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Ana M. B. Nunes and John O. Roads

. 1990 ), Puri and Miller ( Puri and Miller 1990 ), Puri and Davidson ( Puri and Davidson 1992 ), Aonashi ( Aonashi 1993 ), and Kasahara et al. ( Kasahara et al. 1994 ). Precipitation assimilation as part of a physical initialization (PI) procedure has also been proposed to reduce the analysis imbalance due mostly to misrepresentation of the moisture fields by the analyses. The PI procedure was first described by Krishnamurti et al. ( Krishnamurti et al. 1984 ), and subsequently in Krishnamurti et al

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Ronald M. Errico, Peter Bauer, and Jean-François Mahfouf

the presence of clouds and precipitation is sensitive to the characterization of these details. Since such details currently are neither analyzed nor modeled well, if at all, radiance observations suspected of being affected by them are often discarded. This includes perhaps half of all current satellite observations that could otherwise be considered for data assimilation. The discarded satellite data presumably contain useful information, not only about the standard dynamical and moisture fields

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Philippe Lopez

parameterizations of moist processes still rely on crude simplifying assumptions either for the sake of computational efficiency or because of uncertainties about individual processes, in particular microphysics and cloud-scale transport. In the past 10 yr, some progress has also been achieved in the assimilation of observations affected by clouds and precipitation in NWPMs with the aim of producing more realistic initial atmospheric states (or analyses ). Such measurements are already widely available with a

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Arthur Y. Hou and Sara Q. Zhang

sounders (e.g., AMSU, ATMS, MHS) (see list of acronyms in the appendix ), as well as precipitation rates and latent heating profiles derived from these measurements ( Simpson et al. 2000 ). In recent years, significant progress has been made in using these observations in data assimilation to improve atmospheric analyses and forecasts. Numerical weather prediction centers such as the NCEP, JMA, and ECMWF have begun using precipitation data or rain-affected microwave brightness temperatures in

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Rafael L. Bras, Alejandro N. Flores, and Jingfeng Wang

speed) that are physically consistent with the downscaled precipitation and required by many hydrological models. To this end, this paper attempts to use a physically based mesoscale weather forecasting model together with a variational data assimilation (DA) scheme for producing high-resolution hourly precipitation products with a spatial scale of less than 10 km in grid spacing. Data assimilation—a mathematical approach integrating observations into a dynamic model—is used to dynamically downscale

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Yudong Tian, Christa D. Peters-Lidard, Bhaskar J. Choudhury, and Matthew Garcia

ground-based estimates toward the low-intensity end of the spectrum. The “spectrum” difference shown in Fig. 11 has remarkable implications for land surface data assimilation, especially for surface runoff and flood modeling. Because the land surface runoff processes are strongly nonlinear ( Fekete et al. 2003 ), the shift in the distribution of precipitation intensity will cause significant differences in runoff production, partly because strong rainfall events are much more efficient in

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Soichiro Sugimoto, N. Andrew Crook, Juanzhen Sun, Qingnong Xiao, and Dale M. Barker

1. Introduction In recent years, there has been an increased demand for improved forecasting of severe convective weather and its associated hazards. Although the general skill of the numerical weather prediction is being steadily improved thanks to the increased model resolution and enhanced data assimilation systems, a very low skill for quantitative precipitation forecasts (QPFs) persists especially in the warm season ( Fritsch and Carbone 2004 ). One of the causes for the low QPF skill is

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Dusanka Zupanski, Sara Q. Zhang, Milija Zupanski, Arthur Y. Hou, and Samson H. Cheung

1. Introduction Hydrological forecasts for floods and landslides often require precipitation information at finer space and time scales than those available from spaceborne microwave observations. Statistical approaches have been used commonly to merge and downscale precipitation observations ( Huffman et al. 2007 ). There is an emerging interest in using data assimilation techniques to extract information from multiple data sources, combining with high-resolution modeling to downscale

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Philippe Lopez and Peter Bauer

1. Introduction During the last decade, the data assimilation community has attempted to find the optimal way to extract information from observations that are affected by clouds and/or precipitation, with the hope that this could help improve operational weather analyses and forecasts. A large amount of such observations is already available from various spaceborne platforms such as the Special Sensor Microwave Imager (SSM/I), Special Sensor Microwave Imager Sounder (SSM/IS), Tropical Rainfall

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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

into these numerical models. Although predictions of precipitation and soil moisture are intertwined ( Case et al. 2011 ; Jiménez et al. 2014 ; Feng and Houser 2015 ), modern weather data assimilation systems often do not include soil moisture as a control state variable ( Parrish and Derber 1992 ; Derber and Bouttier 1999 ; Barker et al. 2004 ; Wang et al. 2013 ). Therefore, the relative usefulness of assimilating satellite soil moisture observations into a coupled land–atmosphere model

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