Testing a polarimetric cloud imager aboard research vessel Polarstern: comparison of color-based and polarimetric cloud detection algorithms

摘要: Cloud cover estimation is an important part of routine meteorological observations. Cloudiness measurements are used in climate model evaluation, nowcasting solar radiation, parameterizing the fluctuations sea surface insolation, and building energy transfer models atmosphere. Currently, most widespread ground-based method to measure cloudiness based on analyzing unpolarized intensity color distribution sky obtained by digital cameras. As a new approach, we propose that cloud detection can be aided additional use skylight polarization measured 180° field-of-view imaging polarimetry. In fall 2010, tested such novel polarimetric detector aboard research vessel Polarstern during expedition ANT-XXVII/1. One our goals was test durability measurement hardware under extreme conditions trans-Atlantic cruise. Here, describe instrument compare results several different algorithms, some conventional newly developed. We also discuss weaknesses design its possible improvements. The comparison with algorithms developed for traditional nonpolarimetric full-sky imagers allowed us evaluate added value quantities. found (1) neural-network-based perform best among investigated schemes (2) global information (the mean variance intensity), nonoptical (e.g., sun-view geometry), degree polarization) improve accuracy detection, albeit slightly.