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README.md

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-license: apache-2.0
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+# CMORPH VirtualiZarr Parquet Catalog (1998-2024)
+
+## Dataset Overview
+
+This repository contains a **Parquet-based virtual dataset (VDS) catalog** for the [NOAA CMORPH](https://www.ncei.noaa.gov/products/climate-data-records/precipitation-cmorph) (CPC MORPHing technique) global precipitation dataset, hosted on AWS S3 at `s3://noaa-cdr-precip-cmorph-pds/`.
+
+The catalog was built using [VirtualiZarr](https://github.com/zarr-developers/VirtualiZarr) and [Kerchunk](https://github.com/fsspec/kerchunk) to create a single-file index of **236,688 NetCDF files** spanning **January 1998 to October 2024**, enabling cloud-native access to the entire CMORPH archive without downloading or converting the original files.
+
+| Property | Value |
+|---|---|
+| **File** | `cmorph-aws-s3-1998-2024.parquet` |
+| **Size** | 223 MB (zstd compressed) |
+| **Rows** | 236,688 (100% success) |
+| **Time range** | 1998-01-17 to 2024-10-14 |
+| **Years** | 27 (1998-2024) |
+| **Unique months** | 324 |
+| **Temporal resolution** | 30-minute (half-hourly) |
+| **Spatial resolution** | 8 km (~0.073 deg) |
+| **Spatial coverage** | Global |
+| **Variable** | `cmorph` — precipitation rate (mm/hr) |
+| **Source bucket** | `s3://noaa-cdr-precip-cmorph-pds/` (public, no auth required) |
+
+### Parquet Schema
+
+Each row represents one CMORPH NetCDF file:
+
+| Column | Type | Description |
+|---|---|---|
+| `s3_url` | string | Full S3 path (e.g., `s3://noaa-cdr-precip-cmorph-pds/data/30min/8km/2020/01/01/CMORPH_V1.0_ADJ_8km-30min_2020010100.nc`) |
+| `filename` | string | NetCDF filename |
+| `datetime` | timestamp | Parsed timestamp (half-hour slot decoded) |
+| `year` | int32 | Year |
+| `month` | int32 | Month |
+| `day` | int32 | Day |
+| `hour` | int32 | Hour (0-23) |
+| `minute` | int32 | Minute (0 or 30) |
+| `month_key` | string | Year-month key (e.g., `2020-01`) |
+| `status` | string | Virtualization status (`success` or `error: ...`) |
+| `kerchunk_refs` | string | Kerchunk JSON references (~84 KB per row) — byte ranges, codecs, array metadata for cloud-native reads |
+
+## How It Was Created
+
+The catalog was built by [`cmorph_parquet_vds_catalog.py`](https://github.com/icpac-igad/ibf-thresholds-triggers/blob/xarray-method/thresholds/CMORPH/cmorph_parquet_vds_catalog.py) using:
+
+1. **File discovery** — `fsspec` lists all `*.nc` files on S3 for the requested year range
+2. **Distributed virtualization** — [Coiled](https://coiled.io/) workers run `VirtualiZarr.open_virtual_dataset()` on batches of 100 files, extracting Kerchunk reference metadata (byte offsets, chunk shapes, codecs) without downloading the full data
+3. **Streaming Parquet write** — A PyArrow `ParquetWriter` streams each completed batch to a single zstd-compressed Parquet file, keeping coordinator memory constant
+
+```bash
+# Full catalog build (requires Coiled account)
+micromamba run -n aifs-etl python cmorph_parquet_vds_catalog.py catalog \
+    --start-year 1998 --end-year 2024 --n-workers 10
+
+# Lite mode (listing only, no Coiled)
+micromamba run -n aifs-etl python cmorph_parquet_vds_catalog.py catalog \
+    --start-year 1998 --end-year 2024 --lite
+
+# Inspect catalog stats
+micromamba run -n aifs-etl python cmorph_parquet_vds_catalog.py info \
+    --catalog cmorph-aws-s3-1998-2024.parquet
+```
+
+## How to Open the Dataset
+
+### 1. Read the Parquet catalog
+
+```python
+import pandas as pd
+
+catalog = pd.read_parquet(
+    "cmorph-aws-s3-1998-2024.parquet",
+    columns=["s3_url", "datetime", "year", "month_key", "status"],  # skip kerchunk_refs for fast loads
+)
+print(f"Files: {len(catalog)}")
+print(f"Range: {catalog['datetime'].min()} to {catalog['datetime'].max()}")
+```
+
+### 2. Open a single file via Kerchunk refs (zero download)
+
+```python
+import json
+import fsspec
+import xarray as xr
+import pyarrow.parquet as pq
+
+# Read one row's kerchunk refs (memory-efficient iter_batches)
+pf = pq.ParquetFile("cmorph-aws-s3-1998-2024.parquet")
+for batch in pf.iter_batches(batch_size=1, columns=["kerchunk_refs", "status"]):
+    row = batch.to_pydict()
