Machine-learning based observational cloud products for process-oriented climate model evaluation
The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kaps, Arndt [verfasserIn] Lauer, Axel - 1975- [akademischer betreuerIn] Eyring, Veronika [akademischer betreuerIn] Bösch, Hartmut [akademischer betreuerIn] |
|---|---|
| Körperschaften: |
Universität Bremen [Grad-verleihende Institution] |
| Hochschulschrift: |
Dissertation ; Universität Bremen ; 2024 |
| Format: |
E-Book |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
Bremen: 2023 |
|---|
| Rechteinformationen: |
Open Access Namensnennung 4.0 International ; CC BY 4.0 |
|---|
| Schlagwörter: | |
|---|---|
| Formangabe: |
Hochschulschrift |
| Umfang: |
1 Online-Ressource (xiii, 152 Seiten) ; Illustrationen |
|---|
| Weitere Ausgabe: |
Erscheint auch als Druck-Ausgabe Kaps, Arndt: Machine-learning based observational cloud products for process-oriented climate model evaluation - Bremen, 2023 |
|---|
| Links: |
Link aufrufen |
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| DOI / URN: |
urn:nbn:de:gbv:46-elib79485 10.26092/elib/2997 |
|---|
| Katalog-ID: |
1891339419 |
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| 520 | |a The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. | ||
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urn:nbn:de:gbv:46-elib79485 urn 10.26092/elib/2997 doi (DE-627)1891339419 (DE-599)KXP1891339419 (OCoLC)1439695722 (OAEPFHB)elib/2997 DE-627 ger DE-627 rda eng XA-DE-HB 551.576 DE-101 550 DE-101 Kaps, Arndt verfasserin (orcid)0000-0002-5368-5950 aut Machine-learning based observational cloud products for process-oriented climate model evaluation Arndt Kaps Bremen 2023 1 Online-Ressource (xiii, 152 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB pdager DE-46 Clouds Remote sensing Climate Modeling Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Lauer, Axel 1975- akademischer betreuerin (DE-588)130323217 (DE-627)497980657 (DE-576)189926325 dgs Eyring, Veronika akademischer betreuerin (DE-588)1255681446 (DE-627)1799770168 dgs Bösch, Hartmut akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kaps, Arndt Machine-learning based observational cloud products for process-oriented climate model evaluation Bremen, 2023 xiii, 152 Seiten (DE-627)1891339532 https://doi.org/10.26092/elib/2997 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 Resolving-System kostenfrei https://d-nb.info/133951222X/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7948 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459308346X x 12-10-24 21 01 0046 4539460471 ebook_2024_dissbremen Kostenloser Zugriff zza 17-06-24 22 01 0018 4593184614 SUBolrd xu 12-10-24 23 01 0830 4593232724 olr-d x 12-10-24 30 01 0104 4593279119 z 12-10-24 40 01 0007 459331559X xsn 12-10-24 60 01 0705 4593373778 OLRD z 12-10-24 63 01 3401 4593426995 ORD x 12-10-24 70 01 0089 4593482593 z 12-10-24 105 01 0841 4593872146 z 12-10-24 110 01 3110 4593583241 x 12-10-24 132 01 0959 459362682X OLR-DISS x 12-10-24 151 01 0546 4593667569 OLR-ODISS z 12-10-24 161 01 0960 4593691605 ORD z 12-10-24 293 01 3293 4593821150 ORD z 12-10-24 370 01 4370 459385461X x 12-10-24 2403 01 DE-LFER 4547776075 00 --%%-- --%%-- n --%%-- l01 08-07-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 21 01 0046 https://doi.org/10.26092/elib/2997 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 2403 01 DE-LFER https://doi.org/10.26092/elib/2997 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16 |
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urn:nbn:de:gbv:46-elib79485 urn 10.26092/elib/2997 doi (DE-627)1891339419 (DE-599)KXP1891339419 (OCoLC)1439695722 (OAEPFHB)elib/2997 DE-627 ger DE-627 rda eng XA-DE-HB 551.576 DE-101 550 DE-101 Kaps, Arndt verfasserin (orcid)0000-0002-5368-5950 aut Machine-learning based observational cloud products for process-oriented climate model evaluation Arndt Kaps Bremen 2023 1 Online-Ressource (xiii, 152 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB pdager DE-46 Clouds Remote sensing Climate Modeling Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Lauer, Axel 1975- akademischer betreuerin (DE-588)130323217 (DE-627)497980657 (DE-576)189926325 dgs Eyring, Veronika akademischer betreuerin (DE-588)1255681446 (DE-627)1799770168 dgs Bösch, Hartmut akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kaps, Arndt Machine-learning