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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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Arunchandra S. Chandra, Pavlos Kollias, and Bruce A. Albrecht

, which is limited because of the short lifetime of fair-weather cumulus clouds. Furthermore, the high cost associated with routine aircraft observations make their use challenging. Addressing these problems demands technologies to sample clouds with high-resolution capabilities beyond one dimension. In the last 20 years there has been substantial progress in cloud remote sensing with the development of sophisticated cloud radars, lidars, and microwave radiometers (e.g., Spinhirne 1993 ; Moran et al

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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 , https://doi.org/10.1029/2001JD900243 . 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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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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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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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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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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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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Yasutaka Murakami, Christian D. Kummerow, and Susan C. van den Heever

’s spaceborne lidar ( Winker et al. 2009 ), and Aqua’s Moderate Resolution Imaging Spectroradiometer (MODIS) ( Parkinson 2003 ), all of which fly in the A-Train, are used for estimating the cloud parameters in this study. All of the data are matched up to CloudSat footprints. The horizontal and vertical resolutions of CloudSat observations are approximately 1.75 km and 240 m, respectively. Cloud-base geometrical height and rain rates are estimated from the radar reflectivity profile through

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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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