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The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

Eight spatial interpolation models were used to map the spatial distribution of precipitation in the southwestern sector of the Iberian Peninsula (22330 km2) over 40 years (1980/1981–2019/2020). Rainfall data from 103 meteorological stations were used to generate the interpolation models, namely inverse distance weight (IDW) with 6, 12 and 24 points, regression spline (RS), thin spline (TS), universal kriging with spherical and Gaussian variogram (UK_Sphe and UK_gauss, respectively) and multilinear regression (MR), based on physiographic and geographic variables. Furthermore, 32 rainfall stations were used to assess the performance of the previous methods through 7 statistical metrics Ordinary Least Squares (OLS), Root Mean Square Error (RMSE), Normalized root mean square error (NRMSE), Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (Bias and MAE). Based on these metrics, UK_gauss and IDW_6 provided the best adjustments, whereas MR presented the highest errors. All methods were suitable to predict the spatial distribution of rainfall, but adjustments are conditioned by the features of the study area, gauge density and gauge spatial distribution. [ABSTRACT FROM AUTHOR]

Due to low adaptive capacities and high reliance on weather-sensitive natural ecosystems for their livelihoods and food production, smallholder farmers are highly vulnerable to risks associated with climate change and variability. Such risks are location-specific; thus, to cope with them, smallholder farmers require tailored adaptation strategies. To support these farmers, this study aimed to identify climatic risks threatening sustainable smallholder farming in the Limpopo Province regions of Gavaza, Ga-Makanye and Giyani. Weather station records spanning up to 1980–2021 were used to identify climatic risks, for the October–April dryland maize growing season. Risks were identified based on temporal trends and/or interannual variability patterns for reference evapotranspiration and a suite of agriculturally relevant rainfall and temperature indices. Based on the identified risks, tailor-made adaptation strategies were devised for application by farmers within the study region. The results revealed the specific risks of growing seasons becoming shorter and increasingly hotter with highly irregular rainfall patterns. To adapt and improve agricultural productivity despite these prevailing climatic risks, farmers within the study region will benefit if they plant drought tolerant, early maturing and higher yielding crop varieties, apply soil water conservation techniques and make use of seasonal and daily weather forecasts to guide their decision-making. [ABSTRACT FROM AUTHOR]

The article discusses the 1934 German Nanga Parbat Expedition, which aimed to climb the 26,660-foot Nanga Parbat mountain in Pakistan. The expedition was well-prepared and well-funded, with advanced technology and equipment. However, tragedy struck when climber Alfred Drexel fell ill and died, and a snowstorm and powerful storm prevented the team from reaching the summit, resulting in the deaths of six porters and three climbers, including the expedition leader. The expedition was ahead of its time in terms of high-altitude physiology and meteorology, and the first successful summit of Nanga Parbat was achieved in 1953. [Extracted from the article]

The aim of this study was to assess the historical and projected climate trend analysis in the Upper East Region, Ghana, using time series and modelled climate data under shared socio-economic pathways scenarios. Due to the climate data scarcity challenge in Ghana, there is a need to rely on high-resolution satellite-based climate products for climate change studies. Accordingly, temperature and precipitation data of six stations were obtained from Ghana meteorological stations; in addition, precipitation data of five satellite stations were obtained from CHIRPS data with 0.05' (5 km) resolution to complement the in-situ datasets; and the temperature data for the satellite station obtained from the NASA power project were extracted. Modelled climate data were obtained from five GCMs in the CMIP data portal on the WCRP website. R software was used to extract NetCDF-format GCM data to Excel format and CMhyd was used for downscaling GCM data. The variance scaling method and local intensity scaling methods were used for temperature and precipitation bias correction, respectively. The Mann-Kendall (MK) method was used to determine the statistical significance of a change and identify trends in temperature and precipitation. The results of historical temperatures were significantly increased, while historical precipitation was significantly decreased. The projected mean annual temperature will increase significantly, peaking at 1.80°C by 2065, precipitation will, conversely, show significant spatial variability and seasonal dependency. For instance, by 2065, precipitation is projected to decrease between 10.05% and 12.66%. The results concluded by recommending policymakers develop climate change adaptation strategies guided by future climate scenarios. [ABSTRACT FROM AUTHOR]

The Sustainable Development Goals (SDGs) emphasise the importance of clean air (SDG 3 and SDG 11.6). Japan faces challenges in air quality prediction, especially in rural areas, because of the absence of a climate-specific calibration model and limited deployment of low-cost sensors. Our study compared three sensor types: low-cost pre-assembled and pre-calibrated sensors (PurpleAir); self-assembled low-cost sensors (Citizen Science Project); and highly accurate weather stations, focusing on PM2.5 values. Linear and generalised linear mixed models show potential for low-cost sensor calibration. [ABSTRACT FROM AUTHOR]

Relying on a biographical approach that reconstructs the life and work of Johannes [Hovhanness/Յովհաննէս] Jakob [Hagop/Յակոբ] Manissadjian [Manisacıyan/Մանիսաճեան] (1862–1942), a highly successful scientist at the Anatolia College (Merzifon/Marsovan/Մարզվան), who established a meteorological station and a natural history museum with an extensive collection of specimens, the paper traces the routes of disappearance, dispersal and ruination of indigenous lives, people, and knowledge within the context of the Armenian genocide. Drawing on documents from Ottoman, German, and American archives, I stress the potential of biographical methods to study the processes and structures of mass violence targeting the Ottoman Armenians, as well as to foreground the agency and subjectivity of genocide survivors. The article also focuses on post-genocide scientific (dis)engagements of Manissadjian in light of Theodor W. Adorno’s ‘after Auschwitz’ discussions and from the perspective of indigenous knowledge production. In particular, his two ‘archival acts’ in the post-genocide context, the ‘Catalogue’ of the collection of the Anatolia College Museum that he prepared as ‘the former Curator’ and his small pamphlet entitled Proverbs of Turkey, which provided an ethnographic portrait of Anatolia, were his humble acts of saving a treasure trove of knowledge that was in danger of becoming debris. [ABSTRACT FROM AUTHOR]

Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real datasets from various application areas. This article introduces a new outlier detection method that accounts for a (continuously) varying covariance structure, depending on the spatial neighborhood of the observations. The underlying estimator thus constitutes a compromise between a unified global covariance estimation, and local covariances estimated for individual neighborhoods. Theoretical properties of the estimator are presented, in particular related to robustness properties, and an efficient algorithm for its computation is introduced. The performance of the method is evaluated and compared based on simulated data and for a dataset recorded from Austrian weather stations. to the article are available online. [ABSTRACT FROM AUTHOR]

Integrated STEM approaches are acknowledged as important for making school subjects relevant and engaging for students and are reinforced with the current emphasis on computational thinking. Still, such approaches are rarely realized in schools. This study investigates how the traditional 'grammar of schooling' may prevent realization of more holistic teaching in line with ideas from integrated STEM. A concrete teaching project where students design, program and build their own weather station is presented, and we investigate the values and challenges teachers see in the project and the obstacles that may prevent them from running it in class. This way it throws light on why STEM approaches may be difficult to realize in schools. The sample consists of teachers involved in a collaborative project in which Project Weather Station was developed, including the teacher who was undertaking the project in his grade 9 class. The study follows a case study design, with group interviews with teachers. Observation in class, video recording and interviews with students form a backdrop of the main interview data. Results show that teachers see value in the weather station project in line with the overarching aims of the curriculum and consistent with STEM ideas. Challenges they see are related to own competence, time use, risk of failure and logistics across subjects. It is concluded that teachers' challenges reflect a tension between overarching aims in education and the more specific competence aims in the curriculum. On a systemic level there is therefore a need for an acceptance for the value of teaching that targets overarching aims of education. It is important to acknowledge teachers' challenges and fulfil their needs for support when undertaking extensive projects that have potential for realising the more holistic aims of education typical for STEM projects. [ABSTRACT FROM AUTHOR]

Existing research on outdoor thermal comfort in urban areas focuses on meteorological factors such as temperature, relative humidity, wind speed, and radiation on sunny or cloudy days. However, climate conditions before, during, and after rainfall events can cause changes in meteorological factors in subtropical regions. Rainfall is an atmospheric condition with a large influence on thermal comfort, particularly in subtropical areas with abundant rain. Big data on temperature, relative humidity, wind speed, precipitation, and total cloud cover from the Taipei Weather Station under the Central Weather Administration from June to August each year from 2011 to 2020 were analyzed, dividing the data into categories based on precipitation. Using a k-means clustering algorithm, we quantified the relationships between meteorological factors and apparent temperature as well as physiological equivalent temperature in different rainfall scenarios. The results indicate that rainfall in the summers of subtropical oceanic climates indeed influence outdoor thermal comfort in urban areas: 37.7% of the rainfall weather patterns improved thermal comfort and 62.3% of the rainfall weather patterns reduced thermal comfort. RWP 46 represent the weather conditions that most significantly improve thermal comfort, at 1:00 PM, with a rainfall of 15 mm, there is a decrease in ΔAT by 14.4 ℃ and ΔPET by 22.1 ℃. With the hot, humid, and rainy climate conditions in Taiwan, apparent temperature is the most accurate index of thermal comfort. KEY POLICY HIGHLIGHTS Rainfall events may affect outdoor thermal comfort in subtropical cities. Different rainfall weather patterns have different impacts on urban outdoor thermal environments. 62.3% of rainfall weather patterns resulted in worsened thermal comfort, for low and concentrated rainfall resulting in a stuffier thermal environment. [ABSTRACT FROM AUTHOR]

Mountain glaciers are sensitive to climate variability and can be of great importance for downstream populations due to their hydrological significance. Synthetic Aperture Radar (SAR) images are often used to monitor the characteristics of glaciers based on the backscattering coefficient. However, the influence of satellite orbit and polarization when collecting images for wide regions has not been well considered. This study focuses on the extraction of wet snow in summer and firn in winter in West Kunlun Shan and the Tibet Interior Mountains by using Sentinel-1 C-band SAR data. The investigated regions have different climate patterns. We compare backscatter coefficient distributions for wet snow and firn, derived from maximum likelihood classification under various polarizations, alongside their respective ratios and show that polarization has a minor impact on the identification and monitoring of both wet snow and firn. However, a comparison of the wet snow ratios at different satellite orbits reveals notable differences between ascending and descending orbits in summer. We furthermore show, by analyzing weather stations on glaciers, that such effect can be related to the different acquisition time and different temperatures in the morning and afternoon and therefore to the orbit. In contrast, firn ratios across different orbits show less variation in winter, and the monitoring results consistently align with the patterns of ablation and accumulation typical under both climatic influences. These findings demonstrate glaciers' sensitivity to temperature fluctuations and the radar wave's responsiveness to surface characteristics. Consequently, when employing SAR for glacier monitoring, it is crucial to consider the influence of orbit and polarization, in combination with temperature variations, and whether the season is winter or summer. [ABSTRACT FROM AUTHOR]

The article discusses a weather map from July 8, 2024, showing Hurricane Beryl making landfall in Texas as a tropical storm. The storm had previously caused extensive damage in Jamaica and weakened as it crossed Mexico's Yucatan Peninsula. The article also highlights the importance of analyzing weather maps accurately, considering factors such as corrupted data and the scale of analysis. It further explains the presence of high-pressure areas in Colorado and the Appalachian Mountains, as well as the characteristics of different air masses across the United States. The author emphasizes the need for a good understanding of meteorology and basic rules for surface analysis when interpreting weather maps. [Extracted from the article]

