Investigating the Impact of Weather Parameters on Signal Strength of Satellite Dish in Enugu Metropolis

Effects of some weather variables on the signal strength of Maloney FM radio, Nasarawa State, Nigeria

In this paper, the impact of atmospheric parameters (atmospheric temperature, pressure, relative humidity and wind speed) and noise temperature on digital terrestrial television signal strength over Karshi area, Abuja, North-Central part of Nigeria has been examined. Measurements were taken for a period of nine months (April 2019 – December 2019) to cover the commencement of rainy period and towards the end of rainy period. The results showed that atmospheric and noise temperature as well as wind speed weakly influenced the signal strength while relative humidity strongly influenced the signal strength. The effect of atmospheric pressure over the signal strength was not consistent during the period of measurement. The signal strength of AIT, NTA, Channels TV and ETV were negatively affected by the atmospheric temperature up to about88.8%, 25.6%, 16.5% and 93.2%, respectively; the noise temperature up to about 85.3%, 13.4%, 5.9% and 90.7% respectively and wind speed by about 89.2%, 26.8%, 16.3%, and 92.4%respectively. However, the signal strength was affected positively by the relative humidity up to about 87.9%, 18.7%, 10.1% and 92.4% respectively, while the atmospheric pressure affected NTA and Channels TV negatively by about 21.3% and 26.8%, respectively and affected AIT and ETV positively by about 22.7% and 26.8%, respectively. Findings from this study will be of help to satellite link designers, policy makers, antenna modelers and other TV transmission equipment designers on how to mitigate the effect of some of the atmospheric components on digital terrestrial signal strength reception in Nigeria.

The modeling of DTTV signal strength (AIT, NTA, ETV and Channels TV) in Abuja and Unity TV, Jos, Plateau State, with atmospheric parameters (temperature, pressure, relative humidity, wind speed and rainfall) as well as noise temperature, from April 2019 to December 2020 have been carried out using the atmospheric parameters measured with an automatic weather station and noise temperature measured with a non-contact infra-red thermometer, with TV signal strength measured with a CATV signal strength meter from 5.00 am – 9.00 pm daily, at 30 minutes interval. The results show that atmospheric temperature (58%), noise temperature (60%) and atmospheric pressure (100%) affect the Received Signal Strength (RSS) negatively, while the wind speed (60%), relative humidity (80%), and rainfall (100%) affect the signal strength positively. The RSS measured in Jos on altitude 1,400m was higher than that in Abuja on altitude 470m which may likely be due to an increase in altitude resulting in a decrease in atmospheric pressure and air density which implies less signal attenuation. It can be inferred that atmospheric parameters and noise temperature have a great impact on the RSS in the study areas. Consequently, the higher signal received in the rainy season compared to the dry season may be due to less dust in the atmosphere as well as the enhanced digitalized television signal and its robustness to interference and signal attenuation compared to the analogue signal. The developed models have low RMSE values; hence will be able to predict the influence of atmospheric parameters on TV RSS in the areas. Generally, the result will be useful to the Nigeria Broadcasting Commission to improve the quality of the signal transmission of digital television broadcasting for the future digital switchover in other parts of the country especially on the choice of antennas on their transmitters. The findings will also be useful to spectrum users such as mobile telecommunication, wireless internet, and radio broadcasting in managing and improving the quality of their services.

Relationship of meteorological factors and human brucellosis in Hebei province, China

Statistical analysis of atmospheric parameters, noise temperature and digital terrestrial television signal strength over Jos metropolis, Plateau state, Nigeria

The present study aimed to determine the plausibility of forecasting the outbreak of diseases based on the weather by analysing the impact of atmospheric temperature and humidity on the occurrence of different diseases. The subjects of the present study were the 30,434 patients who visited the ED in 1 year from 1 February 2005 to 3 February 2006. The present study analysed the correlation between the daily number of patients who suffered from 22 types of traumatic and non-traumatic diseases and the data on atmospheric temperature and humidity provided by the Korea Meteorological Administration. With traumatic disease, the occurrence tended to increase in proportion to the rise in temperature and humidity; whereas with non-traumatic disease, the occurrence tended to increase according to the rise in temperature, irrespective of humidity changes. The research on the impact of atmospheric temperature and humidity on different diseases revealed a high level of distribution of most diseases in an environment with high temperature and humidity. However, in the case of pulmonary diseases and trauma to multiple body regions, the occurrence increased in environments with low temperature and high humidity for pulmonary diseases, and with low temperature and low humidity for trauma to multiple body regions. Most diseases tend to increase in proportion to the rise in atmospheric temperature whereas being less affected by humidity. However, an increase in humidity in an optimum range of atmospheric temperature (12 degrees C or higher) triggers an increase in the occurrence of diseases.

