly rainfall in India is paramount due to its pivotal role in determining the country’s agricultural productivity. Due to this phenomenon's highly nonlinear dynamic nature, linear models are deemed inadequate. Parametric non-linear models also face limitations due to stringent assumptions. Consequently, there has been a notable surge in the adoption of machine learning approaches in recent times, owing to their data-driven nature. However, it is acknowledged that machine learning algorithms lack automatic feature extraction capabilities. This limitation has propelled the popularity of deep learning models, particularly in the domain of rainfall forecasting. Nevertheless, conventional deep learning architectures typically engage in the sequential processing of input data, a task that can prove challenging and time-consuming, especially when dealing with lengthy sequences. To address this concern, the present article proposes a rainfall modelling algorithm founded on a transformer-based deep learning architecture. The primary distinguishing feature of this approach lies in its capacity to parallelize sequential input data through an attention mechanism. This attribute facilitates expedited processing and training of larger datasets. The predictive performance of the transformer-based architecture was assessed using monthly rainfall data spanning 41 years, from 1980 to 2021, in India. Comparative evaluations were conducted with conventional recurrent neural networks, long short-term memory, and gated recurrent unit architectures. Experimental findings reveal that the transformer architecture outperforms other conventional deep learning architectures based on root mean square error and mean absolute percentage error. Furthermore, the accuracy of each architecture's predictions underwent testing using the Diebold–Mariano test. The conclusive findings highlight the discernible and noteworthy advantages of the transformer-based architecture in comparison to the sequential-based architectures.
      PubDate: 2024-02-08
       
  • Mitigating the intensity of heat waves through optimal control of carbon
           dioxide emission by spraying water droplets: a modeling approach

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      Abstract: Abstract Intensifying heat waves, exacerbated by rise in carbon dioxide (CO \({_2}\) ) emissions, pose severe threats to global human health. Therefore, it becomes imperative to conduct analytical research on the control mechanisms that help mitigate the intensity of heat waves. This manuscript introduces a mathematical model to analyse the efficacy of mitigating heat wave intensity through optimal control of atmospheric CO \({_2}\) by spraying water droplets. The model integrates key variables including human population density, atmospheric CO \({_2}\) concentration, atmospheric temperature, and amount of water droplets. Using the suggested model, we also examine how heat waves affect the human population. According to the study of the model, when the atmospheric concentration of \(CO_2\) decreases due to the spraying of water droplets, heat wave intensity reduces, and human population density rises. The optimal control analysis is utilised to determine the dynamic spray rate of water droplets to control the atmospheric CO \({_2}\) , incurring the least associated cost. The numerical simulation of the model has been done to portray the analytical findings. Policies to optimally control the atmospheric CO \({_2}\) by spraying water droplets at strategic locations are found more suitable than constant heuristic control.
      PubDate: 2024-02-07
       
  • Numerical analysis of a laboratory-modelled geosynthetic-partially encased
           columns applied as shallow foundation support in frictional-cohesive soils
           

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      Abstract: Abstract The utilization of geosynthetic-encased granular columns is an advantageous and environmentally friendly technique for enhancing fine-grained soil foundations, particularly for supporting footings in specific conditions. These conditions may include collapsible and porous soils, serving as a viable alternative to conventional concrete piles. This study evaluates the geomechanical behavior of GECs as an improvement technique of fine-grained soils beneath shallow foundations, with a particular emphasis on evaluating the impact of encasement length and footing diameter. The investigation involves laboratory tests using 1-g small-scale models of granular columns subjected to incremental static load stages. These models were instrumented not only to measure the applied loads and resulting settlements, but also to assess the earth pressures developed along the depth. This approach was adopted for a comprehensive understanding of the stress transfer mechanisms inherent in employing partial encasement techniques. The responses of these models contribute to the development, calibration, and validation of a numerical model, based on the finite element method, facilitating further analyses to explore the impact of encasement length and granular column diameter in relation to footing dimensions. The findings demonstrate that employing partial encasements in granular columns effectively prevents columns bulging within shallow depths, concurrently improving stress transfer mechanisms in deeper soil strata. Additionally, the study highlights the critical influence of the encasement length concerning the column height (R/L) and the ratio between footing and column diameters (D/d). These factors significantly affect column failure mechanisms, thus bearing substantial implications for the design of shallow foundations supported by columns.
      PubDate: 2024-02-05
       
