Calculation of Mie derivatives

Grainger, R. G.; Lucas, Jonathan; Thomas, G. E.; Ewen, G. B. L.

doi:10.1364/ao.43.005386

Cited by 77 publications

(46 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we define η = C f / C f + C c as the fine mode fraction of aerosol, and this will be used later. The optical properties of the aerosols were computed using Mie theory [52,53]. Prior to this, a fine mode and the corresponding coarse mode (the same cluster number) were combined by the weight of the fine mode fraction.…”

Section: Aerosol Retrieval Methodsmentioning

confidence: 99%

Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China

et al. 2017

View full text Add to dashboard Cite

Abstract:The airborne Atmosphere Multi-angle Polarization Radiometer (AMPR) was employed to perform airborne measurements over North China between 2012 and 2016. Seven flights and synchronous ground-based observations were acquired. These data were used to test the sensor's measurements and associated aerosol retrieval algorithm. According to the AMPR measurements, a successive surface-atmosphere decoupling based algorithm was developed to retrieve the aerosol optical depth (AOD). It works via an iteration method, and the lookup table was employed in the aerosol inversion. Throughout the results of the AMPR retrievals, the surface polarized reflectances derived from air-and ground-based instruments were well matched; the measured and simulated reflectances at the aircraft level, which were simulated based on in situ sun photometer observed aerosol properties, were in good agreement; and the AOD measurements were validated against the automatic sun-photometer (CE318) at the nearest time and location. The AOD results were close; the average deviation was less than 0.03. The MODIS AODs were also employed to test the AMPR retrievals, and they showed the same trend. These results illustrate that (i) the successive surface-atmosphere decoupling method in the retrieved program completed its mission and (ii) the aerosol retrieval method has its rationality and potential ability in the regionally accurate remote sensing of aerosol.

show abstract

Section: Aerosol Retrieval Methodsmentioning

confidence: 99%

Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Computational efficiency is optimized by pre-computing these properties of the ash layer using DISORT (discrete ordinate method for radiative transfer; Stamnes et al, 1988) and storing the results in look-up-tables (LUTs), which are linearly interpolated spectrally to the appropriate values. The spectral aerosol optical properties (extinction coefficient, singlescattering albedo and the phase function) for ash are calculated using Mie theory (Grainger et al (2004); code available at http://eodg.atm.ox.ac.uk/MIE/) and external mixing. The ash particles are assumed to be spherical with a monomodal log-normal aerosol size distribution, which has been shown to be a suitable representation of the size distribution of airborne volcanic ash (Wohletz et al, 1989).…”

Section: Forward Model Descriptionmentioning

confidence: 99%

Retrieval of ash properties from IASI measurements

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. A new optimal estimation algorithm for the retrieval of volcanic ash properties has been developed for use with the Infrared Atmospheric Sounding Interferometer (IASI). The retrieval method uses the wave number range 680-1200 cm −1 , which contains window channels, the CO 2 ν 2 band (used for the height retrieval), and the O 3 ν 3 band.Assuming a single infinitely (geometrically) thin ash plume and combining this with the output from the radiative transfer model RTTOV, the retrieval algorithm produces the most probable values for the ash optical depth (AOD), particle effective radius, plume top height, and effective radiating temperature. A comprehensive uncertainty budget is obtained for each pixel. Improvements to the algorithm through the use of different measurement error covariance matrices are explored, comparing the results from a sensitivity study of the retrieval process using covariance matrices trained on either clear-sky or cloudy scenes. The result showed that, due to the smaller variance contained within it, the clear-sky covariance matrix is preferable. However, if the retrieval fails to pass the quality control tests, the cloudy covariance matrix is implemented.The retrieval algorithm is applied to scenes from the Eyjafjallajökull eruption in 2010, and the retrieved parameters are compared to ancillary data sources. The ash optical depth gives a root mean square error (RMSE) difference of 0.46 when compared to retrievals from the MODerate-resolution Imaging Spectroradiometer (MODIS) instrument for all pixels and an improved RMSE of 0.2 for low optical depths (AOD < 0.1). Measurements from the Facility for Airborne Atmospheric Measurements (FAAM) and Deutsches Zentrum für Luft-und Raumfahrt e.V. (DLR) flight campaigns are used to verify the retrieved particle effective radius, with the retrieved distribution of sizes for the scene showing excellent consistency. Further, the plume top altitudes are compared to derived cloud-top altitudes from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument and show agreement with RMSE values of less than 1 km.

show abstract

“…For example, when both refractive indices and particle size parameters are to be extracted from large aerosol particle spectra, the CDHO fitting can be employed where only a handful of parameters are need optimisation. 37,48 Based on the quantitative analysis in this study, we conclude that the Fourier series fitting is the most desirable method, especially when spectral features are not very narrow (> 10 cm -1 ). In cases where the spectral features are narrow (< 10 cm -1 ) the CDHO and the Triangular fitting are the most accurate and fast of the four methods.…”

Section: Discussionmentioning

confidence: 70%

Spectral curve fitting of dielectric constants

2017

View full text Add to dashboard Cite

Optical constants are important properties governing the response of a material to incident light. It follows that they are often extracted from spectra measured by absorbance, transmittance or reflectance. One convenient method to obtain optical constants is by curve fitting. Here, model curves should satisfy Kramer-Kronig relations, and preferably can be expressed in closed form or easily calculable. In this study we use dielectric constants of three different molecular ices in the infrared region to evaluate four different model curves that are generally used for fitting optical constants: (1) the classical damped harmonic oscillator, (2) Voigt line shape, (3) Fourier series, and (4) the Triangular basis. Among these, only the classical damped harmonic oscillator model strictly satisfies the Kramer-Kronig relation. If considering the trade-off between accuracy and speed, Fourier series fitting is the best option when spectral bands are broad while for narrow peaks the classical damped harmonic oscillator and the Triangular basis fitting model are the best choice.

show abstract

Calculation of Mie derivatives

Cited by 77 publications

References 14 publications

Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China

Remote Sensing of Aerosol Optical Depth Using an Airborne Polarimeter over North China

Retrieval of ash properties from IASI measurements

Spectral curve fitting of dielectric constants

Contact Info

Product

Resources

About