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V. Mohan Karyampudi, Stephen P. Palm, John A. Reagen, Hui Fang, William B. Grant, Raymond M. Hoff, Cyril Moulin, Harold F. Pierce, Omar Torres, Edward V. Browell, and S. Harvey Melfi

Lidar observations collected during the Lidar In-space Technology Experiment experiment in conjunction with the Meteosat and European Centre for Medium-Range Weather Forecasts data have been used not only to validate the Saharan dust plume conceptual model constructed from the GARP (Global Atmospheric Research Programme) Atlantic Tropical Experiment data, but also to examine the vicissitudes of the Saharan aerosol including their optical depths across the west Africa and east Atlantic regions. Optical depths were evaluated from both the Meteosat and lidar data. Back trajectory calculations were also made along selected lidar orbits to verify the characteristic anticyclonic rotation of the dust plume over the eastern Atlantic as well as to trace the origin of a dust outbreak over West Africa.

A detailed synoptic analysis including the satellite-derived optical depths, vertical lidar backscattering cross section profiles, and back trajectories of the 16–19 September 1994 Saharan dust outbreak over the eastern Atlantic and its origin over West Africa during the 12–15 September period have been presented. In addition, lidar-derived backscattering profiles and optical depths were objectively analyzed to investigate the general features of the dust plume and its geographical variations in optical thickness. These analyses validated many of the familiar characteristic features of the Saharan dust plume conceptual model such as (i) the lifting of the aerosol over central Sahara and its subsequent transport to the top of the Saharan air layer (SAL), (ii) the westward rise of the dust layer above the gradually deepening marine mixed layer and the sinking of the dust-layer top, (iii) the anticyclonic gyration of the dust pulse between two consecutive trough axes, (iv) the dome-shaped structure of the dust-layer top and bottom, (v) occurrence of a middle-level jet near the southern boundary of the SAL, (vi) transverse–vertical circulations across the SAL front including their possible role in the initiation of a squall line to the southside of the jet that ultimately developed into a tropical storm, and (vii) existence of satellite-based high optical depths to the north of the middle-level jet in the ridge region of the wave.

Furthermore, the combined analyses reveal a complex structure of the dust plume including its origin over North Africa and its subsequent westward migration over the Atlantic Ocean. The dust plume over the west African coastline appears to be composed of two separate but narrow plumes originating over the central Sahara and Lake Chad regions, in contrast to one single large plume shown in the conceptual model. Lidar observations clearly show that the Saharan aerosol over North Africa not only consist of background dust (Harmattan haze) but also wind-blown aerosol from fresh dust outbreaks. They further exhibit maximum dust concentration near the middle-level jet axis with downward extension of heavy dust into the marine boundary layer including a clean dust-free trade wind inversion to the north of the dust layer over the eastern Atlantic region. The lidar-derived optical depths show a rapid decrease of optical depths away from land with maximum optical depths located close to the midlevel jet, in contrast to north of the jet shown by satellite estimates and the conceptual model. To reduce the uncertainties in estimating extinction-to-backscattering ratio for optical depth calculations from lidar data, direct aircraft measurements of optical and physical properties of the Saharan dust layer are needed.

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Herbert Jacobowitz, Larry L. Stowe, George Ohring, Andrew Heidinger, Kenneth Knapp, and Nicholas R. Nalli

