Enhancing Rainfall Nowcasting Using Generative Deep Learning Model with Multi-Temporal Optical Flow
Bibliographic record
Abstract
Precipitation nowcasting is critical for preventing damage to human life and the economy. Radar echo tracking methods such as optical flow algorithms have been widely employed for precipitation nowcasting because they can track precipitation motions well. Thus, this method, including the McGill algorithm for precipitation nowcasting by Lagrangian extrapolation (MAPLE), was implemented for operational precipitation nowcasting. However, advection-based methods struggle to predict the nonlinear motions of precipitation fields and dynamic processes, such as the growth and decay of precipitation. This study proposes an enhanced optical flow model using a multi-temporal optical flow field and a conditional generative adversarial network (cGAN). We trained the proposed model using a 3-year radar dataset provided by the Korean Meteorological Administration and performed forecast skill evaluations using both qualitative and quantitative methods. In particular, the model featuring multi-temporal optical flow enhances prediction accuracy for the nonlinear motion of precipitation fields, and the model’s accuracy can be further improved through the use of the cGAN structure. We have verified that these improvements hold for 0–3 h lead times. Based on this performance enhancement, we conclude that the multi-temporal optical flow model with cGAN has a potential role in operational precipitation nowcasting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".