Abstract Understanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite‐based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near‐global scale. This study aims to validate three satellite‐based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal‐altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed‐phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global‐scale in situ observational data set to assess the accuracy of satellite‐based cloud phase products and investigates the key factors affecting the distributions of cloud phases.
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The Cloud-resolving model Radar SIMulator (CR-SIM) Version 3.3: description and applications of a virtual observatory
Abstract. Ground-based observatories use multisensor observations to characterize cloud and precipitation properties. One of the challenges is how to designstrategies to best use these observations to understand these properties and evaluate weather and climate models. This paper introduces the Cloud-resolving model Radar SIMulator (CR-SIM), which uses output from high-resolution cloud-resolving models (CRMs) to emulate multiwavelength,zenith-pointing, and scanning radar observables and multisensor (radar and lidar) products. CR-SIM allows for direct comparison between an atmosphericmodel simulation and remote-sensing products using a forward-modeling framework consistent with the microphysical assumptions used in the atmosphericmodel. CR-SIM has the flexibility to easily incorporate additional microphysical modules, such as microphysical schemes and scattering calculations,and expand the applications to simulate multisensor retrieval products. In this paper, we present several applications of CR-SIM for evaluating therepresentativeness of cloud microphysics and dynamics in a CRM, quantifying uncertainties in radar–lidar integrated cloud products and multi-Dopplerwind retrievals, and optimizing radar sampling strategy using observing system simulation experiments. These applications demonstrate CR-SIM as a virtual observatory operator on high-resolution model output for a consistent comparison between model results and observations to aidinterpretation of the differences and improve understanding of the representativeness errors due to the sampling limitations of the ground-basedmeasurements. CR-SIM is licensed under the GNU GPL package and both the software and the user guide are publicly available to the scientificcommunity.
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- Award ID(s):
- 1841215
- PAR ID:
- 10215118
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 13
- Issue:
- 4
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 1975 to 1998
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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