  • Modelling the potential impact of climate change on Carapa procera DC. in
           Benin and Burkina Faso (West Africa)

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      Abstract: Abstract Carapa procera plays an important socio-cultural and economic role for local people. The species is threatened by several factors including climate changes. This study explored the current and future distribution of the species in Benin and Burkina Faso. The maximum entropy (Maxent) software was used which combined the occurrence data of the species with a set of environmental layers. The future distribution of the species was assessed using the shared socioeconomic pathways (SSP) 245 and 585 for 2061–2080 and 2081–2100 periods. Globally, the models performed well, with mean AUC and TSS values of 0.90 and 0.67, respectively, suggesting good performance of the models. A set of five (05) variables drives the distribution of the species with rainfall and isothermality as the most important. For the current distribution, the findings showed that the highly suitable areas were mainly located in Guinea Congolian, and Sudanian zones respectively in Benin and Burkina Faso. The model indicated similar future patterns regardless of the general circulation models (GCMs) and shared socioeconomic pathways (SSPs). The MIROC6 model predicted that the species could lose around 10% of its currently suitable areas, whereas the CNRM model predicted that it would lose 8%. The WAPOK complex was identified to harbor the species in the natural habitats. Our study provides good insight into the current and future distribution of C. procera which can be decisive for the species management.
      PubDate: 2024-02-05
       
  • A mathematical approach of drug addiction and rehabilitation control
           dynamic

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      Abstract: Abstract Drug addiction is a widespread problem worldwide, inflicting substantial harm in terms of a large number of deaths and disability. Mostly people are recognizing it as a major public health challenge, and this awareness extends to both less developed and wealthier nations. This research introduced an innovative mathematical model, represented as \(S_D, E_D, H_D, L_D, R_D, C_D\) , to comprehensively explore the dynamics of drug addiction. The main goal is to analyze the extent of drug addiction in society. This involves considering two groups of individuals: those heavily addicted and those with a light addiction with rehabilitation. The model’s behavior has been examined through RK4 method conducted with Maple. We have been proposed the early identification and rehabilitation method as a control dynamic. This control strategy has been involved identifying drug users early on and providing treatment to minimize their influence on society and prevent them from becoming heavy drug addicts. Our simulations emphasize the pivotal role of interaction rates ( \(\gamma\) ) and rehabilitation rates ( \(\delta _1\) ) and ( \(\delta _2\) ) in controlling drug user population dynamics.
      PubDate: 2024-02-04
       
  • How accurate is the SALTMED model in simulating rapeseed yield and growth
           under different irrigation and salinity levels'

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      Abstract: Abstract Crop modeling is a valuable tool in agricultural research for studying the response of plants to environmental parameters. The purpose of this study was to assess the precision of the SALTMED model in simulating the growth and yield of rapeseed under different levels of irrigation water and soil salinity in a semi-arid region. The normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency coefficient (EF), and index of agreement (d) for grain yield simulation were 0.052, 0.917, and 0.96, respectively, at the calibration stage of the model, and 0.054, 0.881, and 0.96, respectively, at the validation stage. The results showed that the accuracy of the model was satisfactory and acceptable in simulating the grain yield and dry matter of rapeseed, as well as the soil water content and soil salinity. Therefore, the results of this study can be used for modeling the effect of climate change and abiotic environmental parameter variations such as terrain, climate, and soil and water properties on agricultural production systems. However, there were some discrepancies observed between the measured and estimated data for soil water content simulation. Some possible defects in the model theory that may contribute to its inaccuracy were discussed.
      PubDate: 2024-02-03
       
  • Advancing air quality prediction models in urban India: a deep learning
           approach integrating DCNN and LSTM architectures for AQI time-series
           classification

