A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing
Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. r...
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ftnilu:oai:nilu.brage.unit.no:11250/2642263 2023-07-30T03:55:33+02:00 A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing Sayer, Andrew M. Goaverts, Yves Kolmonen, Pekka Lipponen, Antti Luffarelli, Marta Mielonen, Tero Patadia, Falguni Popp, Thomas Povey, Adam C. Stebel, Kerstin Witek, Marcin L. 2020 application/pdf https://hdl.handle.net/11250/2642263 https://doi.org/10.5194/amt-13-373-2020 eng eng NILU - Norsk institutt for luftforskning: 114071 Atmospheric Measurement Techniques. 2020, 373-404. urn:issn:1867-1381 https://hdl.handle.net/11250/2642263 https://doi.org/10.5194/amt-13-373-2020 cristin:1772004 Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © Author(s) 2020 373-404 Atmospheric Measurement Techniques Peer reviewed Journal article 2020 ftnilu https://doi.org/10.5194/amt-13-373-2020 2023-07-08T19:54:04Z Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and presents a general framework to evaluate them, based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those where it is expected that the techniques will perform well, and those for which some complexity about the site may provide a more severe test. Overall all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small/large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as Optimal Estimation the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the ... Article in Journal/Newspaper Aerosol Robotic Network NILU – Norwegian Institute for Air Research: NILU Brage Atmospheric Measurement Techniques 13 2 373 404 |
institution |
Open Polar |
collection |
NILU – Norwegian Institute for Air Research: NILU Brage |
op_collection_id |
ftnilu |
language |
English |
description |
Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and presents a general framework to evaluate them, based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those where it is expected that the techniques will perform well, and those for which some complexity about the site may provide a more severe test. Overall all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small/large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as Optimal Estimation the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the ... |
format |
Article in Journal/Newspaper |
author |
Sayer, Andrew M. Goaverts, Yves Kolmonen, Pekka Lipponen, Antti Luffarelli, Marta Mielonen, Tero Patadia, Falguni Popp, Thomas Povey, Adam C. Stebel, Kerstin Witek, Marcin L. |
spellingShingle |
Sayer, Andrew M. Goaverts, Yves Kolmonen, Pekka Lipponen, Antti Luffarelli, Marta Mielonen, Tero Patadia, Falguni Popp, Thomas Povey, Adam C. Stebel, Kerstin Witek, Marcin L. A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
author_facet |
Sayer, Andrew M. Goaverts, Yves Kolmonen, Pekka Lipponen, Antti Luffarelli, Marta Mielonen, Tero Patadia, Falguni Popp, Thomas Povey, Adam C. Stebel, Kerstin Witek, Marcin L. |
author_sort |
Sayer, Andrew M. |
title |
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
title_short |
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
title_full |
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
title_fullStr |
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
title_full_unstemmed |
A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
title_sort |
review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing |
publishDate |
2020 |
url |
https://hdl.handle.net/11250/2642263 https://doi.org/10.5194/amt-13-373-2020 |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
373-404 Atmospheric Measurement Techniques |
op_relation |
NILU - Norsk institutt for luftforskning: 114071 Atmospheric Measurement Techniques. 2020, 373-404. urn:issn:1867-1381 https://hdl.handle.net/11250/2642263 https://doi.org/10.5194/amt-13-373-2020 cristin:1772004 |
op_rights |
Navngivelse 4.0 Internasjonal http://creativecommons.org/licenses/by/4.0/deed.no © Author(s) 2020 |
op_doi |
https://doi.org/10.5194/amt-13-373-2020 |
container_title |
Atmospheric Measurement Techniques |
container_volume |
13 |
container_issue |
2 |
container_start_page |
373 |
op_container_end_page |
404 |
_version_ |
1772817001543106560 |