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Iterative method to determine an averaged backscatter-to-extinction ratio in cirrus clouds.

S Elouragini, P H Flamant

|November 19, 2010

Summary

This summary is machine-generated.

This study introduces a new iterative method to calculate the backscatter-to-extinction ratio and extinction coefficient in cirrus clouds using lidar data. The technique requires a signal-to-noise ratio above 3 for accurate results.

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Area of Science:

Atmospheric Science
Cloud Physics
Remote Sensing

Background:

Cirrus clouds play a crucial role in Earth's radiative balance.
Accurate retrieval of cloud optical properties, such as extinction coefficient and backscatter-to-extinction ratio, is essential for climate modeling.
Lidar (Light Detection and Ranging) is a key remote sensing technique for profiling atmospheric properties.

Purpose of the Study:

To develop and validate an iterative method for simultaneously determining the backscatter-to-extinction ratio and extinction coefficient in cirrus clouds.
To establish the signal-to-noise ratio requirements for achieving a specific error tolerance in the retrieved parameters.

Main Methods:

The proposed method utilizes an iterative approach based on Klett's inversion algorithm.
The inversion process is constrained by the total optical depth of the cirrus cloud.
The method's performance is evaluated using simulated lidar signals and an experimental lidar dataset.

Main Results:

The iterative method successfully determines both the backscatter-to-extinction ratio and extinction coefficient simultaneously.
A signal-to-noise ratio greater than 3 at cloud top is identified as a requirement for achieving an error below 20% in the backscatter-to-extinction ratio.
Validation with simulated and experimental data demonstrates the method's applicability.

Conclusions:

The developed iterative method provides a robust approach for retrieving key cirrus cloud optical properties from lidar measurements.
The findings highlight the importance of signal quality for accurate remote sensing of cloud parameters.
This method can enhance the understanding of cirrus cloud radiative effects and improve climate models.