To meet the growing needs of higher data rates for present day communications and multimedia systems, use of electromagnetic spectrum above 10 GHz in microwave and millimeter wave region is an obvious solution. However the signals in this frequency range get impaired by rain that causes serious attenuation especially in tropical and equatorial countries that are characterized by heavy rainfall. A ground based microwave radiometer
Applications of the groundbased microwave radiometer to measure meteorological parameters have been widely accepted for years
Ulaby suggested that the brightness temperature given by ground based microwave radiometer obeyed the radiative transfer equation under scatterfree conditions
The identification of rainy periods is of importance also for the telecommunication systems operating at Ka and Q/V bands, where a major impairment derives from the effect of the lowest layers of the atmosphere on radiowave propagation
High values of power margin can be reduced if a priori information of atmospheric conditions along a propagation path is known to us and favours the adoption of adaptive fade mitigation technique
The idea of short term rain prediction by considering the pronounced increase of brightness temperature from two hours before a rain event in the water vapour channel
The ratio between T_{b}’s appears more suitable as an indicator than the single channel brightness temperature itself to discriminate sky conditions. In fact, a linear relationship between T_{b}’s jointly measured around 20 and 30 GHz exists under clear sky conditions, while it becomes strongly nonlinear in the presence of heavy clouds or rain events
In recent years, a number of novel approaches to the problem have been proposed by authors. Machine learningbased methods have been implemented in
The authors of this paper have used the relationship between T_{b}’s to gather information to discriminate between clear and rainy atmospheric conditions from the output of a radiometer placed at the tropical location of Cachoeira Paulista (22.57 deg. S, 89 deg. W), INPE, Brazil. The ratio between T_{b}’s is seen as the critical factor which can be used to differentiate between different sky conditions and consequently evaluate the occurrence of meteorological phenomena such as rain.
Under clear sky, the brightness temperature T_{b }(K) at a radiometric frequency f_{i} is given by Bosisio
Here a_{i }(Kmm^{1}) and b_{i} (K) are frequency and elevation angle dependent coefficients. The Precipitable water vapour (PWV) is expressed by the term V (mm^{1}).
These coefficients are evaluated by linear curve fitting for a large radio sounding database and concurrent radiative transfer forward modelling by Bosisio and Mallet
In the present work, data for one year (2009) was obtained from Cachoeira Paulista in Brazil, where the variations in meteorological phenomena showed variance equivalent to the observations expected at tropical locations in Asian countries such as India. Thus the specific location allowed for more extensive modelling as well as helped to augment the statistical validity of the results obtained through application of the model. The data obtained was not modified in any manner to ensure generalizability of the results obtained, especially considering the fact that a slight change in the data might result in the loss of significant information. The observed brightness temperatures at the two frequency channels 23.834 GHz and 30 GHz are taken as T_{b}(23.8) and T_{b}(30) respectively and are related in the following equation 2,
Here, c_{0}= T_{b}(30)/ T_{b}(23.8), c_{1}= T_{b}(30) c_{0} T_{b}(23.8)
The choice of frequency 23.834 GHz lies in the fact that it is far away from the pressure broadened water vapour resonance line at 22.234 GHz; hence its independent nature with respect to pressure broadening can help in elimination of any unwanted signal. The other one i.e. 30 GHz lies in weak water vapour attenuation region. It has also been found that the choice of this frequency pair is optimal
So long as the sky remains clear the relation between the brightness temperatures at the chosen frequencies stands linear. The building of cloud and rain events however causes a change in the nature of the relationship
Considering a change in the linear relationship between T_{b}(23.8) and T_{b}(30) when sky conditions change from clear to cloudy, a ratio T_{b}(R) is proposed to discriminate between clear, cloudy and rainy weather.
The time series presentation of brightness temperatures at the two frequencies 23.834 GHz and 30 GHz is shown in
The linear relation between the two brightness temperatures for the said frequencies are studied by plotting the brightness temperatures(K) at two frequencies for a period of more than 225 days along with the entire event of rainy and non rainy period of same length of time and is presented in
The ratio between the temperatures as described in equation 4 along with its time derivative for the time series of the brightness temperatures (K) for the same length of time span are depicted in the
A statistical analysis of the number of rain events over Cachoeira Paulista (CP, 220 S), Brazil during the year 2009 reveals that the total number of rain events observed is above 50 (shown in
Scatter plot of the ratio R and the brightness temperature (K) for rainy events {light rain, medium/heavy rain and all rainy events} are presented in
The regression analysis of light rain (T_{b30}<65) shows the linear relationship corresponding to the regression relation shown below in equation 5.
The correlation coefficient corresponding to the regression relationship depicted in equation 5 is 0.894. Similarly, for medium and heavy rain (T_{b30}>65) the regression relationship has a nonlinear nature, which is shown in the following equation 6.
The correlation coefficient corresponding to the above equation is 0.923.
The plot of the histogram of T_{b}Ratio in the following

T_{b}Ratio 
T_{b} 30(K) 
Clear sky 
0.3210.459 
13.7529.75 
Cloudy sky 
0.4590.768 
29.7565.25 
Rainy sky 
>0.768 
>65.25 
The ratio of radiometric brightness temperatures at two different frequencies seems to be a good tool for identifying the sky status. In this paper brightness temperature data corresponding to 23.8 and 30 GHz is used to determine sky conditions by differentiation of clear, cloudy and rainy sky T_{b }Ratio value ranges of 0.3210.459, 0.4590.768 and greater than 0.768 respectively. Considering the analyzed data, sky status indicator has been associated at clear, cloudy and rainy sky conditions assuming values up to 0.39, between 0.4 to 0.88 and greater than 0.88, respectively. A validation of the said parameter classification capability has been performed using concurrent brightness temperatures at 15 GHz, collected by an independent radiometric unit with the ability to sense emission processes. The validation, although limited, has indicated that the proposed indicator has a very good potential for correctly assessing sky conditions to adjust communication systems accordingly. A significant level of similarity was found in the findings presented in this work and the results obtained in
Sky status is important for designing fade margins for satellite communication systems. Advantages in the use of the sky status indicator for optimal functioning of satellite communication systems are in the easy software implementation of the measurement and sky status determination algorithm and in the online system performance monitoring capability so that dynamic fade mitigation techniques could be designed and systems set up, to contrast possible degradation of a satellite propagation channel due to scattering processes arising from rain. The determination of the brightness temperature ratio as a critical factor in the estimation process is a major contribution of the present work. This allows for greater statistical accuracy of the proposed model with fairly linear time complexity for quadratic model establishment, which can allow distributed simultaneous estimation to be implemented using IoT based devices, in future. The accurate estimation of sky status is therefore made possible. As a consequence, through application of the technique illustrated in this paper, rain events can be accurately predicted from sky status, which in turn can allow communication systems to adjust signal parameters accordingly to mitigate rain related effects. In the future, the authors intend to implement the technique outlined in the work on a distributed platform, with the estimation technique enhanced by the application of low timecomplexity machine learning algorithms, which are expected to significantly increase the efficacy of the proposed technique.