As part of the joint National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) Pathfinder program, the NOAA National Environmental Satellite, Data, and Information Service (NESDIS) has created a research-quality global atmospheric dataset through the reprocessing of Advanced Very High Resolution Radiometer (AVHRR) observations since 1981. The AVHRR is an imaging radiometer that flies on NOAA polar-orbiting operational environmental satellites (POES) measuring radiation reflected and emitted by the earth in five spectral channels. Raw AVHRR observations were recalibrated using a vicarious calibration technique for the reflectance channels and an appropriate treatment of the nonlinearity of the infrared channels. The observations are analyzed in the Pathfinder Atmosphere (PATMOS) project to obtain statistics of channel radiances, cloud amount, top of the atmosphere radiation budget, and aerosol optical thickness over ocean. The radiances and radiation budget components are determined for clear-sky and all-sky conditions. The output products are generated on a quasi-equalarea grid with an approximate 110 km × 110 km spatial resolution and twice-a-day temporal resolution, and averaged over 5-day (pentad) and monthly time periods. PATMOS data span the period from September 1981 through June 2001. Analyses show that the PATMOS data in their current archived form are sufficiently accurate for studies of the interaction of clouds and aerosol with solar and terrestrial radiation, and of climatic phenomena with large signals (e.g., the annual cycle, monsoons, ENSOs, or major volcanic eruptions). Global maps of the annual average of selected products are displayed to illustrate the capability of the dataset to depict the climatological fields and the spatial detail and relationships between the fields, further demonstrating how PATMOS is a unique resource for climate studies. Smaller climate signals, such as those associated with global warming, may be more difficult to detect due to the presence of artifacts in the time series of the products. Principally, these are caused by the drift of each satellite's observation time over its mission. A statistical method, which removes most of these artifacts, is briefly discussed. Quality of the products is assessed by comparing the adjusted monthly mean time series for each product with those derived from independent satellite observations. The PATMOS dataset for the monthly means is accessible at

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Toshi Matsui, Charles Ichoku, Cynthia Randles, Tianle Yuan, Arlindo M. da Silva, Peter Colarco, Dongchul Kim, Robert Levy, Andrew Sayer, Mian Chin, David Giles, Brent Holben, Ellsworth Welton, Thomas Eck, and Lorraine Remer

for their support. APPENDIX: SUMMARY OF ACRONYMS. ACE Aerosol–Cloud–Ecosystem AERONET Aerosol Robotic Network AOT Aerosol optical thickness ARCTAS Arctic Research of the Composition of the Troposphere from Aircraft and Satellites AVHRR Advanced Very High Resolution Radiometer CALIOP Cloud–Aerosol Lidar with Orthogonal Polarization CALIPSO Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations DISCOVER-AQ Deriving Information on Surface Conditions from Column and Vertically Resolved

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Robert Wood, Michael P. Jensen, Jian Wang, Christopher S. Bretherton, Susannah M. Burrows, Anthony D. Del Genio, Ann M. Fridlind, Steven J. Ghan, Virendra P. Ghate, Pavlos Kollias, Steven K. Krueger, Robert L. McGraw, Mark A. Miller, David Painemal, Lynn M. Russell, Sandra E. Yuter, and Paquita Zuidema

. CCN influence low clouds by changing the cloud droplet concentration ( N d ), which impacts cloud optical thickness. Changing N d also modifies the efficiency of drizzle formation, which can alter the cloud macrophysical properties. Cloud responses to aerosol-induced drizzle suppression are complex and sensitive to both meteorological and cloud conditions, but are a critical contributor to intermodel spread in aerosol indirect forcing. Besides being susceptible to aerosols, drizzle is also the

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Jinyuan Xin, Yuesi Wang, Yuepeng Pan, Dongsheng Ji, Zirui Liu, Tianxue Wen, Yinghong Wang, Xingru Li, Yang Sun, Jie Sun, Pucai Wang, Gehui Wang, Xinming Wang, Zhiyuan Cong, Tao Song, Bo Hu, Lili Wang, Guiqian Tang, Wenkang Gao, Yuhong Guo, Hongyan Miao, Shili Tian, and Lu Wang

performance characteristics of the five-channel Microtops II Sun photometer for measuring aerosol optical thickness and perceptible water vapor . J. Geophys. Res. , 107 , doi: 10.1029/2001JD001302 . Jones , A. , D. L. Roberts , and A. Slingo , 1994 : A climate model study of indirect radiative forcing by anthropogenic sulphate aerosols . Nature , 370 , 450 – 453 , doi: 10.1038/370450a0 . Kaufman , Y. J. , D. Tanré , and O. Boucher , 2002 : A satellite view of aerosols in the climate

