The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach
The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickn...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Journal article |
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European Geosciences Union
2017
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| _version_ | 1797061151328043008 |
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| author | McGarragh, G Poulsen, C Thomas, G Povey, A Sus, O Stapelberg, S Schlundt, C Proud, S Christensen, M Stengel, M Hollmann, R Grainger, R |
| author_facet | McGarragh, G Poulsen, C Thomas, G Povey, A Sus, O Stapelberg, S Schlundt, C Proud, S Christensen, M Stengel, M Hollmann, R Grainger, R |
| author_sort | McGarragh, G |
| collection | OXFORD |
| description | The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%. |
| first_indexed | 2024-03-06T20:26:57Z |
| format | Journal article |
| id | oxford-uuid:2fb8bfcf-ccc6-43c3-abc2-ba442b6c1fe1 |
| institution | University of Oxford |
| last_indexed | 2024-03-06T20:26:57Z |
| publishDate | 2017 |
| publisher | European Geosciences Union |
| record_format | dspace |
| spelling | oxford-uuid:2fb8bfcf-ccc6-43c3-abc2-ba442b6c1fe12022-03-26T12:57:05ZThe Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approachJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2fb8bfcf-ccc6-43c3-abc2-ba442b6c1fe1Symplectic Elements at OxfordEuropean Geosciences Union2017McGarragh, GPoulsen, CThomas, GPovey, ASus, OStapelberg, SSchlundt, CProud, SChristensen, MStengel, MHollmann, RGrainger, RThe Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%. |
| spellingShingle | McGarragh, G Poulsen, C Thomas, G Povey, A Sus, O Stapelberg, S Schlundt, C Proud, S Christensen, M Stengel, M Hollmann, R Grainger, R The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach |
| title | The Community Cloud retrieval for CLimate (CC4CL). Part II: The
optimal estimation approach |
| title_full | The Community Cloud retrieval for CLimate (CC4CL). Part II: The
optimal estimation approach |
| title_fullStr | The Community Cloud retrieval for CLimate (CC4CL). Part II: The
optimal estimation approach |
| title_full_unstemmed | The Community Cloud retrieval for CLimate (CC4CL). Part II: The
optimal estimation approach |
| title_short | The Community Cloud retrieval for CLimate (CC4CL). Part II: The
optimal estimation approach |
| title_sort | community cloud retrieval for climate cc4cl part ii the optimal estimation approach |
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