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Rainfall forecasting plays a crucial role in managing water resources, agricultural planning, and mitigating the risks of disasters. In this study, we propose an approach using multi-output neural network to predict rainfall rates specifically in East Java Province, Indonesia. The neural network model is trained using historical rainfall data obtained from eleven meteorological stations across East Java Province. To optimize the model's performance, we used resilient propagation algorithm during the training process. VAR (Vector Autoregressive) modeling was utilized to determine the significant lags to be used as input variables in the neural network model. This enabled us to capture the relationships and dependencies between different time steps. The model's performance was evaluated using two metrics, Root Mean Squared Error (RMSE) and Akaike's Information Criterion (AIC). The best-performing model was the Feed Forward Neural Network Model, which outperformed the VAR models, with a lower score of RMSE and AIC value. Furthermore, the model consistently delivered accurate predictions across all sampled meteorological stations, indicating its suitability and reliability for rainfall forecasting in East Java Province. The proposed multi-output neural network approach offers an effective solution for rainfall forecasting in East Java Province. Its implementation can significantly contribute for decision-making in water resource management and disaster mitigation. Providing accurate rainfall forecasts can aid in planning and preparing for potential weather-related challenges and minimizing the adverse impacts of extreme weather events in the region. [ABSTRACT FROM AUTHOR]

Iraq is considered one of the five most important countries in the world because of climate change, and there are three locations in Iraq out of ten in the world that are classified as having the highest temperature due to the lack of vegetation cover, decreased rainfall, and water revenues. This study aims to calculate the drought index using the De Martonne equation and then determine the climate type for several stations in Iraq (Dohuk, Mosul, Arbil, Sulaymaniyah, Baghdad, Rutba, Karbala, Najaf, Nasiriyah, and Basra) using (precipitation and temperatures) for the period (1980-2022). Precipitation data includes the annual total of (rain, hail, snow, and ice), while temperature data is the annual average air temperature. The results showed the dominion of the wet drought index in the northern stations and the arid and semi-arid drought index as we move toward the southern regions, due to the variation in the values of the amount of precipitation, temperature, and vegetation cover. [ABSTRACT FROM AUTHOR]

Asuransi Usaha Tani Padi (AUTP) is a Multiperil Crop Insurance (MPCI). However, in practice AUTP has a weakness in which they have a lengthy claim process. Nevertheless, this weakness can be overcome by implementing weather index-based agriculture insurance. To create a weather index based agricultural insurance, a forecasting model is needed. The two most crop damaging disasters in Indonesia are flood and drought, and the climate factor that is responsible for it is precipitation. There are two data commonly used in climate research, the weather station and satellite data. In this research, satellite data is selected because the insured crops are not always near a weather station. It is known that the variance of precipitation is not constant, hence the GARCH method will be used in this research in order to overcome the assumption of constant variance in a conventional forecasting method. The Sangkapura, Banyuwangi and Karangkates district is chosen to represent districts with high, low and medium rainfall among districts in East Java as the province with the widest crop land in Indonesia. In this research, SARIMA is first used to create the initial model, then heteroskedasticity is not found on the residual model, hence the GARCH model is not needed and only the SARIMA model will be used. The MAPE for the Sangkapura and Banyuwangi model is 3.62% and 3.48%, however the Karangkates model MAPE is at 10.7%. This happens because the SARIMA model is too heavily affected by the last yearly pattern of the training dataset. With a little shift on the training cut-off, the MAPE became 4.8%. The only models that are able to predict flood and drought with decent accuracy, precision and recall are the Sangkapura and Banyuwangi flood models. This prediction can later be used to calculate the premium using the indemnity function. The auto ARIMA function in R has a lower BIC, MAPE, precision and recall on the auto ARIMA forecasting results, and some of the models created fail to pass the white noise assumption in ARIMA. In conclusion, heteroskedasticity are not present on precipitation data in East Java, SARIMA is able to create a good model for precipitation, however it is important to properly choose the cut-off for train testing data since SARIMA is greatly affected by the last yearly pattern of the training dataset, model still needs improvement on predicting disasters and auto ARIMA is not yet able to be a substitute to the conventional way of deciding ARIMA order. [ABSTRACT FROM AUTHOR]

Hidden Markov Model (HMM) is the development of a Markov chain with a state that cannot be observed directly (hidden). One of the HMM applications is rainfall modeling. Sometimes rain has intermittent characteristics, seasonal variations, and variance inhomogeneities, so this study develops HMM on spatial rainfall data with a Bayesian approach. In this study, there is a general linear model (GLM) that supports exogenous variables directly affecting the rainfall characteristics of precipitation at each site over time, while the Markovian transition between weather stations represents seasonal and sub-seasonal weather variability. The data used is monthly rainfall data from January 2018 to June 2021 at eleven weather stations in East Java supported by location and altitude information. Rainfall is classified into some states to build a model. The best model is obtained based on the smallest AIC, namely the model with four hidden states. The results of the analysis show that in the steady-state development, the potential for rainfall in East Java has the highest chance of 0.346. [ABSTRACT FROM AUTHOR]

The effects of climate change are becoming increasingly noticeable in Malaysia which has been experiencing extreme weather events in recent years. The phenomenon of extreme weather has resulted in unpredictable variations in rainfall distribution, highlighting the critical need for accurate rainfall prediction. In addition, accurate rainfall prediction in airport meteorological stations is equally important because airports are considered one of the top priorities in meteorological stations. Therefore, the aim of this study is to determine the best rainfall level prediction model, focusing on three airport meteorological stations; Subang, Kota Bharu, and Senai, by using 15 years (2007-2021) of rainfall data. Two data-mining models, Support Vector Machine and Artificial Neural Network are employed to build the prediction model with different approaches, including data splitting mechanism and feature selection methods (optimize selection, forward selection, and backward elimination). The performance of the best model is assessed by using misclassification rate, accuracy, RMSE, and MAE. The results indicate that the Support Vector Machine, combined with optimized feature selection using a 90:10 ratio, outperforms the Artificial Neural Network models in terms of accuracy (76.36%), misclassification rate (23.64%), RMSE (48.60%) and MAE (23.60%) values. This developed model could serve as a reliable tool for rainfall prediction, benefiting agencies responsible for air traffic control in their planning and decision-making processes. [ABSTRACT FROM AUTHOR]

