The Community Cloud retrieval for CLimate (CC4CL) – Part 2: 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 th...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-06-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://www.atmos-meas-tech.net/11/3397/2018/amt-11-3397-2018.pdf |
| _version_ | 1811269750968287232 |
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| author | G. R. McGarragh C. A. Poulsen C. A. Poulsen G. E. Thomas G. E. Thomas A. C. Povey O. Sus S. Stapelberg C. Schlundt S. Proud M. W. Christensen M. W. Christensen M. W. Christensen M. Stengel R. Hollmann R. G. Grainger |
| author_facet | G. R. McGarragh C. A. Poulsen C. A. Poulsen G. E. Thomas G. E. Thomas A. C. Povey O. Sus S. Stapelberg C. Schlundt S. Proud M. W. Christensen M. W. Christensen M. W. Christensen M. Stengel R. Hollmann R. G. Grainger |
| author_sort | G. R. McGarragh |
| collection | DOAJ |
| 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 clear-sky model, the liquid water
and ice cloud models, the surface model including a bidirectional reflectance
distribution function (BRDF), and 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 modeling errors become more
significant. The retrieval method is then presented describing optimal
estimation in general, the nonlinear 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 up
to 20 %. |
| first_indexed | 2024-04-12T21:47:37Z |
| format | Article |
| id | doaj.art-a60ae6393fdb4d7fac832f5ae5b44f14 |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-12T21:47:37Z |
| publishDate | 2018-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-a60ae6393fdb4d7fac832f5ae5b44f142022-12-22T03:15:34ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482018-06-01113397343110.5194/amt-11-3397-2018The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approachG. R. McGarragh0C. A. Poulsen1C. A. Poulsen2G. E. Thomas3G. E. Thomas4A. C. Povey5O. Sus6S. Stapelberg7C. Schlundt8S. Proud9M. W. Christensen10M. W. Christensen11M. W. Christensen12M. Stengel13R. Hollmann14R. G. Grainger15Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UKRAL Space, STFC, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKNCEO, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKRAL Space, STFC, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKNCEO, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKNational Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UKDeutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, GermanyDeutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, GermanyDeutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, GermanyDepartment of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UKDepartment of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UKRAL Space, STFC, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKNCEO, Rutherford Appleton Laboratory, Didcot, OX11 0QX, UKDeutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, GermanyDeutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, GermanyNational Centre for Earth Observation, Atmospheric, Oceanic and Planetary Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UKThe 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 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and 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 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear 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 up to 20 %.https://www.atmos-meas-tech.net/11/3397/2018/amt-11-3397-2018.pdf |
| spellingShingle | G. R. McGarragh C. A. Poulsen C. A. Poulsen G. E. Thomas G. E. Thomas A. C. Povey O. Sus S. Stapelberg C. Schlundt S. Proud M. W. Christensen M. W. Christensen M. W. Christensen M. Stengel R. Hollmann R. G. Grainger The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach Atmospheric Measurement Techniques |
| title | The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach |
| title_full | The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach |
| title_fullStr | The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach |
| title_full_unstemmed | The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach |
| title_short | The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach |
| title_sort | community cloud retrieval for climate cc4cl ndash part 2 the optimal estimation approach |
| url | https://www.atmos-meas-tech.net/11/3397/2018/amt-11-3397-2018.pdf |
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