[A-CG41] Satellite Earth Environment Observation

[ACG41-23] Synergy of millimeter-wave radar and radiometer measurements for estimating microphysical properties of frozen particles in deep convective systems

*Keiichi Ohara^1,2, Hirohiko Masunaga³ (1.Japan Aerospace Exploration Agency, 2.Nagoya University (Environmental Studies), 3.Nagoya University (Institute for Space-Earth Environmental Research))

Keywords:Retrieval of frozen hydrometers, Deep convective system, Satellite remote sensing, Radiative transfer, Cloud microphysical property

Frozen particles such as cloud ice, snow, and graupel in deep convective systems play an important role in precipitation and upper-level anvil cloud formation. A significant amount of frozen particles produced in deep convective clouds are a primary source of heavy precipitation. In addition, nearly one half of tropical upper-level clouds are formed from cirrus anvils detrained from deep convection, and the ice clouds have significant radiative forcing in both shortwave and longwave spectra. To observationally clarify the properties of frozen particles formed within convective clouds is important for understanding the formation processes of heavy precipitation and tropical upper-level clouds.
To clarify the microphysical properties of frozen particles, a combined use of multiple sensors is a promising strategy. The signals observed by satellite sensors depend on various cloud microphysical properties such as ice water content (IWC), number concentration (Nt), mass-weighted diameter (Dm) and particle shape. It is difficult to simultaneously estimate all these cloud microphysical properties by using the limited information available from a single sensor. Previous studies have developed methods for a combined use of radar and lidar. However, radar and lidar observations are not optimal combination for the retrieval of frozen particles within convective clouds, because lidar signals experience a sever attenuation in thick clouds. The estimation of cloud microphysical properties within convective clouds remains a significant challenge.
This study explores the combined use of CloudSat Cloud Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) to retrieve the vertical profiles of IWC, Nt and Dm (an example is shown in figure). Both CPR reflectivity and GMI high-frequency brightness temperature (Tb) are sensitive to frozen particles inside deep convective clouds. The retrieval algorithm is developed by a combination of Deep Neural Network (DNN) and Optimal Estimation Method (OEM). In the first step of the algorithm, the DNN components estimate an initial guess based on an a priori database, followed by the next step where OEM components seek a more optimal frozen particle profile consistent with CPR and GMI observations.
The retrieval performance is evaluated using the match-up observations of CloudSat and GPM satellites. The synergy of CPR and GMI observations reduces retrieval errors compared to the CPR-only observations. When simulating satellite observations using retrieved IWC, Nt, and Dm profiles as input, not only CPR reflectivity and GMI Tb but also dual-frequency precipitation radar (DPR) reflectivity are reasonably reproduced. These results confirm the self-consistency of the current algorithm and indicate some ability to retrieve large snow and graupel particles inside deep convective clouds. Among different particle shape models tested, dendrite snowflake and soft sphere are found to be the optimal models which best reproduce satellite observations. This work implies the potential of combining passive and active millimeter-wave sensors for obtaining multiple aspects of the cloud ice properties. Future work will incorporate new satellite millimeter-wave radars and radiometers, including EarthCARE/CPR and GOSAT-GW/AMSR3.