Year: 2022
Type: article
Abstract: Precipitation nowcasting is important for a multitude of applications like the planning and preparation of flights in the aviation industry and also for daily routine management.Existing methods are complex, computationally extensive.Also, inherent model configurations involved in assimilation and simulation of current atmospheric state limit their application in forecasting precipitation for next two to four hours.So, in the proposed paper, the development of a data driven algorithm for precipitation nowcasting using the Automatic Weather Station (AWS) data obtained from Vikram Sarabhai Space Centre, Thiruvananthapuram has been carried out.Three different artificial intelligence (AI) models have been developed based on ensemble learning techniques because of low variance and robustness i.e., bagging and boosting, and the potential of these ensembles viz.Random Forest, Adaboost, and Xgboost have been investigated.The accuracy of Random Forest and Xgboost are comparable and slightly better than Adaboost in predicting the occurrence of rainfall events.The model can capture the instances with good accuracy of 80 % for the Random Forest and Xgboost and 65 % for the Adaboost.Sensitivity of the in-situ observations has been carried out.The results indicate that for the prediction of rainfall, time series of pressure and temperature are found to be more influential than other predictors in all the State-of-the-art, machine learning techniques.The study is important from the perspective to apply as an approach for rainfall nowcasting applications and also can be applied for the other heterogenous and dynamic systems.
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4296504865