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Eugene S. Takle, Daniel A. Rajewski, and Samantha L. Purdy

source: Iowa NAIP 2017 Orthophotos. USDA-FS-APFO Aerial Photography Field Office Image created from the Iowa State University GIS Facility Iowa Geographic Map Server ( ). Both towers are sited in agricultural fields that are planted on a corn–soybean annual rotation. In 2016, the first agricultural year the towers were operational, A1 had a triangular area, defined by the guy wire anchors, of grass around its base within a field of corn

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J. Marshall Shepherd

sequence in the animation is an enhanced Geostationary Operational Environmental Satellite (GOES) thermal image that clearly shows a thunderstorm (green and red contours representing cloud-top temperature) develop over the metropolitan Atlanta area and drift downwind. There is increasing evidence that large coastal cities, like Tokyo, Japan, and Houston, Texas, can influence weather through complex urban land use–weather–climate feedbacks. An engineering study by Bouvette et al. ( Bouvette et al. 1982

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Kingtse C. Mo, Lindsey N. Long, and Jae-Kyung E. Schemm

Environmental Prediction (NCEP) Climate Forecast System (CFS) model ( Saha et al. 2006 ; Saha et al. 2010 ). The model physics and dynamics are the same, but the horizontal resolution is different. Three coupled runs were made with the horizontal resolutions of T382, T126, and T62, respectively. The model is similar to the CFS version 2 but coupled with the GFDL Modular Ocean Model, version 3 (MOM3). It is also coupled with the Noah LSM, which has four soil layers and more realistic boundary layer physics

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Diandong Ren, Lance M. Leslie, Rong Fu, Robert E. Dickinson, and Xiang Xin

an important factor in desertification over mountainous regions, because they are very effective in transferring biomass from live to dead respiring pools ( Ren et al. 2009 ). Forecasting landslide timings and impacts has been a significant research topic in geotechnical engineering over the last four decades. However, because of the complexity of natural conditions (e.g., geometrical and geological variability, nonlinear time-displacements relationships, and the superposition of seasonal

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Mohammad H. Mokhtari, Ibrahim Busu, Hossein Mokhtari, Gholamreza Zahedi, Leila Sheikhattar, and Mohammad A. Movahed

: Generalized regression neural network in monthly flow forecasting . Civ. Eng. Environ. Syst. , 22 , 71 – 81 , doi:10.1080/10286600500126256 . Clark , R. N. , G. A. Swayze , A. Gallagher , T. V. V. King , and W. M. Calvin , 1993 : The U.S. Geological Survey Digital Spectral Library: Version 1: 0.2 to 3.0 μm. U.S. Geological Survey Open File Rep. 93-592, 1326 pp. Dickinson , R. E. , 1983 : Land surface processes and climate—Surface albedos and energy balance . Adv. Geophys. , 25

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Paolina Bongioannini Cerlini, Lorenzo Silvestri, Silvia Meniconi, and Bruno Brunone

resource availability in India is based on the use of satellite-derived remote sensing data and the obtained results are compared with data from wells. In Bjerklie et al. (2011) , future trends of groundwater recharge in Long Island Sound have been evaluated from GCM forecasts by assuming different scenarios in terms of carbon emissions. Within the water table fluctuation (WTF) method, valid for unconfined aquifers, the recharge is assumed as proportional to the measured rise of the water table, with

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Christine Wiedinmyer, Michael Barlage, Mukul Tewari, and Fei Chen

investigated the sensitivity of meteorological predictions to land-cover characteristics (e.g., Zhang and Gao 2009 ; Chen et al. 2001 ; Gallucci et al. 2011 ) or the impact of forest mortality due to mountain pine beetle infestation on carbon exchange ( Kurz et al. 2008 ; Pfeifer et al. 2010 ). Yet, this is the first such test of the sensitivity of the coupled Noah land surface model with the Weather Research and Forecasting (WRF) model to analyze the interactions between forest dieback and the

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Daniel M. Brown, Gerhard W. Reuter, and Thomas K. Flesch

the accumulated precipitation. In addition, we calculated the average water vapor mixing ratio for each summer. We have not used satellite data to augment the surface temperature measurements because the Geostationary Operational Environmental Satellite (GOES) west infrared images have a horizontal spatial resolution of greater than 10 km at Fort McMurray (57°N). Furthermore, it is difficult to distinguish between cloud and snow, making temperature estimates difficult. 2.2. Lightning To determine

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Xin-Zhong Liang, Hyun I. Choi, Kenneth E. Kunkel, Yongjiu Dai, Everette Joseph, Julian X. L. Wang, and Praveen Kumar

). Several RCMs built upon the MM5 then emerged to address a wide range of applications ( Leung and Ghan 1999 ; Liang et al. 2001 ; Liang et al. 2004a ; Liang et al. 2004b ; Wei et al. 2002 ). Meanwhile, the next-generation Weather Research and Forecasting (WRF) model has been developed ( Klemp et al. 2000 ; Michalakes 2000 ; Chen and Dudhia 2000 ; more information available online at ) to supersede the MM5. Accordingly, the Illinois State Water Survey then

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Dev Niyogi, Elin M. Jacobs, Xing Liu, Anil Kumar, Larry Biehl, and P. Suresh C. Rao

° longitude and had a grid spacing (Δ X , Δ Y ) of 4 km. The choice of a 4-km grid spacing was dictated by the availability of precipitation datasets [e.g., NCEP/Environmental Modeling Center (EMC) Stage IV data; Baldwin and Mitchell 1996 ] and some initial work involving LDAS indicating that the results were most stable associated with a 4-km grid spacing. In addition, when running the LDAS in a coupled mode for regional climate studies or meteorological studies with the Weather Research and Forecasting

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