Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images

Ranganayakulu, S. V.; Subrahmanyam, K. V.; Niranjan, Akella

ISBN 10: 6203194816 / ISBN 13: 9786203194814

Editore: LAP LAMBERT Academic Publishing, 2020

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Codice articolo ABLIING23Apr0316110261530

Dati bibliografici

• Titolo: Convective Cell Tracking through Deep Learning based Computer Vision: Python-Based Algorithm for Identification & Tracking of Convective Cells using Doppler Weather Radar Reflectivity Images
• Editore: LAP LAMBERT Academic Publishing
• Data di pubblicazione: 2020
• Lingua: Inglese
• ISBN 10: 6203194816
• ISBN 13: 9786203194814
• Rilegatura: Brossura
• Condizione: New

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Riassunto

The present study developed an autonomous algorithm for the Convective cell Identification and TRAcking (CITRA) using DWR reflectivity images. The CITRA algorithm is implemented in Python using Deep learning technique of Neural Networks. Optical Character Recognition is used in the present study through "Tesseract" which is an unsupervised Neural Network module based on LSTM which analyses the input dimensional pixel array/image and outputs high-level strings. The algorithm runs through the DWR reflectivity image pixel values and recognizes the intensities of the pixels (>=30 dB) and segregates convective cells along with other estimated cell properties such as centroid of the storm, the area covered, distance and direction from the radar centre. The performance of CITRA algorithm was tested on different convective storms and it could successfully identify and track them along with other physical properties of the convective cells. Further, we have demonstrated the potential application of CITRA algorithm on the evolution of convective cells detected within the radar range. Presently, CITRA algorithm takes only reflectivity images as a single input parameter.