An efficient gradient method for maximum entropy regularizing retrieval of atmospheric aerosol particle size distribution function

Wang, Yanfei

doi:10.1016/j.jaerosci.2007.11.008

Cited by 28 publications

(11 citation statements)

References 30 publications

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“…Another example can be found in [8], which takes special care to incorporate a priori information on the solution in the form of a nonnegativity constraint. Also, new mathematical methods have been considered, like for instance the maximum entropy method in [9] or Runge-Kutta type iteration methods in [10]. In this paper, we propose a novel method of finding a general formula for retrieving the aerosol microphsyical properties from inversion of multiwavelength lidar data, which can be used in a wide area of applications, not specializing on certain cases where lots of a priori information about the particles is already given.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Base Point Algorithm for the Retrieval of Aerosol Microphysical Properties

Osterloh

Böckmann²,

Mamouri³

et al. 2011

TOASCJ

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Abstract:We propose a new iterative algorithm for the retrieval of the microphysical properties of stratospheric and tropospheric aerosols from multiwavelength lidar data. We consider the basic equation as an ill-posed problem and solve the system derived from spline collocation via a Padé iteration. The algorithm takes special care of the fact that the reconstruction of the distribution via spline collocation is very sensitive to the choice of base points of the chosen spline basis. The algorithm makes use of this fact by changing the base points used for the spline collocation at certain iteration steps. In addition, the effects of projection to ensure a nonnegative solution are examined. We tested how well this and other algorithms are suitable for retrieving the complex refractive index of the particles as well. We also examine whether the algorithm is capable of distinguishing between different, very small imaginary parts of the refractive index, which is often a main problem in practice. Finally, the algorithm is applied to real multiwavelength Raman lidar data and our results are partially validated by the thermodynamic chemical model Isorropia II.

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Section: Introductionmentioning

confidence: 99%

An Adaptive Base Point Algorithm for the Retrieval of Aerosol Microphysical Properties

Osterloh

Böckmann²,

Mamouri³

et al. 2011

TOASCJ

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“…The other evolution conditions of the GA are floating point coding, random crossing points, random mating ratios, a 1% mutation ratio, and the remainder selection rule. The fitness function is Fitness minfcondPg; (4) where condP corresponds to the CN of the kernel matrix and minf g forces fitness to the minimum CN value. For the simulation in this paper, the groups evolved through 600 generations before accepting the angles as optimized.…”

Section: Angle Optimizationmentioning

confidence: 99%

“…That is to say, a small measuring error in the F Λ inp θ matrix introduces a large error to n i as a result. A number of previous research works have looked at solutions to this problem [4][5][6][7][8]. In this paper, we further study this issue.…”

Section: Introductionmentioning

confidence: 98%

Discussion concerning ill-conditioned kernel matrices when retrieving particle size distributions based on scattered radiation distribution

Zuo

Luo

2012

J. Opt. Soc. Am. B

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This paper presents two conclusions concerning the scattered light distribution method for particle sizing: (1) a large difference in vector values is one of the primary causes of an ill-conditioned kernel matrix, and (2) under narrow band and high resolution particle size conditions, the forward scattering region would introduce a larger error into the particle size distribution. Numerical simulations verify these results.

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“…This method initially designs for wellposed convex quadratic programming problems. However, it reveals that the method is also applicable for ill-posed problems and non quadratic programming problems provided that the deviation of the non quadratic model is not far away from the quadratic model (Wang and Ma 2007;Wang 2008). Let us consider the quasi-Newton equation of the minimization problem (10).…”

Section: Gradient Descent Solution Using Rayleigh Quotientmentioning

confidence: 99%

Hybrid regularization methods for seismic reflectivity inversion

2011

Self Cite

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In this paper, we consider new solution methods for seismic deconvolution problem in seismic data processing. We study regularization methods and introduce a hybrid regularization scheme for seismic deconvolution. Fast gradient descent methods are considered for solving the hybrid regularization model. An l 1 minimization model based on primal-dual interior point methods is presented and used for comparison. Numerical experiments for synthetic signal simulations and field data applications are given. The results show the potentials for applications of the developed method.

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An efficient gradient method for maximum entropy regularizing retrieval of atmospheric aerosol particle size distribution function

Cited by 28 publications

References 30 publications

An Adaptive Base Point Algorithm for the Retrieval of Aerosol Microphysical Properties

An Adaptive Base Point Algorithm for the Retrieval of Aerosol Microphysical Properties

Discussion concerning ill-conditioned kernel matrices when retrieving particle size distributions based on scattered radiation distribution

Hybrid regularization methods for seismic reflectivity inversion

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