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Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.

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Abstract-This paper aims to study the influence of sunspot number on High Frequency (HF) radio communications in Peninsular Malaysia for the years 2009 to 2011. Sunspots, which are a natural phenomenon that occurs due to magnetic activities on the Sun's surface, can be counted using smoothed sunspot number (SSN). HF signal propagates through the ionosphere where the ionospheric properties have been ionized by flares and prominences from sunspot number. This has significant effect on the stability of the ionosphere, resulting in the frequencies that can be used for HF communications to vary depending on the time of day, season, year and the 11-year solar cycle. This study was carried out during a period when the sunspot values rose from a low level in 2009 to a much higher level in 2011, making it suitable to observe the influence of sunspot number values on the HF frequencies employed. Maximum Usable Frequency (MUF) was determined based on HF transmission tests that were conducted from April 2009 to September 2011. It was observed that as the SSN values increase, the range of the operating HF frequencies and the numbers of frequencies that can be used also increase. This will, therefore, affect the median frequency that can be used for daily and monthly HF communications.

This paper presents the prediction of hourly Vertical Total Electron Content (VTEC) using a neural network by utilizing the data from a GPS Ionospheric Scintillation and TEC Monitor (GISTM) receiver for six years (from 2005 to 2010) during low to medium solar activity (Sunspot number (SSN) between 0.0 and 42.6). Several network configurations were investigated to observe the effect of the number of neurons, and hidden layers. Overall testing process for several network set-up yielded Root Mean Square Error (RMSE) value of 3 to 7 TECU, absolute error of 2 to 6 TECU and relative error of 8% to 28%.  Testing using April 2010 to November 2010 data (SSN from 8.0 to 25.2) produced RMSE value of 2.95 to 3.88 TECU,absolute error of 2.39 to 3.09 TECU and relative error of 8.11% to 16.18%, which are within the acceptable range.