Solar radiation can be calculated using the actual daily solar irradiation time (n), maximum daily sunshine duration on the same day (N), extraterrestrial radiation (Ra), and the coefficient representing the fraction Ra are as = 0.25 and bs = 0.50. This study was developed and tested with a computational approach that relates Rs, Ra to the observed weather variables including the long-term average of the temperature differences Tmax−Tmin (TDavg), the long term average of the mean air temperatures Tmean (Tavg), the long term average of the ratios Tmean/Tmax-Tmin (T/TD) avg, the long term average of the wind speed (ffavg), the long term average of RHmean (RHavg) and rainfall (RR). This study uses daily data relative to 3 weather stations in Semarang City are Central Java Climatology Station, Tanjung Emas Maritime Meteorological Station and Ahmad Yani Meteorological Station referring to data from BMKG. The prediction system was built using Deep Learning. The Deep Learning method used is H20. Based on the results of RMSE (Root Mean Square Error) for a combination of three variables TDavg, ffavg and RHavg as the best predictors. The prediction result with the smallest RMSE value of 0.009 is the Tanjung Emas Maritime Meteorological Station. [ABSTRACT FROM AUTHOR]

El Niño Southern Oscillation (ENSO) 'is an event where the increase in sea surface temperature becomes unnatural. Stronger events where the sea surface temperature anomaly becomes large, then this disrupts local fish and bird populations. The ENSO phenomenon also affects rainfall in several parts of Indonesia. In this study, an analysis of the effect of the ENSO phenomenon on rainfall in Pelambang City from 2000 to 2020 was carried out and the correlation between Consecutive Dry Days (CDD) and Rainfall in Palembang City for the last 20 years. ENSO data and rainfall data as well as CDD and rainfall data were analyzed using a simple linear regression method. In addition, a correlation analysis was also carried out between the Niño 3.4 and SOI indices with rainfall at two stations, namely Palembang Climatology Station and Sultan Mahmud Badaruddin II Meteorological Station to determine how much influence the ENSO phenomenon has on rainfall in the Palembang City area. The results of the analysis show that the effect of ENSO on rainfall at each station in general is almost not influenced by El Niño and La Niña, this is presumably due to the location of Palembang City which is in the Munsonal area. Monsoon areas are characterized by a unimodal type of rainfall (one peak of the rainy season) where in June, July and August the dry season occurs, while December, January and February are wet months. The remaining six months are a transitional or transitional period (three months of transition from the dry season to the rainy season and three months of transition from the rainy season to the dry season. [ABSTRACT FROM AUTHOR]

Hurricanes are commonly situated over enormous waterways where weather conditions stations are scanty, meteorologists frequently need to appraise the breeze speed of hurricanes. They as a rule use float perceptions, microwave satellite symbolism, and infrared satellite symbolism to make these appraisals. There is developing interest in applying AI and AI procedures to work on the precision of functional meteorological errands, including assessing hurricane wind speed. We started investigating profoundly figuring out how to control hurricane wind speed using convolutional neural networks (CNN) and deep learning techniques. This report presents a framework for evaluating parametric probabilistic models of cyclone wind speeds from existing data on assessed breeze speed with different mean repeat scans (MRIs). The core of the model is a convolutional neural network in which both the residual learning and attention processes are integrated into the version in order to optimise the model's form and validated using k-fold validation. The result shows that our model spot structural changes that occur when tropical cyclones develop, intensify, and approach land. By using CNN modelling, it also provides Tropical Cyclone intensity estimates for the use of satellite TV for PC bands that previously didn't play a major part in quantitative methods. [ABSTRACT FROM AUTHOR]

East Java is the largest sugarcane development area dominated by dry land contributing to almost half of the total national sugar production in Indonesia. The water needs of sugarcane on dry land are met by rainfall. The water availability from rainfall in dry land affects crop evapotranspiration describing the actual water use by crops. The actual water use determines the crop yield. This study aimed to determine the correlation concerning sugarcane evapotranspiration and rainfall in the area of East Java. The climatological data were collected from the meteorological station nearby the sugarcane area, consisting of the minimum and maximum air temperature, air humidity, sunshine duration, wind speed, and rainfall. The crop evapotranspiration of sugarcane was estimated by multiplying potential evapotranspiration and crop coefficient. Potential evapotranspiration describing evapotranspiration at maximum available water conditions was estimated using the FAO Penman-Monteith method. The rainfall used in the analysis was rainfall effectively used by the crop. Research results show that crop evapotranspiration by sugarcane (y) has a positive linear relationship with effective rainfall (x) expressed by the equation y=0.5018x+600.95 (R²= 0.899). The mean effective rainfall is 1,104.2mm and the crop evapotranspiration is 1,155.0mm. The crop evapotranspiration is useful in assessing and planning the expansion of new areas of sugarcane to minimize yield reduction. Further analysis of crop evapotranspiration can be done for other areas of sugarcane. [ABSTRACT FROM AUTHOR]

This paper gives the idea of solving some of the problems faced by farmers. The population rate of the world is growing rapidly and will double in the coming decades and the need of food is also increasing simultaneously. To meet this rapid growth in demand for food, automation in agriculture is the best solution. The aim of this paper is to design and develop an automated system to monitor the various conditions of soil and moisture on the crops, and the proper supply of water, which affects the crops [5]. Such a system will contain sensors, a pump and a device called a humidifier, and various advanced techniques like IoT, machine learning, and artificial intelligence. The technical field is racing to provide more optimum solutions. The incorporation of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide enough scope to predicrocess, analyze situations and improve activities in a real-time situation. Green House is one of the best solutions as the crops can be grown in a controlled climate without the natural environmental conditions. In the last few decades there has been a huge advancement in technology for agriculture, with the help of technology we can help farmers for increasing their rate of yielding. With this, we can overcome the major problems of farmers. This small processor-based unit can be used to control anything from a fan, air-conditioner, humidifier, and watering system from 4 relay outputs. A temperature and humidity range can be set to control relay outputs. The main focus of this paper is to implement the Internet of Things (IoT) in the monitoring of humidity, soil condition, temperature, supply of water to the field, level of water, environmental condition, etc. The IoT-based Smart agriculture System being planned via this paper is integrated with different Sensors. [ABSTRACT FROM AUTHOR]

Using cluster analysis, the climatic zones of Malaysia are classified based on minimum temperature, maximum temperature, precipitation, and wind speed data from meteorological stations between 1994 and 2017. The method of hierarchical clustering analysis which are the complete linkage, single linkage, average linkage and the Ward's linkage was applied. Meteorological data is clustered and related station pairs are identified using hierarchical clustering techniques like Ward's linkage and Euclidean distance. We conclude that the meteorological stations can be clustered into 4 groups namely, a developed centre city, an urban forest, a coastal city facing the Malacca strait and a coastal city. [ABSTRACT FROM AUTHOR]

