Weather Characteristics Research Articles

Pengelompokan Cuaca Kota Palembang Menggunakan Algoritma K-Means Clustering Untuk Mengetahui Pola Karakteristik Cuaca

Weather related information is one of the things that is very important and has a big influence on all kinds of life activities such as in public safety, socio-economics, agriculture, aviation, and so on.The weather in each place or region is different, this happens because of the different weather elements in each place/region. By using data mining clustering techniques, weather clustering will be carried out in the city of Palembang. K-means is the algorithm chosen for clustering the weather in the city of Palembang. The test was carried out using daily weather data for 2020-2021 from BMKG by utilizing rapidminer application as learning techniques for data. So that we will get a group of weather characteristics of Palembang city based on similarities and dissimilarities. From the test results, the best k was obtained at k=3 with the parameters Measure Types ( NumericalMeasure ) and Divergences ( DynamicTimeWarpingDistance ) as well as a local random seed of 2500 seen from the results of the Davies-Bouldin Index (DBI). This weather grouping can later provide information on how the weather character is and reduce the impact of sudden changes in weather conditions.

Modeling shallow soil moisture dynamics in mountainous landslide active regions

Under the worsening climate change, the mountainous landslide active regions are more likely to suffer severe disasters threatening residents. To predict the occurrence of landslides, shallow soil moisture lying in the interface of the hydrological processes has been found as one of the critical factors. However, shallow soil moisture data are often scarce in the landslide active regions. To overcome the severe measurement deficiencies and provide predictions of soil moisture dynamics, we construct a physically-based shallow soil moisture model based on the assumptions of ideal flow, homogeneous and isotropic soil textures, and 1-dimensional water movement dominant by gravity forces. In the model, the meteorological conditions and the physical soil properties are taken into consideration. With limited field measurements, the model can provide reasonably accurate soil moisture predictions. In recognition of the seasonal weather characteristics, we perform a series of sensitivity analyses to examine the response of shallow soil moisture and relate the hydrological processes to air temperature, precipitation intensity, duration, and combinations thereof. Complex interactions of hydrological processes are found with variations in precipitation and air temperature, depending on the interlinked boundary conditions of the soil and water. It demonstrates a strong need for a decent forecast of the complex shallow soil moisture dynamics and the associated hydrologic processes in mountain regions to cope with climate change for landslide preparation and agricultural adaptation in the future.

Application of elements of the information and analytical system of crop management to ensure resource-saving production of flax seeds in the Tver region

Relevance. The concept of precision agriculture call for the use of physical and mathematical, technical and software tools for obtaining and processing information about agroecosystems, as well as for the implementation of agricultural practices directly in the field. To date, there are quite a few software products that can control the production of crops, in particular, the management of differentiated fertilization and compliance with elements of cultivation technology. The purpose of our research conducted over two years was to develop and test in real field conditions algorithms for the functioning of the module of the information and analytical system of crop management for specialized crop rotations involving flax.Methods. The objects of research were the elements of the information and analytical management system for resource-saving production of crop production "Vash urozhaj" (IASUR) and flax-long-lived variety Tonus (selection of the FSBI FNC LC). The experiment used an electronic field map in kml file format containing the coordinates of points, quantitative characteristics of indicators of the content of nutrients and chemical elements in elementary areas within the surveyed spatial object, a technological map of the cultivation of flax, developed specifically for this variety, task maps for the application of solid and liquid fertilizers in .csv file format.Results. Developed and formed in the information and analytical system "Vash urozhaj" electronic technological map of the cultivation of flax-long-legged variety Tonus, containing a list of technological operations, timing and quality characteristics of their implementation, including a possible set of machinery and equipment. The possibility has been tested and the effectiveness of the module for obtaining climatically secured flax yield has been determined — the planned estimated yield for this flax variety at the pilot site is 4.3 c/ha. When cultivated according to the technology adopted in the Federal Scientific Center of Bast Cultures, introduced into the agroplatform, a yield of 4.0 c/ha was obtained. The yield deviation by 6.98% relative to the calculated one is due to the weather features of the growing season of 2021.

