,,,- Aalborg University, Faculty of Engineering and Science, Department of the Built Environment, Aalborg, Denmark (jmn@build.aau.dk)
Weather radar nowcasting is a crucial technique in real-time urban hydrological applications, as weather radars provide spatially distributed rainfall measurements. Uncertainties in weather radar nowcasting stemming from errors in rainfall observations, motion field estimates, and rainfall evolution predictions are, however, inevitable. In this study, we implement a well-established deep learning model within computer science and image processing to estimate weather radar motion fields for nowcasting.
The deep learning model, Recurrent All-Pairs Field Transform (RAFT), developed by Teed and Deng (2020), is demonstrated to outperform several existing deep learning models for optical flow estimation. The RAFT model consists of a feature encoder that extracts features from consecutive images, a correlation layer that computes visual similarities, and a recurrent unit that iteratively updates the estimated flow field. The method is computationally efficient and highly accurate, making it relevant in real-time applications. Due to the similarities between image processing and weather radar rainfall nowcasting, the method has the potential to produce accurate motion fields for extrapolating weather radar rainfall.
In this study, three years of observation data from a Danish C-band weather radar are used to nowcast 51 rainfall events. The rainfall events consist of both linear and non-linear rainfall pattern motions. We systematically compare weather radar rainfall forecasted with Lagrangian persistence using six different motion field approaches: Global vector, COTREC (Li et al., 1995), VET (Variational Echo Tracking; Germann and Zawadski, 2002), Lucas-Kanade (Lucas and Kanade, 1981), DARTS (Dynamic and Adaptive Radar Tracking of Storms; Ruzanski et al., 2011), and RAFT.
The optical flow with RAFT is shown to statistically perform as well as the well-established methods VET and Lucas-Kanade and to outperform the global vector, COTREC, and DARTS. It is demonstrated that RAFT produces accurate and robust motion fields for both linear and non-linear rainfall motion. Thus, the RAFT model for optical flow estimation is shown to be highly relevant for weather radar nowcasting in urban hydrological applications.
References:
Germann, U., Zawadzki, I., 2002. Scale-Dependence of the Predictability of Precipitation from Continental Radar Images. Part I: Description of the Methodology. Mon Weather Rev 130, 2859–2873. https://doi.org/10.1175/1520-0493(2002)130<2859:SDOTPO>2.0.CO;2
Li, L., Schmid, W., Joss, J., 1995. Nowcasting of Motion and Growth of Precipitation with Radar over a Complex Orography. J Appl Meteorol Climatol 34, 1286–1300. https://doi.org/10.1175/1520-0450(1995)034<1286:NOMAGO>2.0.CO;2
Lucas, B.D., Kanade, T., 1981. An iterative image registration technique with an application to stereo vision, in: IJCAI’81: 7th International Joint Conference on Artificial Intelligence. pp. 674–679
Ruzanski, E., Chandrasekar, V., Wang, Y., 2011. The CASA nowcasting system. J Atmos Ocean Technol 28, 640–655. https://doi.org/10.1175/2011JTECHA1496.1
Teed, Z., Deng, J., 2020. Raft: Recurrent all-pairs field transforms for optical flow, in: European Conference on Computer Vision. pp. 402–419
How to cite: Nielsen, J. M., Rasmussen, M. R., Thorndahl, S., Vester, I. K., Ahm, M. K. S., and Nielsen, J. E.: Optical Flow with Recurrent All-Pairs Field Transform (RAFT) for weather radar nowcasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7614, https://doi.org/10.5194/egusphere-egu26-7614, 2026.
