Rational for the project

Clouds play an important role in the energy budget of our planet: optically thick clouds reflect the incoming solar radiation, leading to cooling the Earth, while thinner clouds act as “greenhouse films”, preventing escape of the Earth’s long-wave radiation to space. Cloud response to ongoing greenhouse gases climate warming is the largest source of uncertainty for model-based estimates of climate sensitivity and therefore for predicting the evolution of future climate. Understanding the Earth's energy budget requires knowing the cloud coverage, its vertical distributions and optical properties. Predicting how the Earth climate will evolve requires understanding how these cloud variables respond to climate warming.

Active space-borne instruments have been providing a continuous survey of clouds over the whole globe for more than a decade. All active instruments share the same measuring principle – a short pulse of laser or radar electromagnetic radiation is sent to the atmosphere and the time-resolved backscatter signal is collected by the telescope and is registered in one or several receiver channels. However, the wavelength, pulse energy, pulse repetition frequency (PRF), telescope diameter, orbit, detector, or optical filtering are not the same for any pair of instruments.

We propose to merge the measurements performed by the relatively young space-borne lidar ALADIN/Aeolus, which has been orbiting the Earth since August 2018 and operating at 355nm wavelength with the measurements performed since 2006 by CALIPSO lidar, which is operating at 532nm and is near the end of its life-time. Even though the primary goal of ALADIN is wind detection, its products include profiles of atmospheric optical properties (aerosols/clouds). The proposed study includes: (a) developing a cloud layer detection method for ALADIN measurements, which is compliant with CALIPSO cloud layer detection; (b) comparing/validating the resulting cloud ALADIN product with the well-established CALIOP/CALIPSO cloud data set; (c) developing an algorithm for merging the CALIOP and ALADIN cloud datasets; (d) applying the merging algorithm to CALIOP and ALADIN data and build a continuous cloud profile record; (e) adapting this approach to future missions (e.g. ATLID/EarthCare). .