A008-0025
Development of NASA VIIRS-Like Cloud Property Algorithms for Next Generation Geostationary Imagers

Monday, 7 December 2020
Poster
Robert Holz1, Kerry Meyer2, Steven E Platnick2, Andrew Heidinger3, Nandana Amarasinghe4, Galina Wind4, Richard Frey5, Steven A Ackerman6 and Steve Dutcher7, (1)UW SSEC, Madison, WI, United States, (2)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (3)Center for Satellite Applications and Research (STAR), NESDIS, Madison, WI, United States, (4)Science Systems and Applications, Inc., Lanham, MD, United States, (5)CIMSS/UW-Madison, Evansville, WI, United States, (6)University of Wisconsin Madison, Department of Atmospheric and Oceanic Sciences, Madison, WI, United States, (7)Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI, United States
Abstract:
A new generation of geosynchronous multispectral imagers (ABI on the GOES-R series, AHI on Himawari-8/-9) dramatically improves upon the spatial, spectral and temporal resolution capabilities of heritage GEO imagers and provides for the first time from a geo-stationary platform MODIS/VIIRS like spectral and spatial resolution. We will report on our continued efforts to port the MODIS/VIIRS continuity product (CLDPROP) to the geo-stationary observations. CLDPROP is based on the legacy MODIS cloud products suit that leverages heritage algorithms–MODIS cloud mask (MOD35), MODIS optical and microphysical properties product (MOD06), and the NOAA AWG Cloud Height Algorithm. In addition, the GEO sensors have additional capabilities expected to be useful for cloud retrievals (e.g., atmospheric absorption channels, temporal information). This presentation will focus on the challenges that need to be addressed in order to achieve a LEO/GEO cloud product that is consistent across all sensors. These include:

  1. Pixel growth and view zenith angles that are geographically fixed for the GEO imagers can be expected to induce systematic biases in cloud masking and property retrievals relative to LEO products.
  2. Scattering angles that are correlated with time of day for GEO (see Figure 2) have additional sensitivities to the liquid and ice cloud radiative models used for optical property retrievals; these models can have distinct angular features (e.g., cloud bows, glories, etc.) that can cause biases relative to LEO products when concurrent LEO/GEO observations have differing view geometries.
  3. Relative radiometric inconsistency across sensors that has been shown to induce large inter-sensor differences in cloud optical property retrievals between MODIS and VIIRS; such inconsistency is also possible with GEO.
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