Chemical weathering characteristics and controls in the Yarlung Tsangpo River Basin: Evidence from hydrochemical composition

The uplift of the Tibetan Plateau (TP) is thought to be closely related to the global climate. For the Yarlung Tsangpo River (YTR), the largest river on the TP, the hot spring input is an important source of solutes. Without considering hot spring input will lead to an overestimation of chemical weathering and CO2 consumption rate. Two sample series were collected in the basin before and after the monsoon season to evaluate the chemical weathering rates and controlling factors based on the hydrochemical composition. The results of improved forward model indicate that the highest contribution to the total cations comes from rock weathering (59%–99%, average value 93%), of which carbonate dissolution contributes the most (25%–79%, average value 61%), followed by evaporite dissolution (5%–42%, average value 19%) and silicate weathering (1%–29%, average value 13%). The hydrochemistry of tributaries in the middle reaches (Dogxung Tsangpo and Lhasa River) is affected by hot springs significantly, and the maximum contribution reaches 40%. The chemical weathering rates for the sub-basins show a sequence of Lhasa River > Dogxung Tsangpo > Nyang Qu, primarily controlled by lithology and runoff. For the mainstream above the YTR canyon and tributaries in the upper and middle reaches, chemical weathering is controlled by a transport limited regime, and the rest of the basin is governed by a kinetically limited regime. Overall, after deducting the hot spring input from the solutes, the silicate and carbonate weathering rates in the YTR Basin are 1.8 t/km2/yr and 6.3 t/km2/yr, respectively. The atmospheric CO2 consumption by silicate and carbonate weathering in the YTR Basin accounts for ∼0.17% and ∼0.28% of global, respectively, on ∼0.21% of the global land surface area. The carbon sink caused by chemical weathering and its effect on global carbon cycle in the YTR Basin are insignificant.

Quantitative assessment of weathering of cretaceous sandstone relics in Longdong Area from the surface to the interior

Numerous cretaceous sandstone grottoes in Longdong area have been suffering from serious weathering, and the weathering is proceeding at a considerable rate, resulting in severe challenges for conservation practice. However, relatively little is known about the weathering behavior and mechanism of cretaceous sandstone in Longdong Area. In this study, cretaceous sandstone in North Grotto Temple was selected as a typical case, and the weathering characteristics from the surface to the interior in real environment were studied by field investigation and laboratory analysis (particle and pore size distribution, soluble salt content, microstructure, mineral and chemical composition). Cretaceous sandstones are mainly litharenite and feldspathic litharenite at the North Grotto Temple (NGT). Efflorescence and subflorescence were confirmed as the main weathering forms for litharenite and feldspathic litharenite, respectively. Sodium sulfate and sodium chloride observed from the SEM images were the most active soluble salts tend to accumulate at the certain depth below the surface (1 cm for litharenite and 3 cm for feldspathic litharenite). The salts migration kinetics inside the cliff were concluded based on the distribution of salts. The K-feldspar increased from the surface to the interior with the kaolinite, illite decreasing, indicating the corresponding alteration of K-feldspar in the surface layer. The weathering indexes indicated that the weathering degree along the depth was nonlinear, whereas the weathering degree was firstly increased and then decreased from the surface to the interior. The experimental results in this study can provide a better understanding for the weathering mechanisms of cretaceous sandstone in Longdong Area.

Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models

Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change.

