Abstract
In the present study, a model has been developed for nowcasting using the Automatic Weather Station (AWS) data collected from Thiruvananthapuram, Kerala, India. The Proposed model is based on machine learning techniques: Random Forest (RF), which had been coupled with Principal Component Analysis (PCA). PCA minimizes the presence of multicollinearity issue in the AWS data, which enables the RF to access independent effects of predictors efficiently to predict rainy or non-rainy conditions of the atmosphere for the next 4 hours during the peak summer monsoon of month July. The sensitivity and feasibility of the model were tested for different predictors such as wind speed, temperature, pressure, relative humidity, sunshine, and rainfall, where the demarcation between rainy and non-rainy events was computed using a precision-recall curve. The performance of proposed algorithms for rainfall events is evaluated by using different statistics such as accuracy, precision, recall, probability of detection (POD), and false alarm rate (FAR). The proposed algorithm is found to nowcast with an accuracy rate of 90% and the probability of detection is 68%. The analysis of in-situ observations establishes that the most influential predictors for the nowcasting of rainfall are atmospheric pressure and wind speed.
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The pre-processed data used in this study, could be made available if requested.
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Acknowledgments
The authors thank Editor-in-Chief and reviewers for their constructive comments to increase the impact of research. The authors acknowledge Space Applications Centre (SAC), Ahmedabad ISRO as data for the study was downloaded from the website www.mosdac.gov.in and the two anonymous reviewers for their constructive suggestions for the betterment of the study.
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Conceptualization: Bipasha Paul Shukla and Anupam Priamvada; Methodology: Anupam Priamvada.
Formal analysis and Investigation: Anupam Priamvada; Writing—original draft preparation: Anupam Priamvada; Writing—review and editing: Bipasha Paul Shukla and Nita H. Shah; Resources: Bipasha Paul Shukla and Nita H. Shah.
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Communicated by: H. Babaie
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Shah, N.H., Priamvada, A. & Shukla, B.P. Random forest-based nowcast model for rainfall. Earth Sci Inform 16, 2391–2403 (2023). https://doi.org/10.1007/s12145-023-01037-0
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DOI: https://doi.org/10.1007/s12145-023-01037-0