For the functional linear models in which the dependent variable is functional and the predictors are scalar, robust regularization for simultaneous variable selection and regression parameter estimation is an important yet challenging issue. In this paper, we propose two types of regularized robust estimation methods. The first estimator adopts the ideas of reproducing kernel Hilbert space, least absolute deviation and group Lasso techniques. Based on the first method, the second estimator applies the pre-whitening technique and estimates the error covariance function by using functional principal component analysis. Simulation studies are conducted to examine the performance of the proposed methods in small sample sizes. The method is also applied to the Canadian weather data set, which consists of the daily average temperature and precipitation observed by 35 meteorological stations across Canada from 1960 to 1994. Numerical simulations and real data analysis show a good performance of the proposed robust methods for function-on-scalar models. [ABSTRACT FROM AUTHOR]

A solar eclipse in April could affect local weather by causing a drop in temperatures, wind speeds, and humidity. During the 2017 eclipse, wind speeds dropped by an average of 6 mph. Additionally, a significant temperature drop can reduce cloud cover. In other news, Maureen Sweeney, an Irish woman whose weather forecast helped determine the date for the D-Day invasion in World War II, passed away at the age of 100. Lastly, a study found that thunderstorm-induced straight-line winds are covering a larger area in the central United States, causing significant damage. Extreme weather events were ranked as the second-highest risk factor for major catastrophic events in a survey conducted by the World Economic Forum. [Extracted from the article]

Determination of optimal location for wind farm is a challenging task influenced by several decision criteria and factors subject to human. This case study done proposes an integrated decision support framework to investigate the spatial wind farm suitability based on F-AHP (Fuzzy-Analytical Hierarchy Process) together with Q-GIS (Quantum-Geographic Information System) tool. Firstly, spatial analysis and integration of nine-exclusion factors were included to identify the unsuitable sites. Next, F-AHP-based MCDM (Multi Criteria Decision Making) analysis was employed to obtain criteria weights of seven evaluation factors. Unlike traditional AHP method, the proposed F-AHP method is much flexible to deal with intermediate linguistic variable. F-AHP results reveal that wind speed is the most important criteria followed by power Tr. lines with weighted value of 33.05% and 20.95% respectively. The thematic suitability map was developed on Q-GIS platform to classify from "least suitable" to "most suitable" classes. The final suitable map indicates 26.09 km2 and 138.42 km2 area are most suitable and highly suitable respectively for wind farm establishment. This study is also supported with ground measurement wind resource data collected through a WMS (Weather Monitoring Station) installed at "most suitable" region of suitability indexed class. [ABSTRACT FROM AUTHOR]

In this study, a time series is analyzed with daily historical data from 1989 to December 2021 of the precipitation variable with a total of 12,053 observations, these data are obtained from the Tunguativa weather station located in Paipa a small town in Colombia. For this research, the records of the 'precipitation' variable were taken into account. The objective was to analyze the trends, use the data until 31 December 2020, to estimate a forecast for the year 2021. The observed data is provided by the weather station, and the data of the year 2021 are compared with the data forecast by the Arima method and the change point. Finally, statistical tests are carried out to contrast the degree of similarity of the data obtained from the forecasts with the real data produced by the station. This study can be used to estimate precipitation forecasts and avoid environmental damage. [ABSTRACT FROM AUTHOR]

Weigh-in-motion technology plays a crucial role in efficiently collecting data for traffic monitoring and controlling freight weight limits, with accuracy influenced by pavement temperature. However, research in asphalt pavement temperature modeling faces challenges due to limited environmental data and a lack of comprehensive insight into prediction performance across various sensor installation depths. To address this, an investigation was conducted through a field study at MnROAD in Minnesota, USA, predicting asphalt pavement temperature using data from an on-site weather station. Nine weather factors were considered, and these weather factors were utilized to predict pavement temperature through four regression analysis methods, including linear regression, two types of polynomial regression, and an artificial neural network (ANN) model. To validate the predictions, real pavement temperature data was collected at various depths using embedded sensors. The results showed that the ANN model outperformed other statistical regression models, achieving an R-squared value of up to 0.96. The findings also suggested an optimal sensor installation depth of 2.25 inches, achieving an R-squared over 80% with the ANN model while only considering three highly correlated weather factors for pavement temperature. Additionally, the consistent results from a secondary weather station validate the developed pavement temperature models in this study.. [ABSTRACT FROM AUTHOR]

The structural performance of an asphalt pavement is substantially affected by its temperature state. The in-depth temperature is usually measured through drilling a hole in the pavement. As an alternative, researchers have proposed temperature prediction models considering attributes like air temperature, depth, time, location, solar intensity, wind speed, and relative humidity. But procuring data on many attributes is tedious for the field professionals and at times is not available locally in developing countries. It was observed that most of the existing models predict maximum and minimum temperatures, whereas few models predict the temperature at a fixed depth. This paper presents a pavement temperature prediction model which uses air temperature of preceding hours, time in a day, and depth of the measurement. These attributes are easily available in developing countries. Data were collected through an instrumented track and from a weather station. Contrary to the use of 1–5-day air temperature, it was found that the average air temperature of the preceding few hours is sufficient in predicting the pavement temperature. The prediction accuracy and validation results of the model were found good as compared to some prevailing models. [ABSTRACT FROM AUTHOR]

Natural conditions tend to affect the performance of the asphalt pavement. Climatic factors must be taken into account in the structural design of the asphalt pavement. Thus it is important to classify asphalt scientifically and reasonably based on the local climatic conditions. Based on 56-year meteorological data from 747 representative meteorological stations in China, temperature and precipitation were divided into eight meteorological indices. Three spatial interpolation methods are used for interpolation, namely Inverse Distance Weight, Ordinary Kriging and Collaborative Kriging considering Digital Elevation Model (DEM). After the comparison, the Collaborative Kriging considering DEM interpolation results of temperature and the Inverse Distance Weight interpolation results of precipitation were used to establish the standard climate zoning system in China. The classification of asphalt pavement is introduced on the basis of performance grading. According to the pavement high and low temperature design formula obtained by comparison, climatic suitability zoning of road asphalt is performed, and the classification table with performance grading of road asphalt in China is presented. The research results provide a theoretical basis for the selection of road asphalt and provide guidance for scientific and reasonable design of asphalt pavement. [ABSTRACT FROM AUTHOR]

