SOLR
| _version_ |
1797365333014609920 |
| author |
Devasthale, Abhay |
| author2 |
Karlsson, Karl-Göran, Quaas, Johannes, Graßl, Hartmut |
| author2_role |
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| author2_variant |
k g k kgk, j q jq, h g hg |
| author_facet |
Devasthale, Abhay, Karlsson, Karl-Göran, Quaas, Johannes, Graßl, Hartmut |
| author_role |
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| author_sort |
Devasthale, Abhay |
| author_variant |
a d ad |
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Library A |
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sid-22-col-qucosa |
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Atmospheric measurement techniques (2012) 5, S. 267-273 |
| contents |
The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections. |
| dewey-full |
551 |
| dewey-hundreds |
500 - Natural sciences and mathematics |
| dewey-ones |
551 - Geology, hydrology, meteorology |
| dewey-raw |
551 |
| dewey-search |
551 |
| dewey-sort |
3551 |
| dewey-tens |
550 - Earth sciences |
| facet_avail |
Online, Free |
| finc_class_facet |
Geographie, Geologie und Paläontologie |
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science-geology |
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ElectronicSerialComponentPart |
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Article, E-Article |
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Ebook |
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Article, E-Article |
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Article, E-Article |
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Buch |
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text-online-journal-child |
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Journal |
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Article, E-Article |
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ElectronicArticle |
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Article, E-Article |
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Article, E-Article |
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E-Article |
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not assigned |
| geogr_code_person |
not assigned |
| hierarchy_sequence |
Atmospheric measurement techniques (2012) 5, S. 267-273 |
| id |
22-15-qucosa-177609 |
| illustrated |
Not Illustrated |
| imprint |
Göttingen, Copernicus Publications, 2012 |
| imprint_str_mv |
Online-Ausg.: 2015 |
| institution |
DE-105, DE-Gla1, DE-Brt1, DE-D161, DE-540, DE-Pl11, DE-Rs1, DE-Bn3, DE-Zi4, DE-Zwi2, DE-D117, DE-Mh31, DE-D275, DE-Ch1, DE-15, DE-D13, DE-L242, DE-L229, DE-L328 |
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| language |
English |
| last_indexed |
2024-04-26T03:12:32.212Z |
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devasthale2012correctingorbitaldriftsignalinthetimeseriesofavhrrderivedconvectivecloudfractionusingrotatedempiricalorthogonalfunctioncorrectingorbitaldriftsignalinthetimeseriesofavhrrderivedconvectivecloudfractionusingrotatedempiricalorthogonalfunction |
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Qucosa |
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2012 |
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2012 |
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Göttingen |
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Copernicus Publications |
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marcfinc |
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15-qucosa-177609 |
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marcfinc |
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No subject assigned |
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22 |
| spelling |
Devasthale, Abhay, Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function, Göttingen Copernicus Publications 2012, Online-Ausg. 2015 Online-Ressource (Text) Universitätsbibliothek Leipzig, The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections., Wolken, Satelliten, Atmosphäre, Clouds, Satellites, Atmosphere, Karlsson, Karl-Göran, Quaas, Johannes, Graßl, Hartmut, Atmospheric measurement techniques (2012) 5, S. 267-273, text/html https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa-177609 Online-Zugriff |
| spellingShingle |
Devasthale, Abhay, Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function: Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function, The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections., Wolken, Satelliten, Atmosphäre, Clouds, Satellites, Atmosphere |
| title |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function: Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_auth |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_full |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_fullStr |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_full_unstemmed |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_in_hierarchy |
|
| title_short |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function |
| title_sort |
correcting orbital drift signal in the time series of avhrr derived convective cloud fraction using rotated empirical orthogonal function correcting orbital drift signal in the time series of avhrr derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_sub |
Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| title_unstemmed |
Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function: Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function |
| topic |
Wolken, Satelliten, Atmosphäre, Clouds, Satellites, Atmosphere |
| topic_facet |
Wolken, Satelliten, Atmosphäre, Clouds, Satellites, Atmosphere |
| url |
https://nbn-resolving.org/urn:nbn:de:bsz:15-qucosa-177609 |
| urn |
urn:nbn:de:bsz:15-qucosa-177609 |
| work_keys_str_mv |
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