Over the years, researches used various statistical methods to analyse the daily temperature and daily wind speed data. This study uses the method of repeated measures ANOVA and the result shows that there are significant differences in the daily maximum temperature means among the meteorological stations in Malaysia. The post hoc pairwise comparisons using paired 푡- test with Holm's and Bonferroni's methods are conducted to determine which station differs so that the results become more evident. The tests indicate that the means between all possible pairs of stations differ except for Kota Bharu and Labuan, Senai and Kuantan as well as for Melaka and Kota Kinabalu. Analysis on the daily wind speed data is performed to investigate similar pattern which resulted in the existence of significant differences among the stations. However, pairwise comparison tests indicate that the means between all possible pairs of stations differ. It is noticeable that there is no similar pattern between the daily maximum temperature and the wind speed among the stations. This study successfully make use the method of repeated measures ANOVA in analysing environmental data, which in turn, allowing more future environment and climate research to apply this method. [ABSTRACT FROM AUTHOR]

We analyzed the results of long-term UV observations and reconstruction model at the Meteorological Observatory of Moscow State University (MO MSU, 55.7N. 37.5E) since 1968. The UV measurements in the spectral range of 300-380 nm have been carried out since 1968 using the series of UV pyranometers developed at the MO MSU and then by UVA-1 YES pyranometers and UVA Kipp&Zonen radiometers. UVB-1, YES and Kipp&Zonen UVS-E-T radiometers are used for biologically active erythemal irradiance monitoring since 1999. According to measurements and reconstruction model the long-term interannual UV variability over the period of 1968-2019 has a pronounced increase, especially for erythemal irradiance (of about 5% per decade) since the end of 1970s. However, during the last years the positive trend has been slowed down. The positive UV erythemal trend is explained by the simultaneous decrease in cloudiness and total ozone content and to some extent, by the decrease in aerosol optical thickness, while smaller trend for 300-380nm is explained by the negligible ozone effect on longwave UV irradiance. [ABSTRACT FROM AUTHOR]

A study of surface and columnar atmospheric aerosol and radiative aerosol effects according to the measurements at the Meteorological Observatory of Moscow State University and mesoscale COSMO-ART model in the urban conditions of Moscow megacity has been presented. The observations of columnar aerosol AERONET retrievals, surface PM10, black carbon (BC) mass concentrations were used as well as radiative and meteorological datasets. We found a positive significant dependence of total and fine aerosol optical depth (AOD) mode (R2∼0.4) with PM10 concentrations with a pronounced bifurcation point around PM10=0.05 mgm−3. We also showed the dependence of single scattering albedo with the BC/PM10 fraction both in model and experimental datasets, which is more pronounced in model. Radiative effect of aerosol at TOA varies from -20 Wm−2 to - 1 Wm−2 with a noticeable decrease (in absolute value) in conditions with high absorbing aerosol. The validation of city aerosol pollution obtained from the COSMO- ART model against the observed dataset demonstrates an agreement in mostly cases. The difference in AOD between urban and background conditions in Moscow and Zvenigorod (Moscow suburb) due to urban effects comprises about 19% of the total AOD. [ABSTRACT FROM AUTHOR]

The project "Twin Stations Berlin/Lindenberg" consists in a double suite of instruments of measurements of radiation and of remote sensing (photometry) of aerosols at a rural site: LIN (Meteorological Observatory Lindenberg in Tauche, Germany) and a site in a suburban environment: FUB (Freie Universität Berlin in the South/West edge of the city of Berlin, Germany). The two stations are geographically close to each other (70 km airline distance) thus in the same climate region. Therefore, differences in aerosols and radiation between Lindenberg and Berlin are only due to the rural/urban character of each station. A comparison study shows that the differences in broadband radiation FUB-LIN varies depending on the season from 20 to 40 W.m−2 for diffuse (D) and from 40 to 70 W.m−2 for direct (I) radiation, but only in the morning. In the afternoon, these differences are close to 0 W.m−2. Also the Aerosol Optical Depth (AOD) and aerosol properties Single Scattering Albedo (SSA) and Ångstrøm's α-coefficient have substantial differences between FUB and LIN only in the morning. A statistical analysis of the geometry of observation of these measurements (radiation and photometry for the aerosols), using the sun azimuth angle as reference put forward that these differences in radiation and in photometry measurements occurs only when the city of Berlin is between the station FUB and the sun, giving an urban character to station FUB. [ABSTRACT FROM AUTHOR]

The maximal ice thickness and the lowest average monthly temperature of ambient air are among the influential factors of influence on hydraulic structures. Hydraulic power systems that are formed by cascades of hydroelectric stations have considerable geographic extent. This leads to presence of correlations between natural climatic factors, that are observed when passing from one hydro scheme of cascade to another. For the first time the correlation between the maximal ice thickness and the lowest average monthly temperature of ambient air has been investigated from the data of direct measurements of hydrometric and meteorological stations at hydro schemes of the Dnieper cascade of hydroelectric stations with the use of methods of multivariate correlation. Application of approaches of multivariate correlation and regression turned out to be acceptable during modeling relationship between ice conditions of water storages of Dnieper cascade and temperature of ambient air. Confidence intervals of regression equations and accuracy of the obtained results have been estimated. From the viewpoint of better approximation of real data by predicted ones the results can be improved by using more complex nonlinear polynomial regression models of high orders. The procedure can be used in probabilistic calculations of reliability of cascades of hydroelectric stations. [ABSTRACT FROM AUTHOR]

Weather forecasting is the use of science and technology to forecast the state of the atmosphere for a specific date and location in the future. It's very important to know the weather condition such as temperature & humidity which has main effects on our life especially when you are going out to work or anywhere. Therefore, human-being invented the weather station in order to help them to predict the state of the weather. The weather station is a structure with instruments and equipment for measuring atmospheric conditions in order to give data for weather forecasts and to research weather and climate. Currently meteorologists get weather data from old way which is weather balloon. Thus, we invent a new alternative solution by using modern technology to act as weather station which is Drone weather station. In addition to that we find that there is another problem which effect in the weather and the environment which is Wildfire. It has the potential to be fatal, destroying homes, wildlife habitat, and lumber, as well as contaminating the air with toxic fumes to human health. Therefore, we develop the project to acts also as fire detection and fire extinguisher. [ABSTRACT FROM AUTHOR]

Paper presents theoretical and real hygrothermal behaviour of the south-east timber wall component. The analysed wall component represents current trends in wooden house design and it is a part of experimental timber building in campus of Mendel University in Brno – woodenHAT experimental module. Long-term exposure of the experimental module to real weather conditions brings information about real of hygrothermal behaviour. The component contains several temperature and moisture sensors which together with the weather station permanently monitor temperature and moisture content in the wall component and conditions in front of the façade during the whole year. Experimental data are compared with results of dynamic simulation earned by well-established software for prediction of heat and moisture transport Delphin which has been developed by Institute of Bauklimatik at TU Dresden. Boundary conditions of the numerical model are based on local weather data as well as reference year for closest places near to Brno. The analysis shows and discusses differences between results of experimental measurement and selected boundary conditions. It represents the validation of HAM-modelling for cases neglecting air flows and insufficient airtightness. [ABSTRACT FROM AUTHOR]

