ASDC | DSCOVR_EPIC_L2_O3SO2AI

DSCOVR Level 2

Entry Title: DSCOVR EPIC Level 2 O3SO2AI

Entry ID: DSCOVR_EPIC_L2_O3SO2AI_03

Aerosols Clouds Radiation Budget

Description

Robust cloud products are critical for Deep Space Climate Observatory (DSCOVR) to contribute to climate studies significantly. 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 Earth Polychromatic Imaging Camera (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 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 by 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 interpret better The National Institute of Standards and Technology Advanced Radiometer (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 improve “the calibration of EPIC based on in-flight data.”

DOI

10.5067/EPIC/DSCOVR/L2_O3SO2AI.003

Citation Styles for this Dataset

Publications which Cite this Collection

Huang, Xinzhou; Yang, Kai (2016). Algorithm theoretical basis for ozone and sulfur dioxide retrievals from DSCOVR EPIC.

Resources and Documentation

Earthdata Forum

PROJECT HOME PAGE

EPIC Website

VIEW RELATED INFORMATION

DATA CITATION POLICY

ASDC Citation Policy

From GCMD Science Keywords:

ATMOSPHERIC OZONE > OXYGEN COMPOUNDS
SULFUR DIOXIDE > SULFUR COMPOUNDS
ATMOSPHERIC CHEMISTRY
OXYGEN COMPOUNDS
AIR QUALITY
SULFUR COMPOUNDS

Data Distribution

File Format(s):

HDF5

Get Dataset

Note: "Get Dataset" is a link to our recommended order method. The down arrow will show you additional options.

Spatial Information

Spatial Coverage: (S: -90, N: 90), (W: -180, E: 180)
Spatial Coverage Type: Not provided
Coordinate System: Cartesian
Granule Spatial Representation: Cartesian

Locations

GLOBAL

Temporal Information

Temporal Coverage: 2015-06-13 - Present

Platforms

Solar/Space Observation Satellites

DSCOVR

Deep Space Climate Observatory

EPIC

Earth Polychromatic Imaging Camera

Metadata Dates

Created on 2022-02-09

Last updated on 2023-08-16