Comment on egusphere-2022-1195

Abstract

The ATLID-CPR-MSI retrieval of Clouds, Aerosols and Precipitation (ACM-CAP) product provides a synergistic “best-estimate” retrieval of the quantities and properties of all aerosols and hydrometeors detected by EarthCARE. While synergistic retrieval algorithms are now mature in many contexts, ACM-CAP is unique in providing a single unified retrieval product for all classes of hydrometeor—ice cloud and snow, drizzle and rain, and liquid clouds—and aerosol species, informed by the synergistic target classification (AC-TC). The simultaneous retrieval of the entire atmosphere with a single optimal estimation retrieval, called Cloud, Aerosol and Precipitation from mulTiple Instruments using a VAriational TEchnique (CAPTIVATE), allows for a robust accounting of observational and retrieval errors and the contributions of passive and integrated measurements in the context of layered and complex regimes, and for enforcing physical relationships between components (e.g. the conservation of precipitating mass flux through the melting layer). We have demonstrated and evaluated the ACM-CAP product as applied to the three EarthCARE test scenes simulated from numerical weather model forecasts, using both case studies and statistical evaluation against the simulated measurements and the “true” quantities from the numerical model. We show that the retrievals are both strongly constrained by the observations from the active and passive instruments, and overall closely resemble the underlying model fields in terms of bulk quantities (e.g. cloud water content, precipitation mass flux, and aerosol extinction) and microphysical properties (e.g. cloud effective radius, median volume diameter, and aerosols lidar ratio). The retrieval performs best where the active instruments have strong and unambiguous signal: in ice clouds and snow, which is observed by both ATLID and CPR, and in light to moderate rain, where CPR signal is strong. In precipitation, CPR's Doppler capability permits enhanced retrievals of snow particle density and raindrop size. In complex and layered scenes where ATLID is obscured, we have shown that making a simple assumption about the presence and vertical distribution of liquid cloud in rain and mixed-phase clouds allows improved assimilation of MSI solar radiances. In combination with a constraint on CPR path-integrated attenuation from the ocean surface, this leads to improved retrievals of both liquid cloud and rain in mid-latitude stratiform precipitation. In the heaviest convective precipitation (i.e. greater than around 10 mm h-1), both active instruments are strongly attenuated and dominated by multiple scattering; in these situations ACM-CAP provides a seamless retrieval of cloud and precipitation, but one which is subject to a high degree of uncertainty. ACM-CAP's aerosol retrieval, constrained by ATLID and MSI solar radiances, is performed in hydrometeor-free parts of the atmosphere. The lidar backscatter is subject to high noise, while the solar radiances are expected to be dominated by uncertainties in surface properties especially over land. While the aerosol optical depth is well-constrained in the test scenes, there is a high degree of noise at the ~1 km resolution of the ACM-CAP product. The use of numerical forecast models to simulate test scenes for testing and evaluation puts EarthCARE L2 processors at an unprecedented degree of readiness ahead of launch. While exposure to further simulated test scenes, campaign data, and ultimately in-flight EarthCARE measurements will motivate ongoing improvements to the representation of cloud and precipitation, the instrument forward-models, and their uncertainties, the present evaluation demonstrates that ACM-CAP will provide a novel unified and synergistic retrieval of clouds, aerosols and precipitation of high quality, including a robust accounting of the contributions of observations, and of measurement and retrieval errors.