GRESO: Growth Rate from Satellite Observations

Figure: Growth rates from OCO-2 satellite observations compared to NOAA ground-based measurements

Overview

The GRESO code analyzes OCO-2 satellite data to compute global annual CO2 growth rates.

Publication: Pandey, S., et al. (2024). Toward low-latency estimation of atmospheric CO₂ growth rates using satellite observations. AGU Advances, 5, e2023AV001145.

Quick Start

Prerequisites

• Python 3.7+
• Packages: numpy, pandas, xarray, matplotlib, scipy, python-dateutil

Installation

pip install numpy pandas xarray matplotlib scipy python-dateutil

Running the Analysis

# Default analysis (MIPV11 format)
python greso_analysis.py

# Specific data type analysis
python greso_analysis.py --data-type LNLG

Python API Usage

from greso_analysis import AnalysisConfig, CO2GrowthRateAnalyzer

config = AnalysisConfig(
    data_file="/path/to/OCO2_b11.2_10sec_GOOD_r3.nc4",
    lat_range=[-50, 50],
    uncertainty_threshold=2.0
)

analyzer = CO2GrowthRateAnalyzer(config)
results = analyzer.process_oco_data(["all"])

Data Sources

OCO-2 Satellite Data

• Format: MIPV11 (NetCDF4, .nc4 files) - recommended
  • Uses xco2_2019_scale variable with improved calibration
  • Download: NOAA GML OCO-2 MIP
  • Direct link: OCO2_b11.2_10sec_GOOD_r2.nc4
• Legacy Format: Standard HDF5 (.h5 files)
  • Uses xco2 variable

NOAA Reference Data

• Automatically downloaded from: ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_gr_gl.txt

Methodology

1. Data Processing:

   • Quality filtering (uncertainty threshold, data type)
   • Spatial filtering (latitude range: -50° to 50°)
   • Temporal processing and area-weighted averaging
   • Seasonal detrending using harmonic analysis

2. Growth Rate Calculation:

   • Annual differences in deseasonalized CO2
   • Direct comparison with NOAA global mean growth rates

Results

Example output:

============================================================
ANNUAL GROWTH RATES - ALL DATA
============================================================
Year       | OCO-2 Growth Rate (ppm/yr) | NOAA Growth Rate (ppm/yr)
------------------------------------------------------------
2015       |               2.99 | 2.95
2016       |               2.71 | 2.84
2017       |               2.05 | 2.14
2018       |               2.46 | 2.39
2019       |               2.75 | 2.50
2020       |               2.31 | 2.33
2021       |               2.30 | 2.37
2022       |               1.92 | 2.29
2023       |               3.06 | 2.73
2024       |               3.22 | 3.73
------------------------------------------------------------
MEAN       |               2.50 | 2.50
============================================================

Code Structure

greso_analysis.py    # Main analysis script
utils.py            # Utility functions and classes

Key Components

• AnalysisConfig: Configuration management
• OCODataProcessor: Data loading and preprocessing
• CO2GrowthRateAnalyzer: Core analysis logic
• VisualizationManager: Plotting and visualization

Citation

Pandey, S., Miller, J.B., Basu, S., Liu, J., Weir, B., Byrne, B., Chevallier, F., 
Bowman, K.W., Liu, Z., Deng, F. and O'dell, C.W., 2024. Toward low‐latency 
estimation of atmospheric CO2 growth rates using satellite observations: 
Evaluating sampling errors of satellite and in situ observing approaches. 
AGU Advances, 5, e2023AV001145. https://doi.org/10.1029/2023AV001145

Contact

Developer: Sudhanshu Pandey
Affiliation: NASA Jet Propulsion Laboratory / California Institute of Technology
Email: sudhanshu.pandey[at]jpl.nasa.gov