OpenBench

The Open Land Surface Model Benchmark Evaluation System

Overview

This system is designed for the evaluation of land surface model outputs against reference data. It handles configuration, data processing, validation, comparison, and statistical analysis.

Key Features

• Supports multiple land surface models (e.g., CLM5, CoLM)
• Handles various data types (grid and station-based)
• Performs comprehensive evaluations across multiple variables
• Generates detailed metrics, scores, comparisons, and statistical analysis

Main Components

1. 'openbench.py': Main entry point for the evaluation system
2. 'Mod_Validation.py': Handles validation of model outputs
3. 'Mod_DatasetProcessing.py': Processes datasets for evaluation
4. 'Mod_Comparison.py': Performs comparisons between models and reference data
5. 'Mod_Namelist.py': Manages configuration and namelist reading
6. 'Mod_Metrics.py': Calculates various statistical metrics
7. 'Mod_Scores.py': Computes performance scores
8. 'Mod_Statistics': Conducts statistical analysis
9. 'Lib_Unit': convert the unit
10. Folder 'figlib': lib for vitalization
11. Folder 'regrid': lib for remap--modified from xarray-regrid
12. Floder 'custom': lib for dataset filter

Setup and Configuration

1. Ensure all dependencies are installed (xarray, pandas, numpy, etc.)
2. Configure the system using the provided namelist files:
   • 'main.nml': configuration
   • 'ref.nml': Reference data configuration
   • 'sim.nml': Simulation data configuration
   • Model-specific namelists (e.g., 'CLM5.nml', 'CoLM.nml')
   • Reference-specific namelists (e.g., 'GRDC.nml', 'ILAMB.nml')

Usage

Run the evaluation system using:

''' python script/openbench.py nml/main.nml [or path_to_main_namelist] '''

Supported Evaluations

• Radiation and Energy Cycle
• Ecosystem and Carbon Cycle
• Hydrology Cycle
• Human Activity (Urban, Crop, Dam)
• Forcing Variables

Output

The system generates various outputs including:

• Metric and score calculations
• Comparison plots and statistics
• Detailed logs and summaries

Customization

• Custom filters can be added for handling specific variables, models or datasets
• Evaluation items, metrics, and scores can be configured in the namelists

Contributors

• Zhongwang Wei ([email protected])
• Qingchen Xu (******)

Version

Current Version: 0.1 Release Date: Sep 2024