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Applying X-ray Imaging Crystal Spectroscopy for Use as a High Temperature Plasma Diagnostic
Published on: August 25, 2016
Efficient nonlinear inversion for atmospheric sounding and other applications.
Richard Lynch1, Jean-Luc Moncet, Xu Liu
1Atmospheric and Environmental Research, Inc., 131 Hartwell Avenue, Lexington, Massachusetts 02421-3126, USA. rlynch@aer.com
Applied Optics
|April 3, 2009
Summary
A new method, DRAD, efficiently retrieves atmospheric variables from remote sensing data, even with highly nonlinear problems and uncertain initial guesses. This technique improves upon existing methods like Levenberg-Marquardt for atmospheric profile retrieval.
Area of Science:
- Atmospheric Science
- Remote Sensing
- Data Assimilation
Background:
- Retrieving atmospheric and surface variables from remote observations commonly requires minimizing nonlinear cost functions.
- The success of minimization methods heavily depends on the nonlinearity of the cost function and the accuracy of the initial guess.
- Existing methods may struggle with highly nonlinear problems or limited prior information.
Purpose of the Study:
- To introduce and evaluate a novel minimization method, DRAD (iterative), for atmospheric state variable retrieval.
- To demonstrate DRAD's applicability to highly nonlinear cost functions and scenarios with minimal a priori information.
- To assess the efficiency of DRAD across a range of initial guess errors.
Main Methods:
- Development and application of the DRAD minimization method.
- Retrieval of water vapor and temperature profiles using simulated Atmospheric Infrared Sounder (AIRS) observations.
- Comparative analysis with the Levenberg-Marquardt method for retrieval efficiency.
Main Results:
- DRAD demonstrates effectiveness in retrieving atmospheric profiles under conditions of high nonlinearity.
- The method shows efficiency across a wide spectrum of initial guess errors, outperforming comparisons in challenging cases.
- Simulated AIRS data retrieval confirms DRAD's capability for water vapor and temperature profile estimation.
Conclusions:
- DRAD offers a robust and efficient solution for atmospheric retrieval problems characterized by significant nonlinearity.
- The method's performance across varying initial guess errors highlights its practical utility in remote sensing applications.
- DRAD represents a valuable advancement for improving the accuracy and reliability of atmospheric state variable retrieval.
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