nwpio 0.1.0

Download and process NWP forecast data from cloud archives

NWPIO

A Python library for downloading and processing Numerical Weather Prediction (NWP) forecast data from cloud archives.

Features

Data Download

• Multiple NWP models - GFS, ECMWF HRES/ENS support
• Flexible resolutions - 0.1°, 0.25°, 0.5°, 1.0° depending on product
• Configurable cycles - 00z, 06z, 12z, 18z with variable lead times (up to 384h)
• Parallel downloads - Configurable workers for fast transfers
• GCS-to-GCS copying - No local storage needed for large files
• File validation - Ensures all files are complete before downloading
• Smart skipping - Avoid re-downloading existing files

GRIB Processing

• Variable extraction - Select specific variables from GRIB files
• Time concatenation - Combine multiple files along time dimension
• Zarr conversion - Efficient chunked storage format
• Configurable chunking - Optimize for your access patterns
• Compression support - Multiple algorithms (zstd, lz4)
• GRIB key filtering - Filter by level, type, etc.
• Parallel GRIB loading - Fast processing with multiple workers

Production Ready

• Type-safe configuration - Pydantic models with validation
• Flexible cycle configuration - CLI (--cycle), environment ($CYCLE), or config file
• Multi-process workflow - Download once, process multiple variable sets
• Cycle-based formatting - Dynamic paths with {cycle:%Y%m%d} placeholders
• Comprehensive logging - Track progress and debug issues
• Error handling - Robust recovery and retry logic
• Automatic cleanup - Optional GRIB file deletion after processing
• Docker support - Container-ready for cloud deployment

Installation

pip install -e .

For development:

pip install -e ".[dev]"

Quick Start

Download GRIB files

from nwpio import GribDownloader, DownloadConfig
from datetime import datetime

config = DownloadConfig(
    product="gfs",
    resolution="0p25",
    forecast_time=datetime(2024, 1, 1, 0),
    cycle="00z",
    max_lead_time=120,  # hours
    source_bucket="gcp-public-data-arco-era5",
    destination_bucket="your-bucket-name",
)

downloader = GribDownloader(config)
downloaded_files = downloader.download()

Process GRIB to Zarr

from nwpio import GribProcessor, ProcessConfig

config = ProcessConfig(
    grib_files=downloaded_files,
    variables=["t2m", "u10", "v10", "tp"],
    output_path="gs://your-bucket/output.zarr",
)

processor = GribProcessor(config)
processor.process()

Using the CLI

# Download GRIB files
nwpio download \
    --product gfs \
    --resolution 0p25 \
    --time 2024-01-01T00:00:00 \
    --cycle 00z \
    --max-lead-time 120 \
    --source-bucket gcp-public-data-arco-era5 \
    --dest-bucket your-bucket-name

# Process GRIB to Zarr
nwpio process \
    --grib-path gs://your-bucket/grib/ \
    --variables t2m,u10,v10,tp \
    --output gs://your-bucket/output.zarr

# Combined workflow
nwpio run \
    --config config.yaml

Configuration File Example

Single Process Configuration

# config.yaml
download:
  product: gfs
  resolution: 0p25
  cycle: "2024-01-01T00:00:00"
  max_lead_time: 6
  source_bucket: global-forecast-system
  destination_bucket: your-bucket-name
  destination_prefix: nwp-data/

process:
  - filter_by_keys:
      typeOfLevel: heightAboveGround
      level: 10
    zarr_path: gs://your-bucket/wind_{cycle:%Y%m%d}_{cycle:%Hz}.zarr
    variables: [u10, v10]
    write_local_first: true
    max_upload_workers: 16

Multi-Process Configuration (Recommended)

Download once, create multiple Zarr archives with different variable sets:

# config-multi.yaml
cleanup_grib: true  # Delete GRIB files after all processing

download:
  product: gfs
  resolution: 0p25
  cycle: "2024-01-01T00:00:00"
  max_lead_time: 6
  source_bucket: global-forecast-system
  destination_bucket: your-bucket-name

process:
  # Process 1: 10m winds
  - filter_by_keys:
      typeOfLevel: heightAboveGround
      level: 10
    zarr_path: gs://your-bucket/wind10m_{cycle:%Y%m%d}_{cycle:%Hz}.zarr
    variables: [u10, v10]
    max_upload_workers: 16
    
  # Process 2: 2m temperature and humidity
  - filter_by_keys:
      typeOfLevel: heightAboveGround
      level: 2
    zarr_path: gs://your-bucket/surface_{cycle:%Y%m%d}_{cycle:%Hz}.zarr
    variables: [t2m, d2m]
    max_upload_workers: 16

Run with:

nwpio run --config config-multi.yaml --max-workers 8

ECMWF Source Selection

ECMWF data is available from two sources. Simply specify source_type to choose:

# Use GCS (Google Cloud Storage) - Official ECMWF bucket (default)
download:
  product: ecmwf-hres
  resolution: 0p25
  source_type: gcs  # Uses ecmwf-open-data bucket (default)
  max_lead_time: 120

# Use AWS S3 - Alternative source
download:
  product: ecmwf-hres
  resolution: 0p25
  source_type: aws  # Uses ecmwf-forecasts bucket
  max_lead_time: 120

The source_type defaults to gcs. The appropriate bucket is automatically selected based on the product and source type. You can override with a custom source_bucket if needed.

Supported Products

GFS (Global Forecast System)

• Resolutions: 0p25 (0.25°), 0p50 (0.5°), 1p00 (1.0°)
• Cycles: 00z, 06z, 12z, 18z
• Lead times: Up to 384 hours

ECMWF

• Products: HRES (High Resolution), ENS (Ensemble)
• Resolutions: 0p1 (0.1°), 0p25 (0.25°)
• Cycles: 00z, 12z
• Lead times: Up to 240 hours (HRES), 360 hours (ENS)
• Sources:
  • GCS: gs://ecmwf-open-data (official ECMWF bucket)
  • AWS: s3://ecmwf-forecasts (alternative source)

Architecture

nwpio/
├── __init__.py
├── config.py          # Configuration models using Pydantic
├── sources.py         # Data source definitions for GFS/ECMWF
├── downloader.py      # GRIB file download logic
├── processor.py       # GRIB to Zarr conversion
├── utils.py           # Utility functions
└── cli.py             # Command-line interface

Requirements

• Python 3.9+
• Google Cloud Storage access (with appropriate credentials)
• GRIB file support (eccodes library)

License

MIT