Estimating regional cloud base altitudes from local CloudSat observations via type-dependent statistical extrapolation

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Tuesday, 19 January 2010: 11:45 AM
B314 (GWCC)
Steven D. Miller, CIRA/Colorado State Univ., Fort Collins, CO; and J. M. Forsythe, R. Bankert, P. T. Partain, and T. H. Vonder Haar

Civilian and military flight operations are impacted by the height of the cloud base (ceiling) in many ways. Ceilings impact choice of flight paths (routing), the ability to launch/recover aircraft, gauging potential for aircraft icing, and selecting flight levels for line-of-sight visibility at the surface. Airports typically have instrumentation to measure the ceiling directly, but regions away from airports and surface observations require some other method to ascertain the cloud base. Remote sensing of cloud base from satellite, which provides coverage in data sparse or data denied regions (in the case of military operations), has traditionally been a difficult problem. A new technique to estimate cloud base from satellite observations is presented in this paper.

The NASA CloudSat mission has been providing global cloud base, layering and top measurements since 2006. CloudSat is a nadir-looking instrument with vertical resolution of a few hundred meters and a 1.1 km sampling interval along the ground track. It provides a good two-dimensional view of clouds in the vertical plane, but very limited information on horizontal extent (in the direction of flight path). Conventional geostationary or polar orbiting satellite radiometers provide good two-dimensional views of clouds and their spatial extent, but provide very limited vertical information including cloud base heights. This work uses the strengths of each measurement to generate three-dimensional cloud fields and to estimate cloud base height in data sparse regions.

A cloud type classification derived from infrared and visible measurements onboard the Geostationary Operational Environmental Satellite (GOES) is used to provide the spatial context for the CloudSat vertical profiles. Three years of global statistics on the spatial correlation of cloud base by cloud type were derived from CloudSat and used to generate weighted estimates. Cloud base predictions are made and then validated by withholding a subset of the CloudSat data. Results indicate that there is skill in extending the active sensor measurements away from their ground track. This provides a capability to create three-dimensional cloud occurrence fields. Applications of these cloud fields to the aviation community will be discussed.