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      Abstract: Abstract Every year, a large number of people in India lose their lives as a result of extreme degradation of urban air quality. This study introduces a deep learning architecture designed to analyze and forecast outdoor air conditions in the Indian capital, Delhi. Meteorological and air quality data are sourced from monitoring stations at fourteen locations across Delhi, focusing on carbon monoxide (CO) and particulate matter (PM2.5 and PM10) concentrations to derive the Air Quality Index (AQI) time series spanning September 2014–July 2020. The extensive 1765-day AQI dataset is divided into training (1412 days, 80% of the total dataset) and testing (353 days, the remaining 20%) sets. The AQI values, categorized into five classes ranging from satisfactory to severe, serve as the basis for training and testing the proposed models. The development of deep convolutional neural networks (DCNN) and combined DCNN-long short-term memory (DCNN-LSTM) architectures incorporates meteorological parameters such as temperature, humidity, atmospheric pressure, and AQI time-series data. While deep convolutional layers excel in extracting valuable information and discerning the internal structure of input time series, long short-term memory (LSTM) networks are adept at discerning long-term and short-term correlations. The DCNN-LSTM architecture is proposed for its effective integration of the strengths of both DCNN and LSTM architectures. Evaluation metrics including sensitivity, specificity, accuracy, F1 score, and the AUC-ROC curve gauge the predictive performance of the architectures. The DCNN network attains an overall accuracy of 94.48%, an F1 score of 97%, and an AUC of 0.94. Notably, the DCNN-LSTM architecture surpasses other predictive models, achieving the highest classification accuracy of 97.48%, an AUC of 0.97, and an overall F1 score of 97.48%. These results underscore the efficacy of the proposed deep learning approach in advancing air quality prediction models.
      PubDate: 2024-02-02
       
  • Modeling future (2021–2050) meteorological drought characteristics using
           CMIP6 climate scenarios in the Western Cape Province, South Africa

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      Abstract: Abstract Consistent drought modelling under plausible shared socioeconomic–representative concentration pathways (SSP–RCPs) are crucial for effectively managing future drought risk in agricultural environments. The Western Cape (WC) is one of South Africa’s main agro-based provinces and faces a mounting threat of water insecurity due to recurrent drought. The objective of this study was to predict meteorological drought hazard for 2021–2050 based on three CMIP6 scenarios: SSP5–8.5, SSP2–4.5 and SSP1–2.6. Precipitation simulations generated by the sixth version of Model for Interdisciplinary Research on Climate (MIROC6) under the SSP5–8.5, SSP2–4.5 and SSP1–2.6 scenarios were used from fifteen stations across the six AEZs of the WC province. The Standardised Precipitation Index (SPI) was computed at 12-month timescales. Trend analysis of precipitation datasets and the SPI-values were done at p < 0.05 using the Mann–Kendall (M–K) test. The findings revealed negative precipitation trends of − 7.6 mm/year in Ceres, while positive trends of 0.3 mm/year were observed in Malmesbury. These findings indicate an improvement from − 7.8 and − 6.4 mm/year in the same regions, respectively, compared to historical trends observed between 1980 and 2020. The results suggest that in 2042 and 2044, Bredasdorp will experience − 2 < SPI < − 1.5 under the SSP2–4.5 scenarios, while Matroosberg in 2038 under the SSP5–8.5 will experience SPI > − 2. The findings of this study will assist in the development of proactive planning and implementation of drought mitigation strategies and policies aimed at reducing water insecurity in AEZs.
      PubDate: 2024-02-02
       
  • Mathematical modeling of HIV transmission in a heterosexual population:
           incorporating memory conservation

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      Abstract: Abstract HIV disease is a major global public health concern since its appearance in the early 1980s. Many mathematical models have been conducted to understand, control, and predict its spread. In this paper we propose a mathematical model with memory effect modelling HIV transmission in a heterosexual population divided into two age classes; young-class (15–24 years old) and grown-class (25 years old and over). The goal of dividing the population according to age is to identify the most vulnerable class to the virus based on their sexual activity and make accurate predictions about HIV transmission. First, we determine the biologically significant space for the study, and we prove the existence of a unique solution. Then we divide the principal model into four sub-models: young-people, grown-people, young-men linked to grown-women, grown-men linked to young-women. The basic reproduction number associated to each sub-model is derived. According to the four sub-models, we have found that, if the basic reproduction number is below unity, then the free disease equilibrium state is locally asymptotically stable. Numerical simulations are provided to validate the theoretical results and discuss the local stability of the endemic equilibrium states of each sub-model. We conclude that incorporating memory conservation gives more realistic results, where reaching a stable state takes higher time. As well, memory effect can play the role of prior knowledge about the disease and experience accumulated over years.
      PubDate: 2024-02-01
       
  • Mathematical model and analysis of the soil-transmitted helminth
           infections with optimal control