Satellite observations of precipitation have greatly improved our understanding of its temporal and spatial distribution. In view of the high spatiotemporal heterogeneity and severely skewed distribution characteristics of precipitation, it is necessary and of considerable application value to understand the ability of satellite-derived precipitation products (SPPs) to characterize the variations of precipitation in the time and space dimensions separately. However, to date, research concerning this is scarce. In this study, we explore the ability of 13 SPPs to characterize the temporal and spatial variations of precipitation based on observations from more than 2400 meteorological stations in Chinese mainland from 2001 to 2018. The results show that (1) SPPs tend to perform better in identifying the temporal than the spatial variation of precipitation. Most SPPs can reliably monitor the temporal and spatial variation of monthly and seasonal precipitation in Chinese mainland, although the identification of the spatial variation of daily precipitation involves larger uncertainty; (2) CMORPH-B, IMERG-F, 3B42, and MSWEP generally had better performance in identifying the temporal and spatial variations of precipitation, while PERSIANN and GSMaP-N performed poorly. However, no single SPP outperformed others in all scenarios; (3) with respect to monitoring the temporal variation of daily precipitation, SPPs performed better in southern China than in the north, with three-quarters and half of median KGE values above 0.5 In comparison, no significant spatial difference was observed in their ability to monitor the spatial variation of daily precipitation; and (4) SPPs had large uncertainties in capturing the temporal and spatial variations of precipitation in winter, while they performed best in identifying the temporal and spatial variations of daily precipitation in summer. The results of this paper provide an important reference for data users needing to select suitable SPPs and for satellite developers desiring to further improve data quality. [ABSTRACT FROM AUTHOR]

The weather system over the Tibetan Plateau can cause disasters around the plateau and its downstream areas. Since there are few surface meteorological observation stations over the Tibetan Plateau, the precipitation here cannot be comprehensively detected. Therefore, satellite precipitation estimation products with high timeliness, accuracy, and full coverage are relied upon. This paper presents an AI-based precipitation estimation method using multi-frame FY-4A meteorological satellite data (FY-4A PRE-M). Firstly, multiple-derived features are constructed according to the brightness temperature of the satellite different channels, and the regional precipitation estimation model is then trained using the light gradient boosting machine (LGBM) algorithm. Based on the research results from precipitation retrieval using the infrared channel radiation features, this model analyzes the possible contribution of the radiation difference between channels at the same time and the radiation change in the same channel at different periods (15, 30, 45 and 60 min) to the precipitation estimation. The algorithm continuously updates the model with the history of available precipitation data. From 1 September 2021 to 31 August 2022, the precipitation estimations using the multi-frame FY-4A satellite data were evaluated through the ground rainfall gauge data based on the spatial -- temporal matching. The results show that the multi-frame FY- 4A satellite data have advantages in precipitation estimation. The heavy rainfall caused by a Tibetan Plateau shear line is evaluated using this algorithm. The results show that the precipitation estimations of FY-4A PRE-M and IMERG-L are close to the values of rain gauges. The FY-4A PRE-M can well describe the diurnal variations and the distribution of 24 h cumulative precipitation over the Tibetan Plateau, and the heavy rainfall belts in the central and northern Tibetan Plateau are also well monitored. [ABSTRACT FROM AUTHOR]

This study investigates the accuracy of the experimental models in two standard and optimized cases using data from meteorological stations located on the northern and southern coasts of Iran. The results show that the spatial distribution / different hydrometeorological conditions are quite effective in the accuracy of the models. However, KNF and Papadakis lead to the most accurate estimation among the standard models. The optimization significantly increases all models' accuracy except Papadakis, indicating its remarkable robustness in coastal cities. Comparison between all models demonstrates that the Linacre-1994 optimal model has the best accuracy. Findings reveal that wind speed is the second variable affecting pan evaporation in coastal cities after the vapor pressure deficit. In addition, the form of wind speed inclusion in the Trabert model significantly affects its inaccuracy. Elevation and latitude variables do not affect estimating pan evaporation in coastal cities considering optimization findings. [ABSTRACT FROM AUTHOR]

Lake ecosystem is facing a threat of shrink in many parts of the world and the same in Ethiopia. Lake Haramaya, one of the highland lakes in Ethiopia, has been dried a decade ago in 2004. This study was conducted in Lake Haramaya watershed, Eastern Hararge, Ethiopia, with the objectives of identifying drivers of dried Lake Haramaya and proposing rehabilitation strategies. Various methodologies were employed to meet the intended objectives. Drivers are identified by the study of triangulated data from landsat images of five decades, trend of climate change, focus group discussions, and review of previous research results. To see the effect of land use dynamics on the lake environment, major LULC types were detected and the change analysis of the watershed was executed. To investigate association between climate change and lake environment, the rainfall index method was employed to evaluate 30 years annual rainfall distribution of Haramaya meteorological stations. The results of this method reveal that land use for settlement, climate change, and overexploitation of the lake within and out of the watershed were primary drivers. To propose lake rehabilitation strategies, the study employed the United Nation man and biosphere protection zones. The study classified the watershed into three classes as core, buffer, and transition zones. The size of the lake in 1985 was decided as the core area and 100 metre surrounding the core area as the buffer zone. The result of the study proposed protection of the core area and introduction of apple fruits replacing chat in the buffer zone with prohibition of human settlement. In addition, this study suggests that all activities that take place in the transition zone should comply with sustainable development goal strategies. Therefore, it is highly recommended to adopt the output and proposed strategies of this study in future rehabilitation strategies of Lake Haramaya and its watershed. [ABSTRACT FROM AUTHOR]