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Abstract. Abdulloh A, Chaiphongpachara T, Laojun S. 2024. Assessing the climatic impacts on abundance of Mansonia annulifera, Ma. indiana, and Ma. uniformis (Diptera: Culicidae) in Central Thailand. Biodiversitas 25: 4736-4744. Mansonia mosquitoes (Diptera: Culicidae) are important vectors for transmitting filarial nematodes, including Brugia malayi and Wuchereria bancrofti, which cause lymphatic filariasis in humans. In this study, we used a Poisson regression model to evaluate the impact of climatic variables atmospheric pressure, rainfall, relative humidity, temperature, and wind speed on the abundance of three Mansonia mosquito species, including Ma. annulifera, Ma. indiana, and Ma. uniformis in Central Thailand. Over the period from November 2021 to October 2022, we collected the mosquitoes in three villages: Rua Village, Wat Khok Ket Village, and Khlong Bang Kae Village using BG-Pro CDC-style traps equipped with dry ice, BG-lure, and an ultraviolet light system, operating from 6:00 PM to 6:00 AM for three consecutive days each month. Our analysis revealed significant associations between several climatic factors and mosquito abundance. For Ma. annulifera, each unit increase in temperature and relative humidity significantly increased mosquito abundance by 83.2% and 16.2%, respectively (p<0.05). In contrast, increases in wind speed, atmospheric pressure, and rainfall correspondingly decreased abundance by 57.6%, 10.3%, and 0.7% (p<0.05). For Ma. indiana, rises in temperature, atmospheric pressure, and rainfall notably increased mosquito counts by 119.3%, 30.1%, and 0.6%, respectively (p<0.05). Conversely, an increase in wind speed led to a significant reduction of 43.2% (p<0.05). In the case of Ma. uniformis, temperature, and rainfall positively influenced mosquito abundance by 114.5% and 0.7%, respectively (p<0.05), while a rise in relative humidity resulted in a 3% reduction (p<0.05). These findings have significant practical implications, providing crucial insights into predicting future shifts in mosquito populations in response to climatic changes, thereby empowering the surveillance and control of mosquito-borne diseases in this region.

The Influences of Key Factors on the Consequences Following the Natural Gas Leakage from Pipeline

Changes in the weather influence symptom severity in Ménière's disease (MD). MD is an unpredictable condition that significantly impacts on quality of life. It is suggested that fluctuations in the weather, especially atmospheric pressure may influence the symptoms of MD. However, to date, limited research has investigated the impact of the weather on MD. In a longitudinal study, a mobile phone application collected data from 397 individuals (277 females and 120 males with an average age of 50 yr) from the UK reporting consultant-diagnosed MD. Daily symptoms (vertigo, aural fullness, tinnitus, hearing loss, and attack prevalence) and GPS locations were collected; these data were linked with Met Office weather data (including atmospheric pressure, humidity, temperature, visibility, and wind speed). Symptom severity and attack prevalence were reduced on days when atmospheric pressure was higher. When atmospheric pressure was below 1,013 hectopascals, the risk of an attack was 1.30 (95% confidence interval: 1.10, 1.54); when the humidity was above 90%, the risk of an attack was 1.26 (95% confidence interval 1.06, 1.49). This study provides the strongest evidence to date that changes in atmospheric pressure and humidity are associated with symptom exacerbation in MD. Improving our understanding of the role of weather and other environmental triggers in Ménière's may reduce the uncertainty associated with living with this condition, significantly contributing to improved quality of life.

