We propose to continue the development of our NASA cloud continuity products, applying them to the next generation geostationary imagers that include goes-16+ ABI, japan's himawari AHI, and south…

The University of Wisconsin System has been awarded a $470,580 grant by the National Aeronautics and Space Administration (NASA) to continue the development of cloud retrieval algorithms for the next generation of geostationary weather satellite imagers. The goal is to provide algorithmic continuity with NASA's existing low-Earth orbit (LEO) satellite data record, while adding the high temporal sampling of geostationary satellites to enable new capabilities for the NASA program. This effort will leverage complementary work being done with the National Oceanic and Atmospheric Administration (NOAA) to improve infrared cloud products. The University of Wisconsin will focus on software and algorithm development to efficiently process the geostationary observations, and build an evaluation infrastructure to directly compare the LEO and geostationary cloud retrievals. This will help refine the algorithms and ensure consistency across the different satellite platforms. There is no set-aside designation associated with this grant, which has a period of performance ending on July 31, 2025.

We propose to continue the development of our NASA cloud continuity products, applying them to the next generation geostationary imagers that include goes-16+ ABI, japan's himawari AHI, and south korea s recently launched GEO-kompsat-2A AMI. The goal of this effort is to develop a suite of geostationary cloud retrieval algorithms that provides algorithm continuity with NASA s LEO data record (modis, viirs) adding 10-minute temporal sampling that will enable new and important capabilities for the NASA program of record. Further-more, this work will leverage an independent but complementary proposal that is being submitted by CO-i heidinger requesting NOAA support to develop new techniques that utilize the time-dependent information provided by the geostationary imagers to improve IR cloud products (including cloud-top properties). These proposed collaborative efforts continue our longstanding NASA/NOAA partnership that was instrumental in the development of the NASA EOS-snpp imager cloud climate continuity products cldmsk (cloud mask) and cldprop (cloud-top and optical properties). The cldmsk and cldprop product algorithms share heritage with the NASA modis MOD35/MOD06 collection 6 cloud mask and cloud optical property algorithms and the NOAA IR cloud-top property algorithms developed by CO-i heidinger as part of the clouds from avhrr extended (clavr-x) product. Both the modis and viirs cldmsk and cldprop continuity products are processed at the university of wisconsin NASA atmosphere sips and are publicly archived and distributed by laads at NASA gsfc. The proposed effort will focus on continued software and algorithm development to efficiently process the geostationary observations, and development of an evaluation infrastructure allowing direct inter-comparisons between the LEO and GEO observations that can then be used to investigate and further refine the algorithms. We expect to have a well-characterized geostationary cloud retrieval algorithm processed on a limited time interval of geostationary observations (goes-16/-17, AHI, GEO-kompsat-2A) that will be used to evaluate consistency with the same products from modis and viirs. In parallel with heritage cloud detection and thermodynamic phase classification approaches, IT is proposed that a recently developed machine learning cloud classification algorithm that makes use of spatial information be extended to include GEO temporal information.