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Joseph A. Santanello Jr., Christa D. Peters-Lidard, Aaron Kennedy, and Sujay V. Kumar

arise in sections 4 and 5 . c. Data and evaluation The ARM-SGP program provides a wealth of surface flux, meteorological, and hydrological observations along with atmospheric profiles from radiosonde and lidar for a network of sites in and near the winter wheat belts of Oklahoma and Kansas. This includes collocated soil moisture, net radiation, and sensible, latent, and soil heat, along with collocated surface meteorology data that provide the full set of variables needed to calculate the LoCo

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Joseph A. Santanello Jr., Christa D. Peters-Lidard, Sujay V. Kumar, Charles Alonge, and Wei-Kuo Tao

1. Introduction Land–atmosphere (L–A) interactions and coupling remain weak links in current observational and modeling approaches to understanding and predicting the earth–atmosphere system. The degree to which the land affects the atmosphere (and vice versa) is difficult to quantify, given the disparate resolutions and complexities of land surface and atmospheric models and the lack of comprehensive observations at the process level ( Betts et al. 1996 ; Angevine 1999 ; Entekhabi et al

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Long Yang, James A. Smith, Mary Lynn Baeck, Elie Bou-Zeid, Stephen M. Jessup, Fuqiang Tian, and Heping Hu

2005) through Chicago and its surrounding areas (including Milwaukee) by means of radar and surface observations. They found that the lake-breeze front could be slowed because of the UHI effect. Their conclusions are supported by Sarkar et al. (1998) . The contrasting response of lake- and sea-breeze circulations in different settings to the UHI points to the need for additional study. Instead of examining storm systems that initiate and develop in close proximity to the urban region (e.g., Miao

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June K. Yeung, James A. Smith, Mary Lynn Baeck, and Gabriele Villarini

areas masked. The region depicts the primary study domain that was used to select the top 50 convective storm days based on the CG lightning observations. The location of the KDIX WSR-88D weather radar located at Monmouth, NJ (40.03°N, 74.62°W), is denoted by the closed green circle. We examine organized thunderstorm systems as dominant agents of flash flooding in urban environments of the United States east of the Rocky Mountains ( Smith et al. 2001 , 2005 ; Doswell et al. 1996 ; Ntelekos et al

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Camille Garnaud, Stéphane Bélair, Marco L. Carrera, Chris Derksen, Bernard Bilodeau, Maria Abrahamowicz, Nathalie Gauthier, and Vincent Vionnet

on the atmosphere and hydrosphere. Snow mass observations are thus crucial for quality environmental and hydrological forecasts. In eastern Canada, for example, 1 mm of snow water equivalent (SWE) over the James Bay territory could amount to CAN $1 million in hydroelectric power production ( Brown and Tapsoba 2007 ). Given trends in the evolution of operational environmental prediction systems, it is expected that satellite observations sensitive to snow mass at the scale of a few hundred meters

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Filipe Aires, Léo Miolane, Catherine Prigent, Binh Pham, Etienne Fluet-Chouinard, Bernhard Lehner, and Fabrice Papa

observations. Moderate Resolution Imaging Spectroradiometer (MODIS) observations have been used to derive global products every 2 days ( http://oas.gsfc.nasa.gov/floodmap/ ), but of course, visible/infrared observations suffer from the presence of clouds (about 70% of Earth’s surface at any time) and vegetation. However, this type of data is of great value to the community to detect open water. In Yamazaki et al. (2015) , a global water body map at 90-m resolution is proposed from Landsat imagery. This

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Dongyue Li, Dennis P. Lettenmaier, Steven A. Margulis, and Konstantinos Andreadis

depth at ultrahigh resolution. Combined with distributed snow density modeling, ASO provides the first operational lidar SWE measurement to quantify the volume of water stored in the seasonal snow cover at regional scales ( Painter et al. 2016 ). Li et al. (2017b) developed an ensemble Kalman batch smoother to combine the complementary SWE information from modeling and the enhanced spaceborne passive microwave radiance observations. The resulting high-resolution SWE from the assimilation

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Yonghwan Kwon, Zong-Liang Yang, Long Zhao, Timothy J. Hoar, Ally M. Toure, and Matthew Rodell

1. Introduction Estimates of snow depth and snow water equivalent (SWE) are critical for climate studies and water resource management. Data assimilation (DA) has been identified as a powerful method to generate improved estimates by merging observations and model forecasts based on their uncertainties. As a DA method, radiance assimilation (RA) incorporates microwave brightness temperature T B observations into a land surface model (LSM) coupled with a microwave radiative transfer model (RTM

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Ahmed B. Tawfik, Paul A. Dirmeyer, and Joseph A. Santanello Jr.

identify the influence of local surface energy fluxes on boundary layer development. Santanello et al. (2013) present a comprehensive mixing diagram analysis comparing nine combinations of land surface and boundary layer schemes in the NASA Land Information System (LIS) coupled to the Weather Research and Forecasting (WRF) Model against observations over the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) stations. They showed that the sensitivity of planetary boundary

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Kamil Mroz, Mario Montopoli, Alessandro Battaglia, Giulia Panegrossi, Pierre Kirstetter, and Luca Baldini

regions, and are fraught with problems like undercatch and wind-blown snow biases ( Fassnacht 2004 ). This measurement gap can be bridged by spaceborne active and passive microwave (PMW) sensors that are tailored to detect and quantify snowfall thanks to their ability to probe within clouds ( Levizzani et al. 2011 ; Skofronick-Jackson et al. 2017 ). Two spaceborne radars paved the way toward ground-breaking vertically resolved observations of falling snow over much of the globe: the CloudSat Cloud

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