Understanding the effects of atmospheric conditions with respect to microwave propagation and performance is critical to the design and placement of microwave antennas for modern communication systems. Weather data acquisition in the state of Florida is underdeveloped and the published effects of weather on microwave communications are limited to general models based on large regional climate models. The goal of this research is to correlate atmospheric conditions and microwave transmission via the existing Florida Department of Transportation (FDOT) Road Weather Information System (RWIS) network, new Environmental Sensor Station (ESS) sites, and Harris Corporation network management software – Netboss. The microwave radios in the FDOT microwave infrastructure through powerful Netboss scripting tools and options are utilized to record the received signal level (RSL) output of the microwave radios for signal analysis. This RSL data is analyzed and correlated with the acquired ESS weather data to determine basic atmospheric effects on microwave propagation.
Methods for analysis of correlated data include existing atmospheric attenuation models, such as the Global (Crane) and International Telecommunications Union (ITU) models, and empirical methods such as the Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Discrete Wavelet Transform (DWT) and wavelet decomposition, and correlation analysis of each method used. The data is treated as a discrete non-stationary signal. Results do not show a clear correlation between receiver signal level (RSL) and weather parameters for several of the test methods. Testing the correlation and cross correlation of the raw data yielded weak correlation. The simulation of rain attenuation via the ITU model displayed weak insignificant results for the sets of RSL data. The FFT and STFT both incorporate too much noise and distortion to accurately compute a correlation.
Wavelet decomposition shows a strong correlation between several weather parameters and a weak correlation for others. This result confirms the wavelet decomposition analysis and agrees with trends found in the RSL and weather parameters. Further analysis points to multipath fading and atmospheric ducting. During early hours of the morning, reflections from moist surfaces, such as tree foliage and other terrestrial objects, water vapor and dew will cause transmitted signals to reach the receive antenna out of phase, which will cause attenuation or gain while atmospheric ducting will cause gain in the RSL and is visible in the acquired data. It is concluded that weather conditions such as water vapor, mist, and rising fog have an effect on microwave propagation.
