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. However, measurements of cloud radar alone cannot provide necessary information for cloud scenario classification. The formation fly (A-train) of Aqua, CloudSat and CALIPSO provides other cloud information from lidar and passive radiometer measurements. We identify eight basic cloud types that are recognized by surface observers internationally by combining information available mainly from the CloudSat and CALIPSO satellites.
Combining lidar and radar measurements provide better cloud detection and characterization because of their unique compatible capabilities. Now combining radar and lidar measurements are widely used for cloud studies from cloud macrophysical and microphysical properties. CloudSat and CALIPSO satellites will provide us first opportunity to study cloud from space by combining lidar and radar. There are more advantages to combining lidar and radar measurements from space (than from the ground) because lidar is able to detect more cloud layers from a nadir view from space in the presence of multi-layer cloud systems. In general, cloud optical thickness decreases with altitude, thus lidar has a better chance of penetrating high and mid-level cloud than low-level clouds.
Also see the CloudSat Data I/O Interface Specifications