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P. Ducharme, A. Houdayer, Y. Choquette, B. Kapfer, and J. P. Martin

. This is the type of function [Eq. (5) ] used so far to extract SWE measurements from data collected by airborne gamma detectors: where N is the counts of gammas detected and α is the exponential coefficient. In Fig. 3 , we present the results of the numerical simulations (dotted line) over a totally dry soil for varying values of SWE. The counts for the different values of SWE are plotted as ratios to the counts ( N o ) detected in absence of a snow cover and soil moisture. An exponential of

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Yong-Keun Lee, Cezar Kongoli, and Jeffrey Key

1. Introduction Snow is one of the most dynamic hydrological variables on the earth’s surface and is the cryospheric component with the largest seasonal variation in spatial extent. Over Northern Hemisphere lands, snow cover ranges from about 45.2 × 10 6 km 2 in January to 1.9 × 10 6 km 2 in August ( Barry et al. 2007 ). Because of its dramatic seasonal variation and high reflectivity, snow plays a key role in the global energy and water budget ( Barry et al. 2007 ; IPCC 2007 ). Snow

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Konstantin V. Khlopenkov and Alexander P. Trishchenko

parameters (the R score and the T score) are analyzed. If both are smaller than zero, then the pixel is considered to be clear-sky snow, and the processing jumps to the thin cirrus test (described below). Otherwise, the pixel is considered to be snow covered and cloud contaminated, and undergoes additional cloud tests. If no snow is detected in the first stage, then the pixel is considered to be potentially cloud contaminated and undergoes additional tests, beginning with the simple ratio test. 6

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W. Kendall Melville, Luc Lenain, Daniel R. Cayan, Mati Kahru, Jan P. Kleissl, P. F. Linden, and Nicholas M. Statom

of California, the Carolinas, and the Gulf of Mexico. We also show a terrestrial use of the lidar in measuring the snow cover in the Sierra Nevada, an important natural seasonal reservoir for California’s water supply and, finally, an example of measuring the built environment of a university campus. 2. Modular Aerial Sensing System The MASS is shown during bench top testing in Fig. 1a along with the aircraft used for the 2011 Gulf of Mexico experiment, a Partenavia P.68 light twin engine

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Amandine Pierre, Sylvain Jutras, Craig Smith, John Kochendorfer, Vincent Fortin, and François Anctil

the Köppen–Geiger climate classification updated . Meteor. Z. , 15 , 259 – 263 , https://doi.org/10.1127/0941-2948/2006/0130 . 10.1127/0941-2948/2006/0130 McKay , G. A. , 1968 : Problems of measuring and evaluating snow cover. Proc. Workshop Seminar of Snow Hydrology , Ottawa, ON, Canada, Canadian National Committee, 49–65. Metcalfe , J. R. , and B. E. Goodison , 1993 : Correction of Canadian winter precipitation data. Eighth Symp. on Meteorological Observations and Instrumentation

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Wendy A. Ryan, Nolan J. Doesken, and Steven R. Fassnacht

measured at least once per day if snow was present on the ground. Multiple total depth samples were taken to obtain one integrated measurement when the observers felt it necessary, based on how spatially variable the snow cover was. The number of depth samples taken to obtain a representative sample was also recorded. The snow depth in the immediate vicinity of the USDS was also recorded. The snowboard beneath the ultrasonic sensors was never cleared. Notes were also made in reference to snow crystal

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Alexander Radkevich, Konstantin Khlopenkov, David Rutan, and Seiji Kato

. The light gray circle encloses a region of interest (ROI) cocentered with a CERES FOV acquired at 17 h, 37 min, 6.368 s UTC. The ROI has a diameter of 29 1-km MODIS pixels and includes 632 pixels. We will consider the ROI as a model of the FOV. The FOV was identified as 100% cloud free and 100% snow covered. One can see from Fig. 1 that the clear-sky identification is correct, but that the snow percent coverage is overestimated as this FOV is only partly snow covered. We shall return to this FOV

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Patrick C. Meyers, Ralph R. Ferraro, and Nai-Yu Wang

-global rain-rate estimates every 3–4 h. The microwave spectrum is particularly sensitive to water in all states, allowing for retrievals of water vapor, liquid precipitation, and surface snow cover. Rainfall interacts with the microwave emissions from the earth’s surface, such that convective regions can be identified by the scattering of surface emissions by suspended snow, ice, and water ( Ferraro et al. 1998 ). Spaceborne passive microwave (PMW) imagers have been used to monitor global precipitation

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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

et al. 2018 ; Pettersen et al. 2020 ). Deeper cloud structures that are characteristic of midlatitude winter cyclones are generally easier for PMWs to detect due to strong scattering signals from ice particles and higher reflectivity values that can be detected by radars with reduced sensitivity. Shallow snowfall, however, presents unique PMW detection complexities at higher latitudes since its radiative signal can be difficult to discern over snow-covered surfaces. Depending on radar

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Gé Verver, Masatomo Fujiwara, Pier Dolmans, Cor Becker, Paul Fortuin, and Larry Miloshevich

layer of condensate on the mirror. The mirror temperature (the dew-/frost-point temperature) is measured because the Snow White mirror is also a thermocouple. The sensor housing is equipped with a heater, which operates in cloud layers to avoid sensor icing and to make total water measurements ( Wang et al. 2003 ). In this study all soundings were equipped with the so-called “night”-type Snow White, that is, an ASW35 sensor without the Styrofoam cover of the sensor housing, in order to avoid the

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