Rainfall Forecasting Using GPS-Derived Atmospheric Gradient and Residual for Tropical Region

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 10
• Main Authors: Naha Biswas, Anik, Lee, Yee Hui, Manandhar, Shilpa
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Atmospheric residual; Delays; False alarms; Forecasting; Fronts (meteorology); Global Positioning System; global positioning system (GPS); Global positioning systems; GPS; horizontal gradient; Meteorology; Positioning systems; precipitable water vapor (PWV); Prediction algorithms; Predictions; Rain; Rainfall; rainfall prediction; Refractive index; Satellite navigation systems; Slope gradients; Software; Tropical climate; Tropical environment; Tropical environments; Water vapor; Water vapour; Weather
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2021.3131217

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.