The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors
<p>We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error source...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
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Copernicus Publications
2018
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| _version_ | 1826279424908066816 |
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| author | Sus, O Stengel, M Stapelberg, S McGarragh, GR Poulsen, C Povey, AC Schlundt, C Thomas, G Christensen, M Proud, S Jerg, M Grainger, R Hollmann, R |
| author_facet | Sus, O Stengel, M Stapelberg, S McGarragh, GR Poulsen, C Povey, AC Schlundt, C Thomas, G Christensen, M Proud, S Jerg, M Grainger, R Hollmann, R |
| author_sort | Sus, O |
| collection | OXFORD |
| description | <p>We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°.</p> <br/> <p>By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud- Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa.</p> <br/> <p>The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.</p> |
| first_indexed | 2024-03-06T23:58:31Z |
| format | Journal article |
| id | oxford-uuid:7515ab6f-5770-45b3-869a-f69ce3569d29 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:58:31Z |
| publishDate | 2018 |
| publisher | Copernicus Publications |
| record_format | dspace |
| spelling | oxford-uuid:7515ab6f-5770-45b3-869a-f69ce3569d292022-03-26T20:07:16ZThe Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensorsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:7515ab6f-5770-45b3-869a-f69ce3569d29EnglishSymplectic Elements at OxfordCopernicus Publications2018Sus, OStengel, MStapelberg, SMcGarragh, GRPoulsen, CPovey, ACSchlundt, CThomas, GChristensen, MProud, SJerg, MGrainger, RHollmann, R<p>We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02°.</p> <br/> <p>By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud- Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa.</p> <br/> <p>The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.</p> |
| spellingShingle | Sus, O Stengel, M Stapelberg, S McGarragh, GR Poulsen, C Povey, AC Schlundt, C Thomas, G Christensen, M Proud, S Jerg, M Grainger, R Hollmann, R The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title | The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title_full | The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title_fullStr | The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title_full_unstemmed | The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title_short | The Community Cloud retrieval for Climate (CC4CL). Part I: A framework applied to multiple satellite imaging sensors |
| title_sort | community cloud retrieval for climate cc4cl part i a framework applied to multiple satellite imaging sensors |
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