Investigating the quality of Karst water resources: A case study on Fars Namdan plain by Mohammadsadegh Talebi

“…The research data consists of meteorological, hydrological, topographic and geological maps and statistics. …”
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Validation of summer temperature interpolation methods in northeastern Iran by hamid salehi, Muhammad motamedi, Ezatollah mafi

“…The study area in this study is northeastern Iran, including the provinces of Khorasan Razavi and North Khorasan, and monthly summer temperature data were used for 21 synoptic and evaporative stations in the northeast belonging to the Meteorological Organization and the Ministry of Energy with appropriate distribution. …”
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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

“…A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. …”
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The Outlook of Precipitation Changes in the Great Karoun Using by CMIP5 series models by zeinab mokhayeri, Ebrahim fatahi, Reza Borna

“…To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). …”
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XCO₂ Data Full-Coverage Mapping in China Based on Random Forest Models by Ruizhi Chen, Zhongting Wang, Chunyan Zhou, Ruijie Zhang, Huizhen Xie, Huayou Li

“…The study integrates XCO₂ satellite observations from SCIAMACHY, GOSAT, OCO-2, and GF-5B, alongside nighttime light remote sensing data, meteorological parameters, vegetation indices, and CO₂ profile data. …”
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Deciphering of emergency construction objects using satellite imagery and sub-satellite monitoring data in the Arctic by M. L. Kazaryan, A. A. Richter, M. A. Shakhramanyan, S. M. Grigoriev

“…For the Arctic territories remote methods are relevant because of unfavorable meteorological conditions of contact methods, as well as because of the depressed nature of most settlements. …”
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Assessment of multidecadal precipitation seasonality in the panama canal watershed by István Gábor Hatvani, Nayara Azevedo de Castro Souza, Russell S. Harmon, Jorge A. Espinosa, Zoltán Kern

“…Study Focus: This study provides insight into the spatiotemporal behavior of precipitation across the PCW11 Panama Canal Watershed utilizing 6-day rainfall records from 29 meteorological stations from 1950 to 2019. The focus interval of 2000–2017 offered the most complete spatiotemporal coverage of the long-term seasonal variability explored by hierarchical cluster analysis. …”
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Unraveling the interplay between NDVI, soil moisture, and snowmelt: A comprehensive analysis of the Tibetan Plateau agroecosystem by Di Wei, Lin Yan, Ziqi Zhang, Jia Yu, Xue’er Luo, Yun Zhang, Bo Wang

“…This study integrates MODIS remote sensing images and ERA5-Land meteorological reanalysis datasets to establish a ternary system encompassing NDVI, soil moisture, and snowmelt. …”
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Monitoring the earth surface temperature and relationship land use with surface temperature using of OLI and TIRS Image by sayyad asghari, hadi emami

“…Due to the limitation of meteorological stations, remote sensing can be an appropriate alternative to the Earth's surface temperature. …”
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Spatiotemporal Changes and Trade-Offs/Synergies of Ecosystem Services in the Qin-Mang River Basin by Jiwei Zhao, Luyao Wang, Dong Jia, Yaowen Wang

“…This study, based on land use type (LUT), and meteorological and soil data from 1992 to 2022, combined with the InVEST model, correlation analysis, and spatial autocorrelation analysis, explores the impacts of land use/land cover changes (LUCCs) on ESs. …”
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Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France by A. Demortier, M. Mandement, V. Pourret, O. Caumont, O. Caumont

“…<p>Personal weather station (PWS) networks owned by citizens now provide near-surface observations at a spatial density unattainable with standard weather stations (SWSs) deployed by national meteorological services. This article aims to assess the benefits of assimilating PWS observations of screen-level temperature and relative humidity in the AROME-France model in the same framework of experiments carried out to assimilate PWS observations of surface pressure in a previous work. …”
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The effect of climatic variables on vegetation indices (Case study: Orange orchards in Hassan Abad, Darab County by ALI hashemi, Hojjatollah Yazdanpanah, Mehdi Momeni

“…Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). …”
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Spatial and Temporal Distribution of Precipitation Characteristics and Fitting Parameters in China by ZHOU Yu, ZHANG Yujia, MIAO Changsheng

