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Howard B. Bluestein, Jana B. Houser, Michael M. French, Jeffrey C. Snyder, George D. Emmitt, Ivan PopStefanija, Chad Baldi, and Robert T. Bluth

ground-based wind observations in the boundary layer in and near supercells (mostly in the inflow region and just behind the rear-flank gust front), some tornadic, from a very high-spatial resolution, mobile, pulsed Doppler lidar and collocated, mobile, phased-array, X-band Doppler radar data ( Bluestein et al. 2010 ); and 2) to make a case for the use of the lidar for probing the boundary layer of tornadoes. The data were collected during the second year of the Second Verification of the Origins of

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Ronny Engelmann, Ulla Wandinger, Albert Ansmann, Detlef Müller, Egidijus Žeromskis, Dietrich Althausen, and Birgit Wehner

of flux parameterizations in mesoscale and general circulation models. Such observations can only be done with remote sensing instruments that provide the parameters of interest with high accuracy (<5%–10%) and with a spatial resolution of the order of 50 m and a temporal resolution of a few seconds. Remote measurements of turbulent fluxes in the planetary boundary layer were first shown by Senff et al. (1994) . A water vapor differential absorption lidar (DIAL) was combined with a radar radio

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Julia W. Fiedler, Lauren Kim, Robert L. Grenzeback, Adam P. Young, and Mark A. Merrifield

Guza 1984 ; Holland et al. 1995 ) and buried pressure sensors, which can also measure nearshore waves (e.g., Raubenheimer et al. 1998 ). These are costly to maintain and not easily moved once deployed. Remote observations of nearshore processes for model validation have historically been based on video techniques (e.g., Holman et al. 2013 ), with the more recent addition of lidar and radar ( Brodie et al. 2015 ; Blenkinsopp et al. 2010 ; Almeida et al. 2013 ; Turner et al. 2016b ; Vousdoukas

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Natalie Midzak, John E. Yorks, Jianglong Zhang, Bastiaan van Diedenhoven, Sarah Woods, and Matthew McGill

derived from airborne polarimeter observations. However, only broad plate-like or column-like categories can be derived using polarimeter observations alone. Noel et al. (2004) found lidar depolarization ratio to be sensitive to modeled aspect ratio which allowed for a coarse classification of habit types. Still only broad ice crystal categories including plates or spheroids, irregulars and columns were derived from the study. Also, distinguishing small from large ice crystals is a challenging task

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Stuart A. Young, Mark A. Vaughan, Ralph E. Kuehn, and David M. Winker

1. Introduction The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations ( CALIPSO ) satellite began acquiring scientific data in mid-June 2006. CALIPSO carries three coaligned, nadir-viewing instruments: a three-channel elastic-backscatter lidar, an imaging infrared radiometer, and a wide-field camera. An overview of the CALIPSO mission, science objectives, and instruments is presented in Winker et al. (2010) . The CALIPSO lidar [Cloud–Aerosol Lidar with Orthogonal

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Michael Bennett, Simon Christie, Angus Graham, and David Raper

field trials at Heathrow and Manchester airports using a rapid-scanning lidar in conjunction with various other observations. This paper describes the field work undertaken and how we have elaborated the hardware and software of the lidar system so that it should be capable of monitoring aviation emissions. We illustrate this capability with images of dispersing aircraft plumes under a range of operational modes. Subsequent papers ( Bennett and Christie 2010 ; A. Graham et al. 2010, unpublished

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Bianca Adler, Olga Kiseleva, Norbert Kalthoff, and Andreas Wieser

correlated), and the spectral peak wavelength λ m (as the size of the eddies with the most energy). Reliable measurement of these parameters is crucial for the understanding of the CBL structure and evolution. Variance profiles can be derived from aircraft observations using spatial averages (e.g., Lenschow and Stephens 1980 ; Lenschow 1986 ; Young 1988 ; Grunwald et al. 1998 ) and from tower or wind lidar measurements using temporal averages (e.g., Neff 1990 ; Grund et al. 2001 ; Emeis 2011

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M. Weissmann, R. Busen, A. Dörnbrack, S. Rahm, and O. Reitebuch

deployed adaptively during A-TreC, including dropsondes launched from research aircrafts, Aircraft Meteorological Data Reporting (AMDAR), Automated Shipboard Aerological Program (ASAP), radiosondes, drifting buoys, and satellite rapid-scan winds. In addition to these sensors, an airborne Doppler lidar system was deployed for targeted observations for the first time. The intention was to test the capability of Doppler lidars to sample sensitive areas. For this purpose, a 2- μ m scanning Doppler lidar

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Timothy A. Bonin, Brian J. Carroll, R. Michael Hardesty, W. Alan Brewer, Kristian Hajny, Olivia E. Salmon, and Paul B. Shepson

difficulty distinguishing the nocturnal mixing layer (ML) from the residual layer at night and during morning and evening transition periods ( Schween et al. 2014 ). Doppler lidar observations have been used to estimate the MH, most often using either backscatter or turbulence information from vertical stares (e.g., Hogan et al. 2009 ; Barlow et al. 2011 ; Huang et al. 2017 ). Tucker et al. (2009) evaluates the accuracy of various techniques and finds that vertical velocity variance generally

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S. A. Ackerman, R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill

yield large differences regionally. The studies presented in this section provide insight into the sensitivity of the cloud mask algorithm results to instrument characteristics and algorithm thresholds. Awareness of this sensitivity is necessary for comparing the MODIS cloud detection to other observations covered in the next section. 4. Comparison with lidar/radar observations a. Ground-based observations The performance of the MODIS cloud mask has been addressed in several recent papers ( King et

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