Abstract

Abstract. The space-borne active sounders have been contributing invaluable vertically resolved information of atmospheric optical properties since the launch of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) in 2006. To build long-term records from space-borne lidars useful for climate studies, one has to understand the differences between successive space lidars operating at different wavelengths, flying on different orbits, and using different viewing geometries, receiving paths, and detectors. In this article, we compare the results of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) lidars for the period from 28 June to 31 December 2019. First, we build a dataset of ALADIN–CALIOP collocated profiles (Δdist<1∘; Δtime<6 h). Then we convert ALADIN's 355 nm particulate backscatter and extinction profiles into the scattering ratio vertical profiles SR(z) at 532 nm using molecular density profiles from Goddard Earth Observing System Data Assimilation System, version 5 (GEOS-5 DAS). And finally, we build the CALIOP and ALADIN globally gridded cloud fraction profiles CF(z) by applying the same cloud detection threshold to the SR(z) profiles of both lidars at the same spatial resolution. Before comparing the SR(z) and CF(z) profiles retrieved from the two analyzed lidar missions, we performed a numerical experiment to estimate the best achievable cloud detection agreement CDAnorm(z) considering the differences between the instruments. We define CDAnorm(z) in each latitude–altitude bin as the occurrence frequency of cloud layers detected by both lidars, divided by a cloud fraction value for the same latitude–altitude bin. We simulated the SR(z) and CF(z) profiles that would be observed by these two lidars if they were flying over the same atmosphere predicted by a global model. By analyzing these simulations, we show that the theoretical limit for CDAnormtheor(z) for a combination of ALADIN and CALIOP instruments is equal to 0.81±0.07 at all altitudes. In other words, 19 % of the clouds cannot be detected simultaneously by two instruments due to said differences. The analyses of the actual observed CALIOP–ALADIN collocated dataset containing ∼78 000 pairs of nighttime SR(z) profiles revealed the following points: (a) the values of SR(z) agree well up to ∼3 km height. (b) The CF(z) profiles show agreement below ∼3 km, where ∼80 % of the clouds detected by CALIOP are detected by ALADIN as expected from the numerical experiment. (c) Above this height, the CDAnormobs(z) reduces to ∼50 %. (d) On average, better sensitivity to lower clouds skews ALADIN's cloud peak height in pairs of ALADIN–CALIOP profiles by ∼0.5±0.6 km downwards, but this effect does not alter the heights of polar stratospheric clouds and high tropical clouds thanks to their strong backscatter signals. (e) The temporal evolution of the observed CDAnormobs(z) does not reveal any statistically significant change during the considered period. This indicates that the instrument-related issues in ALADIN L0/L1 have been mitigated, at least down to the uncertainties of the following CDAnormobs(z) values: 68±12 %, 55±14 %, 34±14 %, 39±13 %, and 42±14 % estimated at 0.75, 2.25, 6.75, 8.75, and 10.25 km, respectively.

Highlights

  • Clouds play an important role in the energy budget of our planet: optically thick clouds reflect the incoming solar radiation, leading to cooling of the Earth, while thinner clouds act as “greenhouse films”, preventing escape of the Earth’s longwave radiation to space

  • We show that the theoretical limit for CDAtnhoeromr(z) for a combination of Atmospheric Laser Doppler INstrument (ALADIN) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments is equal to 0.81±0.07 at all altitudes

  • The Aeolus mission faced several technical issues which hindered getting the planned specifications. These issues are related to several factors: (a) laser power degradation (60 mJ per pulse instead of 80 mJ per pulse) and signal losses in the emission and reception paths (33 %) that result in lower signal-to-noise ratio (SNR) than planned; (b) telescope mirror temperature effects, biasing the wind detection and calibration of Mie and Rayleigh channels of ALADIN; and (c) a constantly increasing number of hot pixels of both Accumulation Charge Coupled Device (ACCD) detectors (Weiler et al, 2021), leading to errors both in wind speed and in retrieved optical parameters of the atmosphere

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Summary

Introduction

Clouds play an important role in the energy budget of our planet: optically thick clouds reflect the incoming solar radiation, leading to cooling of the Earth, while thinner clouds act as “greenhouse films”, preventing escape of the Earth’s longwave radiation to space. Despite an excellent daily coverage and daytime/nighttime observation capability (Menzel et al, 2016; Stubenrauch et al, 2017), the height uncertainty of the cloud products retrieved from the observations performed by these space-borne instruments is large (e.g., Feofilov and Stubenrauch, 2017). This precludes the retrieval of the cloud’s vertical profile with the accuracy needed for climate relevant processes and feedback analysis. The wavelength, pulse energy, pulse repetition frequency (PRF), telescope diameter, orbit, detector, and many other parameters are not the same for any pair of instruments These differences define the active instruments’ capability of detecting atmospheric aerosols and/or clouds for a given atmospheric scenario and observation conditions (day, night, averaging distance). We define the procedures and criteria for the comparison of these two products

AEOLUS
CALIPSO-GOCCP
Collocation of AEOLUS and CALIPSO profiles
Lidar equation
Two definitions of scattering ratio profile
Estimating SR(z) profiles at 532 nm from ALADIN data
Calculating averaged SR(z) profiles from CALIOP data
Cloud detection, cloud fraction, and normalized cloud detection agreement
Setup of the numerical experiment
Horizontal and vertical averaging and its effects on ALADIN’s capability to retrieve clouds
Theoretically achievable cloud detection agreement between ALADIN and CALIOP
Comparison of SR–height histograms in each latitude band
Comparing individual SR profiles
Cloud detection agreement
Cloud altitude detection sensitivity
Temporal evolution of cloud detection agreement
Findings
Conclusions
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