Abstract
Microphysical and optical properties of cloud particles are known to be a primordial source of
information for the understanding of light interactions in the atmosphere system through radiative and chemical
processes, hence of the cloud feedback on terrestrial climate. However, given the complexity and variability of
cloud’s microphysical and structural properties, the representation of the coupling between microphysical and
radiative processes remains limited in climate models. In order to reduce uncertainties in these models, there is
an urgent need to develop accurate microphysical parameterizations that will lead to more realistic cloud
radiative behavior. Therefore, the reliable modeling of radiative processes in a cloudy atmosphere requires well-
documented observations of clouds’ optical and microphysical properties at different spatial scales.
This study lies within this framework presenting an accurate characterization of cloud’s optical and
microphysical properties in different thermodynamic phases using in situ measurements. The use of the
information provided by the synergy between high-resolution scale airborne measurements will thereby enable
the improvement and the validation of global scale satellite retrieval algorithms.
As a first step, the relationship between clouds’ microphysical and optical properties was investigated
considering an hybrid microphysical model based on a combination of spherical water droplets and hexagonal
ice crystals of variable aspect ratio. Even though this approach might not be correct from a microphysical point
of view, it was shown that this hybrid model was able to model optical properties in agreement with in situ
measurements for different types of clouds relative to their particle-phase composition.
Secondly, a database including a large set of in situ microphysical and optical measurements performed
by different airborne instruments in a wide variety of meteorological conditions was constructed. A principal
component analysis (PCA) of the in situ angular scattering coefficients measurements performed by the LaMP’s
airborne “Polar Nephelometer” was implemented in order to assess a representative set of single scattering
characteristics for different groups of clouds. This analysis enables one to gather the measurements as a function
of their “optical signing”, revealing the variability of clouds’ microphysical properties inside the database. Then,
classification of the revealed patterns in the principal component space was achieved by using a neural network
(multilayer perceptron) which has the advantage of involving all the information carried by the angular scattering
coefficients measurements. Consequently, the complementary nature of the PCA and neural network allowed us
to classify the data set into three specific groups of clouds relative to their particle phase composition (liquid-
water droplets, mixed-phase and solid-ice particles). This cloud classification was then validated by the
discrimination of the cloud water phase on the basis of the ratio of bulk microphysical parameters derived from
direct microphysical PMS (FSSP-100 and 2D-C) probe measurements.
The next step was performed by extracting three average angular scattering coefficients, established for
scattering angles ranging from 15° to 155° at a wavelength of 0.8 µm, describing the representative single
scattering properties of the three types of clouds. The interpretation of these typical phase functions, using an
inversion technique based on the physical modeling of light scattering processes computed by the hybrid model,
showed that the information contained in the scattering measurements is sufficient to restore component
composition and particle volume distributions for the three types of clouds. This statement was supported by
rather good agreement of the inversion results (the retrieved particle distributions) with the particle size
composition obtained by the collocated independent FSSP and 2D-C measurements for each selected clouds.
However, the results established by these analyses do not account for the complete set of clouds’ optical
parameters needed for the direct and inverse modeling of radiative transfer.
Consequently, the information contained in the average angular scattering coefficients, measured at 0.8
µm for near uniformly positioned scattering angles from 15° to 155°, was exported to the forward and backward
scattering directions and then projected to two other wavelengths in the near infra red region (1.6 µm and 3.7
µm) which are valuable for radiative transfer modeling. Accordingly, the hybrid direct model was applied to the
retrieved particle volume distributions to compute representative angular scattering coefficients documented
between 0° and 180° for wavelengths 0.8µm, 1.6µm and 3.7µm. This process was implemented for the three
types of clouds as well as for cirrus cloud whose radiative effect is subject to high uncertainties in the scientific
community. Thus, a complete set of average microphysical and optical parameters was established for four types
of clouds and allowed us to construct representative look-up tables which can directly be input in cloud
parameter retrieval algorithm used in passive remote sensing techniques.