Dust storms can suspend large quantities of sand and cause haze in the boundary layer over local and regional scales. Iraq is one of the countries that is often impacted to a large degree by the occurrences of dust storms. Sand and dust storms (SDSs), which present environmental risks and affect the regional climate, have been worsened in the East Asian regions over the last decade. Monitoring SDS from space using satellite remote sensing (RS) has become one of the most important issues in this field. We investigate the sand and dust storms detection in Iraq using Moderate Resolution Imaging Spectroradiometer (MODIS) data, both from Terra and Aqua satellite systems for the year 2022. MODIS Surface Reflectance Daily L2G Global 1 km and 500 m data were utilized to calculate the Normalized Difference Dust Index (NDDI). The MYD09GA V006 product was used to monitor, map, and assess the development and spread of dust storms over the arid and semi-arid territories of Iraq. And BT thermal emissive band (MODIS band 31) can discriminate aerial and surface sand and dust over Iraq. Normalized Difference Dust Index (NDDI) is applied for the detection of sand and dust storms. We set thresholds for NDDI to distinguish between water and/or ice cloud and ground features and dust storms and BT to distinguish airborne dust from ground sand or dust. In addition, the data from Modis satellite, and meteorological stations from IMOS are used to validate NDDI-based sand and dust storm events. [ABSTRACT FROM AUTHOR]

The analysis of the spatial and temporal dust storms and their trends is very important for climate change where dust storms consider a significant environmental problem and one of the most effective meteorological phenomena that affect climate change, deforestation and drought, which are reflected on the human health, economic aspects. This research aims to investigate trends of numbers of dust storms and their behavior in thirteen meteorological stations spreads all over Iraq for the period 1980-2018. The trends of Dust storms included monthly data of dust storms occurrences which were analyzed spatially and temporally using the linear regression method, and spatially represented by using ArcGIS. Nonparametric statistic was applied to determine the significance of the trends. Generally, the months (April May June) have the most occurrence values which mean it occurs between springs and summer seasons. Annual analysis is indicated that highest number of dust storms occurrence happens in Nasiriya, Kerbela, Baghdad, stations respectively. In the summer season eight of the thirteen stations which are (Mosul, Kirkuk, Romadi, Baghdad, Koot Al Hai, Najaf, Diwaniya and Semawa) show a significant positive trend (Increasing) during the study period while the other five stations show a negative trend (Decreasing). Annually, the behavior of the number of dust storms for the thirteen stations shows that five of the thirteen stations which are (Kirkuk, Romadi, Koot Al Hai, Najaf and Semawa) show a significant positive trend (Increasing) during the study period while the other eight stations show a negative trend (Decreasing). [ABSTRACT FROM AUTHOR]

In mining activities, hydrological analysis is needed to determine the dewatering system, pump requirements, and settling pond volume. In addition, hydrological analysis can also affect mine productiv ity, especially the use of availability (UA) value on heavy equipment. The problem is the lack of rainfall observation data in some mining locations. One of the solutions is by using simulated rainfall data generated by the Tropical Rainfall Measuring Mission (TRMM)satellite sensor. The objective of this study is to identify the correlation between TRMM satellite data and observation data from the rainfall station to overcome the limitation of data in some ungauged areas. The evaluation was carried out by analyzing the correlation value between TRMM satellite data and the rainfall data from Citeko Meteorological Station, Bandung Geophysical Station, and Bogor Climatological Station with the data period from January 2011 to December 2019. The results show good correlation results with an r-value of 0.78 in Bandung Geophysical Station, 0.68 in Citeko Meteorological Station, and 0.77 in Bogor Climatologal Station for monthly period data. All in all, the TRMM data can beused in ungauged locations for many purposes, including mining activities. [ABSTRACT FROM AUTHOR]

The work is devoted to the study of the dependence of the results of measuring the contact potential difference on the metal parts surface of aerospace technics on the environment parameters. Environmental parameters: temperature, relative humidity and pressure were estimated by a digital weather station with a measurement accuracy of 2 %. The contact potential difference was measured in laboratory conditions between the surfaces of flat samples of aluminum, titanium, and nickel in comparison with the nickel tip The Kelvin probe tip was set in oscillatory motion with a frequency of 410 Hz by an oscillating system with a piezoelectric element. of the Kelvin probe. The accuracy of measuring the contact potential difference with the device is 0.1 mV. A linear regression analysis of the results of experimental studies of the dependence of the contact potential difference on the parameters of the environment is carried out. A weak dependence of the results of measuring the contact potential difference was found. The greatest influence on the contact potential difference of metals is exerted by temperature. With an increase in the ambient temperature by 1 °С, there is a decrease in the contact potential difference of aluminum samples by 6 mV, titanium samples by 14 mV and nickel samples by 9 mV. The dependences of the values of the contact potential difference measured on the samples surface under study on the relative humidity and air pressure turned out to be even smaller. In the process of measuring the contact potential difference of metals, it was important to ensure the stability of the environment. The results obtained can be used for non-destructive testing of metal machines parts of during their production. [ABSTRACT FROM AUTHOR]

The paper discusses the process of manufacturing a "personal" portable weather station, that can be used in small and ultra-small agricultural enterprises, including individual farms. The issues of manufacturing the printed circuit board of the digital part of the weather module, installation operations, operations for the manufacture and assembly of the hull are considered. Recommendations for laboratory testing of the assembled weather station are described in detail, recommendations for the installation of a weather station in the field are given. [ABSTRACT FROM AUTHOR]

Marine weather observations carried out by Tanjung Perak Maritime Class II Meteorological Station, has an important role in increasing alertness for weather conditions at sea. However, the applied weather observation uses ineffective analog equipment and require employees to be on site. Therefore, supporting an IoT-based weather observation system that suits the new normal era of COVIS-19 pandemic and the ease of use for the observer team at the Tanjung Perak Maritime Class II Meteorological Station is needed. This system support consists of a DHT11 temperature sensor, MS5637 pressure sensor and a NodeMCU microcontroller which transmits data via the internet to Thingspeak that are programmed using the Arduino IDE platform. The measurement parameters are wet bulb temperature, dry bulb temperature, dew point, humidity, QFF and QFE. The six parameters are compared with conventional equipment at Tanjung Perak Maritime Class II Meteorological Station. From the results of data analysis, the error values for Dry Bulb Temperature, Wet Bulb Temperature, Dewpoint, Humidity, QFF and QFE are : 2.4%; 1.5%; 2.1%; 4.9%; 0.025%; 0.030%. This research has succeeded in developing an IoT-based weather observation system that can support the effectiveness of the observer team's work in the new normal era. [ABSTRACT FROM AUTHOR]

Modelling Near-Surface Temperature (NST) is critical for a wide range of environmental applications, including air pollution, human comfort, climate change, and urban planning policy energy management. It impacts most biogeophysical and biogeochemical processes of the earth's surface system, making it one of the most important input factors in land surface and hydrological models. In this research, a new approach was proposed for computing the near-surface temperature, which refers to the air temperature at 2 meters above the land surface based on land surface temperatures. Landsat images, digital elevation models, and ground weather stations over the study area were utilized for this purpose. Parameters like LST and duration day length were computed to be input data in the modeling. The results revealed that the LST data could be utilized to constantly estimate air temperature by some parameters, including (LST, DEM, and DDL) in the model. The finding also shows that the model accuracy of the suggested approach was acceptable. [ABSTRACT FROM AUTHOR]

Depending on the Multiple Linear Regression Equation and its improvement, the daily maximum air temperature was predicted based on the historical observations of the Baghdad meteorological station for the period between (2005-2020) and for the winter and summer seasons separately, where the mentioned station is an example for regions with a subtropical climate It has hot dry summers and cool rainy winters. The values recorded in the hour 0300 GMT related to minimum air temperature, wind speed and relative humidity were depended on. The associated observations were analyzed for net days using multiple liner regression technique to predict the maximum daily air temperature for any day during winter and summer, It was characterized by a positive relationship for minimum air temperature (0.69,0.62) and a negative relationship for wind speed (-0.62,-0.55) and also a negative relationship for relative humidity (-0.07,-0.55) for both seasons, winter and summer, respectively. A comparison between the expected maximum air temperature and the recorded maximum air temperature was made to improve the work of the equation by analyzing the residuals of the daily forecast, the bias by analyzing the number of relative occurrences of the occurrence of these errors in order to add value (- 0.1℃) for the separation equation winter and the value (0.05℃) for the summer equation as the error adjustment term. [ABSTRACT FROM AUTHOR]

Global warming has an impact on people all around the globe, which has a substantial impact on them and is hastening climate change. An accurate forecasting system is required for early detection and enhanced agricultural land management. Rainfall forecasting is a challenging job in reality, and the findings must be precise. Rainfall forecasting is a major problem because of the unreliability of existing methods for predicting rainfall. The purpose of this paper to build an accurate model for the daily prediction of the rainfall in Australia using Python In order to find the optimal model based on testing accuracy, four machine learning algorithms are utilised for training and testing (Logistic Regression, Gaussian Naive Bayes, XGboost classifier, and Random Forest). The data was collected at a variety of meteorological stations around Australia using Kaggle. This data collection has 145460 records and 22 attributes. As a result, in order to find the best suitable technique, a comparative study was undertaken after applying four methodologies. Furthermore, a variety of techniques are used, including multiple linear regression and support vector regression, which may provide the most accurate results (up to 78 %). We observed that our model is less accurate when we compared it to this study. With an accuracy of 84.61 %, the XGBoost classifier surpasses rival approaches. There are several misconceptions, according to the matrix. It will be necessary to combine diverse methodologies in the future to increase prediction accuracy. In addition, new datasets are being used, and new areas are being explored. [ABSTRACT FROM AUTHOR]

Rainfall data is very useful information for agricultural communities, especially those that depend on rainfed irrigation systems. The availability of information regarding rainfall predictions will help the community to make plans and avoid crop failure. Rain cycle can be predicted using rainfall data; however, rainfall data is non-periodic data. The study of prediction of non-periodic phenomena could be discussed using the Fourier approach, especially the Fourier transform. This study aims to predict rain cycle in the district of Lima Puluh Kota. The data in this study were collected from online data published by Meteorology, Climatology, and Geophysics Agency (BMKG) with the Minangkabau Meteorological Station as reference station. Analysis was conducted on rainfall data from January 2010 to December 2020 (132 months). The study results show that the rain cycle period in the district of Lima Puluh Kota ranges from 9 to 10 months. This study explains the rainy season pattern that occurs in the district of Lima Puluh Kota, shifted. Besides, these results also explain the change of the rainy season in the last decade, that started from September to February to be less short. [ABSTRACT FROM AUTHOR]

Finding a reliable computational technique for determining pan evaporation (Ep) may be helpful in the assessment and application of techniques for the development of organic agricultural systems and management of water resources. In this study, monthly evaporation (Ep) was estimated utilising a K-Nearest Neighbours (KNN) model, with monthly climatological statistics obtained from a Malaysian weather station used for training and verifying the model, including climatic factors such as maximum and minimum temperature, mean temperatures, relative humidity, solar radiation, and wind speed, for the period 2000-2019. Several models were devised utilising different combinations of input components and other model factors, and the performance of the devised model was evaluated employing standard statistical indices. The outcomes of evaluation in the testing stage for the final suggested KNN-6 model were R2= 0.946, MAE=0.085, RMSE=0.115, RAE=0.211 and RSE=0.053. The results of the investigations in terms of various performance evaluation criteria highlighted that the proposed KNN structure can model the monthly evaporation losses with reasonable accuracy and thus be used to help local interested parties in discussing the ongoing management of water resources. [ABSTRACT FROM AUTHOR]

This study aims to determine the variability of rainfall on the north coast of Java, namely the areas of Cirebon, Tegal, and Semarang. Daily rainfall data for the period January 2017-June 2021 from three meteorological stations were used to analyze rainfall periodicity. Fast Fourier Transform (FFT) is proposed to convert data from time series domain to frequency domain. Rainfall data during the period 1991-2020 were used to identify maximum-minimum variability, rainfall anomalies, and wavelet analysis. The results showed that the maximum rainfall in Cirebon, Tegal, and Semarang occurred in the northwest season (November to February), which is indicated by the FFT diagram with a period of 341 days. The shift in rainfall occurred in the Tegal region, with the maximum peak shifting from the December-January-February (DJF) season to the June-July-August (JJA) season in 2001-2015. The 2015-2018 period shows minimum rainfall events in response to the 2015/16 Super El Niño event. El Niño signals were found in 1991-1994, 2010-2012, and 2015-2020, while La Niña signals were seen in 2007 and 2010. [ABSTRACT FROM AUTHOR]

The paper analyzes the maximum discharge, and in some cases, the floods caused by them, observed in the Ijevan section of the Aghstev River Basin during the period of 2010-2020 and an attempt was made to predict the expected floods based on the forecast of a high-resolution digital weather forecast, WRF precipitation, temperature, and other elements. The observation data of the Ijevan hydrological observatory of the Aghstev River were used for the work, and the actual meteorological values were taken from the observations of the Ijevan meteorological station. [ABSTRACT FROM AUTHOR]