Ecological drought consequences on sustainable development in Iraq

This study is aimed to analyze the spatiotemporal changes of drought in Iraq . Drought is one of the dangerous ecological phenomenon .That earth planet has been suffering since ancient times from ecological challenges such as drought , which have adverse effect on mankind activities and its damages are reflected on sustainable development ; economically , socially and ecologically, where its severity has recently increased. Iraq is considered the fifth country in the world in suffering of drought consequences , therefore, this research was prepared to analyze the ecosystem factors which related with drought, such as weather with a historical dimension at over the past and currently in Iraq through verification about of the weather features such as a temperatures and rainfall over half a century, for the period between 1970 -2020 . Where is proved from spatial analyze , it was the southwest and south region of Iraq are severely affected by drought due to the lack of rainfall and the temperatures rising . This study showed through temporal analyze of weather through the monitoring data in Iraq according to the (SPI), that is referred the warming is rising +2.1degC through 50 past years . Where this rise continues from 1998 until 2020, , with a linear regression of R2 = 0.7084.And there was an anomaly in the average of annual rain fall between 1970-2020. As The results is revealed the reduction of rainfall by [?]84 mm through the past of 50 years ,that precipitation began in decreasing continuously from 1998 until 2020 , with linear regression R2 = 0.1957. This is considering an indicators of climate change , where the drought is one of its repercussion ,which requires adapting with it ,by creating the solutions ,including changing of the mankind systems activities for reducing greenhouse gas emissions by resorting to renewable energies.

Earth Summit Mission 2022: Scientific Expedition and Research on Mt. Qomolangma Helps Reveal the Synergy between Westerly Winds and Monsoon and the Resulting Climatic and Environmental Effects

“Earth summit mission 2022” is one of the landmark scientific research activities of the Second Tibetan Plateau Scientific Expedition and Research (STEP). This scientific expedition firstly used advanced technology and methods to detect vertical meteorological elements and produce forecasts for mountain climbing. The “Earth summit mission 2022” Qomolangma scientific expedition exceeded an altitude of over 8000 meters for the first time and carried out a comprehensive scientific investigation mission on the summit of Mt. Qomolangma. Among the participants, the westerly-monsoon synergy and influence team stationed in the Mt. Qomolangma region had two tasks: 1) detecting the vertical structure of the atmosphere for parameters such as wind, temperature, humidity, and pressure with advanced instruments for high-altitude detection at the Mt. Qomolangma base camp; and 2) observing extreme weather processes to ensure that members of the mountaineering team could successfully reach the top. Through this scientific expedition, a better understanding of the vertical structure and weather characteristics of the complex area of Mt. Qomolangma is gained.

Spatiotemporal Statistics for Analyzing Climatic Conditions Influencing Lymantria dispar Outbreaks

The spongy moth (Lymantria dispar) is a forest pest that damages a variety of trees in North America and Eurasia. A spongy moth outbreak occurred in part of South Korea in 2020 and caused severe damage to domestic forests and human society. Since the occurrence of spongy moths is influenced by climatic factors, this study examines the causes of spongy moth outbreaks by analyzing the temporal and spatial differences in climatic factors, influencing spongy moth occurrence using specimens collected during field surveys. Climatic factors were identified using global occurrence coordinates to compare the weather characteristics of spongy moth occurrence in domestic regions, using the kernel density function. Spatial and temporal comparisons were performed for monthly weather factors obtained from field surveys in 2020 and 2021 in areas with high and low spongy moth larvae densities. Spongy moth outbreaks may result from particular combinations of variable seasonality in temperature and precipitation, including high temperatures during cold periods and low precipitation during developmental periods.

Meteorological drivers of the eastern Victorian Black Summer (2019–2020) fires

The spring and summer of 2019–2020 (Black Summer) saw the largest and most significant bushfire outbreak recorded in eastern Australia. In Victoria, the fires ran from mid-November through early autumn. In this paper, we use a high-spatial and temporal resolution 48-year fire weather re-analysis data set (VicClim5) to describe fire weather and vertical wind and stability profiles for five significant high Forest Fire Danger Index (FFDI) fire events and compare these with detailed fire reconstructions. A feature of several of these fires was very active overnight fire spread driven by topographically enhanced low-level jets and low fine fuel moisture content. The FFDI values on these nights were either the highest or near highest on record in the 48-year data set. We describe cases of lightning ignition, prefrontal fire spread and two cases of post-frontal fire spread – one into Mallacoota on the early morning of 31 December 2019 and the other a northward overnight run down the Buffalo Valley on 4–5 January 2020. On two of the days studied there were complex wind changes associated with the inland penetration of low-level south-easterly winds under the influence of locally generated pressure gradients. An elevated hot, dry mixed layer above these shallow layers also played an important role. On one occasion there is some evidence of possible mountain-wave modulation of surface wind flows. These events demonstrate a range of features of the fire weather and climate in eastern Victoria and the utility of VicClim5 in 3-dimensional climatological analyses.

