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Xin Li, Guodong Cheng, Shaomin Liu, Qing Xiao, Mingguo Ma, Rui Jin, Tao Che, Qinhuo Liu, Weizhen Wang, Yuan Qi, Jianguang Wen, Hongyi Li, Gaofeng Zhu, Jianwen Guo, Youhua Ran, Shuoguo Wang, Zhongli Zhu, Jian Zhou, Xiaoli Hu, and Ziwei Xu

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses.

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Fan Yang, Qing He, Jianping Huang, Mamtimin Ali, Xinghua Yang, Wen Huo, Chenglong Zhou, Xinchun Liu, Wenshou Wei, Caixia Cui, Minzhong Wang, Hongjun Li, Lianmei Yang, Hongsheng Zhang, Yuzhi Liu, Xinqian Zheng, Honglin Pan, Lili Jin, Han Zou, Libo Zhou, Yongqiang Liu, Jiantao Zhang, Lu Meng, Yu Wang, Xiaolin Qin, Yongjun Yao, Houyong Liu, Fumin Xue, and Wei Zheng

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The Desert Environment and Climate Observation Network (DECON) could promote collaborative research on desert dust-storms, boundary-layer and land-atmosphere interactions to better understand the status and role of the Taklimakan desert.

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Travis A. O’Brien, Ashley E. Payne, Christine A. Shields, Jonathan Rutz, Swen Brands, Christopher Castellano, Jiayi Chen, William Cleveland, Michael J. DeFlorio, Naomi Goldenson, Irina V. Gorodetskaya, Héctor Inda Díaz, Karthik Kashinath, Brian Kawzenuk, Sol Kim, Mikhail Krinitskiy, Juan M. Lora, Beth McClenny, Allison Michaelis, John P. O’Brien, Christina M. Patricola, Alexandre M. Ramos, Eric J. Shearer, Wen-Wen Tung, Paul A. Ullrich, Michael F. Wehner, Kevin Yang, Rudong Zhang, Zhenhai Zhang, and Yang Zhou
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David J. Stensrud, Nusrat Yussouf, Michael E. Baldwin, Jeffery T. McQueen, Jun Du, Binbin Zhou, Brad Ferrier, Geoffrey Manikin, F. Martin Ralph, James M. Wilczak, Allen B. White, Irina Djlalova, Jian-Wen Bao, Robert J. Zamora, Stanley G. Benjamin, Patricia A. Miller, Tracy Lorraine Smith, Tanya Smirnova, and Michael F. Barth

The New England High-Resolution Temperature Program seeks to improve the accuracy of summertime 2-m temperature and dewpoint temperature forecasts in the New England region through a collaborative effort between the research and operational components of the National Oceanic and Atmospheric Administration (NOAA). The four main components of this program are 1) improved surface and boundary layer observations for model initialization, 2) special observations for the assessment and improvement of model physical process parameterization schemes, 3) using model forecast ensemble data to improve upon the operational forecasts for near-surface variables, and 4) transfering knowledge gained to commercial weather services and end users. Since 2002 this program has enhanced surface temperature observations by adding 70 new automated Cooperative Observer Program (COOP) sites, identified and collected data from over 1000 non-NOAA mesonet sites, and deployed boundary layer profilers and other special instrumentation throughout the New England region to better observe the surface energy budget. Comparisons of these special datasets with numerical model forecasts indicate that near-surface temperature errors are strongly correlated to errors in the model-predicted radiation fields. The attenuation of solar radiation by aerosols is one potential source of the model radiation bias. However, even with these model errors, results from bias-corrected ensemble forecasts are more accurate than the operational model output statistics (MOS) forecasts for 2-m temperature and dewpoint temperature, while also providing reliable forecast probabilities. Discussions with commerical weather vendors and end users have emphasized the potential economic value of these probabilistic ensemble-generated forecasts.

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