Abstract
Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally-generated, mid-latitude cloud systems in order to validate the retrieval and asses its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the assumed shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 μm is found for one of the flights for the Large Plate Aggregate and the 6-Bullet Rosette shapes. The variational retrieval fits the observations well although small systematic deviations are observed for some of the sub-millimeter pointing towards issues with the sensor calibration or the modeling of gas absorption. We find that the quality of the fit to the observations is independent of the assumed ice particle shape, indicating that the employed combination of observations is insufficient to constrain the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations improve remote-sensing measurements of ice hydrometeors and may thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increases confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.
Highlights
The representation of clouds in climate models remains an important issue that causes significant uncertainties in their predictions (Zelinka et al, 2020)
We find that the quality of the fit to the observations is independent of the assumed ice particle shape, indicating that the employed combination of observations is insufficient to constrain the shape of ice particles in the observed clouds
For flight B984, we find good agreement between retrieved and in situ measured particle size distributions (PSDs) for larger particles (DMAX > 200 μm) for the Large Plate Aggregate and the 6-Bullet Rosette
Summary
The representation of clouds in climate models remains an important issue that causes significant uncertainties in their predictions (Zelinka et al, 2020). Improving and validating these models requires measurements that accurately characterize the distribution of hydrometeors in the atmosphere. Currently available remote-sensing observations do not constrain the distribution of ice in the atmosphere well (Waliser et al, 2009; Eliasson et al, 2011; Duncan and Eriksson, 2018). 35 In Pfreundschuh et al (2020), we have developed a cloud-ice retrieval based on radar and passive sub-millimeter observations to investigate the potential benefits of a synergistic radar mission to fly in constellation with ICI on MetOp-SG. The principal aim of this study is to validate the synergistic retrieval using real observations and to investigate its ability to retrieve 40 the vertical distributions of ice hydrometeors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
