Metadata Updated: August 9, 2018

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”.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Metadata Created Date August 9, 2018
Metadata Updated Date August 9, 2018

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 9, 2018
Metadata Updated Date August 9, 2018
Publisher ASDC
Unique Identifier C1456630703-LARC_ASDC
Marshall Sutton
Maintainer Email
Public Access Level public
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 0bd6f97b-b4f4-4b6f-b2b3-d09ce400dc1e
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 1970-01-01T00:00:00.000Z
Language en-US
Metadata Type geospatial
Data Last Modified 1970-01-01T00:00:00.000Z
Program Code 026:001
Publisher Hierarchy U.S. Government > National Aeronautics and Space Administration > ASDC
Related Documents https://doi.org/10.5067/EPIC/DSCOVR/L2_O3SO2AI.001, https://epic.gsfc.nasa.gov
Source Datajson Identifier True
Source Hash 9d4b6031c6d61d4a20aabe9e4e707fb875cdc282
Source Schema Version 1.1
Spatial -180.0 -90.0 180.0 90.0
Temporal 2015-06-12T00:00:00Z/2018-04-04T00:00:00Z
Category DSCOVR, geospatial

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