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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
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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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):
End of preview.

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