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The authors studied the atmospheric radon concentration with associated meteorological parameters variation during the dust events from July to November 2017. We obtained the meteorological parameters data in weather station of Sulaymaniyah city, Iraq. In the environmental monitoring plan, the atmospheric radon fluctuated from 15 to 48 Bq m–3 around the mean value of 31.5 ± 7 Bq m–3 within the summer. In autumn, varied from 22 to 46 Bq m–3 with a mean value of 34 ± 12 Bq m–3. We employed this to determine the radon level anomalously. Using the modified statistical coefficients, such as the residual deviation (RD), residual fluctuation ratio (RFR), F-test, and p-value coefficients. Among the atmospheric radon fluctuation values, particularly one anomalous (42 Bq m–3) on 25 July was determined because the excessive value of the RD was 1.9 σ, and the RFR value was 66 %. Corresponding to our coefficients criteria, the minimum level of atmospheric radon (22 Bq m–3) does not consider anomalous because of increasing wind speed. Based on this, our method for determining the atmospheric radon anomalies that are influenced by the missed factors beyond the mentioned meteorological parameters is accurate. [ABSTRACT FROM AUTHOR]

The weather chart is created automatically with Digital Atmosphere, but fronts and lines are added by hand using Adobe Illustrator. i Today we go back in history and look at a major tornado outbreak that brought a total of 64 tornadoes to the Great Plains in 2008. Another tornado outbreak hit far northern Colorado and Wyoming, producing 13 tornadoes in a small area around Cheyenne. The tornado statistics website USTornadoes.com records this as the biggest 24-hour tornado outbreak in the history of the state. May 23, 2008, brought one of the biggest tornado outbreaks on record to Kansas, one of which became well-known to storm chasers after touching down near Quinter, located north of Garden City. [Extracted from the article]

The spatial and spatio-temporal dependence modeling to extreme value distributions have been used to analyze the extremes of daily maximum rainfall data across selected weather stations in South Africa combining generalized Pareto distribution (GPD) with the flexible Bayesian Latent Gaussian Model (LGM). The paper demonstrated the spatio-temporal GPD model for applications in extreme rainfall data that capture systematic variation through spatial and spatio-temporal modeling framework, in which the temporal constitutes the week and month as random separately. The paper uses the Bayesian integrated Nested Laplace approximation (INLA) algorithm to estimate marginal posterior means of the parameters and hyper-parameters for Bayesian spatio-temporal models. The Bayesian inferences using INLA technique were applied to obtain prediction of the return levels at each station, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes. [ABSTRACT FROM AUTHOR]

Despite the progress made by numerous contributions in recent decades on uncertainty in hydrological simulation, there are still knowledge gaps in estimating uncertainty sources, especially associated with precipitation. The aim of this study was to determine the precipitation uncertainty through an error model based on the skew normal distribution function and to evaluate the effect of its propagation towards the simulated flow with the TETIS distributed hydrological model in a poorly instrumented tropical Andean basin. The results show the performance of the hydrological model is more sensitive to the location of the meteorological station used than to the number of stations employed in a real case with scarce information. Implementing the Bayesian approach for the study of uncertainty in input data such as precipitation is essential for its quantification, improving the knowledge of how this source of error propagates to the results of the hydrological simulation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study hourly time series of temperature and humidity from six meteorological stations in Oman from 2009 to 2018. Since our data contains missing values due to device failures, we have compared several methods of imputation and found that regression with lagging is most reasonable. To extract the annual oscillations and daily periodicities from hourly time series we apply Singular Spectrum Analysis. By applying the Mann–Kendall test, Spearman's rho test and the ITM test to the annual oscillations and daily periodicities monotonic trends in temperature and humidity over the ten year period have not been confirmed. [ABSTRACT FROM AUTHOR]

Buffalo, New York, was buried by 22.3 inches of snow on December 23, the first day of a five-day winter barrage that featured 51.9 inches of snow and winds gusting as high as 72 mph. Elsewhere, heavy rain preceded and accompanied the storm system in parts of the South and East, while Northwestern precipitation included wintry weather - snow and freezing rain - as Pacific moisture overran lingering cold air. Heavy, wet snow blanketed parts of the Great Lakes and Northeastern States; on December 15, Rhinelander, Wisconsin, reported daily records for precipitation (1.06 inches, a mix of snow and freezing rain) and snow (7.1 inches). In many areas, snowfall was relatively light, but travel conditions were complicated by sharply falling temperatures and high winds, which reduced visibilities due to blowing and drifting snow. [Extracted from the article]

An understanding of how local knowledge of climate change converges with science-based assessment is important to effectively support adaptation decision making. This study explores farmers' cognition of climate change and their adaptation in Ethiopia. Household and community level data on socio-economic, institutional, climatic and adaptation variables were collected. In addition, we obtained long-term historical meteorological data from local weather stations for consecutive 31 years. Mean and percentage computation, chi-square test, standard precipitation index and qualitative content methods were applied for data analysis. The analytical results of obtained past weather data revealed that the climate parameters have significantly changed over time, and highly varied across locations and these variations were substantiated by farmers' experiences. There is a high level of agreement between climatological data and farmers' observation that the onset of the rainy season is now shifting markedly, accompanied by an increasing dry spell with greater implication for rainfed systems. In contrast, there is a significant divergence concerning the length of the growing season which is explained by the strong influence of the onset rather than end of the rainy season. As a result, a range of strategies were implemented by farmers to fight against climate change dynamics, short to long term. The study concluded how diversification of climate communication outlets by combining local cognition can play a unique role in promoting risk communication. As a result, risk cognition as a function of socio-psychological factor should be combined into comprehensive science-based consensus that impact decision making. [ABSTRACT FROM AUTHOR]

