No abstract

By inspection of the image constructed from the brightness temperature difference between the 11 *m and 12 *m satellite data, semi-transparent cirrus clouds where brightness temperature in 11 *m and visible reflectivity are relatively low are identified clearly as having a bigger brightness temperature difference area than that of cloud free areas.Effective emissivity is determined for semi-transparent cirrus clouds using the simple cloud model where scattering is neglected and only absorption is considered.Cirrus clouds often have 'black' parts in this wavelength. The radiance of the 'black' part of the cirrus clouds whose temperature is assumed to be equal to that of semi-transparent part and the clear radiance just off the cirrus clouds which is assumed to be equal to the radiance at the bottom of the cloud are used for effective emissivity calculation. A simple relationship between the effective emissivity for 11 *m and 12 *m has been determined empirically from 860 satellite measurements for eight cirrus clouds cases which have 'black' parts. The bi-spectral method has been developed to retrieve cloud temperature and effective emissivity for semi-transparent cirrus clouds using the effective emissivity relationship between 11 *m and 12 *m. We have compared the retrieved cloud temperature and effective emissivity by our method with those estimated from the 'black' part of the cirrus cloud. It shows reasonable agreement for the cirrus cloud whose effective emissivity is larger than 0.4.

A simple objective cloud type classification method has been developed, based on split‐window measurements of the Advanced Very High Resolution Radiometer on board the NOAA 7 satellite. Brightness temperature difference between the split‐window data is a good parameter for the detection of cirrus and blackbody clouds. Two‐dimensional histograms of brightness temperature of the 11‐μm channel and the brightness temperature difference between the split‐window data over (64 km)2 subareas are constructed. By selecting appropriate thresholds in the two‐dimensional histogram, cirrus, dense cirrus, cumulonimbus, and cumulus clouds are classified over the tropical ocean. Cloud type classification maps were generated by this method for the western Pacific Ocean and were compared with the nephanalysis chart constructed at the Japan Meteorological Satellite Center from GMS data collected within 1 hour of the NOAA 7 observations. The comparison shows reasonable agreement. Fractional cloud cover for cirrus over each (64 km)2 subarea is calculated as the ratio of the number of samples which belong to the cirrus cloud type in the two‐dimensional histogram to the number of total samples in the subarea. Fractional cloud cover estimations for cumulonimbus and low‐level cumulus are also presented.

[1] This study proposes a method of using a local-area cloud system resolving model (LCRM) to evaluate and improve cloud properties simulated by a global cloud system resolving model (GCRM). We study the sensitivity to cloud microphysics schemes by comparing the simulated data of LCRM with CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) using satellite simulators. In particular, the impacts of the improved cloud microphysics scheme, which is more comprehensive than the scheme used for the GCRM experiment, with six water categories are studied. We focus on the active convective phase over the maritime continent. During the 4 day integration period of LCRM, 11 tracks of A-Train satellite observations are available. Cloud properties along the cross sections of these tracks are first examined to find the bias of the simulated data compared to the CloudSat and CALIPSO observations. The improved cloud microphysics scheme used in the LCRM reproduces the overall characteristics of the observed contoured frequency by altitude diagrams (CFADs) well, although biases are still present in the upper layers. We find that the simulated domain-averaged cloud properties can be evaluated using the observed track data. The comparison of the CFADs between LCRM and GCRM clarifies the bias in GCRM, and the new cloud microphysics scheme improves this bias. Using LCRM, sensitivities to cloud microphysics parameters are examined, and the CFADs are further improved if the ice sedimentation speed is increased. These results indicate that introduction of the graupel category or faster sedimentation of ice clouds reduces the total amount of hydrometeors and leads to more efficiency of precipitation.Citation: Satoh, M., T. Inoue, and H. Miura (2010), Evaluations of cloud properties of global and local cloud system resolving models using CALIPSO and CloudSat simulators,

Cloud horizontal size distributions of cloud clusters were analyzed for global cloud resolving simulations with the global nonhydrostatic model NICAM whose mesh interval is about 3.5 km and 7 km. The 3.5 km-mesh simulation was performed for 7 days starting at 00 UTC 25 Dec 2006 by giving an initial condition of reanalysis data, while the 7 km-mesh simulation was run for 32 days from 00 UTC 15 Dec 2006. We used outgoing long-wave radiation (OLR) simulated by NICAM to calculate size distributions of deep convection, and compared them with those analyzed using equivalent blackbody temperature (T BB ) of the infrared channel of 11 µm of the Japanese geostationary meteorological satellite (MTSAT-1R). We selected two threshold temperatures, 208 K and 253 K, to identify deep convective areas including anvil cloud. Specifically, we call clouds defined by the 208 K-threshold "deeper" convective clouds. Over the tropical region covering the maritime continent and the western tropical Pacific (10S−10N, 90E−160W), we examined the size of cloud areas defined by the two T BB threshold values and corresponding threshold values of OLR of 90 W m −2 and 210 W m −2 , which were chosen by comparing cumulative histograms of T BB and OLR in this region.Resolution dependency by NICAM shows that the overall cloud size distribution of the 3.5 km-mesh simulation is much closer to that of the MTSAT-1R observation than that of the 7 km-mesh simulation. Size distributions of deep convection in both simulations indicate nearly lognormal as is seen in the MTSAT-1R observations. The 3.5 km-mesh simulation shows slightly less frequency than the MTSAT-1R observation for smaller size of deeper convection, and it does not reproduce very large clouds. When comparing cloud characteristics over land and ocean, simulated cloud size statistics are slightly closer to the MT-SAT-1R observation in the maritime continent region (westward of 160E) than in the open ocean region (eastward of 160E). A comparison of temporal variation of cloud area shows that the 3.5 km-mesh Journal of the Meteorological Society of Japan Vol. 86A