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Elin A. McIlhattan, Tristan S. L’Ecuyer, and Nathaniel B. Miller

global map of the observed cloud impact on surface radiation ratio (CISRR), which quantifies the ratio of SWCRE to LWCRE at the surface ( McIlhattan 2015 ): Fig . 2. Annual average of the observed cloud impact on surface radiation ratio (CISRR). CISRR is the ratio of shortwave cloud radiative effect (SWCRE) to the longwave cloud radiative effect (LWCRE). Observations are from the A-Train data product 2B-FLXHR-lidar between January 2007 and December 2010. Regions in blue (red) indicate the SWCRE

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Frida A.-M. Bender, Anders Engström, Robert Wood, and Robert J. Charlson

clouds, compared to a higher-resolution CALIPSO product, and also with Jiang et al. (2012) , who find that most CMIP5 models overestimate liquid water path compared to both passive and active satellite observations, indicating that model clouds are thicker, and accordingly fewer, than observed. However, as pointed out in section 2 , the passive MODIS sensors are likely to underestimate the presence of optically thin clouds compared to active sensors like the lidar on CALIPSO , and the multimodel

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Ellsworth G. Dutton

MA-1990 NOTES AND CORRESPONDENCE 587NOTES AND CORRESPONDENCEComments on "Major Volcanic Eruptions and Climate: A Critical Evaluation" ELLSWORTH G. DUTTONGeophysical Monitoring for Climatic Change, NOAA, Boulder, Colorado1 August 1989 Mass and Portman (1989, referred to hereafter asMP) claim that actinometric observations of total solarradiation following major volcanic eruptions show

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Donald P. Wylie and W. Paul Menzel

, 1992: Seasonal and diurnal changes in cirrus clouds as seen in four years of observations with the VAS. J. Appl. Meteor., 31, 370–385. Minnis, P., D. F. Young, K. Sassen, J. M. Alvarez, and C. J. Grund, 1990: The 27–28 October 1986 FIRE IFO cirrus case study: Cirrus parameter relationships derived from satellite and lidar data., Mon. Wea. Rev., 118, 2402–2425. ——, K. N. Liou, and Y. Takano, 1993a: Inference of cirrus cloud properties using satellite-observed visible and infrared

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Robert Pincus, Steven Platnick, Steven A. Ackerman, Richard S. Hemler, and Robert J. Patrick Hofmann

; Williams and Webb 2009 ; among many others) This has inspired a number of other simulators for cloud-related instruments, including spaceborne radars ( CloudSat ; Haynes et al. 2007 ), lidars [ Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Chepfer et al. 2008 )], and multiangle radiometers [Multiangle Imaging SpectroRadiometer (MISR; see Marchand and Ackerman 2010 )]. CALIPSO and MISR have also produced summary datasets against which the results of the simulator

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Elizabeth Berry, Gerald G. Mace, and Andrew Gettelman

tropical belt, and the overestimate of the low cloud reflectance relative to Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar ( PARASOL ), and documented the inability of GCMs to reproduce the contrast between higher cloud reflectance observed along the eastern Pacific Ocean and the lower values over the tropical trade wind cumulus region. The difficulty in simulating low clouds and their effects is intriguing, given the apparent simplistic

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Yi Zhang, Haoming Chen, and Rucong Yu

Radar (CPR), together with the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ; Winker et al. 2010 ), an aerosol lidar, offer an unprecedented opportunity for investigating the three-dimensional distributions of clouds in the globe. Meanwhile, CloudSat overpasses eastern continental China (20°–40°N, 100°–120°E) mostly at 0100–0300 local solar time (LST) (middle of night) and 1300–1500 LST (afternoon), enabling a comparison between daytime and nighttime profiles

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Joseph Sedlar

reanalysis to capture these anomalies appears to be largely biased in its overrepresentation of cloud, and a general lack of LIY versus HIY variability in cloud fractional anomalies. f. Cloud microphysics and downwelling surface radiation Besides total cloud fraction being important for ASR, the microphysical structure of these clouds is also important. Here, Arctic observations of liquid and ice water content profiles are retrieved from a synergy between the radar and lidar instruments onboard CloudSat

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Jason M. English, Andrew Gettelman, and Gina R. Henderson

remote sensing via the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations –GCM-Oriented CALIPSO Cloud Product ( CALIPSO -GOCCP), which is a part of the A-train ( L’Ecuyer and Jiang 2010 ). Because of differences between modeled clouds and clouds observed by instruments, simulator packages such as the Cloud Feedback Model Intercomparison Project (CFMIP) ( Bony et al. 2011 ) Observation Simulator Package (COSP) ( Bodas-Salcedo et al. 2011 ), including a lidar simulator ( Chepfer et

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Kathryn L. Verlinden, David W. J. Thompson, and Graeme L. Stephens

. 2007 ). The spaceborne Geoscience Laser Altimeter System (GLAS) provides continent-wide imagery of the vertical structure of Antarctic cloudiness, but results based on GLAS over Antarctica were published for only one calendar month (October 2003, Spinhirne et al. 2005 ). Spaceborne Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite measurements have been used to examine clouds over Antarctica, but primarily at stratospheric levels (e.g., Noel et al. 2008 ; Wang

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