based observational cloud products for process-oriented climate model evaluation Bremen, 2023 xiii, 152 Seiten (DE-627)1891339532 https://doi.org/10.26092/elib/2997 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 Resolving-System kostenfrei https://d-nb.info/133951222X/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7948 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459308346X x 12-10-24 21 01 0046 4539460471 ebook_2024_dissbremen Kostenloser Zugriff zza 17-06-24 22 01 0018 4593184614 SUBolrd xu 12-10-24 23 01 0830 4593232724 olr-d x 12-10-24 30 01 0104 4593279119 z 12-10-24 40 01 0007 459331559X xsn 12-10-24 60 01 0705 4593373778 OLRD z 12-10-24 63 01 3401 4593426995 ORD x 12-10-24 70 01 0089 4593482593 z 12-10-24 105 01 0841 4593872146 z 12-10-24 110 01 3110 4593583241 x 12-10-24 132 01 0959 459362682X OLR-DISS x 12-10-24 151 01 0546 4593667569 OLR-ODISS z 12-10-24 161 01 0960 4593691605 ORD z 12-10-24 293 01 3293 4593821150 ORD z 12-10-24 370 01 4370 459385461X x 12-10-24 2403 01 DE-LFER 4547776075 00 --%%-- --%%-- n --%%-- l01 08-07-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 21 01 0046 https://doi.org/10.26092/elib/2997 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 2403 01 DE-LFER https://doi.org/10.26092/elib/2997 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16 |
| allfields_unstemmed |
urn:nbn:de:gbv:46-elib79485 urn 10.26092/elib/2997 doi (DE-627)1891339419 (DE-599)KXP1891339419 (OCoLC)1439695722 (OAEPFHB)elib/2997 DE-627 ger DE-627 rda eng XA-DE-HB 551.576 DE-101 550 DE-101 Kaps, Arndt verfasserin (orcid)0000-0002-5368-5950 aut Machine-learning based observational cloud products for process-oriented climate model evaluation Arndt Kaps Bremen 2023 1 Online-Ressource (xiii, 152 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB pdager DE-46 Clouds Remote sensing Climate Modeling Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Lauer, Axel 1975- akademischer betreuerin (DE-588)130323217 (DE-627)497980657 (DE-576)189926325 dgs Eyring, Veronika akademischer betreuerin (DE-588)1255681446 (DE-627)1799770168 dgs Bösch, Hartmut akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kaps, Arndt Machine-learning based observational cloud products for process-oriented climate model evaluation Bremen, 2023 xiii, 152 Seiten (DE-627)1891339532 https://doi.org/10.26092/elib/2997 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 Resolving-System kostenfrei https://d-nb.info/133951222X/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7948 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459308346X x 12-10-24 21 01 0046 4539460471 ebook_2024_dissbremen Kostenloser Zugriff zza 17-06-24 22 01 0018 4593184614 SUBolrd xu 12-10-24 23 01 0830 4593232724 olr-d x 12-10-24 30 01 0104 4593279119 z 12-10-24 40 01 0007 459331559X xsn 12-10-24 60 01 0705 4593373778 OLRD z 12-10-24 63 01 3401 4593426995 ORD x 12-10-24 70 01 0089 4593482593 z 12-10-24 105 01 0841 4593872146 z 12-10-24 110 01 3110 4593583241 x 12-10-24 132 01 0959 459362682X OLR-DISS x 12-10-24 151 01 0546 4593667569 OLR-ODISS z 12-10-24 161 01 0960 4593691605 ORD z 12-10-24 293 01 3293 4593821150 ORD z 12-10-24 370 01 4370 459385461X x 12-10-24 2403 01 DE-LFER 4547776075 00 --%%-- --%%-- n --%%-- l01 08-07-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 21 01 0046 https://doi.org/10.26092/elib/2997 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 2403 01 DE-LFER https://doi.org/10.26092/elib/2997 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16 |
| allfieldsGer |
urn:nbn:de:gbv:46-elib79485 urn 10.26092/elib/2997 doi (DE-627)1891339419 (DE-599)KXP1891339419 (OCoLC)1439695722 (OAEPFHB)elib/2997 DE-627 ger DE-627 rda eng XA-DE-HB 551.576 DE-101 550 DE-101 Kaps, Arndt verfasserin (orcid)0000-0002-5368-5950 aut Machine-learning based observational cloud products for process-oriented climate model evaluation Arndt Kaps Bremen 2023 1 Online-Ressource (xiii, 152 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB pdager DE-46 Clouds Remote sensing Climate Modeling Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Lauer, Axel 1975- akademischer betreuerin (DE-588)130323217 (DE-627)497980657 (DE-576)189926325 dgs Eyring, Veronika akademischer betreuerin (DE-588)1255681446 (DE-627)1799770168 dgs Bösch, Hartmut akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kaps, Arndt Machine-learning