A.A. Kuzmitsky, M.I. Truphanov, O.B. Tarasova, D.V. Fedosenko

Design Information Technologies Center of RAS (Odintsovo, Moscow region, Russia)

One of the key tasks associated with the fast identification of powerful tropical hurricanes, the assessment of the growth of their power, is the formation of such an input dataset, which is based on data that are technically easy and accurately recorded and calculated using existing sources located in the open accessibility.

The presented work is based on the analysis of satellite images as the main data sources, and on weather data as peripheral. An obvious advantage of satellite images in comparison with other sources of data on weather conditions is their high spatial resolution, as well as the ability to obtain data from various satellites, which increases the timeliness and accuracy of retrieving primary information. The developed approach consists in performing the following main interconnected iteratively performed groups of subtasks: calculation of feature points describing the location of individual cloud areas at different points in time by using different descriptors; comparison of the same cloud areas at specified times to analyze the local directions of cloud movements; tracking of cloudiness for specified time intervals; calculation of local features for selected points of cloudiness to recognize the origin and analyze turbulence; the formation of the dynamics of changes in the local area near the trajectory of the point; recognition of primary characteristic features characterizing the transformation of local turbulences into a stable vortex formation; identification of signs of the growing of a hurricane and assessment of the primary dynamics of the increase in its power; generalization and refinement of a priori given features by analyzing similar features of known cyclones.

To detect points, a modified algorithm for finding them has been introduced. To describe the points, additional descriptors are introduced based on the normalized gradient measured for the neighborhood of neighboring points and cyclically changing in the polar coordinate system.

A comparative analysis of the results of applying the created method and algorithm when compared with known similar solutions revealed the following distinctive features: introduction of additional invariant orientations of features when describing characteristic points and greater stability of detecting characteristic points when analyzing cloudiness, identification of cloudiness turbulence and analysis of changes in their local characteristics and movement parameters, formation of a set of generalizing distributions when analyzing a set of moving points for the subsequent recognition of the signs of a hurricane at its initial stages of formation.

The developed approach was tested experimentally in the analysis of hurricanes video recordings and their movement in the Atlantic region for the period from 2010 to 2020. The developed general approach and a specific algorithm for estimating hurricane parameters based on cloud analysis are presented. The approach is applicable for practical implementation and allows accumulating data for detecting hurricanes in real time based on publicly available data for the development of a physical and mathematical model.

Kuzmitsky A.A., Truphanov M.I., Tarasova O.B., Fedosenko D.V. Early recognizing of a hurricanes from public satellite images. Highly Available Systems. 2021. V. 17. № 2. DOI: https://doi.org/10.18127/j20729472-202102-05 (in Russian)

1. Gridin V.N., Smahtin A.P. Fizicheskoe modelirovanie magnitogidrodinamicheskih processov razvitiya moshchnyh tropicheskih ciklonov / Trudy MAI. 2019. Vyp. № 109. S. 7. DOI: 10.34759/trd-2019-109-7 (in Russian).
2. Chen S., Hong Y., Cao Q., Kirstetter P.-E., Yong B., Qi Y., Zhang J., Howard K., Hu J. Wang J. Performance evaluation of radar and satellite rainfalls for Typhoon Morakot over Taiwan: Are remote-sensing products ready for gauge denial scenario of extreme events? J. Hydrol. 2013. № 506. P. 4–13.
3. Ghorbani M.A., Kazempour R., Ahmadi M., Shamshirband S., Ghazvinei P.T. Forecasting pan evaporation with an integrated artificial neural network quantum-behaved particle swarm optimization model: A case study in Talesh, Northern Iran. Eng. Appl. Comput. Fluid Mech. 2018. № 12. P. 724–737.
4. Alpay B.A., Wanik D., Watson P., Cerrai D., Liang, G., Anagnostou E. Dynamic Modeling of Power Outages Caused by Thunderstorms. Forecasting. 2020. № 2. P. 151–162.
5. Lowe D.G. Distinctive image features from scale-invariant keypoints. int. J. Comput. Vis. 2004. № 60. P. 91–110.
6. Shahid Karim, Ye Zhang, Muhammad Rizwan Asif, Saad Ali. Comparative analysis of feature extraction methods in satellite imagery, Journal of Applied Remote Sensing 11(4), 042618 (2017). https://doi.org/10.1117/1.JRS.11.042618.
7. Chang X., Du S., Li Y., Fang S. A Coarse-to-Fine Geometric Scale-invariant Feature Transform for Large Size High Resolution Satellite Image Registration. Sensors 2018. № 18. P. 1360. https://doi.org/10.3390/s18051360.
8. Lin G.-F., Wang T.-C., Chen L.-H. A forecasting approach combining self-organizing map with support vector regression for reservoir inflow during typhoon periods. Advances in Meteorology. 2016. Article ID 7575126. doi:10.1155/2016/7575126.