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 vapour (PWV) is a crucial atmospheric parameter for initiating rainfall, cloud formation, convection, etc. Nevertheless, PWV is not solely responsible for precipitation, as other atmospheric parameters also play a pivotal role in the occurrence of rain events. Consequently, research on weather forecasts necessitates the investigation of other atmospheric parameters that can improve the forecast of rain events with higher. Gradient is another critical parameter that contributes greatly to analysing the evolution of a weather system. In recent days, machine learning has gained immense importance in the field of meteorology and remote sensing. Various machine-learning models have been used in previous literature to establish an improved algorithm for rainfall prediction. In this paper, we have taken the initial step towards conducting our research for a region to accurately forecast the imminent rain events 6 hours ahead of time. U-net architecture performs better than CNNs, with approximately 80% forecast accuracy, making it a suitable deep-learning model for rainfall prediction in tropical regions. Furthermore, PWV exhibits the greatest impact on rainfall prediction over a region of the various input parameters studied in this article.

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.