based observational cloud products for process-oriented climate model evaluation Bremen, 2023 xiii, 152 Seiten (DE-627)1891339532 https://doi.org/10.26092/elib/2997 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 Resolving-System kostenfrei https://d-nb.info/133951222X/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7948 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459308346X x 12-10-24 21 01 0046 4539460471 ebook_2024_dissbremen Kostenloser Zugriff zza 17-06-24 22 01 0018 4593184614 SUBolrd xu 12-10-24 23 01 0830 4593232724 olr-d x 12-10-24 30 01 0104 4593279119 z 12-10-24 40 01 0007 459331559X xsn 12-10-24 60 01 0705 4593373778 OLRD z 12-10-24 63 01 3401 4593426995 ORD x 12-10-24 70 01 0089 4593482593 z 12-10-24 105 01 0841 4593872146 z 12-10-24 110 01 3110 4593583241 x 12-10-24 132 01 0959 459362682X OLR-DISS x 12-10-24 151 01 0546 4593667569 OLR-ODISS z 12-10-24 161 01 0960 4593691605 ORD z 12-10-24 293 01 3293 4593821150 ORD z 12-10-24 370 01 4370 459385461X x 12-10-24 2403 01 DE-LFER 4547776075 00 --%%-- --%%-- n --%%-- l01 08-07-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 21 01 0046 https://doi.org/10.26092/elib/2997 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 2403 01 DE-LFER https://doi.org/10.26092/elib/2997 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16 |
| allfieldsSound |
urn:nbn:de:gbv:46-elib79485 urn 10.26092/elib/2997 doi (DE-627)1891339419 (DE-599)KXP1891339419 (OCoLC)1439695722 (OAEPFHB)elib/2997 DE-627 ger DE-627 rda eng XA-DE-HB 551.576 DE-101 550 DE-101 Kaps, Arndt verfasserin (orcid)0000-0002-5368-5950 aut Machine-learning based observational cloud products for process-oriented climate model evaluation Arndt Kaps Bremen 2023 1 Online-Ressource (xiii, 152 Seiten) Illustrationen Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Dissertation Universität Bremen 2024 DE-46 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. DE-46 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Archivierung/Langzeitarchivierung gewährleistet PEHB pdager DE-46 Clouds Remote sensing Climate Modeling Hochschulschrift (DE-588)4113937-9 (DE-627)105825778 (DE-576)209480580 gnd-content Lauer, Axel 1975- akademischer betreuerin (DE-588)130323217 (DE-627)497980657 (DE-576)189926325 dgs Eyring, Veronika akademischer betreuerin (DE-588)1255681446 (DE-627)1799770168 dgs Bösch, Hartmut akademischer betreuerin dgs Universität Bremen Grad-verleihende Institution (DE-588)2001386-3 (DE-627)101380429 (DE-576)191575038 dgg Bremen (DE-588)4008135-7 (DE-627)106369636 (DE-576)208874569 uvp Erscheint auch als Druck-Ausgabe Kaps, Arndt Machine-learning based observational cloud products for process-oriented climate model evaluation Bremen, 2023 xiii, 152 Seiten (DE-627)1891339532 https://doi.org/10.26092/elib/2997 Resolving-System kostenfrei https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 Resolving-System kostenfrei https://d-nb.info/133951222X/34 Langzeitarchivierung Nationalbibliothek kostenfrei https://media.suub.uni-bremen.de/handle/elib/7948 Verlag kostenfrei GBV-ODiss GBV_ILN_20 ISIL_DE-84 SYSFLAG_1 GBV_KXP GBV_ILN_21 ISIL_DE-46 GBV_ILN_22 ISIL_DE-18 GBV_ILN_23 ISIL_DE-830 GBV_ILN_30 ISIL_DE-104 GBV_ILN_40 ISIL_DE-7 GBV_ILN_60 ISIL_DE-705 GBV_ILN_63 ISIL_DE-Wim2 GBV_ILN_70 ISIL_DE-89 GBV_ILN_105 ISIL_DE-841 GBV_ILN_110 ISIL_DE-Luen4 GBV_ILN_132 ISIL_DE-959 GBV_ILN_151 ISIL_DE-546 GBV_ILN_161 ISIL_DE-960 GBV_ILN_293 ISIL_DE-960-3 GBV_ILN_370 ISIL_DE-1373 GBV_ILN_2403 ISIL_DE-LFER DSpace BO 20 01 0084 459308346X x 12-10-24 21 01 0046 4539460471 ebook_2024_dissbremen Kostenloser Zugriff zza 17-06-24 22 01 0018 4593184614 SUBolrd xu 12-10-24 23 01 0830 4593232724 olr-d x 12-10-24 30 01 0104 4593279119 z 12-10-24 40 01 0007 459331559X xsn 12-10-24 60 01 0705 4593373778 OLRD z 12-10-24 63 01 3401 4593426995 ORD x 12-10-24 70 01 0089 4593482593 z 12-10-24 105 01 0841 4593872146 z 12-10-24 110 01 3110 4593583241 x 12-10-24 132 01 0959 459362682X OLR-DISS x 12-10-24 151 01 0546 4593667569 OLR-ODISS z 12-10-24 161 01 0960 4593691605 ORD z 12-10-24 293 01 3293 4593821150 ORD z 12-10-24 370 01 4370 459385461X x 12-10-24 2403 01 DE-LFER 4547776075 00 --%%-- --%%-- n --%%-- l01 08-07-24 20 01 0084 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 21 01 0046 https://doi.org/10.26092/elib/2997 LF 22 01 0018 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 23 01 0830 