| # PBC — official probabilistic subseasonal calibration | |
| Guan et al., *Advancing subseasonal forecasting with machine learning*, Nature | |
| 2026. [Paper](https://doi.org/10.1038/s41586-026-11126-x) · | |
| [Author code](https://github.com/mouatadid/pbc/tree/d59e18f720bac712e18563bd469d4679c120f9e5) · | |
| [Archived release](https://zenodo.org/records/22238796). | |
| `PBC` executes the unchanged official scripts pinned to | |
| `d59e18f720bac712e18563bd469d4679c120f9e5`. The paper combines Debias++, | |
| Persistence++, CDF projection and probabilistic ensembling. It is statistical | |
| post-processing, not a new autoregressive neural forecaster. | |
| ```python | |
| from weatherai import PBC | |
| model = PBC(work_dir="pbc-work") | |
| # Official baseline, runnable without reforecast archives: | |
| result = model.run("climatology", "era5-f1_mslp", 19, target_dates="20200106") | |
| # After preparing author-format historical data and component forecasts: | |
| # model.run("ecmwfpp", "era5-f1_mslp", 19, target_dates="20200106", arguments=(...)) | |
| # model.run("perpp_ecmwf", "era5-f1_mslp", 19, target_dates="20200106", arguments=(...)) | |
| # model.run("projector", "era5-mslp", 19, target_dates="20200106", arguments=(...)) | |
| # model.run("pbc_ecmwf", "era5-f1_mslp", 19, target_dates="20200106") | |
| ``` | |
| There is **no universal pretrained checkpoint in the official release**. | |
| Coefficients depend on variable, grid, target date and historical observations/ | |
| reforecasts. `PBC.from_pretrained` raises an explanatory error. Author code/data | |
| layout and selection configuration are preserved; missing data never trigger a | |
| lite substitute. Original scripts sometimes exit successfully after skipping | |
| missing inputs, so inspect their logs and expected NetCDF outputs. | |
| Recommended runtime: author's Linux/Python 3.13 environment; the tested CPU | |
| stages also ran on Windows/Python 3.13. Dependencies include NumPy, pandas, | |
| xarray, netCDF4, dask, scikit-learn, ttictoc and setuptools with pkg_resources. | |
| The full archive preparation uses further tools listed upstream. | |
| `python scripts/validate_official_pbc.py` actually runs the original climatology, | |
| projector and equal-weight PBC scripts. A synthetic fixture verifies clipping, | |
| isotonic projection and final CDF `[0.05, 0.35, 0.55, 0.95]`. | |
| [Execution report](https://github.com/GISWLH/WeatherAI/blob/main/docs/results/pbc_official_execution.json). This is not a | |
| historical fit or reproduction of the paper's skill scores. | |
| `PBC_lite()` is the earlier independent tensor approximation, with `.fit`, | |
| `.predict_proba` and `state_dict`. It requires explicitly prepared CDF histories; | |
| it is not the official pipeline. Official target windows are days 19–25 and | |
| 26–32, distinct from WeatherPre's generic weekly windows. Code license: MIT. | |