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      Abstract: Abstract Soil-transmitted helminth diseases are highly prevalent in impoverished regions and pose a significant health burden on the global population. These diseases are primarily transmitted through the contamination of soil with human faces containing parasite eggs. This study presents a novel deterministic mathematical model to comprehensively investigate the dynamics of helminth infection transmission through the soil. The mathematical model exhibits two equilibrium points: the diseases-free equilibrium point (DFE) and the endemic equilibrium point (EEP). The DFE is proven to be locally and globally asymptotically stable when the basic reproduction number is less than one, indicating the potential for disease eradication. Conversely, the EEP is locally asymptotically stable when the basic reproduction number exceeds unity, representing a persistent endemic state. To explore effective intervention strategies for controlling the spread of these infections, optimal control theory is applied. The study incorporates two time-varying control variables derived from sensitivity analysis: the rate of hygiene consciousness in the susceptible class and the rate of hygiene consciousness in the infectious class. Numerical simulations demonstrate that implementing optimal control strategies can successfully curb and mitigate soil-transmitted helminth infections. Overall, this research highlights the importance of proactive and targeted interventions, emphasizing the significance of hygiene education and awareness campaigns. By implementing optimal control measures based on the proposed strategies, the burden of soil-transmitted helminth diseases can be significantly reduced, improving public health in affected regions.
      PubDate: 2024-02-01
       