Choosing multiple linear regressions for weather-based crop yield prediction with ABSOLUT v1.2 applied to the districts of Germany

ABSOLUT v1.2 is an adaptive algorithm that uses correlations between time-aggregated weather variables and crop yields for yield prediction. In contrast to conventional regression-based yield prediction methods, a very broad range of possible input features and their combinations are exhaustively tested for maximum explanatory power. Weather variables such as temperature, precipitation, and sunshine duration are aggregated over different seasonal time periods preceding the harvest to 45 potential input features per original variable. In a first step, this large set of features is reduced to those aggregates very probably holding explanatory power for observed yields. The second, computationally demanding step evaluates predictions for all districts with all of their possible combinations. Step three selects those combinations of weather features that showed the highest predictive power across districts. Finally, the district-specific best performing regressions among these are used for actual prediction, and the results are spatially aggregated. To evaluate the new approach, ABSOLUT v1.2 is applied to predict the yields of silage maize, winter wheat, and other major crops in Germany based on two decades of data from about 300 districts. It turned out to be absolutely crucial to not only make out-of-sample predictions (solely based on data excluding the target year to predict) but to also consequently separate training and testing years in the process of feature selection. Otherwise, the prediction accuracy would be over-estimated by far. The question arises whether performances claimed for other statistical modelling examples are often upward-biased through input variable selection disregarding the out-of-sample principle.

Leveraging high-resolution weather information to predict hail damage claims: A spatial point process for replicated point patterns

Technological advances in weather data collection allow insurers to incorporate high-resolution data to manage hail risk more effectively, but challenges arise when the response variable and predictors are collected from different locations. To address this issue, we adopt a spatial point pattern viewpoint for modeling hail insurance claims. In particular, we propose a spatial mixed-effects framework for replicated point patterns to model the frequency and geographical distribution of hail damage claims following a hailstorm. Our model simultaneously incorporates traditional property rating characteristics collected from policyholders, as well as densely collected weather features, even when observed at different sets of locations across a region. We discuss likelihood-based inference and demonstrate parameter estimation with simulation studies. Using hail damage insurance claims data from a U.S. insurer, supplemented with hail radar maps and other spatially varying weather features, we show that incorporating granular data to model the development of claim reporting patterns helps insurers anticipate and manage claims more efficiently.

Comparisons of the Synoptic Characteristics of 14-Day Extreme Precipitation Events in Different Regions of Eastern China

How to predict and study persistent extreme precipitation events (PEPEs) with a prediction period of 1–2 weeks is an important scientific problem faced by the meteorological circles at home and abroad. Based on the accurate description of the flood range caused by 14-day PEPEs, the comprehensive analysis method was used to obtain the weather characteristics related to 14-day PEPEs (including abnormal trough/ridge, westerly jet, atmospheric river (AR) activity, teleconnections, etc.). First, we selected three regions in China, North China (NC), the Yangtze River valley (YRV), and South China (SC), analyzed their 14-day PEPEs in summer (June to August), and composited them into an average circulation (500 hPa geopotential height field) to compare the weather patterns related to PEPEs in these regions. Then, several variables are composited to understand the evolution of the atmospheric state before and during the occurrence of PEPEs. Finally, potential applications of several teleconnection types and composites in advance prediction are studied. The main findings include: the common weather signals during the occurrence of PEPEs are characterized by obvious and continuous a high-low-high saddle field circulation configuration (conducive to the formation of frequent heavy rainfall), active westerly jet (westerly jet is the controlling factor of precipitation), and enhanced water vapor transport (significantly increased atmospheric river activity). In this study, some key characteristics and development of PEPEs were identified, the formation mechanism of China’s 14-day PEPEs was revealed, the role of ARs in PEPEs was recognized, and the PEPEs precursor signal was extracted. Furthermore, PEPEs in different regions were also compared, which played an important role in understanding and predicting similar events.

