Exhaust identification product for MARCUS project aboard Aurora Australis 2017/18

Exhaust identification product calculated using CN and CO data from the ARM AOS facility aboard the Aurora Australis during the 2017/18 resupply season (Austral summer) as part of the Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) project. The product is a binary tim...

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Bibliographic Details
Other Authors: HUMPHRIES, RUHI S (hasPrincipalInvestigator), HUMPHRIES, RUHI S (processor), Australian Antarctic Data Centre (publisher)
Format: Dataset
Language:unknown
Published: Australian Antarctic Data Centre
Subjects:
EARTH SCIENCE &gt
ATMOSPHERE &gt
ATMOSPHERIC CHEMISTRY &gt
CARBON AND HYDROCARBON COMPOUNDS &gt
ATMOSPHERIC CARBON MONOXIDE
PARTICULATE MATTER
EARTH SCIENCE
ATMOSPHERE
AEROSOLS
EMISSIONS
AIR QUALITY
CARBON MONOXIDE
ARM AOS
EXHAUST
TSI CONDENSATION PARTICLE COUNTER 3772
LGR N2O_CO 907-0015-1000
PARTICLE DETECTORS
CHN ANALYZERS &gt
Carbon Hydrogen Nitrogen Analyzers
R/V AA &gt
R/V Aurora Australis
OCEAN &gt
SOUTHERN OCEAN
GEOGRAPHIC REGION &gt
POLAR
CONTINENT &gt
ANTARCTICA
Austral
Southern Ocean
Antarc*
Antarctica
aurora australis
Online Access:https://researchdata.edu.au/exhaust-identification-product-australis-201718/1424425
https://doi.org/10.26179/5cf868e1d3414
https://data.aad.gov.au/metadata/records/AAS_4292_ExhaustID_201718_AA_MARCUS
http://nla.gov.au/nla.party-617536
Description
Summary:Exhaust identification product calculated using CN and CO data from the ARM AOS facility aboard the Aurora Australis during the 2017/18 resupply season (Austral summer) as part of the Measurements of Aerosols, Radiation and CloUds over the Southern Ocean (MARCUS) project. The product is a binary timeseries (UTC) indicating the presence (True) or absence (False) of exhaust on the atmospheric measurements. The algorithm is described in https://doi.org/10.5194/amt-12-3019-2019 and is modified for this dataset. Included here are a number of permutations which vary the number of median absolute deviations (MAD) and percentage threshold for the window filter (see section 3.3 of paper). It is left to the end-user to assess the suitability to their dataset and choose the appropriate permutation for their needs or expand the window filtering to make it more stringent. The authors recommend the exhaust_4mad02thresh as the best output. For use in publications, please contact Ruhi Humphries (Ruhi.Humphries@csiro.au) to discuss appropriate acknowledgement. This dataset is associated with AAD projects 4292 and 4431.

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