Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice

The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI...

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Published in:Remote Sensing
Main Author: Stefan Kern
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2021
Subjects:
Q
Online Access:https://doi.org/10.3390/rs13214421
https://doaj.org/article/a4a391dffbe34cd2922e91bdd405b752
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spelling ftdoajarticles:oai:doaj.org/article:a4a391dffbe34cd2922e91bdd405b752 2023-05-15T15:17:34+02:00 Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice Stefan Kern 2021-11-01T00:00:00Z https://doi.org/10.3390/rs13214421 https://doaj.org/article/a4a391dffbe34cd2922e91bdd405b752 EN eng MDPI AG https://www.mdpi.com/2072-4292/13/21/4421 https://doaj.org/toc/2072-4292 doi:10.3390/rs13214421 2072-4292 https://doaj.org/article/a4a391dffbe34cd2922e91bdd405b752 Remote Sensing, Vol 13, Iss 4421, p 4421 (2021) sea-ice concentration satellite microwave radiometry retrieval error bias high-concentration pack ice correlation length scale Science Q article 2021 ftdoajarticles https://doi.org/10.3390/rs13214421 2022-12-31T04:04:13Z The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI-450, provide gridded SIC error estimates in addition to SIC. These error estimates, called total error henceforth, comprise a random, uncorrelated error contribution from retrieval and sensor noise, aka the algorithm standard error, and a locally-to-regionally correlated contribution from gridding and averaging Level-2 SIC into the Level-4 SIC CDRs, aka the representativity error. However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. In addition, larger-scale SIC errors due to, e.g., unaccounted weather influence or mismatch between the actual ice type and the algorithm setup are neither well represented by the total error, nor are their correlation scales known for these CDRs. In this study, I attempt to contribute to filling this knowledge gap by deriving spatial correlation length scales for the total error and the large-scale SIC error for high-concentration pack ice. For every grid cell with >90% SIC, I derive circular one-point correlation maps of 1000 km radius by computing the cross-correlation between the central 31-day time series of the errors and all other 31-day error time series within that circular area (disc) with 1000 km radius. I approximate the observed decrease in the correlation away from the disc’s center with an exponential function that best fits this decrease and thereby obtain the correlation length scale L sought. With this approach, I derive L separately for the total error and the large-scale SIC error for every high-concentration grid cell, and map, present and discuss these for the Arctic and the Southern Ocean for the year 2010 for the above-mentioned products. I ... Article in Journal/Newspaper Arctic Climate change Sea ice Southern Ocean Directory of Open Access Journals: DOAJ Articles Arctic Southern Ocean Remote Sensing 13 21 4421
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic sea-ice concentration
satellite microwave radiometry
retrieval error
bias
high-concentration pack ice
correlation length scale
Science
Q
spellingShingle sea-ice concentration
satellite microwave radiometry
retrieval error
bias
high-concentration pack ice
correlation length scale
Science
Q
Stefan Kern
Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
topic_facet sea-ice concentration
satellite microwave radiometry
retrieval error
bias
high-concentration pack ice
correlation length scale
Science
Q
description The European Organisation for the Exploitation of Meteorological Satellites-Ocean and Sea Ice Satellite Application Facility–European Space Agency-Climate Change Initiative (EUMETSAT-OSISAF–ESA-CCI) Level-4 sea-ice concentration (SIC) climate data records (CDRs), named SICCI-25km, SICCI-50km and OSI-450, provide gridded SIC error estimates in addition to SIC. These error estimates, called total error henceforth, comprise a random, uncorrelated error contribution from retrieval and sensor noise, aka the algorithm standard error, and a locally-to-regionally correlated contribution from gridding and averaging Level-2 SIC into the Level-4 SIC CDRs, aka the representativity error. However, these CDRs do not yet provide an error covariance matrix. Therefore, correlation scales of these error contributions and the total error in particular are unknown. In addition, larger-scale SIC errors due to, e.g., unaccounted weather influence or mismatch between the actual ice type and the algorithm setup are neither well represented by the total error, nor are their correlation scales known for these CDRs. In this study, I attempt to contribute to filling this knowledge gap by deriving spatial correlation length scales for the total error and the large-scale SIC error for high-concentration pack ice. For every grid cell with >90% SIC, I derive circular one-point correlation maps of 1000 km radius by computing the cross-correlation between the central 31-day time series of the errors and all other 31-day error time series within that circular area (disc) with 1000 km radius. I approximate the observed decrease in the correlation away from the disc’s center with an exponential function that best fits this decrease and thereby obtain the correlation length scale L sought. With this approach, I derive L separately for the total error and the large-scale SIC error for every high-concentration grid cell, and map, present and discuss these for the Arctic and the Southern Ocean for the year 2010 for the above-mentioned products. I ...
format Article in Journal/Newspaper
author Stefan Kern
author_facet Stefan Kern
author_sort Stefan Kern
title Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
title_short Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
title_full Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
title_fullStr Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
title_full_unstemmed Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice
title_sort spatial correlation length scales of sea-ice concentration errors for high-concentration pack ice
publisher MDPI AG
publishDate 2021
url https://doi.org/10.3390/rs13214421
https://doaj.org/article/a4a391dffbe34cd2922e91bdd405b752
geographic Arctic
Southern Ocean
geographic_facet Arctic
Southern Ocean
genre Arctic
Climate change
Sea ice
Southern Ocean
genre_facet Arctic
Climate change
Sea ice
Southern Ocean
op_source Remote Sensing, Vol 13, Iss 4421, p 4421 (2021)
op_relation https://www.mdpi.com/2072-4292/13/21/4421
https://doaj.org/toc/2072-4292
doi:10.3390/rs13214421
2072-4292
https://doaj.org/article/a4a391dffbe34cd2922e91bdd405b752
op_doi https://doi.org/10.3390/rs13214421
container_title Remote Sensing
container_volume 13
container_issue 21
container_start_page 4421
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