Statistically based calibration/validation control of space-borne lidars: application to ALADIN lidar onboard ADM/Aeolus

Clouds and aerosols play an important role in the Earth’s energy budget through a complex interaction with solar, atmospheric, and terrestrial radiation, and air humidity. Optically thick clouds efficiently reflect the incoming solar radiation and, globally, clouds are responsible for about two thirds of the planetary albedo. Thin cirrus trap the outgoing longwave radiation and keep the planet warm. Aerosols scatter or absorb sunlight depending on their size and shape and interact with clouds in various ways.

Due to the importance of clouds and aerosols for the Earth’s energy budget, global satellite observations of their properties are essential for climate studies, for constraining climate models, and for evaluating cloud parameterizations. Active sounding from space by lidars and radars is advantageous since it provides the vertically resolved information. This has been proven by CALIOP lidar which has been observing the Earth’s atmosphere since 2006. Another instrument of this kind, CATS lidar on-board ISS provided measurements for over 33 months starting from the beginning of 2015. The ALADIN lidar on-board ADM/Aeolus has been measuring horizontal winds and aerosols/clouds since August 2018. More lidars are planned – in 2021, the ATLID/EarthCare lidar will be launched and other space-borne lidars are currently in the development phase.

Needless to say that the quality of the retrieved data strongly depends on the quality of the calibration of the lidar system and its components. Besides “classical” calibration methods (laboratory calibration, calibration in space using on-board sources and/or known external sources and  calibration through collocation, which involves comparisons with ground-based station-, balloon-, and aircraft measurements), one can also make use of a vicarious calibration, where the sites with known properties are used. In this work, we use the whole atmosphere for quality control of the space-borne lidar system, which includes the laser, the sending optics, the receiver with its telescope, and the detection system.

We describe the quality control approach based on a set of several indicators, which characterize the behavior of the lidar system on a day-to-day basis using the L1 (and even L2) data as an input. With the help of this set one can trace:

(a) the stability of the detection chain for the lidar channels (Rayleigh, Mie);

(b) the drift of crosstalk coefficients;

(c) the stability of day- and nighttime stratospheric noise;

(d) the stability of the radiation detection for all atmospheric scenarios and over the whole globe using a clustering algorithm applied to the scattering ratio (SR) histograms.

We demonstrate the results using the L1B and L2A data flow of Aeolus obtained from the first days of its operation and up to now, compare them with the results obtained for CALIOP, and discuss the applications of the approach.