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Alexandros A. Ntelekos, Konstantine P. Georgakakos, and Witold F. Krajewski

stochastic hydrometeorological model for flood and flash-flood forecasting. 1. Formulation. Water Resour. Res. , 22 , 2083 – 2095 . 10.1029/WR022i013p02083 Georgakakos, K. P. , 1992 : Advances in forecasting flash floods. Proc. Joint Seminar on Prediction and Damage Mitigation of Meteorologically Induced Natural Disasters , Taipei, Taiwan, National Science Council of Taiwan, 280–291 . Georgakakos, K. P. , and Hudlow M. D. , 1984 : Quantitative precipitation forecasts techniques for use in

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Jessica D. Lundquist, Paul J. Neiman, Brooks Martner, Allen B. White, Daniel J. Gottas, and F. Martin Ralph

erosion associated with warm rain events has been repeatedly stressed ( Brunengo 1990 ; Ffolliott and Brooks 1983 ; Hall and Hannaford 1983 ; Harr 1986 ; Kattelmann 1997 ; Marks et al. 1998 ; McCabe et al. 2007 ; Zuzel et al. 1983 ), hydrologic forecasting centers still struggle to predict the elevation at which snow turns to rain in mountain watersheds (E. Strem, California–Nevada River Forecast Center, 2006, personal communication). In the West Coast mountains, particularly warm storms result

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Hui Wang, A. Sankarasubramanian, and Ranji S. Ranjithan

parameterization of weather forecasting models, 2) improved estimates of initial conditions through better data assimilation, and 3) recalibration of predicted variables using model output statistics to reduce marginal bias and conditional bias in predictions. Over the past several decades, continued efforts to improve parameterization of numerical weather models has resulted in better weather forecasting skill ( Kalnay 2003 ; Gneiting and Raftery 2005 ). Data assimilation based on Kalman filter techniques

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Jin-Ho Yoon, Kingtse Mo, and Eric F. Wood

( Schaake et al. 2007 ; Wood and Schaake 2008 ). It is referred to here as the Schaake method. The fourth method is the Bayesian merging technique using all ensemble members ( Luo et al. 2007 ; Luo and Wood 2008 ; Coelho et al. 2004 ). Because different downscaling methods produce skillful forecasts at different spatial locations, the multimethod ensemble forecasts defined as the equally weighted mean of downscaled forecasts from the above methods may have higher skill than a single method. There is

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Enrico Zorzetto and Laifang Li

structure of the models above, the posterior probability distribution of the quantities of interest is not available analytically. Therefore, we approximate numerically the posterior distribution using the Hamiltonian Monte Carlo technique using the Stan language ( Carpenter et al. 2017 ). For each model and each site, we run four parallel Markov chain Monte Carlo (MCMC) chains with 2000 iterations for each chain, and discard the first half for each chain to account for the burn-in period. Therefore, we

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Babak Alizadeh, Reza Ahmad Limon, Dong-Jun Seo, Haksu Lee, and James Brown

. They concluded that the techniques have similar overall performance, and that the regression and dressing methods perform better in terms of resolution and reliability, respectively. Mendoza et al. (2016) used medium-range ensemble streamflow forecasts from the System for Hydrometeorological Applications, Research and Prediction, and compared quantile mapping ( Mendoza et al. 2016 ; Hashino et al. 2006 ; Piani et al. 2010 ; Regonda and Seo 2008 ; Wood and Schaake 2008 ; Zhu and Luo 2015

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Di Tian, Christopher J. Martinez, and Wendy D. Graham

2008 ) and Bayesian merging techniques ( Coelho et al. 2004 ; Luo and Wood 2008 ; Luo et al. 2007 ) that have also been used in downscaling seasonal climate forecasts. While the natural analog and constructed analog downscaling methods have shown good performance ( Abatzoglou and Brown 2012 ; Hidalgo et al. 2008 ; Maurer and Hidalgo 2008 ; Maurer et al. 2010 ; Tian and Martinez 2012a , 2012b ), the seasonal reforecast datasets are generally not long enough to perform those analog

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Kuai Fang and Chaopeng Shen

we use DA to improve soil moisture forecasts. Because DA works through the lens of a dynamical system model (most often a process-based model), its effects critically depend on the structures of the LSM and a number of delicate techniques and user choices, for example, assimilation frequency, variables to be updated, and data preprocessing. For example, DA requires the observation to be unbiased with respect to the model. However, for soil moisture, satellite observation and LSM simulations often

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Francesco Silvestro, Nicola Rebora, and Luca Ferraris

times, which are often much shorter than what is necessary for starting up the “machine of civil protection” and its procedures. To overcome this problem, it is a common practice to resort to the use of numerical precipitation predictions issued by meteorological models as input for hydrological response models (e.g., Lin et al. 2002 ; Bacchi et al. 2002 ; Bartholomes and Todini 2005 ). Various works demonstrate that it is not possible to tackle the hydrological forecasting problem in a

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R. D. Koster and G. K. Walker

difference vegetation index (NDVI) through established techniques (e.g., Sellers et al. 1996 ), can also theoretically be assimilated, given the existence of FPAR-related variables in DVMs. A substantial amount of satellite-based soil moisture and vegetation data does indeed exist (or will soon exist) but remains currently untapped for the subseasonal forecasting problem. We close with a final note about the role of dynamic phenology in forecast systems. We have been careful not to imply that the

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