 
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  Subjects -> METEOROLOGY (Total: 106 journals)
Showing 1 - 36 of 36 Journals sorted by number of followers
Nature Climate Change     Full-text available via subscription   (Followers: 201)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 182)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 84)
Climatic Change     Open Access   (Followers: 72)
Atmospheric Environment     Hybrid Journal   (Followers: 71)
Atmospheric Research     Hybrid Journal   (Followers: 71)
Bulletin of the American Meteorological Society     Open Access   (Followers: 64)
Advances in Climate Change Research     Open Access   (Followers: 61)
Journal of Climate     Hybrid Journal   (Followers: 60)
Climate Policy     Hybrid Journal   (Followers: 60)
Climate Change Economics     Hybrid Journal   (Followers: 52)
Advances in Atmospheric Sciences     Hybrid Journal   (Followers: 50)
Climate Dynamics     Hybrid Journal   (Followers: 46)
Atmospheric Chemistry and Physics (ACP)     Open Access   (Followers: 43)
Nature Reports Climate Change     Full-text available via subscription   (Followers: 42)
Weather and Forecasting     Hybrid Journal   (Followers: 41)
American Journal of Climate Change     Open Access   (Followers: 41)
Journal of Applied Meteorology and Climatology     Hybrid Journal   (Followers: 40)
Atmospheric Science Letters     Open Access   (Followers: 40)
Journal of Hydrology and Meteorology     Open Access   (Followers: 40)
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 35)
Climate Resilience and Sustainability     Open Access   (Followers: 35)
Atmosphere     Open Access   (Followers: 33)
Boundary-Layer Meteorology     Hybrid Journal   (Followers: 32)
The Quarterly Journal of the Royal Meteorological Society     Hybrid Journal   (Followers: 32)
International Journal of Climate Change Strategies and Management     Hybrid Journal   (Followers: 32)
Meteorology and Atmospheric Physics     Hybrid Journal   (Followers: 31)
Monthly Weather Review     Hybrid Journal   (Followers: 30)
International Journal of Climatology     Hybrid Journal   (Followers: 29)
Journal of Space Weather and Space Climate     Open Access   (Followers: 29)
Climate Change Responses     Open Access   (Followers: 29)
Journal of Climate Change     Full-text available via subscription   (Followers: 29)
Space Weather     Full-text available via subscription   (Followers: 28)
International Journal of Environment and Climate Change     Open Access   (Followers: 28)
International Journal of Atmospheric Sciences     Open Access   (Followers: 26)
Current Climate Change Reports     Hybrid Journal   (Followers: 26)
Energy & Environment     Hybrid Journal   (Followers: 25)
Environmental Dynamics and Global Climate Change     Open Access   (Followers: 25)
Journal of Atmospheric Chemistry     Hybrid Journal   (Followers: 24)
Advances in Meteorology     Open Access   (Followers: 24)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 23)
Dynamics of Atmospheres and Oceans     Hybrid Journal   (Followers: 20)
Tellus A     Open Access   (Followers: 20)
Tellus B     Open Access   (Followers: 20)
Journal of Economic Literature     Hybrid Journal   (Followers: 19)
Meteorology     Open Access   (Followers: 19)
Weatherwise     Hybrid Journal   (Followers: 18)
Journal of Meteorology and Climate Science     Full-text available via subscription   (Followers: 18)
Economics of Disasters and Climate Change     Hybrid Journal   (Followers: 18)
Global Meteorology     Open Access   (Followers: 17)
Atmosphere-Ocean     Full-text available via subscription   (Followers: 16)
Atmospheric Chemistry and Physics Discussions (ACPD)     Open Access   (Followers: 16)
Weather and Climate Extremes     Open Access   (Followers: 16)
Monthly Notices of the Royal Astronomical Society     Hybrid Journal   (Followers: 15)
Theoretical and Applied Climatology     Hybrid Journal   (Followers: 13)
The Cryosphere (TC)     Open Access   (Followers: 13)
Climate Risk Management     Open Access   (Followers: 12)
Climate and Energy     Full-text available via subscription   (Followers: 12)
Climate Change Research Letters     Open Access   (Followers: 11)
Advances in Statistical Climatology, Meteorology and Oceanography     Open Access   (Followers: 10)
Journal of Hydrometeorology     Hybrid Journal   (Followers: 9)
Atmospheric and Oceanic Science Letters     Open Access   (Followers: 9)
Journal of Climate Change and Health     Open Access   (Followers: 9)
Climate of the Past (CP)     Open Access   (Followers: 8)
Climate     Open Access   (Followers: 8)
Dynamics and Statistics of the Climate System     Open Access   (Followers: 8)
Aeolian Research     Hybrid Journal   (Followers: 7)
Climate Law     Hybrid Journal   (Followers: 7)
Climate Research     Hybrid Journal   (Followers: 7)
Journal of the Meteorological Society of Japan     Partially Free   (Followers: 7)
Open Atmospheric Science Journal     Open Access   (Followers: 7)
Oxford Open Climate Change     Open Access   (Followers: 7)
Carbon Balance and Management     Open Access   (Followers: 6)
Open Journal of Modern Hydrology     Open Access   (Followers: 6)
Climate Services     Open Access   (Followers: 6)
npj Climate and Atmospheric Science     Open Access   (Followers: 6)
Meteorological Applications     Open Access   (Followers: 5)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 5)
Meteorological Monographs     Hybrid Journal   (Followers: 5)
Frontiers in Climate     Open Access   (Followers: 5)
Bulletin of Atmospheric Science and Technology     Hybrid Journal   (Followers: 5)
International Journal of Biometeorology     Hybrid Journal   (Followers: 4)
Journal of Integrative Environmental Sciences     Hybrid Journal   (Followers: 4)
Acta Meteorologica Sinica     Hybrid Journal   (Followers: 4)
Journal of Climatology     Open Access   (Followers: 4)
Urban Climate     Hybrid Journal   (Followers: 4)
Weather and Climate Dynamics     Open Access   (Followers: 4)
Russian Meteorology and Hydrology     Hybrid Journal   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Environmental and Climate Technologies     Open Access   (Followers: 3)
Atmospheric Environment : X     Open Access   (Followers: 3)
Journal of Meteorological Research     Full-text available via subscription   (Followers: 3)
Meteorologica     Open Access   (Followers: 2)
Atmósfera     Open Access   (Followers: 2)
Journal of Weather Modification     Full-text available via subscription   (Followers: 2)
气候与环境研究     Full-text available via subscription   (Followers: 2)
Mediterranean Marine Science     Open Access   (Followers: 2)
GeoHazards     Open Access   (Followers: 2)
Studia Geophysica et Geodaetica     Hybrid Journal   (Followers: 1)
Climate of the Past Discussions (CPD)     Open Access   (Followers: 1)
Michigan Journal of Sustainability     Open Access   (Followers: 1)
Earth Perspectives - Transdisciplinarity Enabled     Open Access   (Followers: 1)
Revista Iberoamericana de Bioeconomía y Cambio Climático     Open Access   (Followers: 1)
Nīvār     Open Access   (Followers: 1)
Modeling Earth Systems and Environment     Hybrid Journal   (Followers: 1)
Tropical Cyclone Research and Review     Open Access   (Followers: 1)
Ciencia, Ambiente y Clima     Open Access   (Followers: 1)
Journal of Agricultural Meteorology     Open Access  
Mètode Science Studies Journal : Annual Review     Open Access  

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JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


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