Although known as the warmest deserts of the world, rainfall and integrated water of Lut and Kavir Deserts is still unknown due to insufficient weather stations. The Dual-Frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement (GPM) mission satellite created an opportunity for use to study the rainfall and vertically integrated liquid water content (LWC) and integrated non-liquid (ice) water content (IWC) using statistical and distance analyses over the two deserts during winter months (December to March) of 2015–2020. The results showed good similarity between the GPM DPR and station data with a Pearson correlation coefficient of 0.81. March has the highest contribution in rain amount, which is about 37% in both deserts. March also has the largest contribution of the rainy days, being about 41.4 and 37.6% in the Lut and Kavir Deserts, respectively. LWC has the highest amount in the Lut Desert, while IWC is the largest in the Kavir Desert. Distance analysis showed that there is a significant increasing trend of rainfall from west to east in the Lut Desert. Elevation does not affect rainfall distribution strongly, but rainfall is highly influenced by the atmospheric-driven large-scale parameters. [ABSTRACT FROM AUTHOR]

Accurate prediction of the macroscopic traffic stream variables is essential for traffic operation and management in an intelligent transportation system. Adverse weather conditions like fog, rainfall, and snowfall affect the driver's visibility, vehicle mobility, and road capacity. The rainfall effect on traffic is not directly proportional to the distance between the weather station and the road because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. The weather event has a spatiotemporal correlation with traffic stream variables, as waterlogging on the road due to rainfall affects the traffic on adjacent roads. The spatiotemporal and prolonged impact of rainfall is not studied in the literature. In this research, we examine whether the inclusion of the rainfall variable improves the traffic stream variables prediction of a deep learning model or not. We use the RNN and LSTM models to capture the spatiotemporal correlation between traffic and rainfall data using past and current traffic and weather information. To capture the prolonged impact of rainfall more extended past sequence of rainfall data than traffic data is used in this study. The roads prone to waterlogging are more affected due to rainfall compared to freeways. Thus we examine the effect of rain on traffic stream variables prediction for different types of roads. The test experiments show that the inclusion of weather data improves the prediction accuracy of the model. The LSTM outperforms other models to capture the spatiotemporal relationship between the rainfall and traffic stream variables. [ABSTRACT FROM AUTHOR]

The current study proposes a new framework for quantifying and monitoring the severity of meteorological drought. The proposed framework consists of three phases. The first phase of the framework uses K-component Gaussian Mixture Distribution (GMD) in the computation. The second phase is mainly based on the dissimilarity matrix-based clustering using C-index and Monte Carlo Feature-based Selection (MCFS) method. The third phase uses the Markov chain, transition probabilities and a non-homogeneous Poisson process under the Bayesian estimation. The Relative Importance (RI) values are used to choose appropriate stations. The Deviance Information Criteria (DIC) is used to check model suitability, and Root Mean Square Error (RMSE) is utilized for determining model performance. The proposed framework is validated to the 52 meteorological stations in Pakistan for 49 years from 1968 to 2016. Moreover, the outcomes of the current analysis provide insight to quantify and monitor meteorological drought comprehensively and accurately. [ABSTRACT FROM AUTHOR]

The quantitative description of meteorological drought from various geographical locations and indicators is crucial for early drought warning to avoid its negative impacts. Therefore, the current study proposes a new framework to comprehensively accumulate spatial and temporal information for meteorological drought from various stations and drought indicators (indices). The proposed framework is based on two major components such as the Monthly-based Monte Carlo Feature Selection (MMCFS,) and Monthly-based Joint Index Weights (MJIW). Besides, three commonly used SDI are jointly assessed to quantify drought for selected geographical locations. Moreover, the current study uses the monthly data from six meteorological stations in the northern region for 47 years (1971-2017) for calculating SDI values. The outcomes of the current research explicitly accumulate regional spatiotemporal information for meteorological drought. In addition, results may serve as an early warning to the effective management of water resources to avoid negative drought impacts in Pakistan. [ABSTRACT FROM AUTHOR]

Recently, the solar energy is considered as one of the most essential alternative sources of energy, which make it significant to operate the photovoltaic (PV) modules at optimal points to attain maximum output powers at utmost efficiencies. In this work, a novel operational optimization scheme is proposed to maximize the electrical output power and its efficiency for two axis sun tracking PV module. The technique incorporates the influence of the surrounding environmental parameters; such as ambient temperature, wind speed, and wind direction to improve performance. The increase in power and efficiency is accomplished by benefiting from the local wind speed and wind direction to cool the cells temperatures, which in return assist the PV to operate at its Maximum Power Point Tracking (MPPT). These newly proposed operating angles (i.e., Azimuth and Tilt) for two axis sun tracking systems may not necessarily coincide with the perpendicular orientation used by conventional sun tracking PV modules. Finally, the effectiveness of the operational optimization technique and its effect on MPPT was verified using computer simulations using actual collected weather data that was obtained from Shams Maan weather station in Jordan. [ABSTRACT FROM AUTHOR]

In this article, we study snow avalanche activity during the snow seasons of 2017–2020 using four automatic time-lapse cameras strategically positioned along the southwestern slope of Tasiapik Valley, near the village of Umiujaq, in Nunavik (northern Québec, Canada). Over the three snow seasons, cameras helped to detect evidence of 130 avalanche events, scattered over seventy-eight distinct avalanche days. The evolution of weather conditions prior to each avalanche release was detailed according to data from a nearby weather station. Moreover, the time of release, the release type, the surface texture, and whether rocky material was present in the deposits were documented from the photographs. To explore relationships between weather data and avalanche releases, conditional inference tree (CIT) analysis was conducted. Results of the CIT analysis showed that there are different weather patterns associated with avalanche releases depending on the season, and significant thresholds values were defined. In winter, the avalanche probability was greater when three-day snowfall total exceeded 10 cm. In spring, the avalanche probability was greater when cumulative melting degree-days were less than forty-six and when daily minimum air temperature was greater than 2°C. Moreover, cornice failures were found to be a major component of the avalanche dynamic in Tasiapik Valley, mainly because of the slope's morphology. They have also been the cause of the three largest volume and longest runout avalanches observed by cameras in this study, highlighting potential risks for local communities. The probability of observing cornice failures is enhanced on days when maximum air temperature is greater than −8°C in winter conditions, whereas in spring conditions it is enhanced by daily maximum air temperature greater than 2.5°C. This study represents a necessary first step toward avalanche forecasting based on weather data in Nunavik. Efforts should be continued given the expected higher frequency of natural hazards in northern regions as a consequence of recent climate changes. [ABSTRACT FROM AUTHOR]

