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Andrew C. Martin, F. Martin Ralph, Anna Wilson, Laurel DeHaan, and Brian Kawzenuk

weather forecasts of ARs has been documented, primarily at the synoptic scales where ARs are a recognizable feature of the extratropical circulation ( Wick et al. 2013 ; Nayak et al. 2014 ; Guan and Waliser 2015 ; DeFlorio et al. 2018 ). Mesoscale phenomena also create challenges when forecasting AR impacts, including precipitation ( Leung and Qian 2009 ; Neiman et al. 2009 ; Ralph et al. 2013 ; Martin et al. 2018 ). One such mesoscale phenomena is the mesoscale frontal wave ( Parker 1998

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A. Amengual, D. S. Carrió, G. Ravazzani, and V. Homar

and Kalnay 1993 ; Mullen and Baumhefner 1988 ; Houtekamer and Derome 1995 ; Du et al. 1997 ). Indeed, errors of any origin can grow rapidly during the quantitative precipitation forecasting and steer toward misleading predictions, especially when fast-growing modes, such as those leading mesoscale convective developments, are dominant for the predicted field. Therefore, QPF is highly sensitive to errors in the initial conditions (ICs), lateral boundary conditions (LBCs), and model physical

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Hector Macian-Sorribes, Ilias Pechlivanidis, Louise Crochemore, and Manuel Pulido-Velazquez

1. Introduction Predicting the hydrological response in a river basin over the coming seasons can be of significant added value for water resources management ( Contreras et al. 2020 ; Lavers et al. 2020 ). Recent investigations have demonstrated the benefits from the use of seasonal streamflow forecasting services at large (i.e., continental) and regional scales ( Crochemore et al. 2020 ; Y. Li et al. 2017 ; Pechlivanidis et al. 2020 ; Wanders et al. 2019 ). Statistical streamflow

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Daniel E. Comarazamy and Jorge E. González

mesoscale atmospheric models (e.g., Kao and Bossert 1992 ; Pielke et al. 1999 ). A first step toward our ultimate goal of studying and understanding the regional–local impact of climate and environmental change over the Caribbean is to investigate the ability of a mesoscale atmospheric model to reproduce the spatial and temporal pattern of different atmospheric variables at the appropriate resolution in the region of interest. The work presented in this paper focuses on the objective of assessing the

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Syewoon Hwang, Wendy Graham, José L. Hernández, Chris Martinez, James W. Jones, and Alison Adams

regional climate models . J. Geophys. Res. , 111 , D06105 , doi:10.1029/2005JD005965 . Gaudet, B. , and Cotton W. R. , 1998 : Statistical characteristics of a real-time precipitation forecasting model . Wea. Forecasting , 13 , 966 – 982 . Goovaerts, P. , 1997 : Geostatistics for Natural Resources Evaluation . Oxford University Press, 496 pp . Grell, G. A. , Dudhia J. , and Stauffer D. R. , 1994 : A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) . NCAR

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F. M. Ralph, T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger

forecasts and development of regional extreme event thresholds using data from HMT-2006 and COOP observers . J. Hydrometeor. , 11 , 1288 – 1306 . Ralph, F. M. , Neiman P. J. , Kiladis G. N. , Weickman K. , and Reynolds D. W. , 2011 : A multi-scale observational case study of a Pacific atmospheric river exhibiting tropical–extratropical connections and a mesoscale frontal wave . Mon. Wea. Rev. , 139 , 1169 – 1189 . Smith, B. L. , Yuter S. E. , Neiman P. J. , and Kingsmill D. E

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Joseph A. Santanello Jr., Sujay V. Kumar, Christa D. Peters-Lidard, Ken Harrison, and Shujia Zhou

includes explicit treatment of entrainment and counter gradient fluxes. The LSM employed in LIS for this study is the Noah LSM version 3.2 ( Ek et al. 2003 ), and it is identical to the version of Noah packaged in the community version of the WRF-ARW version 3.2 release. Noah is used operationally by the National Centers for Environmental Prediction (NCEP) as the LSM for the North American Mesoscale Model (NAM) and the Global Forecasting System (GFS). As such, Noah is a well-supported, developed, and

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Raju Attada, Hari Prasad Dasari, Ravi Kumar Kunchala, Sabique Langodan, Kondapalli Niranjan Kumar, Omar Knio, and Ibrahim Hoteit

with respect to different CPSs has yet to be studied using a high-resolution model. Several studies have investigated the sensitivity of rainfall simulations to CPSs in various regions. For instance, some studies highlighted the importance of choosing a suitable combination of parameterization schemes within the Weather Research and Forecasting (WRF) Model to simulate the rainfall features over the Indian region ( Mukhopadhyay et al. 2010 ; Srinivas et al. 2013 ; Ratnam et al. 2017 ). Similar

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Xiaohang Wen, Shihua Lu, and Jiming Jin

properties. In this study, land use type data were obtained from EOS MODIS data and fractional vegetation coverage data were estimated by using MODIS level 1 B data according to Carlson and Ripley (1997) . This paper focuses on the application of land surface parameters estimated from MODIS data in the mesoscale model. c. Model and experiment The WRF is a next-generation mesoscale forecast model and data-assimilation system that will advance both the understanding and prediction of mesoscale weather and

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James D. Brown, Dong-Jun Seo, and Jun Du

implemented at the NCEP in May 2001 and initially comprised 10 ensemble members from the Eta and Regional Spectral Model (RSM) models ( Du and Tracton 2001 ). Subsequent updates have increased the physics diversity by adding members from the Weather Research and Forecasting (WRF) models [Nonhydrostatic Mesoscale Model (NMM) and Advanced Research WRF (ARW) cores], which has increased membership from 10 to 21 ensemble members. The SREF has been verified for specific variables, years, model cores, and

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