A Worldwide Three-Dimensional Cloud Analysis System

Cloud cover complicates remote sensing for space-to-ground, space-to-air, and air-to-air lines of sight (LOS) needed to support a variety of aerospace missions ranging from low-level airdrops to overhead imagery collection. Satellite imagery can be used to derive the cloud fraction and cloud-top altitude of the highest layer in a column, but does not yield the altitude of the cloud base and vertical extent of a thick, single layer or of multiple layers. Numerical weather prediction models provide three-dimensional forecasts of cloud cover, but they can be notoriously inaccurate. To improve three-dimensional cloud analyses for aerospace applications, a cloud-data fusion (CDF) algorithm can be used to derive the cloud fraction and probability of a cloud-free LOS (PCFLOS) in each layer of a common model-satellite horizontal domain. The base version of the algorithm uses the Cloud Depiction and Forecast System-II (CDFS-II) from the Air Force Weather Agency to determine which columns are cloudy and model fields to distribute the clouds in the vertical. The algorithm employs several cloud parameterizations to derive fractional cloudiness in each layer. The CDF technique is applied daily to 15-km MM5 fields over the continental U.S. Results will be presented and compared to standard meteorological observations, as well as to scientific cloud observations made at the ARM Southern Great Plains (SGP) CART site in Oklahoma.