Under the severe impacts of climate change, drought has become one of the most undesirable and complex natural phenomena with critical consequences for the environment, economy and society. The orthodox drought monitoring approaches use observations of meteorological stations, which are typically restricted in time and space. Remote sensing, conversely, provides continuous global coverage of a variety of hydro-meteorological variables that are influential in drought, and data extracted from remote sensing and modeling missions are now considered more practical and alluring for researchers. In this study, we applied a combination of field data, remotely sensed data and modeled data to detect and quantitatively analyze drought phenomena. To achieve this objective, we utilized Terrestrial Water Storage Anomalies (TWSA) estimations from GRACE mission, Normalized Difference Vegetation Index (NDVI) from MODIS mission, Surface Runoff (R) and Evapotranspiration from ERA5 reanalysis datasets and Soil Moisture (SM) from GLDAS data model to evaluate their feasibility in detecting recent droughts over Turkey. We validated the accuracy of several remote sensing-based indices (GRACE Drought Severity Index, Water Storage Deficit Index [WSDI], Soil Moisture Index, Standardized Runoff Index and NDVI) with the traditional indices (SPI and SPEI) calculated from in situ observations of precipitation. The results revealed that the GRACE-based WSDI gave the best performance with high correlations with the SPI index both temporally and spatially over Turkey. We also found that monthly and annual time series of WSDI agreed well with the SPI index with correlations of 0.69 and 0.73, respectively. The results of drought analysis also indicated that WSDI could be used as a proxy to standard meteorological drought indices over Turkey as it performed well to detect and characterize the recent droughts of Turkey based on its comparisons to SPI results. [ABSTRACT FROM AUTHOR]

The start and end of the vegetation growing season (SOS and EOS) determine the length of the vegetation growing season and are necessary parameters to assess the primary productivity and carbon stocks of vegetation as well as the key dates to determine the start and end of soil wind erosion in arid zones. However, how SOS and EOS of different types of vegetation respond to changes in temperature and precipitation is still unclear. In this study, Moderate-resolution imaging spectroradiometer (MODIS)- normalized difference vegetation index data from 2001 to 2015 were used to estimate SOS and EOS for each vegetation type, the temperature and precipitation data recorded by the meteorological stations in the study area were used to calculate the average temperature and precipitation in 19 periods from June of the previous year to November of the following year, Spearman’s rank correlation analysis was used to analyze the response relationships of SOS and EOS to average temperature and precipitation in each period. The results showed that SOS, EOS, the average timing of SOS (AvSOS) and the average timing of EOS (AvEOS) all had strong spatial heterogeneity. Precipitation is the key factor controlling SOS and EOS, an increase causing SOS to advance and EOS to be delayed, while higher temperature has the opposite effects. Overall, the average advancement rate of SOS was 0.88 day/year, the average delay rate of EOS was 0.33 day/year. From southeast to northwest, AvSOS gradually changed from 121st day to 141st day, whereas AvEOS was the opposite, advancing from 271st day to 282nd day. We divided the vegetation into 12 types in the soil wind erosion area of northern China according to latitudinal zonation and dominant vegetation species, the results avoided t a shortcoming of previous studies in which SOS and EOS were identified without distinguishing vegetation types. [ABSTRACT FROM AUTHOR]

The imperative of project sustainability has become explicit policy within development. This is especially true for technology transfer: 'development objects' are to be used by prospective beneficiaries long after the project's closure. We argue that the link between project sustainability, technology and 'success' requires deeper scrutiny. We investigate a community-based project in Naryn, Kyrgyzstan, which included the transfer of smartphones, weather stations and camera traps. Upon the project's closure, we compare the stakeholders' viewpoints regarding the future use of the equipment, showing how technological objects attract new actors into the project's network, change its course and enhance its impact. We use actor-network theory to explain how development objects shape development processes by generating own networks and transforming social relations of power. We propose a dynamic view of sustainability as: (i) continuation of delivery of project's goods and services, (ii) durability of the achieved changes and (iii) feasibility of independent growth.. [ABSTRACT FROM AUTHOR]

In the present study, two innovative techniques namely, Deep Learning (DL) and Gradient boosting Machine (GBM) models are developed based on a maximum air temperature 'univariate modeling scheme' for modeling the monthly pan evaporation (Epan) process. Monthly air temperature and pan evaporation are used to build the predictive models. These models are used for evaluating the evaporation prediction for the Kiashahr meteorological station located in the north of Iran and Ranichauri station positioned in Uttarakhand State of India. Findings indicated that the deep learning model was found best at Kiashahr station for testing datasets MAE (0.5691, mm/month), RMSE (0.7111, mm/month), NSE (0.7496), and IOA (0.9413). It can be concluded that in the semi-arid climate of Iran both of the used methods had the good capability in modeling of monthly Epan. However, DL predicted monthly Epan better than GBM. Moreover, the highest accuracy of the deep learning model was also observed for the Ranichauri station in terms of MAE = 0.3693 mm/month, RMSE = 0.4357 mm/month, NSE = 0.8344, & IOA = 0.9507 in testing stage. Overall, results expose the superior performance of DL-based models for both study stations and can also be utilized for various other environmental modeling. [ABSTRACT FROM AUTHOR]

