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Tongtiegang Zhao, James C. Bennett, Q. J. Wang, Andrew Schepen, Andrew W. Wood, David E. Robertson, and Maria-Helena Ramos

1. Introduction Ensemble forecasts of seasonal precipitation from coupled ocean–atmosphere general circulation models (GCMs) have mostly replaced traditional statistical forecasts as the basis of operational outlooks issued by many national weather services. For example, the National Centers for Environmental Prediction (NCEP) in the United States has operated its Climate Forecast System (CFS) since 2004 ( Saha et al. 2014 ), the European Centre for Medium-Range Weather Forecasts (ECMWF) has

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Warren J. Tennant, Zoltan Toth, and Kevin J. Rae

1. Introduction Weather forecasts have potential use at a variety of space and time scales. As a public weather forecast service, the South African Weather Service (SAWS) is tasked to provide a comprehensive forecast service from a few hours ahead through all scales up to several seasons ahead. The medium range (3–14 days) is particularly popular through a number of sectors and thus considerable effort has been invested in improving forecasts for this time scale. To this end the National

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Jianguo Liu and Zhenghui Xie

1. Introduction Rainfall is one of the most important weather phenomena, and improvement of quantitative precipitation forecasts (QPFs) is a primary goal of operational prediction centers and a major challenge facing the research community ( Fritsch et al. 1998 ; Gourley and Vieux 2005 ). To understand the limits of deterministic prediction of the atmospheric state by setting initial state conditions, ensemble forecasting methods have been developed to improve the capabilities of QPFs and

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N. Vigaud, A. W. Robertson, and M. K. Tippett

1. Introduction Predictions on subseasonal time scales, between medium-range weather (up to 2 weeks) and seasonal climate (from 3 to 6 months) forecasts, have recently received increasing interest owing to modeling advances ( Vitart 2014 ) and a better understanding of climate phenomena on these time scales, particularly the MJO ( Zhang 2013 ). Sources of predictability at subseasonal time scales include the inertia of sea surface temperature (SST) anomalies, the MJO ( Waliser et al. 2003

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Paul J. Roebber, Melissa R. Butt, Sarah J. Reinke, and Thomas J. Grafenauer

1. Introduction Recently, there have been attempts to provide improved guidance to forecasters concerning forecasts of snowfall ( Roebber et al. 2003 ; Dubé 2006 ; Cobb and Waldstreicher 2005 ; Baxter et al. 2005 ; Ware et al. 2006 ). Roebber et al. (2003) conducted a principal component analysis of radiosonde and surface data and identified seven factors that influence the diagnosis of snow ratio: solar radiation per month, low- to midlevel temperature, mid- to upper-level temperature

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Kyong-Hwan Seo, Wanqiu Wang, Jon Gottschalck, Qin Zhang, Jae-Kyung E. Schemm, Wayne R. Higgins, and Arun Kumar

al. 2003 ). Especially, a general circulation model (GCM) simulation with prognostic SST anomalies by Waliser et al. (1999) confirms that the frictional wave–conditional instability of the second kind (CISK) process is operative on the equator as the maintenance and propagation mechanisms of the MJO. Compared to the worldwide influence of the MJO on local weather and climate, only limited success has been realized in skillfully forecasting the oscillation evolution, especially using GCMs

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Carolyn A. Reynolds, James D. Doyle, F. Martin Ralph, and Reuben Demirdjian

in a drought state at the end of the water year (and no area of California was in an exceptional drought state). Much of the precipitation occurred during January and the first three weeks of February 2017. The storms during these two months had enormous hydrological impacts, including mudslides and widespread flooding, and were often accompanied by high winds. A description of these storms and their hydrological impacts is given in detail by the NOAA California Nevada River Forecast Center. 2

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Tom H. Durrant, Frank Woodcock, and Diana J. M. Greenslade

the model grid. Interpolation of this output to specific locations may result in systematic biases due to unresolved local effects ( Engel and Ebert 2007 ). Postprocessing techniques aim to reduce these systematic biases. The widely used model output statistics (MOS), for example, uses multiple linear regression based on model output and previous observations to provide improved forecasts at specific locations ( Glahn and Lowry 1972 ). A major drawback to MOS is the long training dataset required

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Grace Zalenski, Witold F. Krajewski, Felipe Quintero, Pedro Restrepo, and Steve Buan

1. Introduction The National Weather Service (NWS) has the mandate of providing streamflow forecast services for the United States. To meet its function, it relies on observations from the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers, and a number of federal, state, and tribal partners that supply streamflow, snow, temperature, and other variables used in the forecast process. Hydrologic forecasts produced by the NWS in real time, specifically river streamflow forecasts, are

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Eugene W. McCaul Jr., Georgios Priftis, Jonathan L. Case, Themis Chronis, Patrick N. Gatlin, Steven J. Goodman, and Fanyou Kong

of these concerns, interest remains high in having reliable forecasts of the amounts of total lightning expected from storms. Early approaches to forecasting lightning were based on analytical studies relating storm lightning rates to storm electrical power and, often, to storm cloud top height ( Price and Rind 1992 ). These studies were consistent with earlier speculations ( Vonnegut 1963 ) and were echoed by later observational research ( Williams 1985 ). Later, other investigators based

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