DSCOVR EPIC L2 Ozone (O3), Sulfur Dioxide (SO2) Aerosol Index (AI) with Epic L1B V03 Input, Version 2

Robust cloud products are critical for DSCOVR to make a significant contribution to climate studies. Building on our team’s track-record in cloud detection, cloud property retrieval, oxygen band exploitation, and DSCOVR-related studies, we propose to develop a suite of algorithms for generating the operational EPIC cloud mask, cloud height and cloud optical thickness products. Multichannel observations will be used for cloud masking; the cloud height will be developed with information from the oxygen A- and B- band pairs (780 nm vs. 779.5 nm and 680 nm vs. 687.75 nm); for the cloud optical thickness retrieval, we propose an approach that combines the EPIC 680 nm observations and numerical weather model outputs. Preliminary results from radiative transfer modeling and from proxy data applications show that the proposed algorithms are viable.

Product validation will be conducted by comparing EPIC observations/retrievals with counterparts from coexisting Low Earth Orbit (LEO) and Geosynchronous Earth Orbit (GEO) satellites. The proposed work will include a rigorous uncertainty analysis based on theoretical and computational radiative transfer modeling that complements standard validation activities with physics-based diagnostics. We also plan to evaluate and improve the calibration of the EPIC O2 A- and B-band absorption channels through tracking the instrument performance over known targets, such as cloud free ocean and ice sheet surfaces.
The deliverables for the proposed work include an Algorithm Theoretical Basis Document (ATBD) for peer-review, products generated with the proposed algorithms and supporting research articles. The data products, which will be archived at the Atmospheric Science Data Center (ASDC) at the NASA Langley Research Center, will provide essential inputs needed for the community to apply EPIC observations to climate research and to better interpret NISTAR observations.

The proposed work directly responds to the solicitation to “develop and implement the necessary algorithms and processes to enable various data products from EPIC sunrise to sunset observations once on orbit”, and also for improving “the calibration of EPIC based on in-flight data”.