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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
batch = f.read(self.config.chunksize)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 839, in read_with_retries
out = read(*args, **kwargs)
File "<frozen codecs>", line 325, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1879, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
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# Monsoon 2026 forecast experiment |
Goal: learn the best weekly rainfall forecast for India from past forecasts and |
observations, validate on monsoon (JJAS) 2025, test on JJAS 2026. Secondary: |
drivers and teleconnections of the 2026 monsoon (`teleconnections/`). |
Started 2026-10-06. Code here; data under `/storage/raj.ayush/monsoon2026/`. |
Nothing in `/home/raj.ayush/s2s/s2s_anlysis` or the standardized archive is |
modified. Everything runs through Slurm (`slurm/`), logs in `logs/`, job ids in |
`logs/submissions.txt`. |
## Contract |
- Target: weekly-mean rainfall (mm/day), lead weeks 1-6 (days +1..+7 to |
+36..+42), grid `india_1p5_27x27_v1`, India-area weighted (174 cells). |
- Truth: IMD gridded rainfall. Final product through 2025; **real-time product |
for 2026** (fewer gauges; see `reports/imd_realtime_check/`). |
- Initializations: Monday/Thursday. Train 2020-2024, validate JJAS 2025 |
(35 inits), then refit on 2020-2025 and test on JJAS 2026 (35 inits, |
1 Jun - 28 Sep; later inits only have their early weeks observed). |
- Scores: ACC (centred spatial anomaly correlation against the IMD 1991-2019 |
normal), RMSE, MAE, bias, CRPS; per case and week, then averaged. These |
definitions reproduce the earlier INDIA-S2S-BENCH numbers for JJAS 2025 |
exactly (raw ECMWF ACC 0.305 / RMSE 5.240; raw FuXi 0.276 / 5.683). |
## Frozen recipe (`config/frozen_recipe.json`, frozen before any 2026 score) |
1. Baseline: observed climatology of the previous 15 years. |
2. Each model's anomaly = forecast minus its own seasonal mean (15-day |
day-of-year kernel over training cases, per lead and cell). |
3. Non-negative ridge stack of the anomalies, per lead week, pooled over India, |
refit for whichever models are available. |
4. Probabilities: zero-censored normal in square-root space around the stack, |
fitted on leave-one-year-out predictions; 51 quantiles. |
## Layout |
| Path | What | |
|---|---| |
| `m26/` | library: `data`, `methods`, `pipeline`, `prob`, `metrics`, `features` | |
| `scripts/build_cube.py` | aligned 2020-2025 cube and FuXi 2002-2021 hindcast cube | |
| `scripts/build_cube_2026.py` | 2026 test cube from raw GRIB / FuXi runs (s2s-hind env) | |
| `scripts/run_validation*.py` | selection experiments, rounds 1-3 | |
| `scripts/run_test_2026.py` | refit on 2020-2025, score JJAS 2026 (new dated folder per run) | |
| `scripts/fetch_obs.py` | IMD real-time and IMERG Late download + regrid | |
| `scripts/download_tp_only.py` | rainfall-only wrapper round the archive's S2S downloader | |
| `scripts/make_figures.py` | figures for a report folder | |
| `reports/validation_v1..v3/` | validation tables and figures | |
| `reports/test_2026/<stamp>/` | test tables, figures, `monsoon2026_forecast.nc` | |
| `/storage/raj.ayush/monsoon2026/cache/` | `cube_v1`, `cube_fuxihind`, `cube_2026` | |
| `/storage/raj.ayush/monsoon2026/forecasts/` | new FuXi-S2S 50-member runs for 35 JJAS 2026 dates | |
| `/storage/raj.ayush/monsoon2026/obs/` | 2026 IMD real-time and IMERG Late | |
## 2026 inputs and their status |
- FuXi-S2S: run here from ARCO-ERA5 (jobs `m26-fuxi-stage`, `m26-fuxi-run`); no publication step. |
- ECMWF: arrives through the existing provider-native array (131156, tasks 133-136); 24 of 35 start dates on disk at the 2026-10-07 05:00 test (tasks time out at 24 h and need resubmitting for the rest). |
- Results: `REPORT.md` (single report with figures; images in `report_assets/`). |
- UKMO, NCEP, CMA, CNRM: **not downloaded here.** ECDS caps queued requests per |
user and array 131156 already runs at that cap. A separate targeted worker |
(`m26-prov-dl`) was tried on 2026-10-06 and cancelled: it completed nothing |
and coincided with a rise in request errors in array 131156. These providers |
will arrive when array 131156 reaches its tasks 263-266 (UKMO), 404-407 |
(NCEP), 545-548 (CMA), 627-630 (CNRM), unless that array is re-prioritised. |
`slurm/provider2026_download.sbatch` is ready to use if it is. |
Validation showed FuXi + ECMWF reach ACC 0.402 / RMSE 4.960 against |
0.414 / 4.918 for all six, so the cost of waiting is small. |
- The test re-runs automatically when FuXi finishes and again when ECMWF lands. |
After other providers arrive, run `build_cube_2026.py` then `run_test_2026.py`. |
# Monsoon 2026: learning the best weekly rainfall forecast for India |
Report date 2026-10-07. Everything here lives under |
`/home/raj.ayush/s2s/monsoon2026/` (code, tables, this file) and |
`/storage/raj.ayush/monsoon2026/` (data). Images are in `report_assets/` next |
to this file. |
## 1. Summary |
**The question.** Learn the best rainfall forecast from past forecasts and |
observations, validate on monsoon 2025, test on monsoon 2026, and find what |
drove monsoon 2026. |
**The answer in five lines.** |
1. A simple calibrated combination of the forecast models ("the stack") is the |
best method found. On monsoon 2025 it beats every raw model, the multi-model |
mean and climatology at all six lead weeks. |
2. On monsoon 2026, the independent test, it still beats every raw model at |
every lead. Against climatology it wins clearly at week 1 and is |
statistically tied from week 2 onward (one small exception: below-normal |
Brier skill at week 2). |
3. 2026 was the hardest season in the 2020-2026 record for the underlying |
models: at lead weeks 3-6 both ECMWF and FuXi-S2S had their lowest skill |
of any year. |
4. Monsoon 2026 delivered 90% of normal rain under a fast-growing, record-strong |
El Nino. ENSO is the only seasonal driver that holds up statistically. |
5. Two improvement rounds after the first test gave two small, validated gains |
and several clean nulls (section 7). |
**Headline scores** (weekly-mean rainfall, mm/day; mean over lead weeks 1-6): |
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