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Steven E. Koch, Martin Fengler, Phillip B. Chilson, Kimberly L. Elmore, Brian Argrow, David L. Andra Jr., and Todd Lindley

1. Introduction It has been more than a decade since the National Research Council (2009 , 2010 ) articulated the need for establishing a nationwide mesoscale network to address severe limitations in sampling the atmosphere. Those reports and a follow-on thermodynamic profiling workshop ( Hardesty and Hoff 2012 ) recommended that profiles of wind, temperature, and moisture should extend to 3 km above ground level (AGL), and that for the prediction of convection initiation (CI), a time

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Adam L. Houston and Jason M. Keeler

-forecast CIN can have a fair amount of noise, and errors in model-forecast low-level moisture can lead to substantial error in CIN forecasts ( Moller 2001 ). In fact, uncertainty regarding the distribution of moisture in the boundary layer was one of the motivating factors for the IHOP campaign ( Weckwerth and Parsons 2006 ). This low-level moisture uncertainty manifests itself in forecast CIN errors as early as model initialization ( Bunkers et al. 2010 ). While the importance of accuracy in

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Rezaul Mahmood, Megan Schargorodski, Stuart Foster, and Andrew Quilligan

of its stations observe and collect 5-min data for temperature, precipitation, relative humidity, wind speed and direction, and solar radiation. Currently, 35 of the 71 stations collect soil moisture and temperature data at up to five depths below surface the (5, 10, 20, 50, and 100 cm), 40 stations observe atmospheric pressure, and 13 have camera observations. The instrumentation platform continues to expand as funding becomes available. Figures 2a – c provide a station photo and relative

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Feiqin Xie, Stig Syndergaard, E. Robert Kursinski, and Benjamin M. Herman

) Over moist marine areas, especially in subtropical regions, a very sharp temperature inversion and a large negative moisture gradient are often observed at the top of the MBL. These sharp temperature and moisture gradients give rise to a large negative refractivity gradient that causes a large bending of GPS RO signal paths. It is called superrefraction, when the vertical refractivity gradient exceeds a critical value, that is, dN / dz < −1/ r E ≈ −157 N -unit km −1 ( r E is the curvature

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D. Jagadheesha, B. Simon, P-K. Pal, P. C. Joshi, and A. Maheshwari

time scales over the tropics and there is also a significant moisture signature present. As we have used radiosonde-derived refractivity and its associated temperature statistics mainly over the tropics as empirical information in the technique, our method may have limited applicability outside the tropics. We also apply our technique on COSMIC refractivity profiles over the Bay of Bengal during May–September 2007 to derive information on vertical thermal and moisture changes between the active and

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A. Molod, H. Salmun, and M. Dempsey

) or specific limits ( Compton et al. 2013 ) that are present in many algorithms to deal with the noisy morning SNR profiles that are measured when the PBL height is below instrument range. The underlying assumption for the algorithm developed here, as well as most lidar or wind profiler PBL height algorithms, is that the gradients of moisture, hydrometeors, or particles at or near the PBL height will be manifest as maxima in the signal backscatter at the detector. The time at which the PBL height

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Mathieu Vrac, Alain Chédin, and Edwin Diday

the average temperature or moisture profiles and associated variances may be very different for each airmass class. Providing full description of the vertical atmospheric column, operational meteorological analyses are at the basis of the method we have developed to determine the type of the air mass observed. This method allows one to cluster a set of atmospheric profiles by characterizing each type of air mass in a statistical sense, opening a way to model the distribution of each variable

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Paul E. Ciesielski, Wen-Ming Chang, Shao-Chin Huang, Richard H. Johnson, Ben Jong-Dao Jou, Wen-Chau Lee, Po-Hsiung Lin, Ching-Hwang Liu, and Junhong Wang

rain gauges, surface observations, and several ground-based GPS systems for monitoring moisture conditions. Measuring the atmospheric state with these multiple platforms allows for rigorous cross calibration that can greatly enhance the accuracy of the data, which is crucial for post–field phase diagnostic, data assimilation, and numerical modeling studies. Considering the numerous applications of upper-air radiosondes (“sondes”), several efforts in recent years have been undertaken to create high

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Courtney D. Buckley, Robbie E. Hood, and Frank J. LaFontaine

water as well as soil moisture, snow, and possibly other parameters ( Spencer et al. 1994 ). Zhao et al. (2001) used the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), which is also a satellite-based radiometer, to monitor soil moisture and flooding in the Yangtze and Huaihe River basins in the summer of 1998. They developed several classification systems to determine which of five categories (dry soil, wet soil, ocean/water, stratiform rain, and convective rain) a brightness

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Jie Yang, Qingquan Liu, Feng Ding, and Renhui Ding

center position of the copper spherical shell by using a heat-conducted silica gel. Because moisture can short-circuit the platinum resistance probe, a sealant is adopted to prevent the moisture going into the inside of the copper spherical shell. The diameter and thickness of the copper spherical shell are 8 and 0.5 mm, respectively ( Fig. 1 ). Fig . 1. Internal structure schematics of the spherical temperature sensor. The sensors A and B are covered with a thin layer of white coating and a thin

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