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Daniel P. Tyndall and John D. Horel

1. Introduction Mesoscale surface observations are vital data sources for applications in many different meteorological subfields, including operational forecasting, wind power management, transportation safety, wildfire management, dispersion modeling, and defense applications ( Dabberdt et al. 2005 ; Horel and Colman 2005 ). Two recent reports ( National Academy of Sciences 2009 , 2010 ) recommend that existing and future mesoscale observations be integrated into a network of networks. The

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Ryan Eastman and Stephen G. Warren

1. Introduction Arctic climate change has been among the most substantial of anywhere on earth in the past two decades. A companion paper, Eastman and Warren (2010 , hereafter EW10) has shown that cloud changes derived from surface observations (SURF) appear to be enhancing the warming seen in the Arctic. EW10 show an increasing trend in Arctic total cloud cover and a positive correlation between total cloud cover and surface air temperature in autumn, winter, and spring. Kay and

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Darren L. Jackson and Gary A. Wick

sensing methods because of limitations in direct retrieval of near-surface parameters, so these inputs have typically been acquired from ship and buoy observations. However, unlike ship and buoy observations, satellite observations can provide global coverage on a daily time scale, thus providing the potential for daily global heat flux products. Accurate global observations of air temperature (Ta) are considered essential for more accurate retrieval of sensible heat flux at the ocean surface ( Curry

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José Roberto Rozante, Demerval Soares Moreira, Luis Gustavo G. de Goncalves, and Daniel A. Vila

1. Introduction Operational climate and weather forecast centers routinely evaluate numerical models at regularly spaced grid points. Generally, surface observations are considered “the truth” in such model validations. However, in most cases observations and numerical model output are presented at distinct spatial and temporal scales. Furthermore, surface observations are irregularly spatially distributed and represent environmental characteristics only at a single point and its nearby

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Yves Quilfen, Bertrand Chapron, and Jean Tournadre

Remote Sensing (ERS), the Adaptive Domain Environment for Operating Systems (ADEOS), the Quick Scatterometer (QuikSCAT), and the Meteorological Operational (MetOp) satellites or synthetic aperture radars (SAR) on board the Environmental Satellite ( Envisat ) and Radarsat satellites, synoptic observations of surface wind and atmospheric water content generally reveal storm structures with impressive detail. However, most microwave sensors suffer severe limitations when attempting to retrieve the

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Joshua P. Hacker and Dorita Rostkier-Edelstein

PBL analyses of stability and mixing depth. Yet deficiencies in NWP models and ineffective use of near-surface observations persist, leading to PBL analyses of dubious quality. Several difficulties prevent the optimal use of surface (shelter and anemometer height) observations in modern data assimilation systems. First, transient coupling with the earth’s surface and the free atmosphere produce intermittent, anisotropic, and nonstationary correlations of the observations with the model background

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Luke E. Madaus and Gregory J. Hakim

case to case. However, Sieglaff et al. (2011) suggests that assimilating satellite-based cloud-top cooling estimates may be useful for CI forecasting. Surface observations are one platform whose relevance for CI forecasting remains underexplored. Surface observation networks designed for synoptic-scale forecasting are ill-suited for constraining short-term, convective-scale forecasts ( Sun et al. 2014 ). However, recent studies suggest that new surface observing platforms including crowdsourced

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Qiuhong Tang, Huilin Gao, Pat Yeh, Taikan Oki, Fengge Su, and Dennis P. Lettenmaier

1. Introduction Terrestrial water storage (TWS) is the water stored on and below the land surface, which includes snow, ice, soil moisture, groundwater, and surface water. It is a fundamental component of the water cycle ( Oki and Kanae 2006 ). However, surface measurements of TWS are essentially nonexistent over large areas. Typical methods to estimate the TWS at basin scales include in situ observations, hydrological modeling, coupled atmospheric and terrestrial water balance, and remote

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Virendra P. Ghate, Bruce A. Albrecht, Christopher W. Fairall, and Robert A. Weller

(CGCMs) (e.g., Kiehl and Gent 2004 ; Wittenberg et al. 2006 ; Tiexeria et al. 2008; and others). Attempts have been made to develop MABL cloud parameterizations that yield higher Scu cloud cover than that in some current models (e.g., Bachiochi and Krishnamurti 2000 ). There have been many observational studies of marine Scu using satellite ( Minnis and Harrison 1984 ; Klein and Hartmann 1993 ; Rozendaal et al. 1995 ) and surface-based observations ( Norris 1998a , b ; Cronin et al. 2006

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Scott N. Williamson, David S. Hik, John A. Gamon, Jeffrey L. Kavanaugh, and Saewan Koh

the identification of cloud contamination, including using meteorological stations in a coordinated approach to produce validated satellite data, should be employed. Further work is required to identify the type and degree of cloud contamination in MODIS land surface temperature. A reevaluation of the MODIS cloud mask could be completed using the technique outlined in this paper in conjunction with more precise observations, such as lidar, to further refine spectral thresholds for cloud

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