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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

infrared (IR) images from low-Earth-orbiting (LEO) or geostationary (GEO) satellites provide regular observations of clouds from which estimates of precipitation may be generated. However, although precipitation originates from clouds, not all clouds produce precipitation. More importantly, the relationship between the cloud-top properties and the precipitation reaching the surface is indirect. Passive microwave (PM) radiometers allow a more direct measure of precipitation to be made since these

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F. Joseph Turk, R. Sikhakolli, P. Kirstetter, and S. L. Durden

1. Introduction The joint National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) Global Precipitation Mission (GPM) core satellite was successfully launched on 28 February 2014. GPM is a constellation mission, whereby the observations and precipitation profile estimates from the core satellite dual-frequency (Ku and Ka band) precipitation radar (DPR) and 13-channel (10–183 GHz) passive microwave (PMW) GPM imager (GMI) act as a reference for the other

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Abebe Sine Gebregiorgis, Pierre-Emmanuel Kirstetter, Yang E. Hong, Nicholas J. Carr, Jonathan J. Gourley, Walt Petersen, and Yaoyao Zheng

/IR data processing first discriminates the brightness of the cloud in the visible spectrum and/or the low temperature of the cloud top as seen in the thermal spectrum ( Arkin and Meisner 1987 ). Then it evaluates further criteria such as cloud area extent, time history or evolutionary information, and textural features to correlate with the rainfall estimates ( Adler and Negri 1988 ). Microwave instruments can provide observations of cloud and precipitation properties, beyond simple cloud

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Ali Behrangi, Bin Guan, Paul J. Neiman, Mathias Schreier, and Bjorn Lambrigtsen

greater than 20 mm. These AR criteria were established by Ralph et al. (2004) based on dropsonde observations of ARs and were later used by Neiman et al. (2008) to create an inventory of landfalling ARs on the west coast of North America between WYs 1998 and 2013. The inventory is based on examining twice-daily (ascending and descending passes) composite satellite images of IWV as observed by the Special Sensor Microwave Imager (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS

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Toshi Matsui, Jiun-Dar Chern, Wei-Kuo Tao, Stephen Lang, Masaki Satoh, Tempei Hashino, and Takuji Kubota

methods are described in previous studies ( Matsui et al. 2009 , 2014 ). Briefly, the effective dialectic function is calculated from the Maxwell–Garnett assumption, and single scattering properties are calculated from the Mie assumption for both microwave and radar simulators. The microwave simulator can account for slant-path beams, mimicking the 3D slant-path observations in conical-scanning microwave radiometers, such as the TMI. These forward models are consistent with the physics assumptions

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Mark S. Kulie, Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer

is not prevalent, greatly increased the number of snowfall observations contained in the CloudSat snowfall dataset and thus reflected an ubiquitous shallow snowfall mode in many oceanic regions. Certain continental regions were also sensitive to these tests, especially over the interior of Russia, thus suggesting frequent shallow snowfall events over land. Finally, Wang et al. (2013) used a multiyear coincident CloudSat and passive microwave radiometer dataset to study cloud liquid water in

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E. Cattani, A. Merino, and V. Levizzani

Meteorology Using Satellite and Ground-Based Observations (TAMSAT) African Rainfall Climatology and Time series (TARCAT). They are representative of different methodologies, either with global coverage or tuned over Africa (TAMSAT and RFE). Two global gauge products from the Global Precipitation Climatology Centre (GPCC) are used as reference data for the analysis of the SPEs: 1) the GPCC Climatology, version 2011 (GPCC_Clim), at 0.25° and 0.5° ( Meyer-Christoffer et al. 2011a , b ; Schneider et al. 2014

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Yumeng Tao, Xiaogang Gao, Kuolin Hsu, Soroosh Sorooshian, and Alexander Ihler

information collected from multiple wavelengths. Common choices include visible (VIS) and infrared (IR) wavelength images of cloud albedo and cloud-top brightness temperature from geosynchronous satellites ( Hsu et al. 1997 ; Nasrollahi et al. 2013 ). Another popular data source is passive microwave (PMW) images from sensors on board low-Earth-orbiting (LEO) satellites. PMW images provide information about the atmospheric constituents and hydrometeorological profiles, which is more directly related to

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Clément Guilloteau, Rémy Roca, and Marielle Gosset

from passive satelliteborne sensors is far from trivial. Microwave and infrared brightness temperatures measured by the sensors cannot be unambiguously associated with a unique hydrometeor profile ( Stephens and Kummerow 2007 ). Spatiotemporal variability of accumulated rain depth is partially driven by the rain/no rain variability. For a given period and over a given area, the cumulated rain depth is the product of precipitation fraction (i.e., fraction of space and time that is precipitating) and

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Yiwen Mei, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Marco Borga

1. Introduction Integration of satellite precipitation products with hydrologic models constitutes a potential solution for simulating hydrological processes at global scale. Such an approach is particularly important for mountainous areas where spatial coverage of precipitation observations is limited because of the generally low number of in situ sensors and the blockage of ground remote sensors due to complex terrain. In this context, satellite sensors offer unique advantages relative to

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