Identification and statistical analysis of global water vapour trends based on satellite data
Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launc...
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ftsubbremen:oai:media.suub.uni-bremen.de:Publications/elib/2712 2023-05-15T16:30:28+02:00 Identification and statistical analysis of global water vapour trends based on satellite data Identifikation und statistische Auswertung von globalen Wasserdampftrends aus Satellitenmessungen Mieruch, Sebastian Burrows, John Freund, Jan 2009-06-26 application/pdf https://media.suub.uni-bremen.de/handle/elib/2712 https://nbn-resolving.org/urn:nbn:de:gbv:46-diss000115889 eng eng Universität Bremen FB1 Physik/Elektrotechnik https://media.suub.uni-bremen.de/handle/elib/2712 urn:nbn:de:gbv:46-diss000115889 info:eu-repo/semantics/openAccess water vapour trends satellite GOME SCIAMACHY Bayes model selection Markov chain 530 530 Physics ddc:530 Dissertation doctoralThesis 2009 ftsubbremen 2022-11-09T07:09:51Z Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather ... Doctoral or Postdoctoral Thesis Greenland Siberia Media SuUB Bremen (Staats- und Universitätsbibliothek Bremen) Greenland |
institution |
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Media SuUB Bremen (Staats- und Universitätsbibliothek Bremen) |
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ftsubbremen |
language |
English |
topic |
water vapour trends satellite GOME SCIAMACHY Bayes model selection Markov chain 530 530 Physics ddc:530 |
spellingShingle |
water vapour trends satellite GOME SCIAMACHY Bayes model selection Markov chain 530 530 Physics ddc:530 Mieruch, Sebastian Identification and statistical analysis of global water vapour trends based on satellite data |
topic_facet |
water vapour trends satellite GOME SCIAMACHY Bayes model selection Markov chain 530 530 Physics ddc:530 |
description |
Global water vapour total column amounts have been retrieved from spectral data provided by the Global Ozone Monitoring Experiment (GOME) flying on ERS-2, which was launched in April 1995, and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard ENVISAT launched in March 2002. For this purpose the Air Mass Corrected Differential Optical Absorption Spectroscopy (AMC-DOAS) approach has been used. The combination of the data from both instruments provides a long-term global data set spanning more than 12 years with the potential of extension up to 2020 by GOME-2 data on MetOp. Using linear and non-linear methods from time series analysis and standard statistics the trends of water vapour columns and their errors have been calculated. In this study, factors affecting the trend such as the length of the time series, the variance of the noise and the autocorrelation of the noise are investigated. Special emphasis has been placed on the calculation of the statistical significance of the observed trends, which reveal significant local changes from -5 % per year to 5 % per year. These significant trends are distributed over the whole globe. Increasing trends have been calculated for Greenland, East Europe, Siberia and Oceania, whereas decreasing trends have been observed for the northwest USA, Central America, Amazonia, Central Africa and the Arabian Peninsular. The idea of the comprehensive trend and significance analysis is to get evidence for the truth of these observed changes. While the significance estimation is based on intrinsic properties such as the length of the data sets, the noise and the autocorrelation, an important aspect of assessing the probability that the real trends have been observed is a validation with independent data. Therefore an intercomparison of the global total column water vapour trends retrieved from GOME and SCIAMACHY with independent water vapour trends measured by radiosonde stations provided by the Deutsche Wetter Dienst DWD (German Weather ... |
author2 |
Burrows, John Freund, Jan |
format |
Doctoral or Postdoctoral Thesis |
author |
Mieruch, Sebastian |
author_facet |
Mieruch, Sebastian |
author_sort |
Mieruch, Sebastian |
title |
Identification and statistical analysis of global water vapour trends based on satellite data |
title_short |
Identification and statistical analysis of global water vapour trends based on satellite data |
title_full |
Identification and statistical analysis of global water vapour trends based on satellite data |
title_fullStr |
Identification and statistical analysis of global water vapour trends based on satellite data |
title_full_unstemmed |
Identification and statistical analysis of global water vapour trends based on satellite data |
title_sort |
identification and statistical analysis of global water vapour trends based on satellite data |
publisher |
Universität Bremen |
publishDate |
2009 |
url |
https://media.suub.uni-bremen.de/handle/elib/2712 https://nbn-resolving.org/urn:nbn:de:gbv:46-diss000115889 |
geographic |
Greenland |
geographic_facet |
Greenland |
genre |
Greenland Siberia |
genre_facet |
Greenland Siberia |
op_relation |
https://media.suub.uni-bremen.de/handle/elib/2712 urn:nbn:de:gbv:46-diss000115889 |
op_rights |
info:eu-repo/semantics/openAccess |
_version_ |
1766020192301744128 |