A look at the mathematics of radar-based nowcasting
DOI:
https://doi.org/10.5902/2179460X87224Keywords:
Mathematics, Image processing, Nowcasting, Radar DataAbstract
Short-term weather prediction systems that rely on radar information, known as nowcasting, involve multiple stages of numerical computation and visualization. The algorithms utilized encompass geometric transformations, statistical analysis, image processing, scalar and vector field calculations, and numerical computations based on various mathematical models. Current computer software used for nowcasting, which depends on data and images, requires a deep understanding of fundamental geometry, calculus, algebra, and various mathematical principles from both users and developers. This paper aims to provide a concise and straightforward overview of the process of handling weather radar data for visualization and nowcasting, while also delving into the mathematical principles underpinning these techniques. As a result, the application of mathematics topics covered in undergraduate courses was presented, in the context of their practical use in precipitation and severe events nowcasting systems.
Downloads
References
Cui, X., Chen, M., Qin, R., Li, C., and Han, L. (2023). The roles of surface convergence line and upper-level forcing on convection initiation ahead of a gust front: A case study. Journal of Geophysical Research: Atmospheres, 128(3):e2022JD036921. DOI: https://doi.org/10.1029/2022JD036921
Guo, S., Sun, N., Pei, Y., and Li, Q. (2023). 3d-unet-lstm: A deep learning-based radar echo extrapolation model for convective nowcasting. Remote Sensing, 15(6):1529. DOI: https://doi.org/10.3390/rs15061529
Heistermann, M., Jacobi, S., and Pfaff, T. (2013). An open source library for processing weather radar data (wradlib). Hydrology and Earth System Sciences, 17(2):863–871. DOI: https://doi.org/10.5194/hess-17-863-2013
Helmus, J. and Collis, S. (2016). The python arm radar toolkit (py-art), a library for working with weather radar data in the python programming language, j. open res. softw., 4, e25. DOI: https://doi.org/10.5334/jors.119
Heymsfield, G. M., Ghosh, K. K., and Chen, L. C. (1983). An interactive system for compositing digital radar and satellite data. Journal of Applied Meteorology and Climatology, 22(5):705–713. DOI: https://doi.org/10.1175/1520-0450(1983)022<0705:AISFCD>2.0.CO;2
Imhoff, R. O., De Cruz, L., Dewettinck, W., Brauer, C. C., Uijlenhoet, R., van Heeringen, K.-J., Velasco-Forero, C., Nerini, D., Van Ginderachter, M., and Weerts, A. H. (2023). Scale-dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open-source pysteps library. Quarterly Journal of the Royal Meteorological Society. DOI: https://doi.org/10.5194/egusphere-egu22-7026
Liu, P., Yang, Z., Wang, X., Qiu, X., and Yang, Y. (2022). Assimilation of the pseudo-water vapor derived from extrapolated radar reflectivity to improve the forecasts of convective events. Atmospheric Research, 279:106386. DOI: https://doi.org/10.1016/j.atmosres.2022.106386
Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In IJCAI’81: 7th international joint conference on Artificial intelligence, volume 2, pages 674–679.
Misra, S. and Wu, Y. (2020). Chapter 10 - machine learning assisted segmentation of scanning electron microscopy images of organic-rich shales with feature extraction and feature ranking. In Misra, S., Li, H., and He, J., editors, Machine Learning for Subsurface Characterization, pages 289–314. Gulf Professional Publishing. DOI: https://doi.org/10.1016/B978-0-12-817736-5.00010-7
Proesmans, M., Van Gool, L., Pauwels, E., and Oosterlinck, A. (1994). Determination of optical flow and its discontinuities using non-linear diffusion. In Computer Vision—ECCV’94: Third European Conference on Computer Vision Stockholm, Sweden,May 2–6 1994 Proceedings, Volume II 3, pages 294–304. Springer. DOI: https://doi.org/10.1007/BFb0028362
Pulkkinen, S., Nerini, D., Pérez Hortal, A. A., Velasco-Forero, C., Seed, A., Germann, U., and Foresti, L. (2019). Pysteps: An open-source python library for probabilistic precipitation nowcasting (v1. 0). Geoscientific Model Development, 12(10):4185–4219. DOI: https://doi.org/10.5194/gmd-12-4185-2019
Van der Walt, S., Schönberger, J. L., Nunez-Iglesias, J., Boulogne, F., Warner, J. D., Yager, N., Gouillart, E., and Yu, T. (2014). scikit-image: image processing in python. PeerJ, 2:e453. DOI: https://doi.org/10.7717/peerj.453
Wang, Y., Coning, E., Harou, A., Jacobs, W., Joe, P., Nikitina, L., Roberts, R., Wang, J., Wilson, J., Atencia, A., Bica, B., Brown, B., Goodmann, S., Kann, A., Li, P. W., Monterio, I., Schmid, F., Seed, A., and Sun, J. (2017). Guidelines for Nowcasting Techniques.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ciência e Natura

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.
Ethical Guidelines for Journal Publication
The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles.
Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.
In particular,
Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.
Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.
Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.