10A.3 Improving Cloud Layer Boundaries from GOES-16

Thursday, 11 January 2018: 9:00 AM
410 (Hilton) (Austin, Texas)
John M. Haynes, CIRA, Fort Collins, CO; and Y. J. Noh, S. D. Miller, D. T. Lindsey, A. Heidinger, and J. M. Forsythe

There is significant interest in the vertical boundaries of cloud layers, particularly in multilayer situations, for both aviation applications and general weather analysis and forecasting. The high spatial, spectral, and temporal resolution of the GOES-16 Advanced Baseline Imager (ABI) is resulting in unprecedented views of clouds from space, but information about cloud vertical extent remains limited. This is a limitation of all passive sensors, since information about clouds tends to be concentrated near cloud-top. In particular, for multiple layer cloud scenes, the boundaries of the lowest cloud layer are oftentimes completely ambiguous from ABI measurements alone.

To address this, a statistical method for estimating cloud base has been developed using active sensor observations of clouds from CloudSat and CALIPSO. The cloud geometric thickness is constrained by cloud top height and retrieved water path in the algorithm. This approach has been applied to a number of sensors, including the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). We now apply it to the GOES-16 ABI using cloud water path estimated from the baseline ABI cloud optical depth and effective radius products. By extending the upper-most cloud layer downward according to its derived geometric thickness, the GOES-16 Cloud Layers product (previously indicating only whether cloud is Low [L], Middle [M], or High [H]), is expanded to include classifications like L+M, M+H, and L+M+H.

This work will also discuss a number of other pathways to improvement of cloud boundary characterization in multilayer situations, including a multispectral approach utilizing the 1.38 μm cirrus channel and 0.64 μm visible channel, and the introduction of layer moisture information from numerical weather prediction models. The latter approach uses an active-sensor derived, near-global a priori database of cloud occurrence in 240 m vertical bins that is correlated with layer moisture. Both of these improvements will allow the addition of a L+H classification to the Cloud Layers product for multilayer clouds.

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