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Notes on Rayleigh scattering in lidar signals.
1Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy. mariana.adam@jrc.ec.europa.eu
Applied Optics
|April 27, 2012
Summary
Accurate lidar analysis requires precise atmospheric molecular composition estimation. Including water vapor in models minimally impacts Rayleigh scattering but improves aerosol retrieval accuracy, especially with known water vapor profiles.
Area of Science:
- Atmospheric optics
- Remote sensing
- Lidar technology
Background:
- Rayleigh scattering is crucial for interpreting lidar signals.
- Accurate atmospheric molecular composition is needed for precise lidar measurements.
- Classical and quantum formulations exist for scattering estimations.
Purpose of the Study:
- To evaluate the impact of different atmospheric molecular compositions on Rayleigh scattering calculations.
- To assess the influence of molecular formulations on lidar-derived aerosol properties.
- To determine the optimal molecular composition model for lidar applications.
Main Methods:
- Utilized classical and quantum formulations for Rayleigh scattering estimation.
- Compared three atmospheric models: 2-component (N2, O2), 4-component (N2, O2, Ar, CO2), and 5-component (N2, O2, Ar, CO2, water vapor).
- Analyzed relative differences in scattering and aerosol coefficients across models.
Main Results:
- The 2(4)-component and 5-component atmospheres showed less than ~1% difference in molecular scattering.
- Lidar retrieval of aerosol coefficients exhibited a ±3% relative difference between 2-component and 5-component models.
- Water vapor's inclusion is significant for accurate aerosol profile retrieval when known.
Conclusions:
- The 5-component atmospheric model provides the most accurate lidar aerosol retrieval when water vapor profiles are known.
- The 2-component model remains adequate for lidar applications when water vapor information is limited.
- Accurate consideration of molecular composition, including water vapor, is vital for precise lidar data interpretation.
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