A STATISTICAL-ANALYSIS FOR PATTERN-RECOGNITION OF SMALL CLOUD PARTICLES SAMPLED WITH A PMS-2DC PROBE

Although small particles (size between 25 mu m and 200 mu m) are frequ ently observed within ice and water clouds, they are not generally use d properly for the calculation of structural, optical and microphysica l quantities. Actually neither the exact shape nor the phase (ice or w ater) of these particles is well defined since the existing pattern re cognition algorithms are only efficient for larger particle sizes. The present study describes a statistical analysis concerning small hexag onal columns and spherical particles sampled with a PMS-2DC probe, and the corresponding images are classified according to the occurrence p robability of various pixels arrangements. This approach was first app lied to synthetic data generated with a numerical model, including the effects of diffraction at a short distance, and then validated agains t actual data sets obtained from in-cloud flights during the pre-ICE'8 9 campaign. Our method allows us to differentiate small hexagonal colu mns from spherical particles, thus making possible the characterizatio n of the three dimensional shape (and consequently evaluation of the v olume) of the particles, and finally to compute e.g., the liquid or th e ice water content.