This paper is collaboration between BMKG and the University of Mataram to mitigate landslides by utilizing C-band radar data and rain gauges in measuring rainfall as the main factor triggering disasters. In heavy and prolonged rains, there is a chance for disasters to occur. Based on the observations of the Lombok International Airport Meteorological Station (-8°76'59"LS and 116°27''91"E), the 24-hour cumulative rain on January 30, 2021, the analysis results of 64 mm/h can be included in the category of extrime rainfall. [ABSTRACT FROM AUTHOR]

Environmental condition is one of the parameters that cannot be neglected in the measurement or calibration activity in the calibration and testing laboratory, especially when it affects the measurement results. Therefore, according to the ISO/IEC 17025:2017 Clause 6.3.3, laboratory must perform the monitoring, recording, and controlling of the environmental condition to meet its requirement. This paper discusses the development of EGS-PWS (Piston Weather Station) Version 2.0 based on Internet of Things (IoT) application with NodeMCU, MicroPython programming, and MQTT (Message Queuing Telemetry Transport) communication protocol, to perform the data acquisition of environmental condition and automatic temperature control in SNSU-BSN. NodeMCU ESP32 as the node sensor or publisher, in which is integrated with the DHT22, BMP180, and MAX31865 module sensor, is able to read and publish the environmental condition data to the ThingSpeak® as the IoT Platform and MQTT Broker. Meanwhile, the NodeMCU ESP8266 as the subscriber of the "field1" or room temperature topic, is able to control the room temperature automatically with relay module and air condition (AC) remote control as the actuator, without the need of waiting the instruction or command from the user remotely. With the initial room temperature of 25.6 °C, the result shows that the EGS-PWS Version 2.0 is successfully demonstrated to read and publish the environmental condition data to the ThingSpeak channel of the user, able to control the increasing or decreasing room temperature of 1°C in each updated message input in "field1" for a specific time interval, and meet the room temperature requirement of (22 ± 1) °C with 100% control accuracy in SNSU-BSN. The respond system characteristic shows that the obtained rise time and settling time are 59 minute 19 second and 1 hour 3 minute 27 second, respectively. Therefore, it can be concluded that the EGS-PWS Version 2.0 is possible to be implemented in all of the calibration and testing laboratory. [ABSTRACT FROM AUTHOR]

The sustainable environment is the ideal solution to reduce the impact of climate changes. Daily and continuous data without interruption is a key factor for studying the behavior and variability of atmospheric elements and knowing the extent of the impact of climate changes on them. Therefore, this study aims to find the best way to estimate the missing values of the daily mean temperature for three adjacent agricultural stations in Najaf Governorate for (2014 ,2015). After conducting a Spatiotemporal analysis of the monthly mean temperature, it was found that stations (Abbasiya, Shabja and Meshkhab) is variable from year to year, and it is closer between two stations (Abbasiya and Shabja) than their proximity to the station (Meshkhab) because of their similar agricultural nature. The daily mean for January for the three stations is very close, with a correlation coefficient of (.97). As for the daily mean for the month of July for the three stations, it varies significantly, with a correlation coefficient ranging (.68-.87). The results showed that for convergent values, the method of compensating the mean value before and after the missing value gives very good results in the event that the values are close, but in the divergent values, it gives a difference of up to 2 °c. The method of compensating the values from the neighboring stations is very good for January because of the convergence of the values of the stations for this month. As for July, this method gives medium to good results due to the temperature variation between the stations. The monthly mean compensation method gives inaccurate results due. [ABSTRACT FROM AUTHOR]

The global atmospheric change has initiated a state of continuous retreat for these glaciers. Data in substantial amount is required to understand the glacier dynamics but lack of weather stations in such inaccessible locations, poses numerous challenges for glaciologists. To overcome such difficulties present study employs remotely sensed data. Further in study velocity maps are derived indicating velocity behavior in different zones of glacier. Present study takes advantages of high precision orthorectification, co-registration and correlation technique and further utilizes integrated form of Glen's flow law to estimate glacier thickness. Using correlation techniques, two scenes from 'different periods' are compared to determine glacier displacement over time. The data used is LANDSAT TM and ETM+ data with a spatial resolution of 30 meters. The mean annualvelocity for the Zing-zing-bar glacier for year 2004-2009 is 12.31 ma-1 and the mean annual Ice thickness 85m [ABSTRACT FROM AUTHOR]

The article considers fluctuations in the resources of heat and moisture supply of the Republic of Turkmenistan based on the data of the Hydrometeorological Service of the Ministry of Agriculture and Environmental Protection of the Republic of Turkmenistan for the period of the beginning of the 21st century. Average annual temperatures and precipitation were analyzed for 34 meteorological stations. The results obtained made it possible to identify five agro-climatic zones of heat and moisture supply in the study area according to the conditions of the thermal regime and moisture regime. At the same time, the leading factors that determine the regimes of moisture and precipitation are global, as well as azonal factors (mountain systems and highlands of Iran, the proximity of the Caspian Sea and the deserts of Central Asia). The nature of moisture supply is determined by the value of the Selyaninov HTC. The analysis showed that the entire territory of Turkmenistan is currently located in a very arid climate zone with a coefficient of less than 0.5. [ABSTRACT FROM AUTHOR]

Hydrological analysis in an area requires rainfall data. The problem is that the availability of rainfall data is very limited due to the lack of rainfall measuring stations in an area. One solution to overcome this problem is by utilizing daily rainfall products from the Tropical Rainfall Measuring Mission (TRMM 3842) satellite. To support the use of these products in an area, it is necessary to test the accuracy of the data to determine the characteristics of the resulting error. The purpose of this study is to evaluate and validate TRMM satellite-based precipitation products using the extreme rainfall data measured in the field at two rainfall stations of the Kendari Meteorological, Climatological, and Geophysics Agency. Data validation for satellite-based precipitation products TRMM was performed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). The results of this study indicate that the TRMM satellite-based precipitation products have good accuracy and can be used to estimate rainfall in areas not covered by ground gauged stations. [ABSTRACT FROM AUTHOR]

This research aimed to examine the thermal comfort performance of students in naturally ventilated secondary schools' classrooms in Makassar. The analysis compared the thermal balance and the adaptive model. The study analyzed data gathered from surveys conducted at eight secondary schools and local weather stations. The data consists of the measured indoor thermal environment of 48 classrooms. The personal data, users' perceptions, and adaptive behaviors of 1,594 students were recorded in the questionnaire sheets. The questionnaire also recorded the thermal perception of students about the thermal environments of classrooms. In terms of thermal sensation, the study found that the neutral temperature estimated by PMV was overestimated the neutral temperature developed by actual votes of students. The PMV model estimated the neutral temperature of students at 23.0 °C which was about 7 °C lower than the neutral temperature calculated using the students' actual thermal sensation votes (30.2 °C). This was a very big discrepancy between the predicted and the actual. The adaptive thermal comfort model produces more appropriate results than the PMV model. The modified Humphrey's model predicted more close neutral temperature to the actual one which was 29.1 °C. Students adapt to the thermal environment through several activities. This adaptation makes students remain comfortable even in the hot thermal environment. [ABSTRACT FROM AUTHOR]