A Novel Approach for Reconstructing Slip Histories for Bedrock Fault Scarps Using Rock Surface Luminescence Dating

AbstractA newly developing method, optically stimulated luminescence (OSL)‐depth profiles (DPs), provides a novel means of reconstructing and quantifying fault slip on timescales of 101−4 years. A targeted bedrock fault scarp along Langshan in north China, used as proof of concept, is divided into four sections based on four groups of OSL‐DPs at different heights up the bedrock fault scarp. These have seismic displacements of 170, 80, 130, and 140 cm, and without considering the erosion rate, the exposure ages at the four displacements are 6–35 a, 19–84 a, 0.5–0.9 ka, and 2–5 ka, respectively. The ages are overall in chronological order, while the actual ages are likely underestimated due to erosion. After assuming erosion amounts according to the weathering characteristics, the calculated ages are mostly consistent with paleoseismic trenching studies. In addition, the method may identify a seismic event that previous studies did not identify.

On the Relationship of Arctic Oscillation with Atmospheric Rivers and Snowpack in the Western United States Using Long-Term Multi-Platform Dataset

Atmospheric rivers (ARs) are narrow bands of enhanced integrated water vapor transport, modulated by large-scale and synoptic-scale variability. Here, we investigate how ARs and snowpack are shaped by large-scale variability such as arctic oscillation (AO) by examining the synoptic conditions and characteristics of ARs and snowpack in the different phases of AO. Using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data, Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) reanalysis data, and in-situ observation data over the eastern Pacific and western United States. we found that more precipitation is observed in lower latitudes (35° N–45° N) during negative AO months and farther north (north of 45° N) in latitude during positive AO months. These are associated with wavelike synoptic patterns in negative AO months and more straight-line type synoptic patterns in positive AO months. The different phases of AO also modulate the AR characteristics: 2.6% less intense (5.3% more intense) integrated water vapor transport and total precipitation, and 16.0% shorter (21.1% longer) duration of ARs than the climatological mean (1980–2019) for positive AO (negative AO) phase. AR frequency is also higher (~50.4%) than the climatological mean for negative AO phase, but there is no statistically significant difference between either negative AO or positive AO phase, especially in southern California. In addition, the snow water equivalent (SWE) tends to be reduced in the positive AO phase and under high-temperature conditions, especially in recent years (2010s). The similar relationships are found in the early 1990s and 2000s, but their statistical significances are low. Considering that lower atmospheric temperature keeps increasing over the eastern Pacific and the western U.S., and SWE tends to be reduced in the positive AO phase in recent years, SWE may decrease over northern California if the warming condition persists. These findings highlight how the characteristics of local extreme weather can be shaped by large-scale climate variability.

Daily Weather Forecasting Based on Deep Learning Model: A Case Study of Shenzhen City, China

Daily weather conditions are closely related to every field of production and life, and the forecasting of weather conditions plays an important role in social development. Based on the data characteristics of urban weather conditions, a deep learning network was designed to forecast urban weather conditions, and its feasibility was proved by experiments. In view of the non-stationary and seasonal fluctuation of the time series of daily weather conditions in Shenzhen from 2015 to 2019, empirical mode decomposition (EMD) was used to carry out the stationary processing for the daily minimum humidity, minimum pressure, maximum temperature, maximum pressure, maximum wind speed and minimum temperature. The decomposed components, residual sequence and original sequence were reconstructed according to the degree of relevance. On this basis, a long short-term memory (LSTM) neural network for the Shenzhen daily weather forecast was used, using the advantages of the LSTM model in time-series data processing, using the grid search algorithm to find the optimal combination of the above parameters and combining with the gradient descent optimization algorithm to find optimal weights and bias, so as to improve the prediction accuracy of Shenzhen weather characteristics. The experimental results show that our design of the EMD-LSTM model has higher forecasting precision and efficiency than traditional models, which provides new ideas for the weather forecast.

