ALBERT

Description: [1]  We describe a method to evaluate cloud microphysics simulated with a global cloud resolving model against CloudSat and CALIPSO satellite data. Output from the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) is run through a satellite-sensor simulator (Joint Simulator for satellite sensors), then directly compared to the radar and lidar signals from CloudSat and CALIPSO. The forward approach allows for consistency in cloud microphysical assumption involved in the evaluation. To investigate the dependence of the signals on the temperature, we use temperature extensively as the vertical coordinate. The global statistical analysis of the radar reflectivity shows that the simulation overestimates all the percentiles above –50 °C, and that snow category contributes significantly to low reflectivity values between –80 and –40 °C. The simulated lidar signals have two modes associated with cloud ice and snow categories, though the observations have only one mode. The synergetic use of radar reflectivity and lidar backscatter enables us to determine the relative magnitudes of ice/liquid water contents and effective radii without use of retrievals. The radar-and-lidar diagnosis for cloud tops shows that, due to snow category, NICAM overestimates the mass-equivalent effective radius and underestimates ice water content. Also, the diagnosis was shown to be useful to investigate sensitivities of the parameters of bulk microphysical schemes on the water contents and sizes. The non-spherical scattering of ice particles was shown to affect the above radar-and-lidar diagnosis for large reflectivity ranges, but not to alter most of the other diagnoses for this simulation.