Currently bioclimatic architecture is booming, it focuses on the design and construction of buildings considering the interrelation with the climate, taking into account the different climatic conditions of each place, region or country, taking advantage of the elements and factors of the climate. The purpose of the study is to determine the areas of cold, comfort and heat throughout the year, for this it is proposed to carry out the Bioclimatic High of the Province of Azuay. To achieve this objective, the statistical analysis of the annual climatological data collected from the meteorological stations located in the province of Azuay was carried out, the data correspond to and belong to the National Institute of Meteorology and Hydrology (INAMHI). To analyse the hygrothermal comfort, we proceeded to calculate the averages of maximum and minimum temperatures, as well as the average of the relative humidity of each station under study, these data are condensed in the Biosol software, and applying the Morillón (2004) methodology the diagram of air conditioning isorequirements is obtained where the cold, comfort and heat zones of each station located throughout the territory of the province of Azuay are identified. As a result of this process, monthly maps are obtained, they were prepared through the geographic information system (GIS) Arc GIS. The maps obtained allow different construction professionals such as engineers, architects, urban planners and the general public, to obtain reliable climate information to plan, design and build sustainable projects that respond to the specific climatological and comfort needs of the province of Azuay. [ABSTRACT FROM AUTHOR]

The presented work deals with assessing the influence of a green roof on the temperature course in individual rooms of a timber structure by the so-called dynamic numerical modelling method, which is based on the principle of non- stationary differential Schmidt method. Climatic data from the meteorological station at Brno-Tuřany Airport were used as boundary conditions for the calculation. [ABSTRACT FROM AUTHOR]

Several statistical distributions are used in generating synthetic temperature data such as normal, gamma and others distribution. However, selecting a best marginal distribution from a variety of distribution is time consuming. If the marginal distribution is misidentified, it led to the underestimation of multivariate model. Hence, this synthetic temperature data failed to represent the actual temperature data. Therefore, a more accurate and general application of probability distribution and a study on how to reduce the discrepancy between variance of observed and synthetic data are needed. In this study, the values of the synthetic variances are observed by incorporating the appropriate level of dependence among the individual monthly amount. Three selected meteorological stations which are Alor Setar, Bayan Lepas and Chuping are used from January 1994 to December 2017. The gamma distribution is proposed to fit the marginal distribution which will be used later to transform the data to uniform unit formed. The correlation coefficient is calculated. Next, the synthetic data will be generated using skew-t copula and the correlation coefficient will be noted. The correlation coefficient of the observed data will be matched to the correlation coefficient of the synthetic data. The result shows gamma distribution is suitable for modelling the marginal distribution of the temperature data. It is found that the skew-t copula can be used to generate synthetic temperature data that is close to the observed data with strong correlation values between the temperature stations. It also shown that the skew-t copula is ideal for modelling synthetic temperature data for strong correlated stations. The synthetic generation of temperature data is important in cases where data is limited or unavailable. The copula model is also expected to reflect approximately real situation of the temperature data in Malaysia that can support flood risk management. [ABSTRACT FROM AUTHOR]

Based on the data of environmental monitoring stations and meteorological stations in typical cities in the Bohai Rim region in 2019 to 2020, The air quality in 2020 was better than that in the same period in 2019. Ozone (O3) had surpassed PM2.5 pollution as the primary pollutant for two consecutive years. The overall meteorological conditions in 2020 were relatively unfavorable. The conditions for the horizontal diffusion of pollutants were basically the same as in 2019. The relative humidity and stable weather index were higher than the same period in 2019. But the height of the mixed layer was lower. Under the overall unfavorable weather conditions throughout in 2020, the PM2.5 concentration decreased year- on-year, which indicated that the air pollution prevention and control measures had achieved remarkable results. [ABSTRACT FROM AUTHOR]

The daily average wind speed at sixteen meteorological stations in Malaysia are modeled using first and second order Markov chains. The data are classified into six scales of Beaufort number. Markov chains are used to determine the probability of the changes on the wind speed from one scale to another. The results of the limiting probabilities show that most of the stations experience the highest probabilities at the states of light air and light breeze for two and three consecutive days. The path diagram indicates that the stations are accessible, communicate and irreducible. It is observed that Mersing, Kota Bharu and Kuala Terengganu stations are potential locations for small turbines as the stations would likely to experience a strong wind compared to the others. [ABSTRACT FROM AUTHOR]

The majority of flood cases in Jakarta are caused by exceeding water in Ciliwung River Basin, the most high-risk basin for flooding in Indonesia. Rainfall characteristics of each flood phenomenon are distinct, as well as the rainfall scheme during the flood in Jakarta on January 1st, 2020. The aim of this research is to understand the characteristics of rainfall triggering floods in Jakarta and its surroundings on January 1st, 2020. A comparison analysis between those characteristics with climatology condition and extreme threshold of precipitation is performed. The composite of hourly-rainfall data from Global Satellite Mapping of Precipitation was analyzed to understand the climatology/general characteristic of rainfall in Ciliwung's upstream, midstream, and downstream. Moreover, analysis of rainfall was performed using observational-rainfall data from weather stations (during the event) and hourly rainfall data from satellite (GSMaP) (for calculating rainfall during the event and the extreme threshold within 2001-2020). The composite analysis results in a diurnal pattern of rainfall shown in the upstream during December-January-February (DJF) and a semidiurnal pattern of rainfall in the midstream and downstream especially during January and February. During the flood on January 1st, 2020, there is a semidiurnal pattern along the watershed with the first peak of rainfall intensity reaching 30 mm/hour at 16.00–18.00 Local Time (LT) and the second peak from night until early morning with lower rainfall intensity. The first peak of rainfall intensity is exceeding the extreme precipitation threshold in the midstream and downstream. The Hovmöller diagrams reveal a propagation of rainfall from upstream to downstream from December 31, 2019, in the afternoon to January 1st, 2020, in the morning. Generally, a semidiurnal rain propagating from upstream to downstream with intensity exceeding the extreme precipitation threshold is objected to be the leading cause of flood in Jakarta and surrounding on January 1st, 2020. The results can be used to increase the awareness of rainfall patterns in Ciliwung River Basin as one of the mitigation measures for flooding in Jakarta and its surroundings. [ABSTRACT FROM AUTHOR]