+    if row["status"][0] == "success":
+        refs = json.loads(row["kerchunk_refs"][0])
+        break
+
+# Open via fsspec reference filesystem (reads bytes from S3 on demand)
+mapper = fsspec.filesystem("reference", fo=refs).get_mapper("")
+ds = xr.open_dataset(mapper, engine="zarr", consolidated=False)
+print(ds)
+```
+
+### 3. Open a regional subset with the Icechunk pipeline
+
+The companion script [`cmorph_east_africa_icechunk.py`](https://github.com/icpac-igad/ibf-thresholds-triggers/blob/xarray-method/thresholds/CMORPH/cmorph_east_africa_icechunk.py) materializes an East Africa subset (lat: -12 to 23, lon: 21 to 53) into an [Icechunk](https://github.com/earth-mover/icechunk) versioned store on GCS, then rechunks to "pencil" chunks (full time x 5 lat x 5 lon) for fast time-series access:
+
+```bash
+# Step 1: Create empty template store
+python cmorph_east_africa_icechunk.py init \
+    --catalog cmorph_vds_catalog/catalog.parquet \
+    --gcs-prefix cmorph_ea_subset
+
+# Step 2: Fill with real data via Coiled workers reading from S3
+python cmorph_east_africa_icechunk.py fill \
+    --catalog cmorph_vds_catalog/catalog.parquet \
+    --target-gcs-prefix cmorph_ea_subset --n-workers 20
+
+# Step 3: Rechunk to pencil chunks (Dask P2P shuffle)
+python cmorph_east_africa_icechunk.py rechunk \
+    --source-gcs-prefix cmorph_ea_subset \
+    --target-path gs://cpc_awc/cmorph_ea_pencil --n-workers 20
+
+# Step 4: Verify
+python cmorph_east_africa_icechunk.py verify \
+    --gcs-prefix cmorph_ea_pencil --store-type zarr
+```
+
+### 4. Filter the catalog by time range
+
+```python
+import pandas as pd
+
+# Load only lightweight columns (fast — skips 223 MB of kerchunk_refs)
+df = pd.read_parquet(
+    "cmorph-aws-s3-1998-2024.parquet",
+    columns=["s3_url", "datetime", "year", "month", "day"],
+    filters=[("year", ">=", 2020), ("year", "<=", 2023)],
+)
+print(f"2020-2023 files: {len(df)}")
+```
+
+## Architecture
+
+```
+NOAA S3 (public)                    Parquet Catalog                  Icechunk Store (GCS)
+┌──────────────────┐    VirtualiZarr    ┌──────────────────┐    materialize    ┌──────────────────┐
+│  236,688 NetCDF  │ ──────────────────>│  cmorph-aws-s3-  │ ───────────────> │  EA Subset       │
+│  files (8km,     │   Coiled workers   │  1998-2024       │   Coiled + S3    │  (Icechunk repo) │
+│  30-min, global) │   + Kerchunk refs  │  .parquet        │   direct reads   │  lat:-12..23     │
+└──────────────────┘                    │  (223 MB)        │                  │  lon: 21..53     │
+                                        └──────────────────┘                  └────────┬─────────┘
+                                                                                       │ rechunk
+                                                                              ┌────────▼─────────┐
+                                                                              │  Pencil Zarr     │
+                                                                              │  (full-time x    │
+                                                                              │   5lat x 5lon)   │
+                                                                              └──────────────────┘
+```
+
+## Dependencies
+
+- Python 3.10+
+- `virtualizarr`, `kerchunk`, `fsspec`, `obstore`
+- `pandas`, `pyarrow`, `xarray`, `zarr`
+- `icechunk` (for the East Africa materialized store)
+- `coiled`, `dask.distributed` (for distributed processing)
+- `pystac`, `stac-geoparquet` (for STAC integration — future)
+
+## Related Scripts
+
+| Script | Purpose | Link |
+|---|---|---|
+| `cmorph_parquet_vds_catalog.py` | Build the Parquet VDS catalog from S3 | [GitHub](https://github.com/icpac-igad/ibf-thresholds-triggers/blob/xarray-method/thresholds/CMORPH/cmorph_parquet_vds_catalog.py) |
+| `cmorph_east_africa_icechunk.py` | Materialize EA subset + pencil rechunk | [GitHub](https://github.com/icpac-igad/ibf-thresholds-triggers/blob/xarray-method/thresholds/CMORPH/cmorph_east_africa_icechunk.py) |
+
+## License
+
+The CMORPH data is produced by NOAA's Climate Prediction Center and is in the public domain. The processing scripts and catalog are part of the [ICPAC IGAD IBF Thresholds & Triggers](https://github.com/icpac-igad/ibf-thresholds-triggers) project.