scholarly journals A statistical analysis for pattern recognition of small cloud particles sampled with a PMS-2DC probe

1997 ◽  
Vol 15 (6) ◽  
pp. 840-846 ◽  
Author(s):  
A. Fouilloux ◽  
J. Iaquinta ◽  
C. Duroure ◽  
F. Albers
Keyword(s):  
Pattern Recognition ◽  
Statistical Analysis ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Spherical Particles ◽  
Data Sets ◽  
Ice Water Content ◽  
Dimensional Shape ◽  
Exact Shape ◽  
Ice Water

Abstract. Although small particles (size between 25 µm and 200 µm) are frequently observed within ice and water clouds, they are not generally used properly for the calculation of structural, optical and microphysical quantities. Actually neither the exact shape nor the phase (ice or water) of these particles is well defined since the existing pattern recognition algorithms are only efficient for larger particle sizes. The present study describes a statistical analysis concerning small hexagonal columns and spherical particles sampled with a PMS-2DC probe, and the corresponding images are classified according to the occurrence probability of various pixels arrangements. This approach was first applied to synthetic data generated with a numerical model, including the effects of diffraction at a short distance, and then validated against actual data sets obtained from in-cloud flights during the pre-ICE'89 campaign. Our method allows us to differentiate small hexagonal columns from spherical particles, thus making possible the characterization of the three dimensional shape (and consequently evaluation of the volume) of the particles, and finally to compute e.g., the liquid or the ice water content.

ANALYSIS OF MORPHOLOGICAL GROUPINGS USING ANOPA, A PATTERN RECOGNITION AND MULTIVARIATE STATISTICAL METHOD: A CASE STUDY INVOLVING CENTRARCHID FISHES

2004 ◽  
Vol 12 (02) ◽  
pp. 137-167 ◽  
Author(s):  
DAVID P. CAVANAUGH ◽  
RICHARD V. STERNBERG
Keyword(s):  
Pattern Recognition ◽  
Statistical Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
Discrete Subgroups ◽  
Multivariate Statistical ◽  
Fish Family ◽  
Cohesive Group ◽  
Statistical Analysis Method ◽  
Taxonomic Groups

Morphological relationships within and among taxonomic groups can be very complicated, with anatomical data often supporting two or more incongruent groupings. One possibility is that incongruent character states are taxonomically informative, although in an N-dimensional taxic space. To test the above, morphological relationships of centrarchid fish species were examined using a new pattern recognition, multivariate correlation, and multivariate statistical analysis method (ANOPA). The objective of ANOPA is to identify N-dimensional pattern space correlations among character states, relations that cannot be detected with standard phenetic or phylogenetic approaches. ANOPA provides a solution to an inherent weakness in statistical analysis which occurs in the face of set classification ambiguity, where there is no a priori reason to assign a membership or class identification within multivariate statistical groups. This approach revealed the percoid fish family Centrarchidae to be a statistically significant, cohesive group with complicated internal relationships. Centrarchid taxa are resolved into three major generic aggregates by two and three-dimensional ANOPA, and discrete subgroups were also detected. The complex interrelationships within the Centrarchidae cannot be readily collapsed to a bifurcating tree-structure, explaining the multitude of conflicting phylogenetic hypotheses that have been presented. This is the first robust study of anatomical disparity in teleostean fishes. Applications of ANOPA to the study of morphological gaps, complex taxonomic patterns, and anatomical disparity are discussed.

scholarly journals A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and the Fourier transform framework

2014 ◽  
Vol 7 (4) ◽  
pp. 1779-1801 ◽  
Author(s):  
F. Szczap ◽  
Y. Gour ◽  
T. Fauchez ◽  
C. Cornet ◽  
T. Faure ◽  
...  
Keyword(s):  
Water Content ◽  
Three Dimensional ◽  
Atmospheric Equations ◽  
Cloudy Atmosphere ◽  
Fourier Filtering ◽  
Ice Water Content ◽  
Inhomogeneity Parameter ◽  
The Mean ◽  
The Fourier Transform ◽  
Ice Water

Abstract. The 3DCLOUD algorithm for generating stochastic three-dimensional (3-D) cloud fields is described in this paper. The generated outputs are 3-D optical depth (τ) for stratocumulus and cumulus fields and 3-D ice water content (IWC) for cirrus clouds. This model is designed to generate cloud fields that share some statistical properties observed in real clouds such as the inhomogeneity parameter ρ (standard deviation normalized by the mean of the studied quantity), the Fourier spectral slope β close to −5/3 between the smallest scale of the simulation to the outer Lout (where the spectrum becomes flat). Firstly, 3DCLOUD assimilates meteorological profiles (humidity, pressure, temperature and wind velocity). The cloud coverage C, defined by the user, can also be assimilated, but only for stratocumulus and cumulus regime. 3DCLOUD solves drastically simplified basic atmospheric equations, in order to simulate 3-D cloud structures of liquid or ice water content. Secondly, the Fourier filtering method is used to constrain the intensity of ρ, β, Lout and the mean of τ or IWC of these 3-D cloud structures. The 3DCLOUD model was developed to run on a personal computer under Matlab environment with the Matlab statistics toolbox. It is used to study 3-D interactions between cloudy atmosphere and radiation.

