Satellite remote sensing of clouds has significantly improved with the launch at the A-Train constellation: a group of satellites sharing nearly the same orbit, and passing over a location on Earth one after another, only seconds-to-minutes apart. The constellation included two active cloud sensors: cloud profiling lidar (onboard the CALIPSO satellite), and cloud radar (onboard the CloudSat satellite).
During the seminar I will address three following topics, related to the CloudSat-CALIPSO lidar-radar mission. First, I will discuss how the synergy between the cloud lidar and the cloud imager can improve our assessments of cloud amount. I will demonstrate how one can calibrate an imperfect MODIS estimates with the state-of-the-art CALIPSO profiles, resulting in a more reliable cloud climatology. Next, the unique nature of CALIPSO’s lidar data will be used for the very first quantitative validation of a surface-based detection of cirrus clouds. Design and orbit of the CALIPSO mission make the satellite the most sensitive and the most accurate tool for obtaining global data on cirrus. On the other hand, surface-based, visual cirrus detections are believed to be the least reliable among all cloud types studied from the ground. I will report how that “unreliability” translates into the probabilities of detection. Finally, I will focus on the dataset formed from a joint CloudSat-CALIPSO observations, namely the unique, lidar-radar, vertically-resoled cloud climatology. The climatology results from 5 years of CloudSat and CALIPSO observations, performed nearly simultaneously, when both spacecrafts flew in a very close orbital formation. However, the orbital configuration only allowed the satellites to sample a location every 16 day (i.e., 22-23 times per year). Is such sampling frequency sufficient to produce a reliable cloud climatology? The answer will be given by a study that reports the confidence intervals for the CloudSat-CALIPSO mean cloud amounts globally.
Andrzej Kotarba home page