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Sujan Pal, Francina Dominguez, María Eugenia Dillon, Javier Alvarez, Carlos Marcelo Garcia, Stephen W. Nesbitt, and David Gochis

measurements in the headwaters of the basin using acoustic Doppler current profiler (ADCP) and large-scale particle image velocimetry (LSPIV). The group measured the hydrologic response of three severe high-flow events during the IOP. The main objectives of the hydrometeorological observations were 1) to quantify the hydrological response associated to the extreme convective events simultaneously measured by the larger RELAMPAGO team, 2) to build suitable stage–discharge curves for the headwater rivers

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Nick Rutter, Don Cline, and Long Li

June 2003. All variables, except for snow depth, were recorded at 30-s intervals and averaged to 10-min values. Snow depth was recorded as a single sample value at the start of each 10-min period. Data were recorded on Campbell CR10X dataloggers using manufacturer-supplied calibration sensitivities for each instrument where appropriate. 2) Snowpit data Snowpit measurements were made at CLPX meteorological sites as close as possible to the footprint of the acoustic depth sounder without disturbing

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M. Jahanzeb Malik, Rogier van der Velde, Zoltan Vekerdy, and Zhongbo Su

precipitation for a coarse grid cell, while on-site (NI) inputs were not received. Indeed, Fig. 1a shows an increase in the simulated snow depth and albedo in response to various snowfall events in that period, which is not in agreement with the measurements. Similarly, Pan et al. (2003) and Niu et al. (2011) highlighted the effects of uncertain precipitation input on snow process simulations. The period 28 January–17 February 2003 is, therefore, not further used for evaluation of the model

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William P. Kustas, Jerry L. Hatfield, and John H. Prueger

forcing from NLDAS. The comparison with METFLUX tower estimates indicated greater scatter, particularly for the soybean sites. Possible factors causing this result for the soybean sites include incorrect parameterizations, emissivity effects and mismatch in the scale of the radiometric surface temperature observations, and the source area contributing to the METFLUX tower measurements. The issue of model validation with tower flux observations is considered in detail by Chávez et al. (2005) using an

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Jessica D. Lundquist, Paul J. Neiman, Brooks Martner, Allen B. White, Daniel J. Gottas, and F. Martin Ralph

the high density of the snowpack and their flat locations with wind shelter from the surrounding trees. Snow depth measurements, which are only available at a small subset of the stations (Blue Canyon, Meadow Lake, and Caple’s Lake; Fig. 1b ), acoustically detect changes in snow depth at an hourly time scale. An increase in snow depth can only occur with snowfall or with deposits of drifting snow. However, the latter is rare during a rain event in the Sierra Nevada. A decrease in snow depth could

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H. Leijnse, R. Uijlenhoet, C. Z. van de Beek, A. Overeem, T. Otto, C. M. H. Unal, Y. Dufournet, H. W. J. Russchenberg, J. Figueras i Ventura, H. Klein Baltink, and I. Holleman

but remain fixed during operation. In the future, a raindrop-size-distribution retrieval algorithm will be devised that makes use of the joint measurements of TARA (through Doppler spectra; see Atlas et al. 1973 ) and IDRA (through polarimetric observables; see Seliga and Bringi 1976 ; Gorgucci et al. 2008 ). KNMI operates a 35-GHz cloud radar (PDN100) and a 1290-MHz (Vaisala LAP-3000) wind profiler/radio acoustic sounding system (RASS) at the CESAR site. The cloud radar is a vertically

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Sean Swenson and John Wahr

, using an exact averaging kernel to extract regional water storage variations leads to highly inaccurate estimates ( Swenson et al. 2003 ). Instead, a modified averaging kernel can be constructed ( Swenson and Wahr 2002 ), which attempts to minimize the effects of satellite measurement errors while faithfully representing the water storage signal in the region of interest. To optimize the averaging kernel, one requires an estimate of the GRACE measurement uncertainty, which the GRACE Project provides

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Anja Goldbach and Wilhelm Kuttler

extent of the source areas. The source areas are shown in Fig. 2 representing the 80% flux-source areas for the measured turbulent heat fluxes. To take the effects of different surface covers during different atmospheric conditions into account, the source areas were calculated for unstable ( ζ < −0.05), neutral (−0.05 < ζ < +0.05), and stable ( ζ > +0.05) stratification conditions, and the dimensionless stability parameter ζ was calculated by , where z is measurement height, z d is

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Xia Feng, Alok Sahoo, Kristi Arsenault, Paul Houser, Yan Luo, and Tara J. Troy

for the other three models. 4. Results and discussion a. RB The snow depth, SWE, snow density, snow albedo, and snow temperature fields from the five models are compared with acoustic depth sounder snow depth (obs) and snow pit measurements (SPs), which are shown in Fig. 2 . The snow starts to accumulate in October and gradually builds up a deep snowpack, with the maximum snow depth reaching 3.5 m. There are eight small snowmelt events followed by major snowmelt in mid-May, until the snow finally

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Charles Talbot, Elie Bou-Zeid, and Jim Smith

the results and to contrast the model sensitivity to the SGS model to the (in)sensitivity we noted for idealized simulations. At ground level, comparisons are done between model outputs from LES domain d6 and the measurements from the Princeton EC stations, as well as between results from LES d4 with a resolution of 450 m and measurements from the New Jersey Weather and Climate Network, ASOS, and Mesonet stations. These stations are near the boundary of domain d4 and boundary effects can be

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