scholarly journals Statistical analysis of ice microphysical properties in tropical mesoscale convective systems derived from cloud radar and in situ microphysical observations

2020 ◽  
Vol 20 (6) ◽  
pp. 3503-3553
Author(s):  
Emmanuel Fontaine ◽  
Alfons Schwarzenboeck ◽  
Delphine Leroy ◽  
Julien Delanoë ◽  
Alain Protat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Content ◽  
Mesoscale Convective Systems ◽  
Size Distributions ◽  
Convective Systems ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
Mesoscale Convective ◽  
Ice Water Content ◽  
Ice Water

Abstract. This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and two optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties are calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, mesoscale convective systems (MCS) microphysical properties (ice water content, visible extinction, mass–size relationship coefficients, total concentrations, and second and third moments of hydrometeor size distribution) are sorted in temperature (and thus altitude) zones, and each individual campaign is subsequently analyzed with respect to median microphysical properties of the merged dataset (merging all four campaign datasets). The study demonstrates that ice water content (IWC), visible extinction, total crystal concentration, and the second and third moments of hydrometeor size distributions are similar in all four types of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first concerns the calculation of the visible extinction as a function of temperature and ice water content. The second concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.

scholarly journals Statistical Analysis of Ice Microphysical Properties in Tropical Mesoscale Convective Systems Derived from Cloud Radar and In-Situ Microphysical Observations

2019 ◽  
Author(s):  
Emmanuel Fontaine ◽  
Alfons Schwarzenboeck ◽  
Delphine Leroy ◽  
Julien Delanoë ◽  
Alain Protat ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Water Content ◽  
Mesoscale Convective Systems ◽  
Size Distributions ◽  
Convective Systems ◽  
Cloud Radar ◽  
Microphysical Properties ◽  
Mesoscale Convective ◽  
Ice Water Content ◽  
Ice Water

Abstract. This study presents a statistical analysis of the properties of ice hydrometeors in tropical mesoscale convective systems observed during four different aircraft campaigns. Among the instruments on board the aircraft, we focus on the synergy of a 94 GHz cloud radar and 2 optical array probes (OAP; measuring hydrometeor sizes from 10 µm to about 1 cm). For two campaigns, an accurate simultaneous measurement of the ice water content is available, while for the two others, ice water content is retrieved from the synergy of the radar reflectivity measurements and hydrometeor size and morphological retrievals from OAP probes. The statistics of ice hydrometeor properties is calculated as a function of radar reflectivity factor measurement percentiles and temperature. Hence, MCS microphysical properties (ice water content, visible extinction, mass-size relationship coefficients, total concentrations and second and third moment of hydrometeors size distribution) are sorted in temperature (thus altitude) zones, and subsequently each individual campaign is analysed with respect to median microphysical properties of the global dataset (merging all 4 campaign datasets). The study demonstrates that ice water content, visible extinction, total crystal concentration, and second and third moments of hydrometeors size distributions are similar in all 4 type of MCS for IWC larger than 0.1 g m−3. Finally, two parameterizations are developed for deep convective systems. The first one concerns the calculation of the visible extinction as a function of temperature and ice water content. The second one concerns the calculation of hydrometeor size distributions as a function of ice water content and temperature that can be used in numerical weather prediction.

scholarly journals 3-D polarised simulations of space-borne passive mm/sub-mm midlatitude cirrus observations: a case study

2007 ◽  
Vol 7 (15) ◽  
pp. 4149-4158 ◽  
Author(s):  
C. P. Davis ◽  
K. F. Evans ◽  
S. A. Buehler ◽  
D. L. Wu ◽  
H. C. Pumphrey
Keyword(s):  
Water Cycle ◽  
Spherical Geometry ◽  
Radar Data ◽  
Spherical Particles ◽  
Radiation Balance ◽  
Ice Water Content ◽  
Actual Field ◽  
Remote Sensing Techniques ◽  
Cloud Ice ◽  
Ice Water

Abstract. Global observations of ice clouds are needed to improve our understanding of their impact on earth's radiation balance and the water-cycle. Passive mm/sub-mm has some advantages compared to other space-borne cloud-ice remote sensing techniques. The physics of scattering makes forward radiative transfer modelling for such instruments challenging. This paper demonstrates the ability of a recently developed RT code, ARTS-MC, to accurately simulate observations of this type for a variety of viewing geometries corresponding to operational (AMSU-B, EOS-MLS) and proposed (CIWSIR) instruments. ARTS-MC employs an adjoint Monte-Carlo method, makes proper account of polarisation, and uses 3-D spherical geometry. The actual field of view characteristics for each instrument are also accounted for. A 3-D midlatitude cirrus scenario is used, which is derived from Chilbolton cloud radar data and a stochastic method for generating 3-D ice water content fields. These demonstration simulations clearly demonstrate the beamfilling effect, significant polarisation effects for non-spherical particles, and also a beamfilling effect with regard to polarisation.

