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: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Subjects:
Online Access:https://doi.org/10.3390/rs13214421
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spelling ftmdpi:oai:mdpi.com:/2072-4292/13/21/4421/ 2023-08-20T04:05:02+02:00 Spatial Correlation Length Scales of Sea-Ice Concentration Errors for High-Concentration Pack Ice Stefan Kern agris 2021-11-03 application/pdf https://doi.org/10.3390/rs13214421 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs13214421 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 21; Pages: 4421 sea-ice concentration satellite microwave radiometry retrieval error bias high-concentration pack ice correlation length scale Text 2021 ftmdpi https://doi.org/10.3390/rs13214421 2023-08-01T03:09:11Z 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 ... Text Arctic Climate change Sea ice Southern Ocean MDPI Open Access Publishing Arctic Southern Ocean Remote Sensing 13 21 4421
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea-ice concentration
satellite microwave radiometry
retrieval error
bias
high-concentration pack ice
correlation length scale
spellingShingle sea-ice concentration
satellite microwave radiometry
retrieval error
bias
high-concentration pack ice
correlation length scale
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
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 Text
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 Multidisciplinary Digital Publishing Institute
publishDate 2021
url https://doi.org/10.3390/rs13214421
op_coverage agris
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; Volume 13; Issue 21; Pages: 4421
op_relation Ocean Remote Sensing
https://dx.doi.org/10.3390/rs13214421
op_rights https://creativecommons.org/licenses/by/4.0/
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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