1 / 19

Laboratoire de METEOROLOGIE PHYSIQUE

Laboratoire de METEOROLOGIE PHYSIQUE. Assessment of cloud optical parameters in the solar region : Retrievals from airborne measurements of scattering phase functions. O. Jourdan 1,2 , V.N. Shcherbakov 1 , S.L. Oshchepkov 3 and J.F. Gayet 1

ziv
Download Presentation

Laboratoire de METEOROLOGIE PHYSIQUE

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Laboratoire de METEOROLOGIE PHYSIQUE Assessment of cloud optical parameters in the solar region : Retrievals from airborne measurements of scattering phase functions O. Jourdan1,2, V.N. Shcherbakov1, S.L. Oshchepkov3 and J.F. Gayet1 1Laboratoire de Météorologie Physique, Clermont-Ferrand, France 2Department of Physics, National University of Ireland, Galway, Ireland 3 National Institute of Environmental Studies, Tsukuba, Japan

  2. Effect of cloud particle shape and size on radiative forcing [Kristjanson et al., 2000] [Miloshevich et Heymsfield, 1997] • Order of magnitude of uncertainties resulting in choosing a particular ice crystal parameterizations in GCM equivalent to 50% of the radiative effect associated with a CO2 doubling • More adequate modeling of ice crystals optical and microphysical properties is necessary for reliable inputs into climate models (Phase function (g), extinction, particle size distribution and shape)

  3. Scientific objectives To perform an accurate and representative characterization of cloud’s optical and microphysical properties in different thermodynamic phases using in situ measurements To use the information provided by high resolution scale airborne measurements to improve and validate satellite retrieval algorithms (LUTs) allowing reliable assessment of cloud parameters at a global scale

  4. Airborne experimental setup • Forward Scattering Spectrometer Probe(FSSP-100) • Droplet size distributions for diameters ranging from de 2 µm to 47µm • Water concentration , Liquid water content, Effective diameter • Bi-dimensional optical array spectrometer(2D-C) • Ice crystal shapes and size-distributions for diameters ranging from 100 µm to 800 µm • Ice concentration, Ice water content, equivalent diameter • Airborne “Polar Nephelometer” • Scattering intensity of an ensemble of cloud particles from 3 µm to 800 µm diameter over 28 angles ranging from 15° to 155° at =0.8 µm • Angular scattering coefficients,(), particle size distributions using an inversion method

  5. Methodology (Part 1) • The analyzed data set consists of measurements from ARAT’97, CIRRUS’98 and JACCS’99 representing more than 60,000 microphysical and optical measurements at 1Hz in a wide variety of meteorological conditions (As, Ac, Sc, Ns, Ci) • A principal component analysis (PCA) is applied to the set of optical measurements (angular scattering coefficients) • Each principal component or eigenvector corresponds to a particular optical behavior within the data base • The angular scattering coefficients are projected in the space of principal component to facilitate the interpretation of physical trends

  6. Methodology (Part II) • The interpretation of the patterns revealed by the PCA is achieved through neural network analysis (multilayer perceptron). Clouds’ “optical signatures” are classified according to their particle phase composition (three thermodynamical phases). • The next step is performed by extracting three average () from 15° to 155° at =0.8 µmdescribing the representative single scattering properties of the three types of clouds (liquid, mixed, ice phase). • The inverse problem needs to be solved to derive representative cloud microphysical properties from scattering measurements. • Accurate resolution of the problem relies on the setting up of physical direct modeling of light scattering process to link cloud’s microphysical to cloud’s optical properties

  7. Mie theory Improved geometric optics [Yang et al., 1996] Principle of the inversion method • The inverse problem is set up to retrieve simultaneously both water and ice particle size distributions. • A direct hybrid model (combination of spherical and hexagonal particles) is implemented to compute scattering efficiency factors Q1 et Q2 (look up tables) • The accuracy and the representativeness of the retrievals mainly depend on the choice of the direct model • The inversion method consists of a non-linear least square fitting of the scattering phase function using smoothness constraints on the desired particle size distributions.[Oshchepkov et al., 2000; Jourdan et al., 2003a, JGR]