Applying data mining techniques for rainfall modeling because of a lack of sufficient memory components may increase uncertainty in rainfall forecasting. To solve this issue, in this research, a deep-learning-based long short-term memory (LSTM) model is developed for the first time for forecasting monthly rainfall data, and its capability is compared with a random forest (RF) data-driven model. To this end, monthly rainfall data for a period of 41 years (1980–2020) from two meteorological stations in Turkey, namely Rize and Konya, with different climatic conditions, are used. The analysis is carried out using optimum window sizes for determining the optimum lag times of rainfall time series. The performance of the models is evaluated using five statistical measures, namely root mean square error (RMSE), RMSE-observations standard deviation ratio (RSR), Legate and McCabe's index (LMI), correlation coefficient (R) and Nash–Sutcliffe efficiency (NSE), and also using two visual means, namely Taylor and violin diagrams. The results reveal that the LSTM model, as a more efficient tool, outperforms the RF model in forecasting rainfall at both stations, with improved RMSE of 12.2–14.9%, RSR of 12.3–14.8%, R of 9.4–13.5% and NSE of 32.9–33.2%. The LSTM-based approach proposed herein could be adopted over any global climatic conditions to forecast the monthly rainfall with reasonable accuracy. [ABSTRACT FROM AUTHOR]

The processes involved in the formation of nocturnal temperature inversions (NTIs) are of great relevance throughout the year, notably influencing the surface distribution of minimum temperatures during nights of atmospheric stability. The low density of surface meteorological stations in the study area motivated the use of thermographies for the mapping and identification of cold air pools CAPs. Thermal distribution during stable nights leads to the formation of CAPs in valley areas and depressed areas, and in areas with warmer air (WAM) in orographically complex areas. The thermographies carried out with satellite products from AQUA and SUOMI-NPP (MODIS and VIIRS LST) represent the only tool capable of fully radiographing the territory under study, thus overcoming the limitations in the interpolation of minimum surface temperatures. The main objective of the research was, therefore, to value thermography as an important tool in the identification of CAPs. The products used were subjected to statistical validation with the surface temperatures recorded in meteorological observatories (R2 0.87/0.88 and Bias −1.2/-1.3) with a new objective of making thermal distribution maps in nocturnal stability processes ... [ABSTRACT FROM AUTHOR]

The ever-increasing importance of irrigation monitoring and water-use optimization in agriculture calls for new solutions for a more complete understanding of the plant growth dynamic and the agricultural water cycle. In this study, the fitness for use of the Flower Power low-cost sensors, not designed for scientific applications, is evaluated in an integrated agricultural monitoring context in contrast to freely available satellite information from Landsat 8, Sentinel 1 and 2. Measurements of air temperature, solar radiation, leaf area index (LAI) and soil moisture are considered. 456 sensors have been deployed in the Capitanata Irrigation Consortium (Italy) as part of the GROW Observatory project with local farmers collaborating as citizen scientists to either deploy these sensors, monitor the environmental variables and control irrigation management. The main results are: (i) positive agreement between Flower Power sensors and high-quality professional stations for measurements of meteorological variables (5.6°C RMSE for Air Temperature); (ii) acceptable estimates of crops LAI (RMSE = 0.55 m2 m−2) and mixed ones of Surface Soil Moisture (m = 0.75, R2 = 0.23) from Flower Power sensors in respect to different satellite data; (iii) potentiality of these sensors combined with remote sensing in providing suitable tools for irrigation management. [ABSTRACT FROM AUTHOR]

In recent environmental studies, the examination of extreme events has great impact. The block maxima of environment-related indices can be analyzed by the tools of extreme value theory. For instance, the monthly maxima of the fire weather index at stations in British Columbia might be modeled by GEV distribution, but it is questionable whether the underlying stochastic process is stationary. This property can lead us to different approaches to determine whether there is a significant trend in the past few years' data or not. One such approach is a likelihood ratio based procedure, which has favorable asymptotic properties, but for realistic sample sizes it might have large decision errors. In this paper, we analyze the properties of the likelihood ratio test for extremes by bootstrap simulations and present a simulation-based procedure to overcome the problem of small sample sizes. We also propose a return level calculation method. Using our theoretical results we reassess the trends of fire weather index monthly maxima in selected stations of British Columbia. [ABSTRACT FROM AUTHOR]

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistical biases. This study validated air temperature from NLDAS-2 and a novel statistically downscaled NLDAS-2 against observational weather station data for the state of Florida. We statistically downscaled NLDAS-2 to a 1-km grid product using MODIS land surface temperature. We investigated mean biases and Pearson correlation coefficients between daily observational data and the two model-derived datasets. We then calculated multiple Climate Extremes Indices to further scrutinize differences in capturing extreme temperatures. Finally, we quantified potential causes of systematic NLDAS-2 biases related to distance from the coast, urban heat island, land cover, and type of observational stations. Two model-derived datasets showed similar mean biases and correspondence with observational data, underestimating maximum temperature by 1°C and overestimating minimum temperature by 2°C. Extreme temperatures were well simulated in both datasets. However, we still found overestimated extreme minimum temperatures and underestimated extreme maximum temperatures. Systematic biases tended to be higher for coastal stations and grids having a high fraction of water. Our study suggests that including physical processes covering land surface and ocean interactions may improve the model accuracy. [ABSTRACT FROM AUTHOR]

Reducing the post-harvest losses of agricultural products is a current problem which deserves special attention, worldwide. In this work, agro-meteorological parameters collected at different weather stations of Benin Republic were analysed in order to highlight their seasonality, and to identify favourable air-drying periods for agricultural products. Periods from March to May and October to December have the peaks of global radiation and insolation; high temperatures values are concentrated between February and May, and rains sequences are observed from March to November. Cross-analysis of different parameters led to statistically identifying favourable drying periods for agricultural products in each region of the country. The period from November to February was shown to be favourable. It includes most post-harvest periods for most of the cultivated agricultural products and also has the best environmental and practical conditions for natural air-drying. [ABSTRACT FROM AUTHOR]