scholarly journals Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content

2016 ◽  
Vol 121 (10) ◽  
pp. 5809-5826 ◽  
Author(s):  
Chenxi Wang ◽  
Steven Platnick ◽  
Zhibo Zhang ◽  
Kerry Meyer ◽  
Ping Yang
Keyword(s):  
Error Analysis ◽  
Information Content ◽  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Model Error ◽  
Forward Model ◽  
Infrared Observations ◽  
Ice Cloud ◽  
Cloud Properties

scholarly journals Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 2. Retrieval evaluation

2016 ◽  
Vol 121 (10) ◽  
pp. 5827-5845 ◽  
Author(s):  
Chenxi Wang ◽  
Steven Platnick ◽  
Zhibo Zhang ◽  
Kerry Meyer ◽  
Gala Wind ◽  
...  
Keyword(s):  
Optimal Estimation ◽  
Estimation Algorithm ◽  
Infrared Observations ◽  
Ice Cloud ◽  
Cloud Properties

scholarly journals The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

2017 ◽  
Author(s):  
Gregory R. McGarragh ◽  
Caroline A. Poulsen ◽  
Gareth E. Thomas ◽  
Adam C. Povey ◽  
Oliver Sus ◽  
...  
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Forward Model ◽  
Inversion Method ◽  
Surface Model ◽  
Linear Inversion ◽  
Retrieval Method ◽  
Ice Cloud ◽  
Cloud Models ◽  
Community Cloud

Abstract. 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), 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 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 %.

scholarly journals Cloud information content analysis of multi-angular measurements in the oxygen A-band: application to 3MI and MSPI

2015 ◽  
Vol 8 (12) ◽  
pp. 12709-12758
Author(s):  
G. Merlin ◽  
J. Riedi ◽  
L. C. Labonnote ◽  
C. Cornet ◽  
A. B. Davis ◽  
...  
Keyword(s):  
Content Analysis ◽  
Information Content ◽  
Polarized Light ◽  
Quantitative Information ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Spectral Bands ◽  
Angular Measurements ◽  
Black Surface ◽  
Almost All

Abstract. The vertical distribution of cloud cover has a significant impact on a large number of meteorological and climatic processes. Cloud top altitude and cloud geometrical thickness are then essential. Previous studies established the possibility of retrieving those parameters from multi-angular oxygen A-band measurements. Here we perform a study and comparison of the performances of future instruments. The 3MI (Multi-angle, Multi-channel and Multi-polarization Imager) instrument developed by EUMETSAT, which is an extension of the POLDER/PARASOL instrument, and MSPI (Multi-angles Spectro-Polarimetric Imager) develoloped by NASA's Jet Propulsion Laboratory will measure total and polarized light reflected by the Earth's atmosphere–surface system in several spectral bands (from UV to SWIR) and several viewing geometries. Those instruments should provide opportunities to observe the links between the cloud structures and the anisotropy of the reflected solar radiation into space. Specific algorithms will need be developed in order to take advantage of the new capabilities of this instrument. However, prior to this effort, we need to understand, through a theoretical Shannon information content analysis, the limits and advantages of these new instruments for retrieving liquid and ice cloud properties, and especially, in this study, the amount of information coming from the A-Band channel on the cloud top altitude (CTOP) and geometrical thickness (CGT). We compare the information content of 3MI A-Band in two configurations and that of MSPI. Quantitative information content estimates show that the retrieval of CTOP with a high accuracy is possible in almost all cases investigated. The retrieval of CGT seems less easy but possible for optically thick clouds above a black surface, at least when CGT > 1–2 km.

scholarly journals Information content of visible and midinfrared radiances for retrieving tropical ice cloud properties

2017 ◽  
Vol 122 (9) ◽  
pp. 4944-4966 ◽  
Author(s):  
Kai-Wei Chang ◽  
Tristan S. L'Ecuyer ◽  
Brian H. Kahn ◽  
Vijay Natraj
Keyword(s):  
Information Content ◽  
Ice Cloud ◽  
Cloud Properties

scholarly journals The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach

2018 ◽  
Vol 11 (6) ◽  
pp. 3397-3431 ◽  
Author(s):  
Gregory R. McGarragh ◽  
Caroline A. Poulsen ◽  
Gareth E. Thomas ◽  
Adam C. Povey ◽  
Oliver Sus ◽  
...  
Keyword(s):  
A Priori ◽  
Optimal Estimation ◽  
Forward Model ◽  
Inversion Method ◽  
Surface Model ◽  
Nonlinear Inversion ◽  
Retrieval Method ◽  
Ice Cloud ◽  
Cloud Models ◽  
Community Cloud

Abstract. 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 %.