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 30 01 0104 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 40 01 0007 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 60 01 0705 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 63 01 3401 E-Book https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 LF 70 01 0089 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 105 01 0841 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 110 01 3110 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 132 01 0959 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 151 01 0546 Volltext https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 161 01 0960 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 293 01 3293 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 370 01 4370 https://nbn-resolving.org/urn:nbn:de:gbv:46-elib79485 2403 01 DE-LFER https://doi.org/10.26092/elib/2997 21 00 DE-46 00 Universität Bremen 21 00 DE-46 00 Fachbereich 01: Physik/Elektrotechnik (FB 01) 60 01 0705 10 ho 20 01 0084 OLRD 110 01 3110 OLRD 370 01 4370 OLRD 21 01 0046 ebook_2024_dissbremen 22 01 0018 SUBolrd 23 01 0830 olr-d 60 01 0705 OLRD 63 01 3401 ORD 132 01 0959 OLR-DISS 151 01 0546 OLR-ODISS 161 01 0960 ORD 293 01 3293 ORD 23 01 0830 2024-10-12 10:31:16 |
| language |
English |
| format_phy_str_mv |
Book |
| building |
20 21:z 22:u 23 30 40:s 60 63 70 105 110 132 151 161 293 370 2403:0 |
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findex.gbv.de |
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20@ 21@za 22@u 23@ 30@ 40@sn 60@ 63@ 70@ 105@ 110@ 132@ 151@ 161@ 293@ 370@ 2403@01 |
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Clouds Remote sensing Climate Modeling |
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Dissertation Universität Bremen 2024 |
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21:Universität Bremen DE-46:Universität Bremen 21:Fachbereich 01: Physik/Elektrotechnik (FB 01) DE-46:Fachbereich 01: Physik/Elektrotechnik (FB 01) |
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551.576 |
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true |
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Kaps, Arndt @@aut@@ Lauer, Axel @@dgs@@ Eyring, Veronika @@dgs@@ Bösch, Hartmut @@dgs@@ |
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2023-01-01T00:00:00Z |
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3551.576 |
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1891339419 |
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Hochschulschrift |
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englisch |
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The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. |
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The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. |
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The importance of clouds in regulating the Earth’s energy balance as well as moisture and heat distributions cannot be overstated. Consequently, clouds have a considerable influence on the trajectory of anthropogenic climate change, of which possible scenarios are being studied with global climate models (GCMs). Uncertainties from the representation of clouds in GCMs have been identified as a leading cause of inter-model spread in climate projections. Our current understanding of clouds and the processes relevant to their formation and effect on climate is informed partly by observations from remote sensing instruments aboard orbital satellites. This thesis introduces new methods of characterizing clouds from space with the help of machine learning and neural networks. The purpose of these methods is to improve the understanding of and reduce the uncertainties in climate projections by providing satellite products that are objectively interpretable and consistently comparable to GCM output. The methods explored in this thesis highlight that machine learning and especially neural networks have the potential to improve multiple aspects of climate science. The presented results show that cloud classes can be reliably obtained from low-resolution data to improve their interpretability. They further show that comparison between climate models and observations can potentially be simplified with machine learning. |
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