Line-by-Line Coefficients for Spectrally Resolved Outgoing Longwave Radiation

Line-by-line regression coefficients for major greenhouse gases derived using the open-source PyLBL radiation code. The coefficients enable computation of an emission temperature shift ratio to predict spectrally-resolved climate feedbacks. This dataset was created by Jing Feng of Princeton University, following the methodology of Feng et al., 2023.

Use Cases

• Compute emission temperature shift ratios based on the provided line-by-line coefficients.
• Predict spectrally-resolved feedback parameters for greenhouse gases.
• Validate analytical solutions for spectrally resolved outgoing longwave radiation.
• Reproduce or extend the radiative transfer calculations from Feng et al., 2023.

Strengths

• Coefficients are derived from an open-source, documented line-by-line radiation code (PyLBL).
• The dataset is directly linked to a published scientific methodology (Feng et al., 2023).
• An example MATLAB script is included to demonstrate usage of the coefficients.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Princeton University (Jing Feng)

Collection Method

Derived using the PyLBL line-by-line radiation code following equations in Feng et al., 2023.