Deriving cloud overlap statistics from radar

Hogan, Robin J.; Illingworth, Anthony J.

doi:10.1002/qj.49712656914

Cited by 224 publications

(221 citation statements)

References 12 publications

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“…In response, a new scheme for representing vertical cloud alignment, referred to as ''exponential-random'' overlap, was introduced by Hogan and Illingworth (2000). They defined an ''overlap parameter'', which allows the combined cloud cover of a pair of gridbox layers to take any value between that of maximum overlap and of random overlap.…”

Section: Parameterization Of Cloudsmentioning

confidence: 99%

Impact of improved representation of horizontal and vertical cloud structure in a climate model

2011

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Many studies have investigated the effects that misrepresentation of sub-grid cloud structure can have on the radiation budget. In this study, we perform 20-year simulations of the current climate using an atmosphere-only version of the Met Office Unified Model to investigate the effects of cloud approximation on model climate. We apply the ''Tripleclouds'' scheme for representing horizontal cloud inhomogeneity and ''exponential-random'' overlap, both separately and in combination, in place of a traditional plane-parallel representation with maximum-random overlap, to the clouds within the radiation scheme. The resulting changes to both the radiation budget and other meteorological variables, averaged over the 20 years, are compared. The combined global effect of the parameterizations on topof-atmosphere short-wave and long-wave radiation budget is less than 1 W m -2 , but changes of up to 10 W m -2 are identified in marine stratocumulus regions. A cooling near the surface over the winter polar regions of up to 3°C is also identified when horizontal cloud inhomogeneity is represented, and a warming of similar magnitude is found when exponential-random overlap is implemented. Corresponding changes of the same sign are also found in zonally averaged temperature, with maximum changes in the upper tropical troposphere of up to 0.5°C. Changes in zonally averaged cloud fraction in this location were of opposite sign and up to 0.02. The individual effects on tropospheric temperature of improving the two components of cloud structure are of similar magnitudes to about 2% of the warming created by a quadrupling of carbon dioxide.

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Section: Parameterization Of Cloudsmentioning

confidence: 99%

Impact of improved representation of horizontal and vertical cloud structure in a climate model

2011

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“…Furthermore, research has been conducted to improve numerical model predictions by verifying and improving cloud radar data or through parameterization and data assimilation (Mace et al, 1998;Hogan and Illingworth, 2000;Ahlgrimm and Forbes, 2014). Moreover, other studies have considered the microphysical characteristics of clouds such as the liquid water content and size distribution of rain droplets (O'Connor et al, 2005;Zhong et al, 2012) and the classification of ice crystal forms in clouds (Aydin and Singh, 2004).…”

Section: Introductionmentioning

confidence: 98%

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Kim

et al. 2015

Adv. Atmos. Sci.

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In this study, cloud base height (CBH) and cloud top height (CTH) observed by the cloud radar at the Boseong National Center for Intensive Observation of Severe Weather during fall 2013 (September-November) were verified and corrected. For comparative verification, CBH and CTH were obtained using a ceilometer (CL51) and the Communication, Ocean and Meteorological Satellite (COMS). During rainfall, the CBH and CTH observed by the cloud radar were lower than observed by the ceilometer and COMS because of signal attenuation due to raindrops, and this difference increased with rainfall intensity. During dry periods, however, the CBH and CTH observed by the cloud radar, ceilometer, and COMS were similar. Thin and low-density clouds were observed more effectively by the cloud radar compared with the ceilometer and COMS. In cases of rainfall or missing cloud radar data, the ceilometer and COMS data were proven effective in correcting or compensating the cloud radar data. These corrected cloud data were used to classify cloud types, which revealed that low clouds occurred most frequently.

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“…As this combined overlap assumption is physically more reasonable than MO and RO, it has been adopted by most GCMs in recent times (Collins, 2001;Barker, 2008a). However, recent studies have found that MRO is still insufficient to fully describe the nature of cloud overlap: the overlap of vertically adjacent clouds is not simply a maximum, as in MRO, but changes depending on the depth of clouds (Hogan and Illingworth, 2000). Additionally, it should be noted that a common deficiency of MRO and RO is that they are sensitive to model vertical resolution (Räisänen, 1998;Bergman and Rasch, 2002), which causes different C tot and radiation output, even for the same cloud field and using the same overlap assumption.…”

Section: Conventional Cloud Overlap Assumptionsmentioning

confidence: 98%

Advances in studies of cloud overlap and its radiative transfer in climate models

Zhang

Jing

2016

J Meteorol Res

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The latest advances in studies on the treatment of cloud overlap and its radiative transfer in global climate models are summarized. Developments with respect to this internationally challenging problem are described from aspects such as the design of cloud overlap assumptions, the realization of cloud overlap assumptions within climate models, and the data and methods used to obtain consistent observations of cloud overlap structure and radiative transfer in overlapping clouds. To date, there has been an appreciable level of achievement in studies on cloud overlap in climate models, demonstrated by the development of scientific assumptions (e.g., e-folding overlap) to describe cloud overlap, the invention and broad application of the fast radiative transfer method for overlapped clouds (Monte Carlo Independent Column Approximation), and the emergence of continuous 3D cloud satellite observation (e.g., CloudSat/CALIPSO) and cloud-resolving models, which provide numerous data valuable for the exact description of cloud overlap structure in climate models. However, present treatments of cloud overlap and its radiative transfer process are far from complete, and there remain many unsettled problems that need to be explored in the future.Citation: Zhang Hua and Jing Xianwen, 2016: Advances in studies of cloud overlap and its radiative transfer in climate models.

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Deriving cloud overlap statistics from radar

Cited by 224 publications

References 12 publications

Impact of improved representation of horizontal and vertical cloud structure in a climate model

Impact of improved representation of horizontal and vertical cloud structure in a climate model

Verification and correction of cloud base and top height retrievals from Ka-band cloud radar in Boseong, Korea

Advances in studies of cloud overlap and its radiative transfer in climate models

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