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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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Camille Birman, Fatima Karbou, Jean-François Mahfouf, Matthieu Lafaysse, Yves Durand, Gérald Giraud, Laurent Mérindol, and Laura Hermozo

reanalysis outputs of numerical weather prediction (NWP) models with appropriate downscaling techniques to account for orography. Crochet (2007) , Crochet et al. (2007) , and Durand et al. (2009a , b) , for example, used the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis over the time period 1958–2002 to compute precipitation estimates over mountainous areas. Crochet (2007) and Crochet et al. (2007) have produced a 1-km-resolution precipitation analysis over Iceland

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Christoph Schär, Lyudmila Vasilina, Felix Pertziger, and Sébastien Dirren

operationally used to provide snow-cover maps ( Kobilov et al. 2001 ). While such techniques yield precise information on the spatial and temporal extent of the snow cover (e.g., Carroll et al. 1999 ), it appears difficult to derive sufficiently reliable quantitative estimates of snow water equivalent from satellite data in complex terrain. In the current study an attempt is undertaken to forecast summer runoff in central Asia based on winter and spring precipitation estimates from meteorological data

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Andreas Stohl and Paul James

forecast E − P estimates ( r 2 = 0.86; see Fig. 3b ) is in fact higher than the correlation between Eulerian and forecast E − P estimates ( r 2 = 0.80), perhaps pointing to a higher accuracy of the Lagrangian method. Figure 4 shows annual P from 24-h forecasts, the Lagrangian technique, and the GPCP observations. All three datasets show the same global patterns, but also differ quite significantly from each other. The forecasts underestimate P over the Sahara and overestimate P over

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Xiang Gao and Paul A. Dirmeyer

model repeatedly from a set of slightly different, equally viable initial conditions and averaging an ensemble of these forecasts. It has been shown that this technique has a beneficial impact on the skill of the forecasts by reducing noise in model predictions, and an ensemble mean, given a large enough sample, should always outperform individual members in predicting nonlinear systems ( Kharin et al. 2001 ). With the availability of climate predictions that are produced by multiple dynamical

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Jeffrey P. Walker, Garry R. Willgoose, and Jetse D. Kalma

1. Introduction An ability to retrieve the soil moisture profile by assimilation of near-surface soil moisture measurements (such as would be obtained from remote sensing) in a soil moisture model has received an increasing amount of attention over the past decade. Recent studies ( Houser et al. 1998 ; Walker et al. 2001 ) have suggested that statistical assimilation techniques such as the Kalman filter, through their ability to modify directly the soil moisture estimates of deeper soil layers

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Isidora Jankov, Lewis D. Grasso, Manajit Sengupta, Paul J. Neiman, Dusanka Zupanski, Milija Zupanski, Daniel Lindsey, Donald W. Hillger, Daniel L. Birkenheuer, Renate Brummer, and Huiling Yuan

1. Introduction Some of the recent activities at the Cooperative Institute for Research in the Atmosphere (CIRA) have been related to the development of synthetic satellite imagery ( Greenwald et al. 2002 ; Grasso and Greenwald 2004 ; Grasso et al. 2008 ). The motivation for this activity was to evaluate the performance of a numerical weather prediction model using synthetic satellite imagery. Synthetic imagery was produced from the European Center for Medium-Range Weather Forecasts (ECMWF

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D. I. F. Grimes, E. Coppola, M. Verdecchia, and G. Visconti

for rainfall amounts. However for hydrological forecasting, the time step needed even for large (>10 000 km 2 ) catchments is of the order of 1 day and for smaller catchments is correspondingly shorter. Daily estimates are also useful for some agricultural applications such as monitoring gaps in the growing season. Although a conventional rain gauge network gives rainfall observations at a daily time step, throughout much of the African continent the network is inadequate both in terms of spatial

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Ming Pan, Eric F. Wood, Dennis B. McLaughlin, Dara Entekhabi, and Lifeng Luo

1. Introduction Data assimilation is essentially to optimally merge information from different sources. As hydrology becomes increasingly data driven in the recent decades, data assimilation also emerges as a powerful tool for hydrologists to integrate a vast amount of information and make improved model forecasts and projections ( McLaughlin 2002 ). Many factors contribute to this trend, including the fast-growing capacity of numerical models to simulate hydrologic processes ( Mitchell et al

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Rongqian Yang, Michael Ek, and Jesse Meng

surface fluxes and to derive realistic land states to initialize climate models. Focusing on the land and atmosphere components of the earth system, there are a few atmospheric reanalyses developed over the past. Examples of these reanalyses include the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 ), the ECMWF interim reanalysis (ERA-Interim; Dee et al. 2011 ), the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA

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