Laboratoire de METEOROLOGIE PHYSIQUE Assessment of cloud optical parameters in the solar region : Retrievals from airborne measurements of scattering phase.

Présentation au sujet: "Laboratoire de METEOROLOGIE PHYSIQUE Assessment of cloud optical parameters in the solar region : Retrievals from airborne measurements of scattering phase."— Transcription de la présentation:

1 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 1 Laboratoire de Météorologie Physique, Clermont-Ferrand, France 2 Department 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 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 CO 2 doubling [Miloshevich et Heymsfield, 1997][Kristjanson et al., 2000] 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 µm describing 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 Principle of the inversion method The inverse problem is set up to retrieve simultaneously both water and ice particle size distributions. 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] Mie theoryImproved geometric optics [Yang et al., 1996] A direct hybrid model (combination of spherical and hexagonal particles) is implemented to compute scattering efficiency factors Q 1 et Q 2 (look up tables) The accuracy and the representativeness of the retrievals mainly depend on the choice of the direct model

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

9 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 σ ext (km -1 ) : 39. ± 0.1 / 40 ± 20  :1.00 - 0.02 / 1.000 g : 0.85 ± 0.01 / 0.84 ± 0.02 Extrapolation and projection in the infra-red

10 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 σ ext (km -1 ) : 0.80 ± 0.05 / 2 ± 2  : 1.00 - 0.06 / 1.0 g : 0.79 ± 0.05 / 0.75 ± 0.1 Extrapolation and projection in the infra-red 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 sensing Look up tables for cirrus clouds

13 Inverse Problem Numerical solution : set of linear algebraic equation Maximum likehood solution is the least squares method (LSM) solution : :log Normal noise distribution : Covariance matrix of measurements The additional terms enable us to improve inversion of the Fisher matrix LSM estimations : Solving in log space, a i =ln  i and f j =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 : Covariance matrix of the retrieved particule size distribution : : Covariance matrix of measurements Covariance matrix of the extrapolated and projected phase function : Optical and Microphysical Parameters :

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

16 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 σ ext (km -1 ) : 9.0 ± 0.4 / 13 ± 10  : 1.00 - 0.04 / 1.0 g : 0.80 ± 0.03 / 0.80 ± 0.07 Application à la télédection Extrapolation et projection dans l’infra-rouge

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

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 glace dans 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