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André April

significant ice events for areas in which there will be active shipping. A 30-day outlook is then issued and reassessed every 15 days throughout the Arctic summer. The new statistical events forecasting model (SEF) presented herein uses the dates on which these various events have occurred each year since the Canadian Ice Service first began detecting them by aerial or satellite reconnaissance almost 46 years ago. These dates form a continuous series for each arctic summer year. The SEF uses the series

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Veronica J. Berrocal, Adrian E. Raftery, and Tilmann Gneiting

Colucci 1997 ; Grimit and Mass 2002 ; Scherrer et al. 2004 ; Eckel and Mass 2005 ). Hence, to realize the full potential of an ensemble forecast it is necessary to apply some form of statistical postprocessing, with the goal of generating probabilistic forecasts that are calibrated and yet sharp. In the spirit of the pioneering work of Glahn and Lowry (1972) , who introduced regression-type model output statistics approaches to a meteorological audience, various statistically based ensemble

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Steven A. Mauget and Jonghan Ko

. 1 ’s annual management cycles can be simulated over similar periods if daily meteorological data are available to drive crop simulation models. By contrast, retrospective forecast records derived from the National Centers for Environmental Prediction (NCEP) climate forecast system, although potentially more skillful, are available for only 24 yr ( Saha et al. 2006 ). Thus basing management simulations on these simple prediction schemes might provide better statistical sampling of the production

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Fumin Ren, Chenchen Ding, Da-Lin Zhang, Deliang Chen, Hong-li Ren, and Wenyu Qiu

1. Introduction Considerable progress has been made in numerical weather prediction (NWP) during the past decades due partly to a steady accumulation of scientific knowledge and partly to technological advances in utilizing a variety of observations and gaining computing power ( Bauer et al. 2015 ). Despite the steady progress, significant forecast errors in today’s NWP models are still present, especially in processing atmospheric statistical properties that are not directly available from the

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Phillip E. Shafer and Henry E. Fuelberg

trees (CART) (e.g., Livingston et al. 1996 ; Mazany et al. 2002 ; Burrows et al. 2004 ; Brenner 2004 ; Lambert et al. 2005 ). These methods attempt to quantify the relationship between a set of predictors and the outcome of interest such as thunderstorm probability or lightning frequency (e.g., Neumann and Nicholson 1972 ; Reap 1994 ). The present study develops a statistical scheme that provides improved forecast guidance of warm season afternoon and evening lightning for 11 areas of the

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

for technique development. This approach is also used in this work. Operational guidance on forecast wind radii comes primarily from numerical weather prediction (NWP) models and purely statistical models, like the wind radii climatology and persistence model (DRCL; Knaff et al. 2007 , hereafter K07 ). NWP comes from a combination of regional hurricane specific models and global models ( NHC 2009 ; CIRA 2016b ). Wind radii are estimated by software developed at the Geophysical Research

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Seung-Eon Lee and Kyong-Hwan Seo

index, an El Niño–Southern Oscillation (ENSO) developing index, and an ENSO decaying index] and showed higher prediction accuracy than the state-of-the-art MMEs. Their study also implied that an empirical model can be used as a real-time forecasting tool. Therefore, the construction of a statistical forecast model for changma precipitation may be valuable for enhancing prediction capabilities. Currently, there is no statistical forecast model for changma precipitation. The aim of the current study

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Richard J. Hall, Adam A. Scaife, Edward Hanna, Julie M. Jones, and Robert Erdélyi

not operational in 2013). Operational forecast ensemble sizes vary in number: 31 in 2014 and 32 for 2015 and 2016. All predictor datasets are normalized by subtracting the monthly mean and dividing by the monthly standard deviation for the period 1981–2010. Any trend in the data is retained. No tuning of the predictors is performed to obtain the initial statistical forecast models, although detrending of sea ice is used in a subsequent model. 3. Methods a. Regression models We use a simple

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

GFS upgrades improve the MJO forecast. These estimates of the ability of MJO forecasting are especially important because the MJO temporal scale bridges the gap between synoptic weather forecasting and seasonal climate forecasting, and the information on the MJO-related weather and climate can benefit global regions with a high population density. MJO prediction beyond lead times that the dynamical forecast models provide is routinely extended through statistical prediction techniques. First

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Q. J. Wang, Andrew Schepen, and David E. Robertson

1. Introduction Seasonal climate forecasts are in high demand in Australia, and seasonal rainfall forecasts in particular are sought by farmers, water managers, and others throughout the year. The Australian Bureau of Meteorology provides probabilistic forecasts of seasonal rainfall using a statistical prediction system based on sea surface temperature (SST) anomaly patterns over the Indian and Pacific Oceans ( Drosdowsky and Chambers 2001 ; Fawcett et al. 2005 ). Different forecast models are

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