Multiple satellite observations of cloud cover in extratropical cyclones. Issue 17 (9th September 2013) - Naud, Catherine M.; Booth, James F.; Posselt, Derek J.; van den Heever, Susan C.
Abstract: [1] Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat‐CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone‐centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold‐warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector. Key points: Cyclone‐centered cloud fraction composites from three data sets are compared Seasonal contrast in cyclone cloudiness is more pronounced in NH than SH The three data sets together better constrain models than individually