Point comparisons of research aircraft data to GOES-derived cloud products

GOES-derived cloud products developed at NASA Langley Research Center are being examined as possible upgrades to the Current Icing Potential (CIP) algorithm, developed at NCAR. This algorithm combines observed and model data to create an hourly diagnosis of icing potential and intensity. The GOES-derived products give real-time information about the clouds that is not available from standard satellite fields. Some of the fields that are candidates for inclusion in CIP are cloud phase, liquid water path, effective radius, and optical depth.

Part of the examination process has involved comparing the derived cloud fields to research aircraft data. The purpose of these comparisons is to determine if there are correlations between individual fields or combinations of fields and measured liquid water content, which is an important indicator of the potential for icing. This will aid in determining which fields should be incorporated into CIP and how to best combine them for a good icing diagnosis. It will also show the situations where the products perform well along with those in which they perform poorly.

The aircraft data were averaged over 4 km segments, to match the resolution of the satellite products. The average supercooled liquid water content (SLWC) for that segment was then compared to the nearest pixel value of various satellite products. This was done for all segments and for segments that were determined to be near cloud top without obscuration by higher clouds. Even though the satellite products are most meaningful near cloud top the comparisons for all segments were done in order to assess how well the products may be able to be applied three dimensionally. Comparing the aircraft data at or near the cloud top does not handicap the satellite products and should give better correlations.

Results of these comparisons will be presented along with cases demonstrating both good and poor correlations and the reasons for them.