8.11 Toward a three-dimensional near-real time cloud product for aviation safety and weather diagnoses

Wednesday, 6 October 2004: 5:15 PM
Patrick Minnis, NASA/LRC, Hampton, Va; and L. Nguyen, R. Palikonda, D. Spangenberg, M. L. Nordeen, and Y. H. Yi

Satellite data have long been used for determining the extent of cloud cover over a given region and for estimating the properties at the cloud tops. The derived properties are also used to develop icing intensity indices to help determine the potential impact on the safety of air traffic in the region. Currently, cloud properties and icing indices are derived in near-real time over the USA from the Geostationary Operational Environmental Satellite (GOES) imagers at 75°W and 135°W. Traditionally, the results have been given in two dimensions because of the lack of knowledge about the vertical extent of clouds and the occurrence of overlapping clouds. Aircraft fly in a three-dimensional space and require vertical as well as horizontal information about clouds, their intensity, and their potential for icing. To improve the vertical component of the derived cloud and icing parameters, this paper explores various methods and datasets for filling in the three-dimensional space with cloud material. Multispectral satellite retrieval techniques are explored to determine their potential for identifying overlapped clouds and, when possible, determining the properties of the upper and lower cloud layers. Empirical techniques are used to infer cloud thickness from the cloud microphysical data. The cloud thickness and top data help provide estimates of cloud base. Other datasets including ceilometer readings and vertical temperature and humidity profiles from radiosondes and numerical weather prediction models are used to help refine the 3-D cloud field. Once the cloud locations are determined, the column-integrated parameters derived from the satellite data are distributed vertically to achieve a complete data set. These data should be valuable for both icing and fog diagnoses and eventually for improving weather forecasts. This paper provides an overview of the process used to develop a prototype dataset and discusses roadblocks that must to be overcome before this type of product becomes reliable.

Supplementary URL: http://www-pm.larc.nasa.gov/

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