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Michael K. Tippett, Timothy DelSole, and Anthony G. Barnston

1. Introduction Linear regression has long played an important role in weather and climate forecasting, both in empirical prediction models and statistical postprocessing of physics-based prediction model output (e.g., Glahn and Lowry 1972 ; Penland and Magorian 1993 ). Here we focus on the use of regression to correct and calibrate climate forecasts, although our findings are generally applicable to all regression-based forecasts. A regression between past model output and observations

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Wansuo Duan and Zhenhua Huo

1. Introduction A forecast is an estimate of the future state of the atmosphere or ocean. Forecasts are generally conducted by estimating the current states using observations and then investigating how these states evolve using numerical models. Because of the effect of instability and related nonlinearity, very small errors in initial states can be nonlinearly amplified and lead to large errors in the forecast results ( Lorenz 1963 ). Because we cannot observe every detail of the atmospheric

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Yan Guo, Jianping Li, and Yun Li

1. Introduction As one of the particular focuses of the World Climate Research Programme’s Climate Variability and Predictability (CLIVAR) project, seasonal forecasting is of great significance. Although state-of-the-art climate models have been improved significantly and have been verified to be useful tools for seasonal forecasting, their forecast skill for precipitation, especially for Asian summer monsoon precipitation, remains limited ( Wu et al. 2009 ; Lee et al. 2011 ). North China

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Eric Gilleland, David Ahijevych, Barbara G. Brown, Barbara Casati, and Elizabeth E. Ebert

1. Introduction Small-scale variability in high-resolution weather forecasts presents a challenging problem for verifying forecast performance. Traditional verification scores provide incomplete information about the quality of a forecast because they only make comparisons on a point-to-point basis with no regard to spatial information [ Baldwin and Kain (2006) ; Casati et al. (2008) ; see Wilks (2005) and Jolliffe and Stephenson (2003) for more on traditional verification scores]. For

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Joao Gari da Silva Fonseca Jr., Fumichika Uno, Hideaki Ohtake, Takashi Oozeki, and Kazuhiko Ogimoto

1. Introduction The worldwide dissemination of photovoltaic (PV) power systems in the current decade has been remarkable. Just between 2010 and 2017, the worldwide installed capacity of PV power increased from 40 to 403 GW ( International Energy Agency 2018 ). Such growth, associated with the intrinsic weather-dependent variability typical of PV power generation, has caused a strong demand for improvements in day-ahead forecasting of solar irradiation. Day-ahead forecasts of solar irradiation

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John A. Knaff, Charles R. Sampson, and Galina Chirokova

1. Introduction The estimation and forecast of surface winds associated with tropical cyclones (TCs) is important to a variety of stakeholders and applications. Important stakeholders include state and local governments, private industry, and the U.S. military. Key applications include wind-based risks and impacts, and wave and surge forecasting. The National Hurricane Center (NHC), the Central Pacific Hurricane Center (CPHC), and the Joint Typhoon Warning Center (JTWC) provide information

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Ian T. Jolliffe and David B. Stephenson

1. Introduction Forecast verification is a crucial aspect of any prediction system. It is important to assess the quality of forecasts if improvements are to be made. A large number of verification measures have been suggested ( Jolliffe and Stephenson 2003 ). To narrow the range of possible measures, a number of desirable properties of measures have been proposed and generally accepted ( Murphy 1993 ; Mason 2003 ). For probability forecasts of a binary event, the two most frequently cited

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Jing-Shan Hong, Chin-Tzu Fong, Ling-Feng Hsiao, Yi-Chiang Yu, and Chian-You Tzeng

to have reliable quantitative precipitation forecast (QPF) products for the prevention and mitigation of typhoon-related disasters in Taiwan. Fig . 1. The terrain of Taiwan (m; shaded) with the rain gauge station locations (dots). However, accurate predictions of typhoon precipitation in Taiwan are challenging, primarily because of the island’s high mountain range. Significant mesoscale variations caused by orographic effects ( Wang 1992 )—including track deflection ( Chang 1982 ; Yeh and

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Mei Hong, Ren Zhang, Dong Wang, Min Wang, Kefeng Liu, and Vijay P. Singh

in China and Japan ( Kurihara 1989 ). Because WPSH plays an important role in the East Asian climate, many scientists have attempted to forecast its patterns and trends ( Park et al. 2010 ; Chang et al. 2000a ). Because the occurrences and aberrations of WPSH are complex processes ( Miyasaka and Nakamura 2005 ), forecasting WPSH, especially abnormal WPSH, remains difficult ( Grinsted et al. 2004 ). Current forecast models can be divided into two main categories: numerical and statistical. The

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

1. Introduction Medium-range (10–15 days or submonthly time scales) weather forecasting has recently gained more attention owing to its practical importance with regard to water allocation and flood control in large basins. For instance, operation of reservoir systems critically depends on precipitation/streamflow over 2–4 weeks to develop water and energy management plans ( Sankarasubramanian et al. 2009a , b ). Chaotic characteristics of the atmosphere ( Lorenz 1963 ) known as the “butterfly

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