---
license: cc0-1.0
task_categories:
  - time-series-forecasting
tags:
  - climate
  - precipitation
  - cmorph
  - noaa
  - virtualizarr
  - kerchunk
  - zarr
  - icechunk
  - east-africa
  - geospatial
size_categories:
  - 100K<n<1M
---

# 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 from Hugging Face

```python
import pandas as pd

HF_PARQUET = "hf://datasets/E4DRR/virtualizarr-stores/cmorph-aws-s3-1998-2024.parquet"

catalog = pd.read_parquet(
    HF_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 zarr
import xarray as xr
import pyarrow.parquet as pq

HF_PARQUET = "hf://datasets/E4DRR/virtualizarr-stores/cmorph-aws-s3-1998-2024.parquet"

# Read one row's kerchunk refs from Hugging Face (memory-efficient iter_batches)
with fsspec.open(HF_PARQUET, "rb") as f:
    pf = pq.ParquetFile(f)
    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 → zarr.storage.FsspecStore (Zarr v3 compatible)
fs = fsspec.filesystem("reference", fo=refs, remote_protocol="s3", remote_options={"anon": True})
store = zarr.storage.FsspecStore(fs, read_only=True)
ds = xr.open_dataset(store, engine="zarr", consolidated=False, zarr_format=2)
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

HF_PARQUET = "hf://datasets/E4DRR/virtualizarr-stores/cmorph-aws-s3-1998-2024.parquet"

# Load only lightweight columns (fast — skips 223 MB of kerchunk_refs)
df = pd.read_parquet(
    HF_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.