ecmwf/climetlab

Python package for easy access to weather and climate data

Structural Verdict

How Data Flows Through the System

Data flows from remote sources through normalization and caching to scientific computing frameworks, with optional visualization output.

1. Source Loading — Load data from CDS, MARS, URLs, or local files using load_source() (config: cache-directory)
2. Argument Normalization — Standardize parameters using @normalize decorators with type conversion
3. Data Caching — Cache downloaded data locally for performance (config: cache-directory)
4. Format Conversion — Convert between GRIB, NetCDF, CSV formats using readers
5. Data Access — Provide unified interface to xarray, pandas, numpy
6. Visualization — Generate maps and plots using Magics or other backends (config: plotting-options)

System Behavior

How the system actually operates at runtime — where data accumulates, what loops, what waits, and what controls what.

Data Pools

Feedback Loops

• Cache Validation (cache-invalidation, balancing) — Trigger: Data access request. Action: Check cache freshness and redownload if stale. Exit: Valid cached data found.

Delays & Async Processing

• Remote Data Download (async-processing, ~seconds to hours) — First access to datasets requires download time
• File Lock Wait (queue-drain, ~milliseconds) — Concurrent cache access serialized via filelock

Control Points

• cache-directory (env-var) — Controls: Where downloaded data is cached locally
• plotting-options (runtime-toggle) — Controls: Default visualization parameters like width and foreground

Technology Stack

Key Components

• CliMetLabApp (class) — Interactive CLI shell that combines multiple command modules through multiple inheritance src/climetlab/scripts/main.py
• load_source (function) — Primary API function for loading data from various sources with unified interface src/climetlab/__init__.py
• load_dataset (function) — High-level API for loading predefined datasets with metadata src/climetlab/__init__.py
• normalize (function) — Decorator for standardizing function arguments with type conversion and validation src/climetlab/decorators.py
• alias_argument (function) — Decorator enabling parameter aliases for backward compatibility src/climetlab/decorators.py
• get_plugins (function) — Dynamic plugin discovery using entrypoints for extensible CLI commands src/climetlab/scripts/main.py
• plot_map (function) — Main plotting function for generating maps with various backend options src/climetlab/__init__.py
• settings (module) — Global configuration system for cache directories and plotting options src/climetlab/__init__.py
• Constants source (module) — Generates derived meteorological constants like cos_latitude and insolation dev/constants.py
• TensorFlow integration (module) — Demonstrates ML model integration with custom PeriodicConv2D layer dev/tf.py

Science Pipeline

1. Load MARS surface data — cml.load_source with levtype='sfc', param='2t', grid='O96' dev/constants.py
2. Generate time constants — Generate cos_latitude, sin_longitude, cos_julian_day parameters [(time, lat, lon) → (time, lat, lon, 9_params)] dev/constants.py
3. Load ERA5 training data — CDS API call with reanalysis-era5-single-levels dev/tf.py
4. CNN processing — PeriodicConv2D -> Dense -> Dropout layers [(batch, height, width, channels) → (batch, predictions)] dev/tf.py

Assumptions & Constraints

• [warning] Assumes input tensor follows standard Conv2D shape conventions but no explicit validation (shape)
• [info] Assumes MARS data has specific grid format 'O96' without validation (format)
• [info] Time parameters assume 0-23 hour range but no bounds checking shown (value-range)

Related Weather Climate Repositories

Frequently Asked Questions

Analyzed on March 31, 2026 by CodeSea. Written by Karolina Sarna.