The present study shows the effects of environmental conditions (atmospheric temperature, pressure and relative humidity) due to altitude changes on performance, fuel consumption and emissions in a naturally aspirated diesel engine. Due to changes in altitude, the atmospheric conditions are altered, mainly the air density, associated to hydrostatic pressure, temperature profile and humidity and relative nitrogen/oxygen ratio, thus modifying the engine intake conditions. The study considers changes in altitude from sea level to 2500 m above sea level, which are representative of the orographic conditions in Ecuador. As a main part of this research, a parametric study of variation of atmospheric temperature, pressure and relative humidity is carried out in AVL BOOST™, showing the effects on mean effective pressure, fuel consumption and specific pollutant emissions (CO2, NOx, CO and soot). The study considers effects at regional level (change from an altitude to another) and local level (changes in the atmospheric conditions due to local anticyclone or storm, temperature and humidity). The quantitative effects are expressed in the form of sensitivity coefficients, e.g., relative change in an engine output variable due to the change in atmospheric pressure, temperature or humidity. In addition, several global correlations have been obtained to provide analytical expressions to summarize all results obtained, showing the separate effect of pressure and temperature on each engine performance variable.

Evapotranspiration is an important hydrological process and its estimation usually needs measurements of many weather variables such as atmospheric pressure, wind speed, air temperature, net radiation and relative humidity. Those weather variables are not easily obtainable from in situ measurements in practical water resources projects. This study explored a potential application of downscaled global reanalysis weather data using mesoscale modelling system 5 (MM5). The MM5 is able to downscale the global data down to much finer resolutions in space and time for use in hydrological investigations. In this study, the ERA‐40 reanalysis data are downscaled to the Brue catchment in southwest England. The results are compared with the observation data. Among the studied weather variables, atmospheric pressure could be derived very accurately with less than 0·2% error. On the other hand, the error in wind speed is about 200–400%. The errors in other weather variables are air temperature (<10%), relative humidity (5–21%) and net radiation (4–23%). The downscaling process generally improves the data quality (except wind speed) and provides higher data resolution in comparison with the original reanalysis data. The evapotranspiration values estimated from the downscaled data are significantly overestimated across all the seasons (27–46%) based on the FAO Penman–Monteith equation. The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period). There are clear patterns among some weather variables and they could be used to correct the biases in the downscaled data from either short‐term in situ measurements or through regionalization from surrounding weather stations. Artificial intelligence tools could be used to map the downscaled data directly into evapotranspiration or even river runoff if rainfall data are available. This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encouraged. Copyright © 2010 John Wiley & Sons, Ltd.

We present an analysis of the factors which control the seasonal variations of the clear-sky greenhouse effect, based on satellite observations and radiative transfer simulations. The satellite observations include the radiation budget at the top of the atmosphere from the Earth Radiation Budget Experiment and the total column moisture content derived from the Special Sensor Microwave/Imager. The simulations were performed with the SAMSON system described in an earlier paper, using atmospheric temperatures and humidities from operational analyses produced by the European Centre for Medium Range Weather Forecasts. At low latitudes, the magnitude of the clear-sky greenhouse effect is dominated by the strong thermodynamic link between the total column moisture content of the atmosphere and sea surface temperatures, with minimal seasonal variations. In contrast, at middle to high latitudes there are strong seasonal variations, the clear-sky greenhouse effect being largest in winter and smallest in summer. These variations cannot be explained by the seasonal cycle in the total column moisture content, as this is largest in summer and smallest in winter. The variations are controlled instead by the seasonal changes in atmospheric temperatures. The colder atmosphere in winter enhances the temperature differential between the atmosphere and the sea surface, leading to a larger greenhouse effect despite the lower moisture contents. The magnitude of the clear-sky greenhouse effect is thus controlled by atmospheric humidity at low latitudes, but by atmospheric temperature at middle and high latitudes. These controls are illustrated by results from sensitivity experiments with SAMSON and are interpreted in terms of a simple model.

This paper evaluates the performance of localized weather forecasting model using Artificial Neural Network (ANN) with different ANN algorithms in a tropical climate. Three ANN algorithms namely, Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient are used in the short-term weather forecasting model. The study focuses on the data from North-West Malaysia (Chuping). Meteorological data such as atmospheric pressure, temperature, dew point, humidity and wind speed are used as input parameters. One hour ahead forecasted results for atmospheric pressure, temperature and humidity were compared and analyzed and they show that ANN with Levenberg-Marquardt algorithm performs best.

Estimation of global solar radiation using ANN over Turkey