Mapping of Space Weathering Features and Vesicle Contents in Lunar Soils

Mapping of Space Weathering Features and Vesicle Contents in Lunar Soils

Application of extreme gradient boosting and Shapley Additive explanations to predict temperature regimes inside forests from standard open-field meteorological data

Forest microclimate can buffer biotic responses to summer heat waves, which are expected to become more extreme under climate warming. Prediction of forest microclimate is limited because meteorological observation standards seldom include situations inside forests. We use eXtreme Gradient Boosting ‒ a Machine Learning technique ‒ to predict the microclimate of forest sites in Brandenburg, Germany, using seasonal data comprising weather features. The analysis was amended by applying a SHapley Additive explanation to show the interaction effect of variables and individualised feature attributions. We evaluate model performance in comparison to artificial neural networks, random forest, support vector machine, and multi-linear regression. After implementing a feature selection, an ensemble approach was applied to combine individual models for each forest and improve robustness over a given single prediction model. The resulting model can be applied to translate climate change scenarios into temperatures inside forests to assess temperature-related ecosystem services provided by forests.

Severity Analysis of Large-Truck Wrong-Way Driving Crashes in the State of Florida

Wrong-way driving (WWD) crashes lead to severe injuries and fatalities, especially when a large truck is involved. This study investigates the factors associated with crash-injury severity in large-truck WWD crashes in Florida. Various driver, roadway, weather, and traffic characteristics were explored as explanatory variables through a random parameter ordered logit model. The study also accounted for heterogeneity by identifying random parameters in the model and introducing interaction effects as potential sources of such heterogeneity. The findings indicate that not using a seatbelt, driving under the influence of drugs, and a driving speed of 50–74 mph were more likely to result in fatal crashes. On the contrary, female drivers, private roadways, and sideswipe collisions showed negative impacts on crash-injury severity. The model identified two random parameters, including a speed of 25–49 mph and early-morning crashes. The interaction effects showed that when driving at a speed of 25–49 mph, young drivers (under 20 years old) and middle-aged drivers (36–50 years old) were the sources of heterogeneity, decreasing crash-injury severity. Understanding the contributing factors of large-truck WWD crashes can help policymakers develop safety countermeasures to reduce the associated injury severity and improve truck safety.

Spatio-temporal characteristics of hazardous weather affecting Chinese airports based on the ERA5/ERA5-land reanalysis dataset

The growing demand for air transportation has led to increased air traffic and airline operations at airports worldwide, while hazardous weather conditions have a considerable impact on the efficiency and safety of air traffic. The long-term and high-resolution state-of-art fifth-generation reanalysis of the European Center for Medium-Range Weather Forecasts (ECMWF) (ERA5) and ERA5-land provide us a perspective on the climatological characteristics of hazardous weather conditions affecting Chinese airports. These hazardous weather conditions include low-level wind shear (LLWS), limited visibility (LIMV), thunderstorms (TSTMs), and snowfall (SNOW). The LLWS is frequent in winter but rare in summer. At the airports in the north of eastern China, managers should take more precautions about LLWS from February to March and October to November. LIMV is major hazardous weather in the south of eastern China that should be carefully monitored in winter, especially from December to January. In northwestern China, LIMV is rare, especially at Yinchuan and Lanzhou airports. TSTMs frequently occur in South China and Southwest China, especially in Yunnan, Guangxi, Guangdong, and eastern Tibet. The corresponding active period is summer. SNOW frequently occurs over the Tibet Plateau and parts of Xinjiang province. SNOW is generally active in early winter (December–January) at Urumchi airport but at Lhasa airport in late winter (February–March). There is no SNOW throughout the year in the south of eastern China. The observations also verify such characteristics of the annual cycles of the four hazardous weather conditions. The trend analysis does not express many warnings on hazardous weather conditions except the SNOW. The SNOW at Lhasa airport shows an increasing trend. Considering the frequent SNOW in Lhasa, more attention should be put to monitoring this weather here.