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.

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.

In recent years, precipitable water vapor has emerged as a key atmospheric parameter in weather prediction research. However, relying on only one parameter does not always accurately predict rainfall, as other atmospheric parameters also contribute to initiating rain events. In our previous study, we explored a methodology for rainfall forecasting based on the atmospheric delay gradient at an individual station in the tropical region. Commonly referred to as the atmospheric gradient, it accounts for the delay in GNSS signals due to changes in horizontal refractivity while propagating through the troposphere to the ground station. This paper discusses the spatial and temporal correlation of the gradient with precipitation occurring over a region. We have investigated different features, such as gradient magnitude, gradient convergence, and the flux of the atmospheric gradient, to propose potential nowcasting criteria for precipitation in the tropical climatic zone. The atmospheric gradient in the surrounding region orients toward the area of precipitation as the rain event approaches. This gradient gradually alters its orientation and converges toward the region of rainfall at the time of precipitation, introducing the concept of gradient convergence. This phenomenon results in an inward gradient flux at the time of the downpour, which presents a potential parameter for rainfall prediction over an area. We also proposed investigating area sizes of 4° × 4° or 8° × 8°, depending on the gradient feature, to establish a forecasting methodology for rainfall. The weather front in the tropical region begins to initiate 12 h before the actual time of occurrence and advances toward the area where it will have an impact from a more distant location. This article presents a detailed investigation of various features of the atmospheric gradient and its potential to nowcast rainfall for a region, as well as proposes the lead time for long-term weather predictions. Furthermore, a deep neural network has been employed to predict rainfall events for the next 6 h over an area in the tropical region.

This paper presents a GPS-derived slant water vapor technique for cloud monitoring in Singapore. The normalized slant wet delay ($SWD$) and slant water vapor ($SWV$) are introduced. The suitability of the normalized $SWV$ over $SWV$ for cloud monitoring is demonstrated, as it is not very sensitive to the satellite elevation angle. For better illustration and representation of the spatial distribution of the normalized $SWV$, the skyplot is discretized into different cells based on the azimuth and elevation angles to produce the spatial plot. The spatial plots are analyzed for cloud monitoring and compared alongside the sky images. The results show that the spatial plots of normalized $SWV$ are generally consistent with the cloud formation observed in the sky images, hence demonstrating their usefulness for cloud monitoring. The probability distribution of the normalized $SWV$ associated with cloudy and clear sky conditions is also analyzed, which shows that the mean values of normalized $SWV$ associated with the former are higher. Finally, the time series of the normalized $SWV$ is explored in relation to the solar irradiance. It is shown that the time series and spatial plots of normalized $SWV$ are also consistent with the ratio of clear sky to measured irradiance.

Precise rainfall estimation is highly essential for investigating water availability, evaluating weather hazards, and understanding rapid climate variations in urban ecosystems. Accurate runoff response is crucial for land use planning, which requires high spatiotemporal precipitation observations, particularly in urban hydrology for groundwater management and the design of efficient drainage systems. Although a rain gauge provides accurate rainfall measurements at a particular location on the surface, it often lacks the spatial extent of rainfall distribution, depending on the gauge network and the complexity of the terrain. Moreover, the rain gauge accumulates the rainwater and records an observation until the minimum threshold of 0.2 mm for rainfall detection is reached, which might miss the precise starting time of the rain event. The Weather radar provides a higher spatiotemporal resolution compared to rain gauge monitoring, which tracks precipitation over a larger region at regular spatial and temporal intervals, with an estimate of instantaneous rainfall intensity. X-band weather radar satisfies the need for higher spatiotemporal observation with more accurate rainfall estimates for precise runoff modelling in comparison to S and C-band radars, but at the cost of greater signal attenuation due to its larger operating frequency. X-band radar suffers from the limitation of overshooting for low-lying clouds relative to its sampling volume, which worsens with the increasing range in proportion to the radar elevation angle. X-band radars are also prone to errors resulting from non-meteorological echoes, reflections from ground clutter, and the cone of silence above the maximum elevation angle that causes the rain cells looming above the radar antenna in the zenith direction to remain undetected by the weather radar. Micro rain radar (MRR) is a vertically pointed, specialised, low-cost radar that can continuously measure the drop size distribution and, hence, rainfall rates at different vertical ranges with high resolution. MRR provides fine-scale vertical rainfall characteristics, which can effectively adjust the X-band radar estimates for vertical layers at various elevation angles.In this research, we have developed a model to perform the bias correction in the rainfall rates of X-band weather radars using the MRR rainfall observation as ground truth. A feed-forward neural network is implemented to improve precipitation estimates from X-band weather radars, utilising rainfall rate, horizontal reflectivity, and specific differential phase as input features. The MRR observations from multiple range gates are averaged over the vertical extent of the X-band radar beam in order to align with the mean rainfall rates from X-band weather radar. The MRR rainfall estimate is pre-processed to mitigate bias by removing outliers caused by evaporation/wind effects, melting particles, or non-meteorological objects, and further verified against collocated rain gauge observations to identify days with actual rainfall events. Thereafter, the rainfall rates at different altitudes from the X-band weather radar nearest to the MRR location are fed to the neural network model as inputs, while the averaged MRR observations from the corresponding range gates are used as the ground truth for training the model. This approach enables bias correction and improves precipitation estimates, particularly across vertical atmospheric layers.