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J. L. Schroeder, W. S. Burgett, K. B. Haynie, I. Sonmez, G. D. Skwira, A. L. Doggett, and J. W. Lipe

28 different parameters with an observation period of 5 min for meteorological data (e.g., air temperature, humidity, etc.) and 15 min for agricultural data (e.g., soil temperature, soil moisture content, etc.). All real-time data from the surface stations are available free of charge online at the WTM Web page at . The WTM also includes two atmospheric profilers located at Reese Technology Center, Texas. 2. West Texas Mesonet surface observation systems The WTM project was

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Lauren M. Candlish, Richard L. Raddatz, Matthew G. Asplin, and David G. Barber

volume ( Kwok and Cunningham 2010 ), average age of ice ( Maslanik et al. 2007 , 2011 ), and an increasing melt season length ( Markus et al. 2009 ). These changes in sea ice cover can release large heat and moisture fluxes into the Arctic atmosphere throughout the cold season, thus modifying the regional boundary layer climate. To better understand the changes being observed in the Arctic, reliable in situ data are required. The use of a Radiometrics profiling radiometer on board a ship in the

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Qi Zhang, Kunde Yang, and Qiulong Yang

1. Introduction In marine environments, rapid decay of moisture at the bottom of the marine atmospheric boundary layer (MABL) can result in a large change to the vertical gradient in humidity ( Saeger et al. 2015 ; Wagner et al. 2016 ). If the gradient extends sufficiently high (always within a few tens of meters), an anomalous atmospheric structure forms in which the propagation of electromagnetic waves is similar as when trapped in a waveguide. This kind of structure is known as the

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Christopher A. Fiebrich, Kevin R. Brinson, Rezaul Mahmood, Stuart A. Foster, Megan Schargorodski, Nathan L. Edwards, Christopher A. Redmond, Jennie R. Atkins, Jeffrey A. Andresen, and Xiaomao Lin

ground surface (e.g., air temperature, relative humidity, rainfall, winds, solar radiation, atmospheric pressure, soil temperature, and soil moisture), 2) report data at a subhourly temporal resolution, and 3) have a spatial density on the order of one station per 1000 km 2 (average spacing of approximately 30 km). We further recognize that an emphasis on data quality, reliability, and completeness is vital to a mesonet’s ability to effectively deliver services in near–real time and document

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Keith D. Hutchison

1. Introduction The Lockheed Martin Missiles & Space science team has successfully demonstrated a highly accurate, nonlinear, physical relaxation algorithm for the retrieval of moisture profiles from Defense Meteorological Satellite Program (DMSP) SSM/T-2 data. The algorithm has also proven invaluable to advanced sensor design concept studies for future satellite programs ( Wilheit et al. 1994 ). More recently, cloud-top temperatures, derived from a small sample of coincident National Oceanic

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Sijie Pan, Jidong Gao, David J. Stensrud, Xuguang Wang, and Thomas A. Jones

itself and thus it is highly beneficial to create a good initial condition for severe weather prediction. Kuo et al. (1996) conducted a set of experiments indicating that even when using a first guess with a very poor initial moisture field, 4DVar was quite effective in improving the vertical moisture distribution when TPW was assimilated. But this study was conducted at only mesoscale resolutions. For convective-scale NWP, the importance of the storm moisture environment has not been extensively

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Jeremiah P. Sjoberg, Richard A. Anthes, and Therese Rieckh

Stoffelen 2009 ; Stoffelen 1998 ; Vogelzang et al. 2011 ), and wave height ( Caires and Sterl 2003 ; Janssen et al. 2007 ). It has also been used in hydrology to estimate errors in measurements of precipitation ( Roebeling et al. 2012 ), fraction of absorbed photosynthetically active radiation ( D’Odorico et al. 2014 ), leaf area index ( Fang et al. 2012 ), and, particularly, soil moisture ( Anderson et al. 2012 ; Dorigo et al. 2010 ; Draper et al. 2013 ; Hain et al. 2011 ; Miralles et al. 2010

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P. L. Heinselman, P. L. Spencer, K. L. Elmore, D. J. Stensrud, R. M. Hluchan, and P. C. Burke

Bragg scattering, the radar reflectivity is related to the refractive-index structure parameter C n 2 . Because C n 2 depends on the spatial variations of moisture and temperature, the sharp gradients in water vapor mixing ratio and temperature characteristic of the mixed layer depth may be depicted by a horizontal layer in the reflectivity field. Rabin and Doviak (1989) hinted at the potential for using WSR-88D reflectivity data to estimate mixed layer depth after noticing the daytime

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Allen B. White, Daniel J. Gottas, Eric T. Strem, F. Martin Ralph, and Paul J. Neiman

the National Weather Service River Forecast System (NWSRFS) for real-time river forecasting. The NWSRFS is a collection of modules or operations that can be selected to perform various hydrologic forecasting procedures ( Smith and Page 1993 ). The two operations that were used in this analysis are the snow accumulation and ablation model, SNOW-17, developed at the Hydrologic Research Laboratory of the Office of Hydrology ( Anderson 1973 ) and the Sacramento Soil Moisture Accounting Model (SAC

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Theodore V. Blanc

averageroughness Reynolds number of 6. Because the analysis generally employed conservative estimates of the measurement uncertainties and left out other sources of error, it is argu~ that the results of the analysis rep~enta best case situation.1. Introduction The fluxes of momentum, heat, and moisture mustbe measured to properly characterize the interactionof the lower atmosphere and the ocean. These turbulentexchanges play a key role in the energy transportmechanism that influences the weather and the

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