Possibilities of warm cloud microstructure profiling with multiple-field-of-view Raman lidar

Area of Science:

• Atmospheric Science
• Remote Sensing
• Lidar Technology

Background:

• Raman lidar provides valuable data for atmospheric studies.
• Accurate retrieval of cloud characteristics is crucial for climate modeling.
• Optimizing lidar system parameters can enhance measurement capabilities.

Purpose of the Study:

• To investigate the potential of multiple-field-of-view (MFOV) Raman lidar for cloud characteristic retrieval.
• To determine the sensitivity of Raman lidar returns to specific cloud properties.
• To recommend optimal receiver fields-of-view (FOVs) for improved cloud sounding.

Main Methods:

• Analysis of Raman lidar return sensitivity to scattering coefficient and effective droplet size.
• Estimation of optimal FOVs based on lidar system parameters (R, H, z).
• Development and verification of a retrieval algorithm using iterative schemes and singular value decomposition.

Main Results:

• Raman lidar return is sensitive to scattering coefficient and effective droplet size.
• Optimal FOVs for scattering coefficient profiles are approximately R/H.
• Optimal FOVs for droplet size measurements are approximately 0.01z/H.

Conclusions:

• MFOV Raman lidar is a viable tool for retrieving key cloud characteristics.
• Specific FOV recommendations can enhance the accuracy of cloud property measurements.
• The developed algorithm and recommendations enable variable altitude resolution profile retrieval.