A measurement of physical quantities requires the right technique to do it. This is done to obtain the characteristics of the system and an accurate measuring sensor. With accurate measurements, the quality of a sensor or measurement system can be known precisely. Educational method about measuring and calibrating a measuring instrument and control require practical and relevant media to be implemented directly in the field. This article discusses the devising of an Internet of Things (IoT)-based system to measure, read and process the physical quantities of weather conditions. The weather conditions mentioned are; temperature and humidity, intensity of sunlight, rainfall, also wind speed and direction. The reading of these quantities was carried out with analog and digital sensors integrated with the ESP 8266 microcontroller. This sensory system was placed in the field station. The results of reading and processing on the microcontroller are uploaded to the online server. A client system, called a base station, requests periodic sensor data to the server. The results of data acquisition are then processed again in Raspberry Pi media to be displayed in layers and stored in Excel form. The results of this study can be used for calibration media analog and digital sensors that can measure the quantities measured by weather stations. Stored data can also be used as a learning medium Measurement analysis and characterization of measuring instruments. [ABSTRACT FROM AUTHOR]

Air pollution is one of the greatest environmental evils. The Multi-Plume industrial source complex Short-term Period Regulatory (MPC-SPR) model is an alternate model to EPA and it is exclusively developed for regulatory purposes in India. The MPC-SPR model is based on a Gaussian Plume dispersion model from continuous elevated point sources. The processes as considered in the models are similar to ISCST3. The data requirements for dispersion analysis consist of three important parts: the emission inventory, the meteorological data and the monitoring data. A detailed emission inventory has been prepared by gathering data such as stack height, stack diameter, stack temperature, velocity, and rate of discharge of SO2 from the industries. Emission inventory has been Neyveli prepared data gathered from CARD such as stack coordinate, stack height, exit stack diameter and emission rate of SO2. The Meteorological data such as wind speed and Wind direction were observed from the Meteorological observatory at Vadalur. The hourly meteorological file to the MPC-SPR models have been prepared, which is similar to that of the ISCST3 model. The MPC-SPR Model was used to simulate the dispersion pattern of SO2 in the month of January 2019 by considering the emission from all the industrial stacks situated within the study area. [ABSTRACT FROM AUTHOR]

The amount of greenhouse gas emissions released during the combustion of fossil fuels directly affects the global temperature in the recent period. The assessment of future climate projections is necessary to plan mitigation and adaptation efforts in various sectors. For global warming studies, different scenarios based on the rate of radiative forcing and mitigation efforts in the future had been prepared as RCP or the Representative Concentration Pathways. In this study, two of the four scenarios will be used to project the future changes of climate parameters – the RCP8.5 scenario, where no mitigation effort is taken into account and the RCP4.5 scenario, which assumes the business-as-usual will still take place in the future. Observational data and Global Circulation Model (GCM) output from the Coupled Model Intercomparison Project 5 (CMIP5) are used to assess the future climate projection. The historical series of 29 models from GCM (Global Circulation Model) will be evaluated using daily observational data from 70 meteorological stations in Indonesia for 20 years period (1986 – 2005) based on the similarity of its spatial and temporal patterns. The output of GCMs is bias-corrected before being used in the analysis process, using quantile mapping method. Five models are chosen based on the correlation values to project the extreme climate events over the Indonesia region, using the Expert Team on Climate Change Detection and Indices (ETCCDI), or the extreme climate indices. The indices used are the total annual rainfall (Prcptot), consecutive dry days (CDD), consecutive rainy days (CWD), monthly maximum temperature values (TXx) and monthly minimum temperature values (TXn). Compared to the baseline period (1981 – 2010), most of the extreme climate events will be increased significantly in the future periods (2011 – 2040, 2041 – 2070, 2071 – 2100), except for CDD. [ABSTRACT FROM AUTHOR]

Students are generally sensitive to and curious about changes in their environment and special phenomena, even though they do not want to invest too much energy into acquiring knowledge in this field. However, the school curriculum is not flexible enough to respond to this demand immediately. Therefore, the science teacher should find the appropriate methods to explain these natural phenomena to the youngsters. This paper is based on a project and experiment, which were applied successfully during the past school years with the Science Club students in Székely Mikó High School. Arduino measuring devices were designed and based on these a school mini meteorological station was built. During the project the students gained knowledge about atmospheric physics phenomena and improved their technical skills. [ABSTRACT FROM AUTHOR]

Heavy rainfall prediction is a noteworthy issue for meteorological division as it is intently connected with the economy and life of human. It is a reason for cataclysmic events like flood and dry season which are experienced by individuals over the globe consistently. Precision of rainfall gauging has extraordinary significance for nations like India whose economy is to a great extent subject to agribusiness. Because of dynamic nature of air, Statistical strategies neglect to give great precision to rainfall anticipating. Spatial insertion of downpour check information is essential for Eco hydrology study (or) demonstrating of land corruption. The month to month rainfall information recorded at meteorological stations is investigated to examine the spatial examples of rainfall. The spatial addition strategies was connected to display the spatial inconstancy of rainfall for the quite a long while. The precision of the addition result was estimated by embracing the spatial introduction method (i.e Inverse Distance Weighted). From the outcomes inferred utilizing the spatial interjection, we foresee the rainfall utilizing three algorithms (i.e Support Vector Machine, Random Forest Algorithm, Deep Learning Algorithm). In the wake of getting the outcomes correlations are made between those three calculations. The principle motivation behind the work is guide of specific locale and to learn about the variable factors that are impacting to anticipate the rainfall and from that investigation we look at three changed calculation and demonstrate the outcome which one best to foresee the rainfall. The calculation which predicts the exact rainfall is viewed as the best calculation for anticipating rainfall. [ABSTRACT FROM AUTHOR]

Changing the physical conditions of the Earth's atmosphere include temperature and distribution of rainfall, which has a wide impact on various sectors of human life. According to the Indonesia Food Security Monitoring Bulletin, Komodo Meteorological Station in East Nusa Tenggara (hereafter denoted as NTT) Province in Indonesia has reported that Komodo was the one of first priority in the classification of districts experiencing drought impacts from climate change that occurred in Indonesia. The study aimed to climate projection in Komodo using the Quantile Matching Bootstrap (QMB) method, which is the block bootstrap simulation technique combined with a quantile prediction and matching method for simulating future daily climate data. The projected data is influenced by the relationship of predictors derived from the Global Climate Models to the observed data. The study was carried out by examining daily rainfall and daily maximum temperature observed data from Komodo Meteorological Station. In this paper, the performance of the QMB approach is improved by three different quantiles i.e. 10th, 50th, and 90th percentiles. The result for QMB produces the average coverage rate for the prediction intervals of around 80%. By using out of sample validation techniques, the results show that this method has successfully projected daily rainfall and daily maximum temperature during rainy season as well as transition from dry season to rainy season. [ABSTRACT FROM AUTHOR]