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Robert Wood, Matthew Wyant, Christopher S. Bretherton, Jasmine Rémillard, Pavlos Kollias, Jennifer Fletcher, Jayson Stemmler, Simone de Szoeke, Sandra Yuter, Matthew Miller, David Mechem, George Tselioudis, J. Christine Chiu, Julian A. L. Mann, Ewan J. O’Connor, Robin J. Hogan, Xiquan Dong, Mark Miller, Virendra Ghate, Anne Jefferson, Qilong Min, Patrick Minnis, Rabindra Palikonda, Bruce Albrecht, Ed Luke, Cecile Hannay, and Yanluan Lin

radiation platform was deployed at a trace-gas site established by the National Oceanic and Atmospheric Administration (NOAA) close to the summit of the volcanic island of Pico (elevation 2350 m) some 60 km south of Graciosa (see, e.g., Honrath et al. 2004 ). This suite included a Multifilter Rotating Shadowband Radiometer (MFRSR) and broadband shortwave and longwave radiometers. The scientific objective of this deployment was to measure the radiative fluxes and aerosol optical thickness above the

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Pedro A. Jimenez, Joshua P. Hacker, Jimy Dudhia, Sue Ellen Haupt, Jose A. Ruiz-Arias, Chris A. Gueymard, Gregory Thompson, Trude Eidhammer, and Aijun Deng

horizontal irradiance (GHI) for the model’s energy budget, the direct normal irradiance (DNI) and diffuse (DIF) components are not commonly output to the user. GHI is much less sensitive to aerosol optical properties than DNI and DIF (e.g., Gueymard 2012 ; Ruiz-Arias et al. 2015 ), and sometimes NWP models do not account for atmospheric aerosols in the radiative transfer equation. This can be a limitation for CSPs, for example, which use the direct irradiance coming from the sun. Aerosol modeling is

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Laura D. Riihimaki, Connor Flynn, Allison McComiskey, Dan Lubin, Yann Blanchard, J. Christine Chiu, Graham Feingold, Daniel R. Feldman, Jake J. Gristey, Christian Herrera, Gary Hodges, Evgueni Kassianov, Samuel E. LeBlanc, Alexander Marshak, Joseph J. Michalsky, Peter Pilewskie, Sebastian Schmidt, Ryan C. Scott, Yolanda Shea, Kurtis Thome, Richard Wagener, and Bruce Wielicki

effects. Spectroradiometers also must cover sufficient range to capture the aerosol optical thickness spectral dependence. Conclusions New observational tools are needed to tackle the challenges of improving and evaluating the climate model parameterizations of fine-scale processes that drive cloud and aerosol climate radiative forcing and feedbacks. We have given several examples demonstrating how the SW spectral dimension can reveal novel information to understand cloud microphysical and radiative

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Arunas P. Kuciauskas, Peng Xian, Edward J. Hyer, Mayra I. Oyola, and James R. Campbell

operational global aerosol model in 2006 with implementation at the Fleet Numerical Meteorology and Oceanography Center (FNMOC). The Navy Atmospheric Variational Data Assimilation System for aerosol optical thickness (NAVDAS-AOT; Zhang et al. 2008 ) was operationally implemented in 2010. The system assimilates quality-assured and quality-controlled (QA/QC) two-dimensional Moderate Resolution Imaging Spectroradiometer (MODIS) AOT at 550 nm ( Zhang and Reid 2006 ; Hyer et al. 2011 ) by default, but it can

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Nilton O. Rennó, Earle Williams, Daniel Rosenfeld, David G. Fischer, Jürgen Fischer, Tibor Kremic, Arun Agrawal, Meinrat O. Andreae, Rosina Bierbaum, Richard Blakeslee, Anko Boerner, Neil Bowles, Hugh Christian, Ann Cox, Jason Dunion, Akos Horvath, Xianglei Huang, Alexander Khain, Stefan Kinne, Maria C. Lemos, Joyce E. Penner, Ulrich Pöschl, Johannes Quaas, Elena Seran, Bjorn Stevens, Thomas Walati, and Thomas Wagner

.4923.1227 . Andreae , M. O. , 2009 : Correlation between cloud condensation nuclei concentration and aerosol optical thickness in remote and polluted regions . Atmos. Chem. Phys. , 9 , 543 – 556 . Andreae , M. O. , and D. Rosenfeld , 2008 : Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols . Earth-Sci. Rev. , 89 , 13 – 41 . Andreae , M. O. , D. Rosenfeld , P. Artaxo , A. A. Costa , G. P. Frank , K. M. Longo , and M. A. F. Silva

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