Articles | Volume 19, issue 20
https://doi.org/10.5194/amt-19-6781-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Research article
| 
28 Oct 2026
Research article |
| 28 Oct 2026
| 28 Oct 2026
Enhancing weather radar data by removing non-meteorological echoes, using neural networks trained on synthetic weather data
Related authors
EGUsphere, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Atmos. Meas. Tech., 19, 1711–1727, 2026
Atmos. Meas. Tech., 17, 4695–4707, 2024
Geosci. Model Dev., 15, 291–313, 2022
Atmos. Chem. Phys., 21, 17291–17314, 2021
Cited articles
Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., Hirsh, B., Huang, S., Kalambarkar, K., Kirsch, L., Lazos, M., Lezcano, M., Liang, Y., Liang, J., Lu, Y., Luk, C. K., Maher, B., Pan, Y., Puhrsch, C., Reso, M., Saroufim, M., Siraichi, M. Y., Suk, H., Suo, M., Tillet, P., Wang, E., Wang, X., Wen, W., Zhang, S., Zhao, X., Zhou, K., Zou, R., Mathews, A., Chanan, G., Wu, P., and Chintala, S.: PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation, in: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 (ASPLOS 2024), edited by: Gupta, R., Abu-Ghazaleh, N. B., Musuvathi, M., and Tsafrir, D., ACM, 929–947, https://doi.org/10.1145/3620665.3640366, 2024. a
Atanbori, J., Frantzidis, C. A., Al-Khafajiy, M., Aliyu, A., Sohani, B., Appiah, K., Moore, H., Sanders, C., and Ward, A. I.: Learning with noisy labels for classifying biological echoes in polarimetric weather radar observations using artificial neural networks, Neurocomputing, 634, 129892, https://doi.org/10.1016/j.neucom.2025.129892, 2025. a
Bauer, S., Tielens, E. K., and Haest, B.: Monitoring aerial insect biodiversity: A radar perspective, Philos. T. Roy. Soc. B, 379, 20230113, https://doi.org/10.1098/rstb.2023.0113, 2024. a
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.: Hydrometeor classification through statistical clustering of polarimetric radar measurements: a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016. a
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler Weather Radar: Principles and Applications, Cambridge University Press, https://doi.org/10.1017/CBO9780511541094, 2001. a
