| # Gray-swan translocation: forecast diagnostics | |
| [Model pages](README.md) 路 [Three-paper integration](https://github.com/GISWLH/WeatherAI/blob/main/docs/research_20261019.md) | |
| Sun et al., **Predicting regional gray swans via translocation: AI weather models | |
| and Dubai's unprecedented 2024 rainfall**, Science Advances, 7 October 2026. | |
| [Paper](https://doi.org/10.1126/sciadv.ady9406). | |
| Classification: extreme-event generalization and interpretability. This paper | |
| does **not** introduce a sixth/new forecasting architecture. It evaluates | |
| GraphCast, FuXi, AIFS, Pangu-Weather and FourCastNet-v2, and investigates | |
| GraphCast/AIFS effective receptive fields (ERFs) and fine-tuning experiments. | |
| Official resources from the paper: | |
| | Resource | Code / model | | |
| |---|---| | |
| | GraphCast | [DeepMind code and weight links](https://github.com/google-deepmind/graphcast) | | |
| | FuXi | [Original cascade code (pinned)](https://github.com/tpys/FuXi/tree/9292fe0692156a01cd3d62bcb427cc3798cf8add), [author ONNX models / sample data](https://doi.org/10.5281/zenodo.10401602) | | |
| | AIFS | [ecmwf/aifs-single-1.0](https://huggingface.co/ecmwf/aifs-single-1.0) | | |
| | Pangu | [Original author code and four ONNX weight links](https://github.com/198808xc/Pangu-Weather), [6h checkpoint](https://drive.google.com/file/d/1a4XTktkZa5GCtjQxDJb_fNaqTAUiEJu4/view) | | |
| | FourCastNet-v2 | [NVIDIA inference code](https://github.com/NVIDIA/earth2mip/blob/main/earth2mip/networks/fcnv2_sm.py), [official NGC v0.2 weights + statistics](https://api.ngc.nvidia.com/v2/models/nvidia/modulus/modulus_fcnv2_sm/versions/v0.2/files/fcnv2_sm.zip) | | |
| | GraphCast fine-tuning | [csubich/graphcast](https://github.com/csubich/graphcast) | | |
| | Dynamically similar event tracking | [cyclonetracking](https://github.com/alexcrawford0927/cyclonetracking) | | |
| | Paper ERF notebook and data | [Zenodo 18111128](https://zenodo.org/records/18111128) | | |
| Zenodo lists the 19,427-byte notebook `graphcast-ERF-cleaned.ipynb`, README, | |
| and three ERF data ZIP files (~3.14, 2.03 and 2.41 GB). These are diagnostics, | |
| not newly released model checkpoints. Model licenses follow each backbone. | |
| The current FuXi repository advertises FuXi 2.1; that newer release must not be | |
| silently substituted for the original cascade used in this study. The table | |
| therefore links the original code snapshot and its author-hosted archive. | |
| Only the GraphCast route was executed for this integration; the other official | |
| backbone artifacts are discovery references, not newly validated implementations. | |
| ```python | |
| from weatherai import Translocation | |
| model = Translocation.from_pretrained() # official 0.25-degree, 13-level GraphCast | |
| inputs, targets, forcings = model.prepare(era5_dataset, lead_time="6h") | |
| forecast = model(inputs, targets * float("nan"), forcings) | |
| erf = model.effective_receptive_field( | |
| inputs, targets_template=targets * float("nan"), forcings=forcings, | |
| variable="total_precipitation_6hr", | |
| location={"batch": 0, "time": -1, "lat": 260, "lon": 220}) | |
| ``` | |
| The main entry downloads the official GraphCast checkpoint and normalization | |
| statistics from `dm_graphcast` and imports unchanged author code at | |
| `08cf73625c9d12bd9aaa038868bcb2fe488f2a22`. This historical revision retains the | |
| `graphcast` package before the upstream repository's WeatherNext reorganization. | |
| Variants are `operational` (0.25°, 13 levels), `37levels` (0.25°, 37 levels), and | |
| `small` (official pretrained 1°, 13 levels; not the paper's main resolution). | |
| Code is Apache-2.0; GraphCast weights retain CC-BY-NC-SA-4.0. | |
| The wrapper order follows the study's released ERF notebook: GraphCast, | |
| Bfloat16Cast, InputsAndResiduals and autoregressive.Predictor. Signed input | |
| sensitivities use JAX reverse-mode AD on a selected physical output. Coordinates, | |
| variable, lead and normalization must match the intended event. ERF alone does | |
| not establish causality. Full 0.25° operational forecasting and the actual Small-model ERF adapter passed | |
| ZeroGPU execution. [Current test evidence](https://github.com/GISWLH/WeatherAI/blob/main/docs/research_20261019.md). | |
| `Translocation_lite()` retains the independent random-weight PyTorch toy model. | |
| For custom PyTorch diagnostic experiments, import `TranslocationLite` from | |
| `weatherai.models.translocation`. Neither is the pretrained main entry. | |
| The complete Dubai reconstruction, event tracking and fine-tuning ablations | |
| have not been reproduced. No study-specific fine-tuned weights were found in | |
| the cited ERF archive; its large ZIP files contain diagnostic data. | |