scholarly journals A Statistical Comparison of Cirrus Particle Size Distributions Measured Using the 2D Stereo Probe During the TC<sup>4</sup>, SPartICus, and MACPEx Flight Campaigns with Historical Cirrus Datasets

2017 ◽  
Author(s):  
M. Christian Schwartz
Keyword(s):  
Particle Size ◽  
Ice Crystals ◽  
Data Sets ◽  
Ice Crystal ◽  
Cross Sectional ◽  
Measuring Systems ◽  
Field Investigations ◽  
Ice Water Content ◽  
Corrected Data ◽  
Ice Water

Abstract. This paper addresses two straightforward questions. First, how similar are the statistics of cirrus particle size distribution (PSD) datasets collected using the 2D Stereo (2D-S) probe to cirrus PSD datasets collected using older Particle Measuring Systems (PMS) 2D Cloud (2DC) and 2D Precipitation (2DP) probes? Second, how similar are the datasets when shatter-correcting post-processing is applied to the 2DC datasets? To answer these questions, a database of measured and parameterized cirrus PSDs, constructed from measurements taken during the Small Particles in Cirrus (SPartICus), Mid-latitude Airborne Cirrus Properties Experiment (MACPEx), and Tropical Composition, Cloud, and Climate Coupling (TC4) flight campaigns is used. Bulk cloud quantities are computed from the 2D-S database in three ways: first, directly from the 2D-S data; second, by applying the 2D-S data to ice PSD parameterizations developed using sets of cirrus measurements collected using the older PMS probes; and third, by applying the 2D-S data to a similar parameterization developed using the 2D-S data itself. Thereby a parameterized version of what the 2DC would have seen had it flown on the above missions next to the 2D-S is compared to a similarly parameterized version of the 2D-S. It is seen, given the same cloud field and given the same assumptions concerning ice crystal cross-sectional area, density, and radar cross section, that the parameterized 2D-S and the parameterized 2DC predict similar distributions of inferred shortwave extinction coefficient, ice water content, and 94 GHz radar reflectivity. However, the parameterization of the 2DC based on uncorrected data predicts a statistically significant higher number of total ice crystals and a larger ratio of small ice crystals to large ice crystals than does the parameterized 2D-S. The 2DC parameterization based on shatter-corrected data also predicts statistically different numbers of ice crystals than does the parameterized 2D-S, but the comparison between the two is nevertheless more favorable. It is concluded that the older data sets continue to be useful for scientific purposes, with certain caveats, and that continuing field investigations of cirrus with more modern probes is desirable.

scholarly journals A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and Fourier transform framework

2014 ◽  
Vol 7 (1) ◽  
pp. 295-337 ◽  
Author(s):  
F. Szczap ◽  
Y. Gour ◽  
T. Fauchez ◽  
C. Cornet ◽  
T. Faure ◽  
...  
Keyword(s):  
Water Content ◽  
Three Dimensional ◽  
Cirrus Cloud ◽  
Atmospheric Equations ◽  
Cloudy Atmosphere ◽  
Fourier Filtering ◽  
Ice Water Content ◽  
Inhomogeneity Parameter ◽  
The Mean ◽  
Ice Water

Abstract. The 3DCLOUD algorithm for generating stochastic three-dimensional (3-D) cloud fields is described in this paper. The generated outputs are 3-D optical depth (τ) for stratocumulus and cumulus fields and 3-D ice water content (IWC) for cirrus clouds. This model is designed to generate cloud fields that share some statistical properties observed in real clouds such as the inhomogeneity parameter ρ (standard deviation normalized by the mean of the studied quantity), the Fourier spectral slope β close to −5/3 between the smallest scale of the simulation to the outer Lout (where the spectrum becomes flat). Firstly, 3DCLOUD assimilates meteorological profiles (humidity, pressure, temperature and wind velocity). The cloud coverage C, defined by the user, can also be assimilated, but only for stratocumulus and cumulus regime. 3DCLOUD solves drastically simplified basic atmospheric equations, in order to simulate 3-D cloud structures of liquid or ice water content. Secondly, Fourier filtering method is used to constrain intensity of ρ, β, Lout and mean of τ or IWC of these 3-D cloud structures. 3DCLOUD model was developed to run on a personnel computer under Matlab environment with the Matlab statistics toolbox. It is used to study 3-D interactions between cloudy atmosphere and radiation.

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