In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal, and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.

In this paper, a simplified latitude and day-of-year (DoY)-based model is proposed for the retrieval of precipitable water vapor (PWV) from global positioning system (GPS) signal. Conventionally, PWV, the total amount of water in a vertical column of a unit cross-sectional area, is estimated from the GPS signal delay and a dimensionless conversion factor PI. This PI value is found to rely on a water vapor weighted mean temperature (T _m ) value which varies widely across the day, month, and year for different regions. It is, therefore, both time specific and site specific. Analysis of the PI value and its effect on the retrieved PWV from the data obtained for tropical, subtropical, and temperate regions show that although the PI value is time and site specific, the change in the median value of PI for different years is minimal and is dependent only on factors like the latitude coordinates of the particular site and the DoY. Therefore, using the data obtained from 174 different sites, a latitude-coordinate and DoY-based PI value model for the retrieval of PWV is proposed in this paper. The proposed model has been successfully validated using data from different databases: the International GNSS Service Global Positioning System National Aeronautics and Space Administration (IGS GPS NASA) database, the International GNSS Service Global Positioning System Global Geodetic Observing System (IGS GPS GGOS) database, and the very-long-baseline interferometry (VLBI) database. Results show strong agreement between PWV values calculated using the proposed model and those calculated using the temperature dependent models with 99%, 98%, and 93% of error within ±1 mm for IGS GPS NASA, IGS GPS GGOS, and VLBI databases, respectively. Moreover, the proposed model allows for the ease of PWV retrieval, which is useful in meteorological studies and also applicable in satellite communications.

Global positioning system (GPS) satellite delay is extensively used in deriving the precipitable water vapor (PWV) with high spatio–temporal resolution. One of the recent applications of GPS derived PWV values are to predict rainfall events. In the literature, there are rainfall prediction algorithms based on GPS-PWV values. Most of the algorithms are developed using data from temperate and sub-tropical regions. Mostly these algorithms use maximum PWV rate, maximum PWV variation and monthly PWV values as a criterion to predict the rain events. This paper examines these algorithms using data from the tropical stations and proposes the use of maximum PWV value for better prediction. When maximum PWV value and maximum rate of increment criteria are implemented on the data from the tropical stations, the false alarm ( $FA$ ) rate is reduced by almost 17% as compared to the results from the literature. There is a significant reduction in $FA$ rates while maintaining the true detection ( $TD$ ) rates as high as that of the literature. A study done on the varying historical length of data and lead time values shows that almost 80% of the rainfall can be predicted with a false alarm of $26.4$ % for a historical data length of 2 hours and a lead time of 45 min to 1 hour.

In recent studies, precipitable water vapor (PWV) has caught interest of researchers in predicting rainfall. However, rainfall depends on several other atmospheric factors which play a vital role in its initiation. With only one atmospheric parameter, the false prediction is high, especially for long-term prediction. In this paper, a new method for rainfall forecasting is proposed using horizontal tropospheric gradient and atmospheric residual which are important weather features. It is observed that the gradient orientation defines the weather front for a larger region, and the gradient slope, gradient magnitude and atmospheric residual play a crucial role in rainfall prediction. The algorithm is based on GPS PWV data from stations in the tropical region. This proposed algorithm obtains an average false alarm and true detection rate of 36.6% and 87%, respectively for a prediction window of 6 hours. The proposed threshold values are found to be similar for three different tropical stations which make the algorithm location independent. The comparison of this approach with several other data suggests that this algorithm is suitable in practical scenario for a long-term rainfall prediction with a better true detection rate and a minimal false alarm rate.