Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States
In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006...
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Molecular Diversity Preservation International
2012
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ftmdpi:oai:mdpi.com:/2072-4292/4/5/1134/ 2023-08-20T04:09:44+02:00 Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States Christine Chen Tarendra Lakhankar Peter Romanov Sean Helfrich Al Powell Reza Khanbilvardi agris 2012-04-25 application/pdf https://doi.org/10.3390/rs4051134 EN eng Molecular Diversity Preservation International https://dx.doi.org/10.3390/rs4051134 https://creativecommons.org/licenses/by/3.0/ Remote Sensing; Volume 4; Issue 5; Pages: 1134-1145 snow IMS NOAA NCDC AVHRR Text 2012 ftmdpi https://doi.org/10.3390/rs4051134 2023-07-31T20:28:48Z In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed. Text Sea ice MDPI Open Access Publishing Remote Sensing 4 5 1134 1145 |
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snow IMS NOAA NCDC AVHRR |
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snow IMS NOAA NCDC AVHRR Christine Chen Tarendra Lakhankar Peter Romanov Sean Helfrich Al Powell Reza Khanbilvardi Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
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snow IMS NOAA NCDC AVHRR |
description |
In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed. |
format |
Text |
author |
Christine Chen Tarendra Lakhankar Peter Romanov Sean Helfrich Al Powell Reza Khanbilvardi |
author_facet |
Christine Chen Tarendra Lakhankar Peter Romanov Sean Helfrich Al Powell Reza Khanbilvardi |
author_sort |
Christine Chen |
title |
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
title_short |
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
title_full |
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
title_fullStr |
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
title_full_unstemmed |
Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States |
title_sort |
validation of noaa-interactive multisensor snow and ice mapping system (ims) by comparison with ground-based measurements over continental united states |
publisher |
Molecular Diversity Preservation International |
publishDate |
2012 |
url |
https://doi.org/10.3390/rs4051134 |
op_coverage |
agris |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Remote Sensing; Volume 4; Issue 5; Pages: 1134-1145 |
op_relation |
https://dx.doi.org/10.3390/rs4051134 |
op_rights |
https://creativecommons.org/licenses/by/3.0/ |
op_doi |
https://doi.org/10.3390/rs4051134 |
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Remote Sensing |
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4 |
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5 |
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1134 |
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1145 |
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1774723382042951680 |