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J. C. B. Hoedjes, A. Chehbouni, J. Ezzahar, R. Escadafal, and H. A. R. De Bruin

, a similar effect will be seen. In this study, an effort is made to quantify differences between sensible heat fluxes, obtained from an LAS and an EC system, caused by differences between the characteristics of the respective footprints. The approach is based on the use of the radiative surface temperature, obtained from thermal infrared satellite data, as indicator of the spatial variability of soil humidity. First, a model for the estimation of sensible heat fluxes from radiative surface

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Tristan S. L’Ecuyer and Greg McGarragh

System (CERES) clouds and radiative swath (CRS) product ( Wielicki et al. 1996 ) offers estimates of Q R that are constrained to match top of the atmosphere (TOA) flux measurements but with reduced temporal sampling, whereas Cloudsat’s level-2B radiative fluxes and heating rates algorithm (2B-FLXHR; L’Ecuyer et al. 2008 ) offers improved cloud boundary information and spatial resolution but at greatly reduced spatial and temporal sampling. All of these algorithms are built on the same basic

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Helen E. Brindley and Jacqueline E. Russell

the two aerosol models introduced in section 2 in terms of their ability to match the angular distribution of the observed GERB radiances. We also assess the performance of the different approaches highlighted in section 3 by analyzing the variability of derived flux fields. In section 5 , we use the best-performing model and approach identified in section 4 to quantify the likely impact of neglecting the effect of dust aerosol on the GERB SW fluxes and derived dust radiative efficiency

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Virendra P. Ghate, Mark A. Miller, Bruce A. Albrecht, and Christopher W. Fairall

) Earth System Research Laboratory (ESRL)’s Physical Sciences Division (PSD), primarily during the Northern Hemisphere fall period. Instrumentation at the ARM sites is described in detail by Mather and Voyles (2013) and that on board the NOAA cruises by de Szoeke et al. (2012) . Described below is the subset of the instrumentation used in this study. The radiative transfer model used to simulate the radiative fluxes is described in appendix A . a. Instrumentation 1) ARM Southern Great Plains

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Laura M. Hinkelman, K. Franklin Evans, Eugene E. Clothiaux, Thomas P. Ackerman, and Paul W. Stackhouse Jr.

; Di Giuseppe and Tompkins 2005 ). The 3D radiative transfer effect on domain-averaged solar fluxes has been divided into two physical processes, as summarized by Várnai and Davies (1999) . The first, which is termed the “one-dimensional (1D) heterogeneity effect,” arises from the nonlinear relationship between cloud optical depth and albedo. The mean transmission of a cloud with horizontally varying optical depth is more than the transmission of a uniform cloud with the mean optical depth. As a

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John W. Bergman and Harry H. Hendon

1. Introduction Cloud variability exerts a strong influence on radiative transfer within the earth’s atmosphere. That influence, or cloud radiative forcing, affects circulations of the atmosphere and ocean by altering surface energy fluxes and atmospheric heating rates. For example, investigations with atmospheric general circulation models (GCM) find that tropical circulations are substantially altered if cloud radiative forcing is neglected (e.g., Slingo and Slingo 1988 , 1991b ; Randall

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Bruce A. Wielicki and Richard N. Green

VOLUME28 JOURNAL OF APPLIED METEOROLOGY NOVEMBER 1989Cloud Identification for ERBE Radiative Flux Retrieval BRUCE A. WIELICKI AND RICHARD N. GREENAtmospheric Sciences Division, NASA Langley Research Center, Hampton, Virginia(Manuscript received 27 September 1988, in final form 15 May 1989) ABSTRACT Derivation of top of atmosphere radiative fluxes requires the use of measured satellite

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Patrick C. Taylor, Robert G. Ellingson, and Ming Cai

is a direct determination of feedback radiative perturbations. The PRP technique is elucidated by a first-order Taylor series expansion. Let us define R as the net flux at TOA, which is dependent on temperature, CO 2 concentration, water vapor specific humidity, surface albedo, and cloud properties denoted by T , x , r , α , and C , respectively. Let us consider R 1 , the net flux at TOA of the initial equilibrium climate (denoted with subscript 1), and R 2 , the net flux at TOA of the

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F. Miskolczi, T. O. Aro, M. Iziomon, and R. T. Pinker

Introduction Surface radiative fluxes play an important role in climate processes on all scales. The key elements involved in the exchange of energy between the surface and the atmosphere are the upwelling and downwelling shortwave (SW: 0.2–4.0 μ m) and longwave (LW: 4.0–50.0 μ m) fluxes. Photosynthetically active radiation (PAR: 0.4–0.7 μ m) is known to play a key role in controlling CO 2 exchange (Bolin 1977; Daughtry et al. 1992 ), modeling of biological heating in oceans

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Nathaniel B. Miller, Matthew D. Shupe, Christopher J. Cox, Von P. Walden, David D. Turner, and Konrad Steffen

the net radiative flux at the surface ( Walsh and Chapman 1998 ), thereby impacting the surface energy budget. The shortwave and longwave radiative effect of clouds, or cloud radiative forcing (CRF), can be quantified by comparing the actual surface radiative flux to the flux during an equivalent clear-sky scene. In general, Arctic clouds have a warming effect on the surface, except for a period in the summer when the sun is highest and surface albedo is lowest ( Curry and Ebert 1992 ; Intrieri

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