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C. W. Fairall, A. B. White, J. B. Edson, and J. E. Hare

given by where c dn , c Tn , and c qn are the transfer coefficients for velocity, temperature, and moisture; the subscript n refers to the neutral value ( ξ = 0), and the transfer coefficients for stress and sensible and latent heat are denoted by C x . Here X s is the mean variable value at the surface, and X r the value at some reference height z r . Note that in this convention X = U 1 , U 2 are the horizontal wind components relative to the fixed earth, and S is the

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Yun Zhang, Zuhang Wu, Lifeng Zhang, Yanqiong Xie, Yanbin Huang, and Hepeng Zheng

an character of “maritime-like,” which could be related to the abundant moisture transported from tropical ocean during EASM. Figure 10 shows average horizontal moisture flux at 925-hPa calculated from reanalysis data during the EASM season. Moisture fluxes from the tropical Indian Ocean and North Pacific Ocean meet and turn northward around the South China Sea (SCS), then converge into the EASM rainband to sustain copious precipitation. Interestingly, it is noted that the D m ( N w ) is even

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Raquel Niclòs, Vicente Caselles, César Coll, Enric Valor, and Eva Rubio

autonomous system. The measurements took place during the 2000 and 2001 Wind and Salinity Experiment (WISE) campaigns conducted in the framework of the Soil Moisture and Ocean Salinity (SMOS) mission, sponsored by the European Space Agency (ESA). The SMOS request in salinity accuracy is ±0.1 psu (1 psu = 1 g of salt in 1 kg of seawater), which is derived from the Global Ocean Data Assimilation Experiment (GODAE) requirements for global ocean-circulation studies ( Kerr et al. 2001 ; Berger et al. 2002

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Nadya T. Vinogradova and Rui M. Ponte

1. Introduction An important component of the calibration and validation effort of any satellite mission is the comparison between remotely sensed and in situ measurements. For the recently launched Aquarius/Satelite de Aplicaciones Cientificas-D ( SAC-D ; Lagerloef et al. 2008 ; Lagerloef 2012 ) and Soil Moisture and Ocean Salinity ( SMOS ; Font et al. 2010 ) satellite missions dedicated to measuring sea surface salinity (SSS), comparison between retrieved and in situ observations is

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Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, and Lars Nerger

sparsely gridded sea ice thickness observations, there are very few studies with ice thickness assimilation. Lisæter et al. (2007) examined the potential for ice thickness assimilation in coupled sea ice–ocean models with an ensemble Kalman filter (EnKF). Yang et al. (2014) assimilated the first near-real-time European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) satellite–based sea ice thickness data into a coupled sea ice–ocean model using a local ensemble-based singular evolutive

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Hengqian Yan, Ren Zhang, Gongjie Wang, Huizan Wang, Jian Chen, and Senliang Bao

1. Introduction Satellite observations have become essential tools to depict phenomena and features in the ocean. Almost all kinds of sea surface fields, including sea surface temperature (SST), sea surface height (SSH), sea surface ice, and chlorophyll concentration, have been mapped by the satellite platforms for a long time. Nevertheless, remote sensing of sea surface salinity (SSS) was not realized until the launch of Soil Moisture Ocean Salinity (SMOS) mission in 2009 ( Font et al. 2010

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Claire Henocq, Jacqueline Boutin, Gilles Reverdin, François Petitcolin, Sabine Arnault, and Philippe Lattes

detection of SSS, will be launched in the next two years. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite mission will derive SSS on individual pixels with a spatial resolution at ground level of about 40 km and global surface ocean coverage of between 3 and 7 days ( Barre et al. 2008 ). The accuracy expected on a single observation goes from 0.5 at the center of the swath to 1.7 practical salinity scale (pss) of 1978 at the edge on SSS, at 35 pss and 15°C ( Zine et al

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Dean Vickers and L. Mahrt

1. Introduction Our purpose is twofold: to (i) study the cospectral gap that separates turbulent and mesoscale contributions to the calculated fluxes of heat, moisture, and momentum; and (ii) present a description of multiresolution decomposition including a simple example. Multiresolution analysis applied to time series decomposes the record into averages on different time scales and represents the simplest possible orthogonal decomposition. Multiresolution (MR) spectra yield information on

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M. Adam, B. B. Demoz, D. D. Venable, E. Joseph, R. Connell, D. N. Whiteman, A. Gambacorta, J. Wei, M. W. Shephard, L. M. Miloshevich, C. D. Barnet, R. L. Herman, and J. Fitzgibbon

1. Introduction Water vapor is an important constituent of the atmosphere. The vertical distribution of moisture is important in determining atmospheric stability. Water vapor is also the most radiatively active atmospheric trace gas in the infrared ( Ramanathan 1988 ) and thus could produce strong forcing from feedback associated with anthropogenically driven climate change ( Cess et al. 1990 ). In addition, there is significant variability in the distribution of water vapor on temporal and

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M. Talone, C. Gabarró, A. Camps, R. Sabia, J. Gourrion, M. Vall-llossera, and J. Font

1. Introduction a. The SMOS mission In May 1999 the European Space Agency (ESA) approved the Soil Moisture and Ocean Salinity (SMOS) Mission as the second of its Living Planet Programme Earth Explorer Opportunity Missions to provide global and frequent soil moisture and sea surface salinity (SSS) maps. SMOS was launched on 2 November 2009, and after the first calibration and checkout period (the so-called “commissioning phase”), SSS level 3 products will be distributed; the expected accuracy is

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