Predicting atmospheric humidity and temperature based on satellite signal field strength measurements

Early measurements of rainfall made using radar systems although have been used qualitatively by weather forecasters for more than 40 years, and by some operational hydrologists, acceptance still contain a lot of uncertainties in the quality of the data. As most of the communication satellites (ie: MEASAT) were limitedly used for communication purpose, very few were looking to extend the application of these satellites for other application such as weather forecasting. Furthermore, weather satellite currently present were expensive to buy, thus this project aims to find a cheaper method to forecast weather. Therefore, this work investigates the application of a communication satellite as a new method for predicting the atmospheric temperature and humidity. The chosen satellite was a MEASAT3 operating in the frequency range between 11 GHz and 12 GHz with 120 kW transmitting power. The electric field strengths of the satellite signals were measured using a Prodig-5 TV Explorer instrument. The measurements were carried out daily between 8.30 am to 5.00 pm from September 2012 to March 2014. The one-metre parabolic dish antenna was positioned on the roof top of the Department of Physics building (3° 1' 19.1928'' N, 101° 42' 19.9476'' E) of the Universiti Putra Malaysia. The mean field strength of the MEASAT3 signal (DVBS) during clear sky was 83.6 dBμV. These signal strengths were affected by the ambient temperature and atmospheric humidity. Torrential rain reduced the field strength signal to 75 dBμV, i.e, the threshold detection level of the Low-noise Block Downconverter (LNB). Empirical models to predict the humidity and temperature based on the measured field strengths were established. The humidity and temperature data were obtained from the WeatherWatcher Live software based on the measured data collected at local meteorological station. The accuracies for temperature and humidity predictions obtained from this work were within 5.9% and 5.5% when compared to the real data obtained from Meteorological Department database.

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