| Current Conditions at CIRA | |
| Temperature: | 14.9 F |
| Dewpoint: | 2.5 F |
| Relative Humidity: | 57 % |
| Barometer: | 29.981 in |
| Wind: | ENE at 0.0 mph |
| Rainfall: | 0.00 in |
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Phil helped design, develop, and implement the CloudSat data processing system. Currently, he manages the Data Processing Center and works with the CloudSat Science Team to develop new data products. He holds a BS in Atmospheric Science from UCLA (1994) and a MS in Atmospheric Science from Colorado State University (1998). He obtained a project management certificate from PMI and Colorado State University in 2007. His work on the CloudSat project has been recognized with several awards:
The launch of the NASA CloudSat in April 2006 enabled the first satellite-based global observation of vertically resolved cloud information. However, CloudSat’s nonscanning W-band (94 GHz) Cloud Profiling Radar (CPR) provides only a nadir cross section, or “curtain,” of the atmosphere along the satellite ground track, precluding a full three-dimensional (3D) characterization and thus limiting its utility for certain model verification and cloud-process studies. This paper details an algorithm for extending a limited set of vertically resolved cloud observations to form regional 3D cloud structure. Predicated on the assumption that clouds of the same type (e.g., cirrus, cumulus, and stratocumulus) often share geometric and microphysical properties as well, the algorithm identifies cloud-type-dependent correlations and uses them to estimate cloud-base height and liquid/ice water content vertical structure. These estimates, when combined with conventional retrievals of cloud-top height, result in a 3D structure for the topmost cloud layer. The technique was developed on multiyear CloudSat data and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) swath data from the NASA Aquasatellite. Data-exclusion experiments along the CloudSat ground track show improved predictive skill over both climatology and type-independent nearest-neighbor estimates. More important, the statistical methods, which employ a dynamic range-dependent weighting scheme, were also found to outperform type-dependent near-neighbor estimates. Application to the 3D cloud rendering of a tropical cyclone is demonstrated.
