This data set identifies different types of clouds.
A great strength of microwave radar measurements of clouds and precipitation is the ability to retrieve quantitative content data from the radar reflectivity factor Z. This is made possible by devising algorithms based on empirical relationships between Z and various microphysical parameters, such as ice water content (IWC) or rainfall rate. However, because of the diversity of microphysical conditions found in the atmosphere, algorithms need to be applied only to those conditions for which they are considered valid. In other words, it is first necessary to identify the target and then select an appropriate algorithm. The algorithm selection process depends on such basic factors as cloud phase, and also the hydrometeor density, shape, and size distribution. For example, although cirrus, altostratus, and the upper portions of cumulonimbus clouds are all predominantly ice phase clouds, it is not possible to apply a single algorithm for retrieving IWC in these targets: cirrus generally contain only single ice crystals, altostratus likely contain low-density ice crystal aggregates at the warmer temperatures, and cumulonimbus may combine ice crystals, snowflakes, rimed particles, graupel, and even hailstones. Due to the different radiative forcings of various cloud types (Hartmann et al. 1992; Chen et al. 2000), classifying clouds into categories based on type is also an important task for cloud remote sensing and global cloud climatology studies.
As the first step in converting the vertical profiles of Z from CloudSat into meaningful microphysical data quantities, we are developing an algorithm for identifying cloud type and precipitation from the information expected to be available. As described here, we identify eight basic cloud types that are recognized by surface observers internationally. Currently, we are relying on CloudSat radar-only Z measurements for cloud identification, but further refinements will incorporate ancillary data such as are available from Aqua and CALIPSO.
Our initial approach is to use an extended cloud dataset obtained over a 1-year period from the Southern Great Plains Clouds and Radiation Testbed site, which identifies these cloud types using a previously developed multiple remote sensor algorithm (Wang and Sassen 2001). We then examine the MMCR (8.7-mm radar) data for each of the identified cloud types to establish relations between the maximum Zmax measured in a particular vertical profile and the temperature at that level. Permissible bounds in temperature and Zmax for each cloud type are established. The temporal consistency of the Zmax is also considered, as well as the presence of precipitation.