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Nikolaos Mastrantonas, Biswa Bhattacharya, Yoshihiro Shibuo, Mohamed Rasmy, Gonzalo Espinoza-Dávalos, and Dimitri Solomatine

1. Introduction Precipitation is a major component of the global water cycle and the main forcing in hydrological processes. Its accurate estimation in space and time is of immense importance for decision-making and planning for a broad range of applications. Lately, due to the limited availability of adequate ground-based observations in many areas and the advances in remote sensing, there is an increasing interest in satellite precipitation products (SPPs). These products have near

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Ronald Stenz, Xiquan Dong, Baike Xi, and Robert J. Kuligowski

-R), SCaMPR, employs IR brightness temperature and microwave data to retrieve rainfall rates ( Kuligowski 2010 ). Numerous other real-time algorithms exist for retrieving rainfall rates from IR and microwave data, including the Climate Prediction Center morphing technique (CMORPH) ( Joyce et al. 2004 ); Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (GSMaP_MVK+; the plus sign refers to the version that utilizes rainfall estimates from the AMSU-B sensor in addition to PMW

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Kun Yang, Toshio Koike, Ichirow Kaihotsu, and Jun Qin

-averaged microwave data to obtain areal mean soil moisture, to save the computational time. a. Case 1: Sensitivity to forcing data sources LDAS-UT and the LSM are driven with AWS, GLDAS, and JMA data. Figure 10 shows the comparison of daily mean soil water content between the observations, LDAS-UT output, and the LSM output, corresponding to three sets of forcing. It shows a general worse tendency of soil moisture estimates for both LDAS-UT and the LSM, when the forcing data was changed from AWS and GLDAS to

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Huilin Gao, Eric F. Wood, Matthias Drusch, Wade Crow, and Thomas J. Jackson

the surface temperatures as part of the North American Land Data Assimilation System (NLDAS) model output. At the same time, the meteorology background during the experimental period was excellent for testing the retrieval over a large dynamic range of moisture conditions. LSMEM-retrieved soil moisture, validated by field observations at the three main field sites (CF, ER, and LW), have rms errors in volumetric soil moisture of 2.8%, 2.3%, and 1.8%, respectively. Compared to other microwave soil

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Chris Derksen, Arvids Silis, Matthew Sturm, Jon Holmgren, Glen E. Liston, Henry Huntington, and Daniel Solie

. Microwave scatterometer observations are well-suited for snowmelt detection (i.e., Kimball et al. 2004 ) but snow extent datasets derived largely from optical data have problems capturing rapid changes in the snow line during spring ( Wang et al. 2005 ; Brown et al. 2007 ), and algorithm development for snow water equivalent (SWE) estimates from passive microwave data lag behind other landscape regions ( Derksen et al. 2005 ) as a result of difficulties in signal interpretation. Given predicted and

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Andreas Colliander, Thomas J. Jackson, Aaron Berg, D. D. Bosch, Todd Caldwell, Steven Chan, Michael H. Cosh, C. Holifield Collins, Jose Martínez-Fernández, Heather McNairn, J. H. Prueger, P. J. Starks, Jeffrey P. Walker, and Simon H. Yueh

. J. Jackson , 2008 : Field observations of soil moisture variability across scales . Water Resour. Res. , 44 , W01423 , . Jackson , T. J. , 1993 : Measuring surface soil moisture using passive microwave remote sensing . Hydrol. Processes , 7 , 139 – 152 , . 10.1002/hyp.3360070205 Jackson , T. J. , and Coauthors , 2010 : Validation of advanced microwave scanning radiometer soil moisture products . IEEE

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Daniel Watters, Alessandro Battaglia, Kamil Mroz, and Frédéric Tridon

1. Introduction The Global Precipitation Measurement Core Observatory (GPM CO ) satellite, launched in February 2014, offers unprecedented spaceborne observations of the three-dimensional structure of precipitating systems ( Hou et al. 2014 ). The satellite detects rain rates in the range 0.2–110.0 mm h −1 and travels in a sun-asynchronous orbit, providing coverage between 68°N and 68°S, thus augmenting the 37°N/S coverage of the predecessor Tropical Rainfall Measuring Mission (TRMM

Open access
Jackson Tan, Walter A. Petersen, Gottfried Kirchengast, David C. Goodrich, and David B. Wolff

1. Introduction Global observation of precipitation relies on spaceborne instruments, but high quality estimates from passive microwave (PMW) sensors are only available on low-Earth-orbiting satellites, each of which has limited coverage. To obtain a complete global estimate of precipitation in a timely fashion, observations from different sensors have to be converted to surface precipitation through a well-calibrated, parametric (i.e., not sensor specific) algorithm and stitched together in a

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

-changing climate. Despite a long, albeit sparse, record [first known observations date back 2000 BCE ( Wang and Zhang 1988 )], globally complete precipitation measurements did not become available until the modern era of satellite Earth-observing systems that employ infrared and microwave radiometric techniques (e.g., Atlas and Thiele 1981 ). Achieving measurement standards of rainfall in atypical (i.e., extreme) environments on small spatiotemporal scales across the globe, however, has turned out to be more

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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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