An individual motion driven CNN-Based AI method for precipitation forecasting Using RADAR Image Sequence
Bibliographic record
Abstract
Precipitation forecasting, especially with high spatial resolution and accurate intensity estimation, remains a critical challenge in the field of Artificial Intelligence (AI). Existing AI-based forecasting models often struggle with key limitations, including mismatched precipitation motion patterns, blurred precipitation field generation, and inaccurate intensity predictions. These issues largely arise from conventional models simulating average motion and neglecting individual motion—which refers to the unique speed, trajectory, and direction of a single precipitation event. To address these limitations, we propose an Individual Motion Driven AI (IMD-AI) method based on a Convolutional Neural Network (CNN). This approach incorporates motion alignment and pattern grouping techniques to correct mismatches in individual motion estimation, thereby enabling more accurate and intact regional precipitation forecasting. Our CNN architecture is designed to extract spatial features from RADAR image sequences and map them directly to real-world parameters such as precipitation intensity, humidity, wind speed, and atmospheric pressure. Furthermore, to enhance precision and sharpness, we integrate strategies like patch embedding, schedule sampling, and adversarial training under the SPA framework. These additions mitigate the tendency of AI models to filter out high-frequency details, improving the model’s ability to preserve fine-scale patterns in precipitation fields. The final system is deployed through a web-based application, allowing users to upload RADAR images and instantly receive multiple weather parameter predictions with high reliability and accuracy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".