“…In order to summarize the precipitation characteristics in China and explore the distribution of precipitation duration,this paper selects 56 years of continuous daily precipitation data from 698 meteorological stations in China and analyzes the spatial distribution and inter-annual variations of precipitation characteristics in terms of seasonal distribution,extreme characteristics,and precipitation duration.The paper also uses the Gamma distribution to fit the precipitation duration curves and estimate the parameters and analyzes the potential factors that affect the fitting parameters.The results show that ① the precipitation generally shows an increase in the southeast part and a decrease in the central part of China,with an increase and a wider distribution of winter precipitation in the east,northwest,and northeast parts.From the southeast to the northwest of China, the trend of precipitation percentile change shows an increase,then a decrease,and then an increase again,and the distribution of the 99-percentile precipitation thresholds is more uneven.The trend of the precipitation percentile at the Linhe station shows the greatest decrease,especially the wet-day precipitation percentile.The wet period is in a decreasing trend in the southwest and central parts and in an increasing trend in the coastal and northwestern parts.The distribution characteristics of drought and flooding durations in the basin show the opposite state in most regions,and the inter-annual variation of the wet period is smaller than that of the dry period.② The Gamma distribution has a better fitting effect on precipitation.The scale parameter β has a higher correlation with precipitation amount and precipitation percentile and better performance on extreme precipitation thresholds for heavy precipitation events.These findings can provide theoretical support for the subsequent research on precipitation processes based on physical influencing factors.…”
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Evaluating different drought products for assessing drought and implications on agriculture in Nepal by Shishir Chaulagain, Manoj Lamichhane, Urusha Chaulagain, Sushant Gyawali, Sadina Shrestha, Vishnu Prasad Pandey

“…By utilizing Soil Water Deficit Index (SWDI) alongside Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Flow Index (SFI), this research highlights the complex progression from meteorological to agricultural and hydrological droughts. …”
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Review on Applications of System Dynamics Method in Regional Water Demand Prediction by QIN Huanhuan

“…Water resources are the most important basic natural resources for the sustainable development of the social economy,the controlling element of the ecological environment,and strategic economic resources.With the growth in population and economic development,regional water consumption is increasing,and the imbalance between the supply and demand of water resources has become the “bottleneck” restricting regional economic development.Water demand prediction is an important basis for the planning,management,and sustainable utilization of regional water resources,which involves complex factors such as economic,hydrological,meteorological,social,engineering,and technological factors as well as the industrial structure and industrial and agricultural development.The system dynamics method based on the feedback control theory has significant advantages in the dynamic change,causal mechanism analysis,quantitative analysis and simulation of a multi-factor coupled complex giant system.It has been widely used in the prediction of regional water demand.The system dynamics model of regional water demand prediction can decompose a complex social water use system into multiple interconnected and mutually constrained subsystems.It can clearly show the dynamic change process of water demand of each water use subsystem and obtain accurate prediction results of water demand.In this paper,firstly,the characteristics of the system dynamics method are briefly introduced.Then,the applications of this method in regional water demand are summarized and analyzed from four aspects,i.e.…”
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Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning by Stefanie Steinbach, Anna Bartels, Andreas Rienow, Bartholomew Thiong’o Kuria, Sander Jaap Zwart, Andrew Nelson

“…Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). …”
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Apply a deep learning hybrid model optimized by an Improved Chimp Optimization Algorithm in PM2.5 prediction by Ming Wei, Xiaopeng Du

“…First, the Random Forest (RF) model is utilized to evaluate the importance of air pollution and meteorological features and select more suitable input features. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…Second, density peak clustering improved by kernel Mahalanobis distance (KMDDPC) is applied to classify the dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation (MKDE) to accomplish the PV power interval prediction. …”
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Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…The proposed method has been contrasted with conventional methods by applying a testing environment incorporating essential Meteorological Factors (MF) and historical SE datasets. …”
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Weather identification using models based on deep learning by Afroza Nahar, Rifat Al Mamun Rudro, Bakhtiar Atiq Faisal, Md. Faruk Abdullah Al Sohan, Laveet Kumar

“…This study evaluates deep learning techniques for weather forecasting based on different meteorological characteristics. This paper examines a few weather variables to evaluate the prediction performance of several deep learning solutions using TensorFlow and pre-trained Keras applications models. …”
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