A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

McFarlane, Sally A.; Evans, K. Franklin; Ackerman, Andrew S.

doi:10.1029/2001jd001011

Cited by 53 publications

(59 citation statements)

References 36 publications

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“…Generally speaking, to retrieve a vertical profile of droplet effective radius, the above approach suggests using a database of stochastic cloud models and corresponding radiative transfer calculations of cloud reflectances at 0.67, 2.1 and 11.6 µm. This is similar to a Bayesian retrieval algorithm (e.g., McFarlane et al, 2002;Evans et al, 2002) that combines prior information about cloud structure and microphysics with radiative transfer calculations, p( x| I 0.67 , I 2.1 , I 11.6 ) = p( I 0.67 , I 2.1 , I 11.6 | x)p(x) p( I 0.67 , I 2.1 , I 11.6 | x)p(x)dx .…”

Section: Proof Of Conceptmentioning

confidence: 99%

“…7. For details on a Bayesian retrieval algorithm applied to microwave radiometer and submillimeter-wave cloud ice radiometer see the excellent descriptions given in McFarlane et al (2002) and Evans et al (2002).…”

Section: Proof Of Conceptmentioning

confidence: 99%

“…At the end of Sect. 4, this approach is generalized in the terms of Bayesian retrievals (McFarlane et al, 2002;Evans et al, 2002). …”

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confidence: 99%

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What does reflection from cloud sides tell us about vertical distribution of cloud droplet sizes?

et al. 2006

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Abstract. Cloud development, the onset of precipitation and the effect of aerosol on clouds depend on the structure of the cloud profiles of droplet size and phase. Aircraft measurements of cloud profiles are limited in their temporal and spatial extent. Satellites were used to observe cloud tops not cloud profiles with vertical profiles of precipitation-sized droplets anticipated from CloudSat. The recently proposed CLAIM-3D satellite mission (cloud aerosol interaction mission in 3-D) suggests to measure profiles of cloud microphysical properties by retrieving them from the solar and infrared radiation reflected or emitted from cloud sides.Inversion of measurements from the cloud sides requires rigorous understanding of the 3-dimentional (3-D) properties of clouds. Here we discuss the reflected sunlight from the cloud sides and top at two wavelengths: one nonabsorbing to solar radiation (0.67 µm) and one with liquid water efficient absorption of solar radiation (2.1 µm). In contrast to the plane-parallel approximation, a conventional approach to all current operational retrievals, 3-D radiative transfer is used for interpreting the observed reflectances. General properties of the radiation reflected from the sides of an isolated cloud are discussed. As a proof of concept, the paper shows a few examples of radiation reflected from cloud fields generated by a simple stochastic cloud model with the prescribed vertically resolved microphysics. To retrieve the information about droplet sizes, we propose to use the probability density function of the droplet size distribution and its first two moments instead of the assumption about fixed values of the droplet effective radius. The retrieval algorithm is based on the Bayesian theorem that combines prior information about cloud structure and microphysics with radiative transfer calculations.

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Section: Proof Of Conceptmentioning

confidence: 99%

Section: Proof Of Conceptmentioning

confidence: 99%

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What does reflection from cloud sides tell us about vertical distribution of cloud droplet sizes?

et al. 2006

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“…However, for future applications we propose retrievals based on stochastic cloud fields, as in Marshak et al (2006), or on cloud resolving models, as in Zinner et al (2008) which use a Bayesian approach (e.g. McFarlane et al, 2002) to provide a distribution of possible effective radii that statistically satisfy to the measured radiances.…”

