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

sensible heat, as well as net radiation and soil heat flux, with a subset measuring carbon dioxide flux. Additional hydrometeorological observations included wind speed and direction, air temperature, vapor pressure, near-surface soil temperature and moisture, and below- and above-canopy radiometric surface temperature. At one site, a ground-based light detection and ranging (lidar) system from the Los Alamos National Laboratory (LANL) and the University of Iowa (UI), measuring ABL water vapor, height

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Melissa L. Wrzesien, Michael T. Durand, Tamlin M. Pavelsky, Ian M. Howat, Steven A. Margulis, and Laurie S. Huning

events linked to atmospheric rivers and surface air temperature via satellite measurements . Geophys. Res. Lett. , 37 , L20401 , https://doi.org/10.1029/2010GL044696 . 10.1029/2010GL044696 Guan , B. , N. P. Molotch , D. E. Waliser , S. M. Jepsen , T. H. Painter , and J. Dozier , 2013 : Snow water equivalent in the Sierra Nevada: Blending snow sensor observations with snowmelt model simulations . Water Resour. Res. , 49 , 5029 – 5046 , https://doi.org/10.1002/wrcr.20387 . 10.1002/wrcr

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R. A. Roebeling, E. L. A. Wolters, J. F. Meirink, and H. Leijnse

imagers ( Kidd and Levizzani 2011 ). The methods developed for geostationary satellites often use thermal infrared observations and relate daily minimum cloud-top temperatures ( Adler and Negri 1988 ) or cold cloud durations (CCD) to rain rates ( Todd et al. 1995 ). The infrared-based methods give fair accuracies over areas where rainfall is governed by deep convection. The use of visible and near-infrared observations offers the opportunity to base precipitation retrievals on cloud physical

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Charles Talbot, Elie Bou-Zeid, and Jim Smith

challenges in performing such simulations are often different from the challenges of idealized cases. Recently, Liu et al. (2011) tested WRF-LES, with data assimilation, over real terrain in a nested mesoscale–LES configuration for wind power applications. Their comparison of modeled fields to observations showed a discrepancy and they recommended further tests to elucidate whether inaccurate synoptic forcing or coarse resolution (100 m in their study) are the sources of the model errors. This paper

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Virendra P. Ghate and Pavlos Kollias

passage of storm systems that originate at a different region and hence is due to nonlocal effects ( Rickenbach 2004 ). However, the daytime precipitation occurs because of local land–atmosphere interactions ( Fitzjarrald et al. 2008 ). Here, observations collected during the Green Ocean Amazon (GOAmazon) field campaign are used to study the controls of daytime (local) precipitation during the Amazon dry season. In particular, the presented study aims to address the following questions: 1) Which

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Matthew Sturm, Brian Taras, Glen E. Liston, Chris Derksen, Tobias Jonas, and Jon Lea

understanding of model accuracy. The method opens the possibility of converting thousands of depth observations that have been, or currently are being, collected in to SWE values. For example, it could be used to convert Environmental Technical Applications Center monthly observed global snow depth climatologies ( Foster and Davy 1988 ) into SWE climatologies. It is also likely to become even more useful in the future if one promising new method of measuring snow depth becomes operational: airborne LiDAR

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Mark S. Kulie, Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer

as the third (fifth) range bin above the declared surface bin over oceanic (land) surfaces. Both 2C-SNOW-PROFILE and 2C-PRECIP-COLUMN utilize the cloud mask field contained in the 2B-GEOPROF product and only consider CPR observations associated with a cloud mask equal to or exceeding 20, where cloud mask values of 20, 30, and 40 are labeled “weak echo,” “good echo,” and “strong echo” and are associated with false detection rates of ~5%, 4%, and 1% when compared against Cloud–Aerosol Lidar and

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Catalina M. Oaida, John T. Reager, Konstantinos M. Andreadis, Cédric H. David, Steve R. Levoe, Thomas H. Painter, Kat J. Bormann, Amy R. Trangsrud, Manuela Girotto, and James S. Famiglietti

.g., Dettinger et al. 2004 ; Clark et al. 2011 ; Girotto et al. 2014a ). Given the importance and highly variable nature of the mountain snowpack, our ability to accurately estimate how much snow water equivalent (SWE) is stored in these mountainous regions is not only greatly desirable, but also a major scientific challenge (e.g., Dozier 2011 ; Margulis et al. 2015 ; Dozier et al. 2016 ; Painter et al. 2016 ). In situ observations, remotely sensed data, modeling, or a combination of these data sources

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Yefim L. Kogan, Zena N. Kogan, and David B. Mechem

reflectivity alone may not be sufficient for an accurate retrieval, especially in drizzling cases where a significant fraction of cloud liquid water is carried by small drops ( r < 25 μ m). To enhance the accuracy of Q l retrievals a number of studies have proposed using Doppler velocity measurements in addition to reflectivity ( Frisch et al. 1995 ; Babb et al. 1999 ; Kollias et al. 2001a ). Others have suggested combining radar observations with other cloud remote sensing instruments such as lidars

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Richard Essery and John Pomeroy

aperture radar (SAR) or lidar ( Schmugge et al. 2002 ). Although predictions of average SWE are useful, the standard deviation is also required for snowmelt models, as this determines the timing and rate at which snow-free ground emerges during melt ( Donald et al. 1995 ). Lacking the detailed meteorological data required to run the model for other years, a sequence of simulations was performed using the meteorological observations for 1996/97 but varying the snowfall rates during the observed events

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