A 4D Cloud Water Content Product Derived from Operational Satellite Data for ARM

William Smith NASA - Langley Research Center
Patrick Minnis NASA - Langley Research Center
Mandana Khaiyer Science Systems and Applications, Inc.
Rabindra Palikonda Science Systems and Applications. Inc./NASA - LRC
Douglas Spangenberg Science Systems and Applications, Inc.
Helen Yi Science Systems and Applications. Inc./NASA - LRC
Kris Bedka NASA

Category: Cloud Properties

Working Group: Cloud Life Cycle

Together, the CALIPSO lidar and the CloudSat cloud profiling radar are providing unprecedented data describing the vertical structure of cloud systems across the Earth. While these instruments provide excellent vertical resolution along the satellite track, they are non-scanning, and by themselves do not provide the three-dimensional representation of clouds over large areas needed for a variety of applications. Thus, it is desirable to try and extend the unique information provided by CloudSat and CALIPSO in time and space in order to improve the characterization of clouds in four dimensions. In the approach taken here, characteristic or climatological cloud water content (CWC) profiles are derived from CloudSat data for a variety of cloud types. A future version of this analysis will incorporate new data products from CALIPSO. The cloud types are defined by cloud parameters typically retrieved from operational satellite data, such as the cloud temperature (Tc), cloud water path (CWP), and geometric thickness (DZ). This information is used in a retrieval system to derive CWC profiles from operational satellite imager data that are constrained by the retrieved CWP and DZ. The technique is demonstrated over the SGP using cloud properties derived routinely for ARM from Geostationary Operational Environmental Satellite (GOES) data and tested with independent CloudSat data. This encouraging new 4D product has a number of potential applications for the ARM community, including the improved description of atmospheric radiative heating and the evaluation of cloud process models on regional scales. Because the product can be produced in near real-time, it also has potential for assimilation into forecast models. A strategy for accomplishing this will also be discussed.

This poster will be displayed at ASR Science Team Meeting.

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