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Kathryn L. Verlinden and Simon P. de Szoeke

parameterization of processes important to the clouds. Cloud radiative forcing, as well as cloud microphysical and dynamical processes, remains as one of the largest sources of uncertainty in projecting future climate ( Bony et al. 2006 ). Model simulations show differing responses by boundary layer clouds to such forcing factors as increasing sea surface temperatures ( Zhang et al. 2013 ), greenhouse gases ( Bretherton et al. 2013 ), and aerosol properties ( Caldwell and Bretherton 2009 ); therefore, models

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Daniel H. DeSlover, William L. Smith, Paivi K. Piironen, and Edwin W. Eloranta

, α ( ν ) −1 , for each AERI spectral “microwindow.” The resultant iterated values of α ( ν ), as shown in section 7b , represent a spectrum of visible-to-infrared optical depth ratios across the atmospheric infrared window. The scale factor α ( ν ) is derived from data near the peak of both downwelling solar radiation (lidar wavelength) and upwelling terrestrial radiation (infrared microwindows); thus, it provides a mechanism for measuring the effective cloud forcing in a spectral region that

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John E. Yorks, Dennis L. Hlavka, William D. Hart, and Matthew J. McGill

parameterization of the lidar ratio for ice and liquid water clouds is fundamental for current space-based lidar systems to accurately compute extinction and backscatter coefficients. This parameterization should account for the dependence of lidar ratio on geographic location. However, more research is needed to improve our understanding of the relationship between lidar ratio and cloud generation mechanism, which consequently should improve the accuracy of cloud radiative forcing estimations from space

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Yu-Tai Hou, Kenneth A. Campana, Kenneth E. Mitchell, Shi-Keng Yang, and Larry L. Stowe

intercompared withtwo independent products, the Air Force Real-Time Nephanalysis (RTNEPH), and the International SatelliteCloud Climatology Project (ISCCP). The ISCCP cloud database is a climate product processed retrospectivelysome years after the data are collected. Thus, only CLAVR and RTNEPH can satisfy the real-time requirementsfor numerical weather prediction (NWP) models. Compared with RTNEPH and ISCCP, which only use twochannels in daytime retrievals and one at night, CLAVR utilizes all five

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Fang-Fang Li, Ying-Hui Jia, Guang-Qian Wang, and Jun Qiu

observational studies have found that the maximum sound pressure level of thunder is around 120 dB under natural conditions, so we think that the sound pressure level in the cloud is likely to be above 120 dB. Table 1. Velocity amplitudes of different sound fields and the corresponding SPL. b. Motion equation A series of studies on the forces acting on spherical particles in a flow field have been carried out, forming a mature theoretical framework. Mei et al. (1991) improved the force analysis of a

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Jarred L. Burley, Steven T. Fiorino, Brannon J. Elmore, and Jaclyn E. Schmidt

limited applicability to the research community. Previous studies have shown the feasibility of using Air Force weather data for characterizing single and multiple scattering through clouds at optical wavelengths ( Roadcap et al. 2015 ). This research seeks to describe the development of an integrated internal LEEDR capability leveraging these external datasets relevant to cloud microphysical properties in a near-real-time environment. LEEDR’s modularity and ability to ingest NWP data are exploited to

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Jaime Garatuza-Payan, Rachel T. Pinker, and W. James Shuttleworth

. Barkstrom, V. Ramanathan, R. D. Cess, and G. G. Gibson, 1990: Seasonal variation of cloud radiative forcing derived from the Earth Radiation Budget Experiment. J. Geophys. Res., 95, 18 687–18 703. 10.1029/JD095iD11p18687 House, F. B., A. Gruber, G. E. Hunt, and A. T. Mecherikunnel, 1986:History of satellite missions and measurements of the Earth Radiation Budget (1957–1984). Rev. Geophys., 24, 357–378. 10.1029/RG024i002p00357 Iqbal, M., 1983: An Introduction to Solar Radiation. Academic

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A. Protat, J. Delanoë, E. J. O’Connor, and T. S. L’Ecuyer

- μ m bias from the 2B-CWC-RO effective radii, which provide the standard microphysical inputs to the product. The algorithm was then rerun over all CloudSat orbits included in the present analysis and the impact of the effective radius bias on the SW and LW cloud forcing (defined as the difference in outgoing SW and LW radiation between clear-sky and all-sky conditions) has been estimated. The result is that cloud LW forcing is increased from 44.6 to 46.9 W m −2 (implying an error of about 5

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H. Wang, R. T. Pinker, P. Minnis, and M. M. Khaiyer

model to refinements in radiative processes. J. Atmos. Sci. , 40 , 605 – 630 . 10.1175/1520-0469(1983)040<0605:TROASG>2.0.CO;2 Ramanathan, V. , Cess R. D. , Harrison E. F. , Minnis P. , Barkstrom B. R. , Ahmad E. , and Hartmann D. , 1989 : Cloud-radiative forcing and climate: Results from the Earth Radiation Budget Experiment. Science , 243 , 57 – 63 . 10.1126/science.243.4887.57 Raschke, E. , Stuhlmann R. , Palz W. , and Steemers T. C. , 1991 : Solar Radiation Atlas

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Ann M. Fridlind and Andrew S. Ackerman

1. Introduction The interactions of aerosols with clouds represent a leading source of uncertainty in quantifying anthropogenic radiative forcing of climate globally since preindustrial times ( Solomon et al. 2007 ). Clouds are also reported to constitute the largest source of uncertainty in climate sensitivity to radiative forcing in current coupled ocean–atmosphere climate models ( Soden and Held 2006 ). In the tropics, differences in the predicted sensitivity of marine boundary layer clouds

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