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Andrew E. Mercer, Alexandria D. Grimes, and Kimberly M. Wood

applied techniques known as supervised learning methods, where the user oversees many aspects of the study, including the case selection, the predictor selection, the classification criteria and method, and traditional cross-validation techniques ( Russell and Norvig 2010 ). Techniques such as regression and classification fall under the broader heading of supervised learning methods. These methods have value as evidenced by the improvements seen in RI forecasts in recent years ( Kaplan et al. 2015

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Ricardo Martins Campos, Vladimir Krasnopolsky, Jose-Henrique G. M. Alves, and Stephen G. Penny

potential. Berbić et al. (2017) applied ANNs and support vector machines for short forecasts of significant wave height. Dixit and Londhe (2016) developed a neuro wavelet technique, combining discrete wavelet transform and ANNs, to explore the predictability of extreme events for five major hurricanes at four locations in the Gulf of Mexico. Deo et al. (2001) , Deo and Sridhar Naidu (1998) , and Mandal and Prabaharan (2006) developed ANN systems to predict significant wave heights in India, and

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

1. Introduction Geostationary Operational Environmental Satellite (GOES) imagery is a key element of U.S. operational weather forecasting, supporting the need for high-resolution, rapidly refreshing imagery for situational awareness ( Line et al. 2016 ). While used extensively by human forecasters, its usage in data assimilation (DA) for numerical weather prediction (NWP) models is limited. Instead DA makes greater usage of microwave and infrared sounder data on low-Earth-orbiting satellites

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Yun Fan, Vladimir Krasnopolsky, Huug van den Dool, Chung-Yu Wu, and Jon Gottschalck

Abstract

Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, post-processing methods, from simple bias correction methods to more complicated multiple linear regression-based Model Output Statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as Week 3-4 forecasts.

In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve Week 3-4 precipitation and 2-meter temperature forecasts made by the NOAA NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and co-variabilities hidden within the multi-dimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model Week 3-4 precipitation and 2-meter temperature forecasts. The results show that to some extent neural network techniques can significantly improve the Week 3-4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

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Ryan Lagerquist, Amy McGovern, Cameron R. Homeyer, David John Gagne II, and Travis Smith

. Kingfield , K. Ortega , and T. Smith , 2019 : Estimates of gradients in radar moments using a linear least squares derivative technique . Wea. Forecasting , 34 , 415 – 434 , https://doi.org/10.1175/WAF-D-18-0095.1 . 10.1175/WAF-D-18-0095.1 Markowski , P. , and Y. Richardson , 2009 : Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research . Atmos. Res. , 93 , 3 – 10 , https://doi.org/10.1016/j.atmosres.2008.09.015 . 10.1016/j

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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

is not a tropical cyclone ( American Meteorological Society 2019 ). Heuristic-based models typically require a specific set of meteorological variables provided by weather model outputs and often cannot be run directly with observations from sources such as satellites without further information or routines. Machine-learning (ML) techniques can be used effectively to identify a range of different meteorological features that may be broadly classified as regions of interest (ROI). For instance, ML

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John L. Cintineo, Michael J. Pavolonis, Justin M. Sieglaff, Anthony Wimmers, Jason Brunner, and Willard Bellon

1. Introduction Since the advent of weather satellites, researchers have been investigating signatures of intense convection from satellite images (e.g., Purdom 1976 ; Adler and Fenn 1979 ; Menzel and Purdom 1994 ; Schmit et al. 2005 , 2015 ). Forecasters frequently scrutinize satellite imagery to help infer storm dynamics and diagnose and forecast the intensity of thunderstorms, which can generate a variety of hazards. Intense convective updrafts frequently penetrate the tropopause

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Eric D. Loken, Adam J. Clark, Amy McGovern, Montgomery Flora, and Kent Knopfmeier

, multiple-physics CAEs is an artificial inflation of ensemble spread due to the existence of systematic biases between ensemble members ( Eckel and Mass 2005 ; Clark et al. 2010b ; Loken et al. 2019 ). These shortcomings are typically resolved using one or more postprocessing techniques, including isotropic (e.g., Sobash et al. 2011 , 2016 ; Loken et al. 2017 , 2019 ; Roberts et al. 2019 ) or anisotropic (e.g., Marsh et al. 2012 ) spatial smoothing of the raw forecast probability field

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

the model resolution, to extend the forecast horizon, and to increase ensemble size). Flexibility. ML techniques can accommodate (i) variables that have not been (and sometimes cannot be) included in physically based models, (ii) physical constrains (like conservation laws or balance equations), (iii) processes that are nonlinear, (iv) non-Gaussian observation errors, and (v) empirical data for processes for which the true physics is poorly understood ( Krasnopolsky 2013 ). Ease of use. Modern

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Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

techniques and logistic regression techniques for postprocessing GEFS precipitation forecasts. For selecting optimal postprocessing methods, it would be informative to compare the performance of analog methods, which requires long-term reforecast archives, with logistic regression, which only needs a small set of training data. Given the research gaps we have identified, this study aims to 1) document the performance of the GEFS and ECMWF daily precipitation ensemble forecasts using Brazil as case study

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