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.
Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog