A quantitative precipitation forecast model using convective-cloud tracking in satellite thermal infrared images and ada
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ORIGINAL ARTICLE
A quantitative precipitation forecast model using convective‑cloud tracking in satellite thermal infrared images and adaptive regression: a case study along East Coast of India Barnali Goswami1,2 · Gupinath Bhandari1 · Sanjay Goswami3 Received: 25 June 2020 / Accepted: 5 September 2020 © Springer Nature Switzerland AG 2020
Abstract The current work addresses issues related to quantitative estimation of precipitation caused by convective clouds, using thermal infrared images, and adaptive regression modeling. The developed methodology has been implemented on the Indian sector during the period 22–25 October 2013. The importance of the developed methodology lies in the fact that the information obtained from it can facilitate further studies intended for the prediction of flood events. This study is the continuation of existing work of identification of convective clouds and the analysis of the Mesoscale Convective Systems (MCS). In the current work, forecast of rainfall in terms of millimeter has been proposed. The entire work has been carried out on thermal infrared (TIR) images obtained from geostationary satellites and the results have been validated by actual rainfall data measured by rain gauges. The results obtained from the developed methodology were found to be fairly close to actual values. Keywords Quantitative precipitation forecast · Convective cloud · Mesoscale convective system · Thermal infrared images · Tracking · Clustering · Meteorology · Satellite image processing
Introduction Generally, heavy precipitation during a short period of time is one of the major influences towards flooding. To efficiently predict a flood event, an adept rainfall prediction system is of paramount priority. The objective of the study is Quantitative Precipitation Forecast (QPF) from convective clouds. The prediction is proposed in terms of millimeters (mm) of daily rainfall. And the entire process is based on detection and identification of Mesoscale Convective Systems (MCS) from satellite images and their extensive study. The output from this study can then be applied to flood prediction models. * Sanjay Goswami [email protected] 1
Department of Civil Engineering, Jadavpur University, Kolkata, India
2
School of Computer Science, Faculty of Science, MIT World Peace University, Pune, India
3
Center for Disaster Preparedness and Management, Jadavpur University, Kolkata, India
The entire study has been categorized into three steps. The first step is to identify the convective clouds in Thermal infrared (TIR) images, as image processing of these images is fast. The convective clouds have been successfully detected by analyzing the gray values of the TIR images in the work by Goswami and Bhandari (2013a, b). The second step is to track the clouds in a series of TIR images, to approximate their future location. This was accomplished by Goswami and Bhandari (2013a, b) by observing the displacement of the clouds in subsequent TIR images. Goswami et al. (2014a) demonstrated how multiple
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