Section: -D Radiative Transfer In Cloudsmentioning

confidence: 99%

Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

et al. 2011

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Abstract. Cloud-aerosol interaction is a key issue in the climate system, affecting the water cycle, the weather, and the total energy balance including the spatial and temporal distribution of latent heat release. Information on the vertical distribution of cloud droplet microphysics and thermodynamic phase as a function of temperature or height, can be correlated with details of the aerosol field to provide insight on how these particles are affecting cloud properties and their consequences to cloud lifetime, precipitation, water cycle, and general energy balance. Unfortunately, today's experimental methods still lack the observational tools that can characterize the true evolution of the cloud microphysical, spatial and temporal structure in the cloud droplet scale, and then link these characteristics to environmental factors and properties of the cloud condensation nuclei.Here we propose and demonstrate a new experimental approach (the cloud scanner instrument) that provides the microphysical information missed in current experiments and remote sensing options. Cloud scanner measurements can be performed from aircraft, ground, or satellite by scanning the side of the clouds from the base to the top, providing us with the unique opportunity of obtaining snapshots of the cloud droplet microphysical and thermodynamic states as a function of height and brightness temperature in clouds at several development stages. The brightness temperature profile Correspondence to: J. V. Martins (martins@umbc.edu) of the cloud side can be directly associated with the thermodynamic phase of the droplets to provide information on the glaciation temperature as a function of different ambient conditions, aerosol concentration, and type. An aircraft prototype of the cloud scanner was built and flew in a field campaign in Brazil.The CLAIM-3D (3-Dimensional Cloud Aerosol Interaction Mission) satellite concept proposed here combines several techniques to simultaneously measure the vertical profile of cloud microphysics, thermodynamic phase, brightness temperature, and aerosol amount and type in the neighborhood of the clouds. The wide wavelength range, and the use of multi-angle polarization measurements proposed for this mission allow us to estimate the availability and characteristics of aerosol particles acting as cloud condensation nuclei, and their effects on the cloud microphysical structure. These results can provide unprecedented details on the response of cloud droplet microphysics to natural and anthropogenic aerosols in the size scale where the interaction really happens.

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“…In addition, the retrieval method is successfully applied on globally real data for the first time. It has previously only been used for case studies (Seo and Liu, 2005;McFarlane et al, 2002;Evans et al, 2005) and instrument concept studies (Zinner et al, 2008;Evans et al, 2002). The development is motivated by the need to improve the Odin-SMR retrievals, but the methodology should be of general interest since it can be used for any similar instrument.…”

Section: Introductionmentioning

confidence: 99%

Non-Gaussian Bayesian retrieval of tropical upper tropospheric cloud ice and water vapour from Odin-SMR measurements

et al. 2009

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Abstract. Improved Odin-SMR retrievals of upper tropospheric water are presented. The new retrieval algorithm retrieves humidity and cloud ice mass simultaneously and takes into account of cloud inhomogeneities. Both these aspects are introduced for microwave limb sounding inversions for the first time. A Bayesian methodology is applied allowing for a formally correct treatment of non-unique retrieval problems involving non-Gaussian statistics. Cloud structure information from CloudSat is incorporated into the retrieval algorithm. This removes a major limitation of earlier inversion methods where uniform cloud layers were assumed and caused a systematic retrieval error. The core part of the retrieval technique is the generation of a database that must closely represent real conditions. Good agreement with Odin-SMR observations indicates that this requirement is met. The retrieval precision is determined to be about 5-17% RHi and 65% for humidity and cloud ice mass, respectively. For both quantities, the vertical resolution is about 5 km and the best retrieval performance is found between 11 and 15 km. New data show a significantly improved agreement with CloudSat cloud ice mass retrievals, at the same time consistency with the Aura MLS humidity results is maintained. The basics of the approach presented can be applied for all passive cloud observations and should be of broad interest. The results can also be taken as a demonstration of the potential of down-looking sub-mm radiometry for global measurements of cloud ice properties.

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A Bayesian algorithm for the retrieval of liquid water cloud properties from microwave radiometer and millimeter radar data

Cited by 53 publications

References 36 publications

What does reflection from cloud sides tell us about vertical distribution of cloud droplet sizes?

What does reflection from cloud sides tell us about vertical distribution of cloud droplet sizes?

Remote sensing the vertical profile of cloud droplet effective radius, thermodynamic phase, and temperature

Non-Gaussian Bayesian retrieval of tropical upper tropospheric cloud ice and water vapour from Odin-SMR measurements

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