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[1] This paper reports on the early mission performance of the radar and other major aspects of the CloudSat mission. The Cloudsat cloud profiling radar (CPR) has been operating since 2 June 2006 and has proven to be remarkably stable since turn-on. A number of products have been developed using these space-borne radar data as principal inputs. Combined with other A-Train sensor data, these new observations offer unique, global views of the vertical structure of clouds and precipitation jointly. Approximately 11% of clouds detected over the global oceans produce precipitation that, in all likelihood, reaches the surface. Warm precipitating clouds are both wetter and composed of larger particles than nonprecipitating clouds. The frequency of precipitation increases significantly with increasing cloud depth, and the increased depth and water path of precipitating clouds leads to increased optical depths and substantially more sunlight reflected from precipitating clouds compared to than nonprecipitating warm clouds. The CloudSat observations also provide an authoritative estimate of global ice water paths. The observed ice water paths are larger than those predicted from most climate models. CloudSat observations also indicate that clouds radiatively heat the global mean atmospheric column (relative to clear skies) by about 10 Wm À2 . Although this heating appears to be contributed almost equally by solar and infrared absorption, the latter contribution is shown to vary significantly with latitude being influenced by the predominant cloud structures of the different region in questions. Citation: Stephens, G. L., et al. (2008), CloudSat mission: Performance and early science after the first year of operation,

The U.S. Department of Energy's Atmospheric Radiation Measurement (ARM) Program is deploying sensitive, millimeter-wave cloud radars at its Cloud and Radiation Test Bed (CART) sites in Oklahoma, Alaska, and the tropical western Pacific Ocean. The radars complement optical devices, including a Belfort or Vaisala laser ceilometer and a micropulse lidar, in providing a comprehensive source of information on the vertical distribution of hydrometeors overhead at the sites. An algorithm is described that combines data from these active remote sensors to produce an objective determination of hydrometeor height distributions and estimates of their radar reflectivities, vertical velocities, and Doppler spectral widths, which are optimized for accuracy. These data provide fundamental information for retrieving cloud microphysical properties and assessing the radiative effects of clouds on climate. The algorithm is applied to nine months of data from the CART site in Oklahoma for initial evaluation. Much of the algorithm's calculations deal with merging and optimizing data from the radar's four sequential operating modes, which have differing advantages and limitations, including problems resulting from range sidelobes, range aliasing, and coherent averaging. Two of the modes use advanced phase-coded pulse compression techniques to yield approximately 10 and 15 dB more sensitivity than is available from the two conventional pulse modes. Comparison of cloud-base heights from the Belfort ceilometer and the micropulse lidar confirms small biases found in earlier studies, but recent information about the ceilometer brings the agreement to within 20-30 m. Merged data of the radar's modes were found to miss approximately 5.9% of the clouds detected by the laser systems. Using data from only the radar's two less-sensitive conventional pulse modes would increase the missed detections to 22%-34%. A significant remaining problem is that the radar's lower-altitude data are often contaminated with echoes from nonhydrometeor targets, such as insects.

In late April 2006, NASA launched Cloudsat, an earth-observing satellite that uses a near-nadir-pointing millimeter-wavelength radar to probe the vertical structure of clouds and precipitation. The first step in using Cloudsat measurements is to distinguish clouds and other hydrometeors from radar noise. In this article the operational Cloudsat hydrometeor detection algorithm is described, difficulties due to surface clutter are discussed, and several examples from the early mission are shown. A preliminary comparison of the Cloudsat hydrometeor detection algorithm with lidar-based results from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite is also provided.

The occurrence statistics of hydrometeor layers covering the Earth's surface is described using the first year of millimeter radar data collected by Cloudsat merged with lidar data collected by CALIPSO (July 2006 to June 2007). These satellites are flown in a tight orbital configuration so that they probe nearly the same volumes of the atmosphere within 10–15 s of each other. This configuration combined with the capacity for millimeter radar to penetrate optically thick hydrometeor layers and the ability of the lidar to detect optically thin clouds has allowed us to characterize the vertical and horizontal structure of hydrometeor layers with unprecedented precision. We find that the global hydrometeor coverage averages 76% and demonstrates a fairly smooth annual cycle with a range of 3% peaking in October 2006 and reaching a minimum in March 2007. The geographic distribution of hydrometeor layers defined in terms of layer base, layer top, and layer thickness is described. The predominance of geometrically thin boundary layer clouds is illustrated as is the spatial distribution of upper tropospheric ice clouds in the tropics. The cooccurrence of multiple layers is shown to be a strong function of latitude and geography with cooccurring middle‐level (3 km < layer base < 6 km) and high‐level (base > 6 km) layers being predominant over the continents. Cloud layer overlap is also examined, and a bias due to an assumption of maximum fractional overlap in coarse resolution models is quantified and shown to be on the order of −5 to −7% globally maximizing over the high‐latitude continents of the Northern Hemisphere.