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Tristan S. L’Ecuyer, H. K. Beaudoing, M. Rodell, W. Olson, B. Lin, S. Kato, C. A. Clayson, E. Wood, J. Sheffield, R. Adler, G. Huffman, M. Bosilovich, G. Gu, F. Robertson, P. R. Houser, D. Chambers, J. S. Famiglietti, E. Fetzer, W. T. Liu, X. Gao, C. A. Schlosser, E. Clark, D. P. Lettenmaier, and K. Hilburn

satellites, for example, have provided improved observations of the exchange of longwave and shortwave radiation at the TOA ( Wielicki et al. 1996 ; Loeb et al. 2001 ). When coupled with water vapor estimates from the Atmospheric Infrared Sounder (AIRS) and cloud and aerosol information from the Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat , and the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ), these observations have also led to significant

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Larry K. Berg, Laura D. Riihimaki, Yun Qian, Huiping Yan, and Maoyi Huang

the NCEP reanalysis and NASA Data Assimilation Office (DAO) reanalysis moisture flux divergence over North America were attributed to uncertainties in the LLJ. Higgins et al. (1996) compared the NCEP and DAO reanalyses to surface observations of precipitation and radiosonde data. They found that while the reanalyses captured key features of the LLJ, there were significant differences in column-integrated moisture flux. Their research was extended to investigate the diurnal cycle of rainfall and

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G. Alexander Sokolowsky, Eugene E. Clothiaux, Cory F. Baggett, Sukyoung Lee, Steven B. Feldstein, Edwin W. Eloranta, Maria P. Cadeddu, Nitin Bharadwaj, and Karen L. Johnson

Lammeren , 2001 : Cloud effective particle size and water content profile retrievals using combined lidar and radar observations: 1. Theory and examples . J. Geophys. Res. , 106 , 27 425 – 27 448 , . 10.1029/2001JD900243 Doyle , J. G. , G. Lesins , C. P. Thackray , C. Perro , G. J. Nott , T. J. Duck , R. Damoah , and J. R. Drummond , 2011 : Water vapor intrusions into the High Arctic during winter . Geophys. Res. Lett. , 38 , L12806

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Catherine M. Naud, Derek J. Posselt, and Susan C. van den Heever

approximately 20 km. We used these profiles to create a cloud mask that utilizes a fixed vertical grid spacing of 250 m; however, we kept the horizontal spacing as in the original files (i.e., CloudSat footprint, or ~1 km). We also utilized the cloud classification product of Wang et al. (2012) , provided in the cloud classification (CLDCLASS)–lidar data files, which combines active observations from the CloudSat radar and CALIPSO lidar with the Moderate Resolution Imaging Spectroradiometer (MODIS

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A. Bodas-Salcedo, K. D. Williams, P. R. Field, and A. P. Lock

(CTH) using a stereo-imaging technique ( Moroney et al. 2002 ; Muller et al. 2002 ). MISR also retrieves cloud optical depth from the visible radiances, although only over ocean. These retrievals allow the computation of joint CTH– τ histograms. The Cloud Profiling Radar (CPR) is onboard CloudSat ( Stephens et al. 2008 ), and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) is onboard the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; Winker et al

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Jun Inoue, Jiping Liu, James O. Pinto, and Judith A. Curry

Clouds, and the Arctic Climate System Studies (ACSYS) Numerical Experimentation Group (NEG). The first ARCMIP experiment occurred between September 1997 and October 1998. Intensive observations from 1997 to 1998 under the auspices of the Surface Heat Budget of the Arctic Ocean (SHEBA) program ( Uttal et al. 2002 ), the First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Clouds Experiment ( Curry et al. 2000 ), and the Atmospheric Radiation Measurement

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Klaus Wyser

habits are complicated and no simple relationship between temperature and habit exists ( Dowling and Radke 1990 ). Despite the variety of naturally occuring habits, all crystals are treated as hexagonal columns for the remainder of this work. This simplification is chosen to make r e from the present work applicable in the radiation scheme of Ebert and Curry (1992) , which has been developed with the same assumption. The restriction to one habit is inconsistent with observations; nevertheless

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Vijayakumar S. Nair, S. Suresh Babu, K. Krishna Moorthy, and S. S. Prijith

(TVM), Goa (GOA), and Hyderabad (HYD)—are indicated by aircraft symbols. b. Satellite data To supplement the temporally limited ICARB data, we have used the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOD (for the March–May period) over a decade (2000–2010) and vertical profiles of aerosols from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) to examine the persistence and vertical structure of aerosols around India. The MODIS sensors, on board

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Ning An, Kaicun Wang, Chunlüe Zhou, and Rachel T. Pinker

introduce large uncertainties ( Welch et al. 2008 ). Moreover, many of these satellite sensors have different spectral channels for day and night retrievals, which introduce intrinsic errors to the observations of cloud diurnal variations. New airborne active sensors, such as the Cloud Profiling Radar on board the CloudSat satellite and Cloud–Aerosol Lidar with Orthogonal Polarization on board the CALIPSO satellite, can provide multiple cloud-layer-top and cloud-layer-base heights ( Kim et al. 2011

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A. S. Daloz, E. Nelson, T. L’Ecuyer, A. D. Rapp, and L. Sun

with precipitation are currently limited. Mean values of cloud cover or cloud fraction are often employed to evaluate observations or models (e.g., Walsh et al. 2009 ; Clark and Walsh 2010 ; Dolinar et al. 2016 ), providing interesting information on the representation of the proportion of clouds, but their use alone provides limited insights into the underlying cloud processes. Cloud cover or fraction is also difficult to compare between models and observations, as the classification technique

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