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Statistical Characterization of Temperature and Pressure Vertical Profiles for the Analysis of Laser Heterodyne
Monica M Flores1, David S Bomse2, J Houston Miller1
1Department of Chemistry, George Washington University, Washington, DC 200521, USA.
Sensors (Basel, Switzerland)
|August 28, 2021
Summary
This study analyzes radiosonde data to improve molecular oxygen (O2) line shape fitting in Laser Heterodyne Radiometry spectra. Statistical analysis of atmospheric pressure and temperature profiles refines greenhouse gas retrievals.
Area of Science:
- Atmospheric Science
- Spectroscopy
- Data Analysis
Background:
- Molecular oxygen (O2) spectral line shapes are crucial for atmospheric remote sensing.
- Variations in atmospheric pressure and temperature significantly impact O2 spectral features.
- Accurate atmospheric profiles are essential for retrieving greenhouse gas (GHG) concentrations.
Purpose of the Study:
- To statistically analyze historical radiosonde pressure and temperature profiles.
- To improve the fitting of molecular oxygen (O2) line shapes in Laser Heterodyne Radiometry (LHR) spectra.
- To refine the retrieval of vertically resolved mixing ratios for greenhouse gases (GHGs).
Main Methods:
- Extraction of radiosonde temperature and pressure data from the Integrated Global Radiosonde Archive (IGRA).
- Statistical characterization of temperature and pressure profiles using polynomial fit coefficients.
- Constraining O2 line shape fits via Nelder-Mead optimization using probability distributions of coefficients.
- Utilizing refined profiles for GHG retrieval.
Main Results:
- Demonstrated that atmospheric pressure and temperature profiles are key factors affecting O2 spectral fits in LHR.
- Developed a statistical method to characterize atmospheric variability from radiosonde data.
- Successfully refined temperature and pressure profiles for improved GHG retrievals.
Conclusions:
- Statistical analysis of radiosonde data provides robust constraints for O2 line shape fitting.
- Improved atmospheric profiling enhances the accuracy of greenhouse gas measurements.
- Vertical profile determinations can serve as Bayesian priors for subsequent measurements, aiding uncertainty estimation.
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