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  • Microwave observations x
  • The Catchment-scale Hydrological Modeling & Data Assimilation (CAHMDA-V) x
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Gabriëlle J. M. De Lannoy, Rolf H. Reichle, and Valentijn R. N. Pauwels

1. Introduction Assimilating low-frequency (1–10 GHz) passive microwave observations into land surface models is expected to improve estimates of land surface conditions and, hence, weather and climate predictions. Global observations of brightness temperatures (Tb) are available from the (late) Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), the Soil Moisture Ocean Salinity (SMOS; Kerr et al. 2010 ) mission, and Aquarius ( Le Vine et al. 2007 ). Soil moisture has a

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Haolu Shang, Li Jia, and Massimo Menenti

-storage capacity of the whole region. The surface runoff could be estimated based on that. In this paper, we retrieved the fractional area of water-saturated soil (WSS) and standing water from the polarization difference brightness temperature (PDBT) at 37 GHz in order to study the inundation pattern of large floodplains by satellite microwave observations. The PDBT at 37 GHz is determined by the land surface temperature, the soil’s polarized effective emissivity difference (PEED), and vegetation transmission

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Mustafa Gokmen, Zoltan Vekerdy, Maciek W. Lubczynski, Joris Timmermans, Okke Batelaan, and Wouter Verhoef

of many recent research efforts ( McCabe et al. 2008 ) because of their potential to provide spatially continuous and temporally recurrent estimates over regional to global scales ( Alsdorf and Lettenmaier 2003 ). Precipitation is regularly retrieved from multisensor microwave and infrared data using a variety of techniques (e.g., Joyce et al. 2004 ). One of the recent datasets is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), which is designed to

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Gift Dumedah and Jeffrey P. Walker

1. Introduction Data assimilation (DA) methods are used widely for finding a compromise between imperfect observations and uncertain model outputs. Generally, the DA procedure encompasses three major components: (i) accurate estimation of the model state, (ii) determination of the measurement/observation errors, and (iii) estimation of model parameter values. The model parameter estimation has a direct impact on the simulated outputs and thus significantly influences the forward model runs

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