  8. Inversion of the averaged scattering phase functions for the three types of cloud Jourdan et al., 2003a, JGR

  9. Extrapolation and projection in the infra-red Microphysics Retrievals / Measurements Conc (cm-3) : 185 / 215 TWC (g.m-3) : 0.16 / 0.13 Reff (µm) : 6.7 / 5.9 Optical parameters at 0.8 µm Retrievals / literature σext (km-1) : 39. ± 0.1 / 40 ± 20  :1.00 - 0.02 / 1.000 g : 0.85 ± 0.01 / 0.84 ± 0.02

  10. Extrapolation and projection in the infra-red • Microphysics • Retrievals / Measurements • Conc (cm-3) : 13 / 0.4 • TWC (g.m-3) : 0.016 / 0.011 • Reff (µm) : 32.3 / 35.7 • Optical parameters at 0.8 µm • Retrievals / literature • σext (km-1) : 0.80 ± 0.05 / 2± 2 • : 1.00 - 0.06 / 1.0 g : 0.79 ± 0.05 / 0.75 ± 0.1 Jourdan et al., 2003b, JGR

  11. Conclusions and outlook • A statistical analysis (PCA) and a neural network classification algorithm allowed us to establish typical phase functions for different type of clouds (liquid, mixed and solid phase). • The information contained in the scattering phase function measurements from 15° to 155° is sufficient to accurately restore component composition and particle size distribution.  • The statistical analysis is in agreement with physical modeling of scattering phase functions using direct PSD measurements for each type of clouds. • Extrapolation and projection in the I.R. (with propagation of errors) enabled us to fully characterize clouds optical properties which could be included in radiative transfer analyses

  12. Application to passive remote sensingLook up tables for cirrus clouds

  13. Inverse Problem • Numerical solution : set of linear algebraic equation :log Normal noise distribution • Maximum likehood solution is the least squares method (LSM) solution : : Covariance matrix of measurements • LSM estimations : • The additional terms enable us to improve inversion of the Fisher matrix • Solving in log space, ai=lni and fj=lnj transforms the problem in a non linear one :

  14. Extrapolation, Projection in IR, Error analysis • Extrapolated (=0.8µm) and Projected (=1.6µm and =3.7µm) Scattering Phase Functions : • Optical and Microphysical Parameters : • Covariance matrix of the retrieved particule size distribution : : Covariance matrix of measurements • Covariance matrix of the extrapolated and projected phase function :

  15. Application à la télédection Extrapolation et projection dans l’infra-rouge • Calcul des matrices de diffusion • Extrapolation et projection • Calcul des paramètres optiques

  16. Application à la télédection Extrapolation et projection dans l’infra-rouge Paramètres microphysiques Restitutions / Mesures Conc (cm-3) : 307 / 56 TWC (g.m-3) : 0.035 / 0.015 Reff (µm) : 7.0 / 6.3 Paramètres optiques à 0.8 µm Restitutions / Littérature σext (km-1) : 9.0 ± 0.4 / 13± 10  : 1.00 - 0.04 / 1.0 g : 0.80 ± 0.03 /0.80 ± 0.07

  17. Problème inverse Contenu en information angulaireSimulations numériques • Quels sont les paramètres microphysiques restituables par inversion des indicatrices de diffusion? • Les simulations numériques montrent qu’il est possible de restituer une information sur la taille, la composition microphysique et laforme des particules même lorsque () n’est que partiellement documentée

  18. Differences between ice crystal and water droplet scattering Liquid-solid phase discrimination

  19. Perspectives • L’ amélioration de l’optique du Néphélomètre Polaire pour caractériser les angles de diffusion avant et arrière et prendre en compte la polarisation permettrait de réduire les erreurs de restitutions • Le couplage du Néphélomètre Polaire avec une sonde telle que le Cloud Particule Imager apporterait une source d’information nouvelle pour valider notre algorithme de restitution • La prise en compte des effets de rugosité et d’inhomogénéité des cristaux de glacedans le modèle direct hybride devrait permettre de reduire les effets de propagation des erreurs lors de l’extrapolation et de la projection des indicatrices de diffusion à d’autres longueurs d’onde • Les indicatrices de diffusion obtenues pour les différents types de nuages et à trois longueurs d’ondes devront être intégrées dans les algorithmes de restitutions utilisés en télédection passive pour évaluer la contribution réelle de ce travail

More Related