Precipitable water vapor (PWV) has caught the interest of researchers for extensive study on weather prediction in recent times. Nevertheless, the prediction of rainfall cannot be solely reliant on a single metric, as other atmospheric factors also play a significant role. Atmospheric gradient has gained little attention in the field of meteorology, especially for the prediction of significant weather events. Moreover, the existing research on rainfall forecasting is mostly focused on an individual station rather than a larger region. This article aims to predict rain events in a certain geographical region instead of a single station, employing a novel methodology for the tropical climatic zone. Different features of the atmospheric gradient, along with PWV, have been rigorously studied in relation to rainfall to determine the potential criteria for predicting rain events over an area. Both gradient convergence and flux shift toward greater positive values even 6 h before the rain event over the rainy region, when compared to the area with no significant rain events, which further increases with the advancement of time. PWV also manifests a similar trend to the atmospheric gradient. In this article, we have proposed a novel dual-parameter algorithm with PWV and either of the two gradient features, which gives approximately 90% true detection (TD) with a much lower false alarm (FA) rate of around 22% for a region of$8 {\mathrm {^{\circ}}} \times 8 {\mathrm {^{\circ}}}$. Furthermore, another two-layer forecasting algorithm has been established, which precisely predicts the location of rainfall for the next 6 h in the tropical region.

Precipitable water vapor (PWV) is a crucial atmospheric parameter in meteorological studies as it evinces a clear correlation with precipitation which makes it incumbent for rainfall prediction. Nevertheless, the rainfall is not solely dependent on PWV, other dynamic factors also initiate the occurrence of rain events. Hence, the research on weather forecast demands the investigation of other atmospheric parameters as well which can better predict the rain events with improved accuracy and better lead time, particularly for tropical region. Atmospheric gradient is another imperative parameter which changes its direction with the movement of impending weather event and provides real atmospheric information. Till date, previous studies have been conducted to predict rainfall for individual stations. Now, we have put a step forward to carry out our research for a region to analyze an imminent weather front and predict it accurately with sufficient lead time. The atmospheric gradient shows a converging nature at the time of precipitation exhibiting the similar behavior as its wet component, however, the hydrostatic gradient increases in magnitude when the rain event starts to occur.

The application of GPS technology has been pervasive in meteorological science besides point positioning for weather prediction with high spatio-temporal resolution. In meteorology, rainfall forecasting is highly imperative to mitigate the destruction of public properties and lives. In recent years, precipitable water vapor has caught the interest of the scientists in the field of research on rainfall prediction. In this article, we have presented the correlation of precipitable water vapor with time and space during the transition of weather condition from a rainy period to dry one or vice-versa. The enhancement in PWV values in a larger region prior to rainfall makes it a potential predictor for rain for a particular area. The amendment in PWV slope with time before precipitation is also significant to forecast the impending rain event. These results substantiate the usefulness of precipitable water vapor as a potential indicator of weather forecasting.

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

In recent years, the research on atmospheric gradient for weather forecasting as well as GPS positioning has significantly increased as it contains real atmospheric information. In this paper, the seasonal dependence of the atmospheric gradient orientation consistent with wind direction has been presented vividly. We have illustrated the gradient time series of four different monsoon seasons for the tropical station Singapore. The tropospheric gradient alters its direction from season to season and mostly remains oriented along the wind flow. Also, the cumulative distribution plot between the abrupt change in gradient and precipitation gives rise to an indicative feature for rainfall forecasting. The results substantiate the fact that gradient can be contemplated as an imperative atmospheric parameter for predicting a weather event for a larger region.

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.

Numerous weather parameters affect the occurrence and amount of rainfall. Therefore, it is important to study these parameters and their interdependency. In this paper, different weather and time-related variables - relative humidity, solar radiation, temperature, dew point, day-of-year, and time-of-day are analyzed systematically using Principal Component Analysis (PCA). We found that four principal components explain a cumulative variance of 85%. The first two principal components are applied to distinguish rain and no-rain scenarios as well. We conclude that all 7 variables have similar contribution towards rainfall detection.

Precipitation is dependent on a myriad of atmospheric conditions. In this paper, we study how certain atmospheric parameters impact the occurrence of rainfall. We propose a data-driven, machine-learning based methodology to detect precipitation using various meteorological sensor data. Our approach achieves a true detection rate of 87.4% and a moderately low false alarm rate of 32.2%.

In this paper, the Precipitable Water Vapor (PWV) content of the atmosphere is derived using the Global Positioning System (GPS) signal delays. The PWV values from GPS are calculated at different elevation cut-off angles. It was found that the significant range of elevation cut-off angles is from 5° to 50°. The PWV values calculated from GPS using varying cutoff angles from this range were then compared to the PWV values calculated using the radiosonde data. The correlation coefficient and the Root Mean Square (RMS) error between the GPS and radiosonde derived PWV decreases with the increasing cut-off angle and the distance between the two. The seasonal parameters also effect the relation between the two.

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 (Tm) 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.

Precipitable Water Vapor (PWV) is a good source to monitor precipitation. It is defined by the amount of water vapor present in atmosphere. Traditionally, radiosondes and microwave radiometers were used to derive PWV. However, these devices have poor temporal resolutions and high operational costs. Therefore, GPS signal delay is now widely used for such purposes. The main aim of this paper is to study relationship between GPS derived PWV and precipitation. We present an analysis which shows that PWV increases before any rainfall event, while it decreases after the rainfall event. We also derive a threshold PWV that detects the occurrence of rainfall, once PWV exceeds the threshold value. PWV and rainfall data of June 2010 and 2011 are used for validation.