scholarly journals Objective Assessment of the Information Content of Visible and Infrared Radiance Measurements for Cloud Microphysical Property Retrievals over the Global Oceans. Part II: Ice Clouds

2006 ◽  
Vol 45 (1) ◽  
pp. 42-62 ◽  
Author(s):  
Steven J. Cooper ◽  
Tristan S. L’Ecuyer ◽  
Philip Gabriel ◽  
Anthony J. Baran ◽  
Graeme L. Stephens
Keyword(s):  
Information Content ◽  
Near Infrared ◽  
Objective Assessment ◽  
Ice Clouds ◽  
Ice Cloud ◽  
Cloud Properties ◽  
Microphysical Property ◽  
Retrieval Scheme ◽  
Retrieval Problem ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Cirrus clouds play an important yet poorly determined role in the earth’s climate system and its various feedback mechanisms. As such, a significant amount of work has been accomplished both in understanding the physics of the ice clouds and in using this knowledge to estimate global distributions of ice cloud properties from satellite-based instruments. This work seeks to build on these past efforts by offering a reexamination of the ice cloud retrieval problem in context of recent advancements in the understanding of optical properties for a variety of realistic ice crystal shapes. In this work, the formal information content analysis outlined in Part I is used to objectively select the optimal combination of measurements for an ice cloud microphysical property retrieval scheme given a realistic assessment of the uncertainties that govern the ice cloud retrieval problem. Although this analysis is for a theoretical retrieval combining simulated measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) with the CloudSat Cloud Profiling Radar (CPR) above an ocean surface, the general methodology is applicable to any instrument package. Channel selection via information content is determined through a realistic characterization of not only the sensitivity of top-of-the-atmosphere radiances to desired retrieval parameters but also to the uncertainties resulting from both the measurements themselves and from the forward model assumptions used in relating observational and retrieval space. Results suggest that the channels that maximize retrieval information are strongly dependent upon the state of the atmosphere, meaning that no combination of two or three channels will always ensure an accurate retrieval. Because of the complexities of this state-dependent nature and the need for a consistent retrieval scheme for an operational retrieval, a five-channel retrieval approach consisting of a combination of error-weighted visible, near-infrared, and infrared channels is suggested. Such an approach ensures high information content regardless of cloud and atmospheric properties through use of the inherent sensitivities in each of these spectral regions.

scholarly journals Inference of ice cloud properties from high spectral resolution infrared observations

2004 ◽  
Vol 42 (4) ◽  
pp. 842-853 ◽  
Author(s):  
Hung-Lung Huang ◽  
Ping Yang ◽  
Heli Wei ◽  
B.A. Baum ◽  
Yongxiang Hu ◽  
...  
Keyword(s):  
Spectral Resolution ◽  
High Spectral Resolution ◽  
Infrared Observations ◽  
Ice Cloud ◽  
Cloud Properties

Information Content and Uncertainties in Thermodynamic Profiles and Liquid Cloud Properties Retrieved from the Ground-Based Atmospheric Emitted Radiance Interferometer (AERI)

2014 ◽  
Vol 53 (3) ◽  
pp. 752-771 ◽  
Author(s):  
D. D. Turner ◽  
U. Löhnert
Keyword(s):  
Water Vapor ◽  
Information Content ◽  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Retrieval Algorithm ◽  
Electromagnetic Spectrum ◽  
Slight Reduction ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Mean Square Errors

AbstractThe Atmospheric Emitted Radiance Interferometer (AERI) observes spectrally resolved downwelling radiance emitted by the atmosphere in the infrared portion of the electromagnetic spectrum. Profiles of temperature and water vapor, and cloud liquid water path and effective radius for a single liquid cloud layer, are retrieved using an optimal estimation–based physical retrieval algorithm from AERI-observed radiance data. This algorithm provides a full error covariance matrix for the solution, and both the degrees of freedom for signal and the Shannon information content. The algorithm is evaluated with both synthetic and real AERI observations. The AERI is shown to have approximately 85% and 70% of its information in the lowest 2 km of the atmosphere for temperature and water vapor profiles, respectively. In clear-sky situations, the mean bias errors with respect to the radiosonde profiles are less than 0.2 K and 0.3 g kg−1 for heights below 2 km for temperature and water vapor mixing ratio, respectively; the maximum root-mean-square errors are less than 1 K and 0.8 g kg−1. The errors in the retrieved profiles in cloudy situations are larger, due in part to the scattering contribution to the downwelling radiance that was not accounted for in the forward model. Scattering is largest in one of the spectral regions used in the retrieval, however, and removing this spectral region results in a slight reduction of the information content but a considerable improvement in the accuracy of the retrieved thermodynamic profiles.

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