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

1. Introduction Precipitation is an important source of water resources and a major driving factor in the functioning of agriculture, forest, and freshwater ecosystems. Accurate precipitation forecasting is one of the most sensible aspects of weather prediction to the society. It strongly affects daily decisions in different sectors, such as public health, water resources, energy production, agriculture, and environmental protection. Numerical weather prediction (NWP) models are the state

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

1. Introduction Over the past 20 years, increases in computing resources have reshaped the state of numerical weather prediction (NWP) in several key ways: by enabling skillful high-resolution ensemble forecasts (e.g., Xue et al. 2007 ; Jirak et al. 2012 , 2016 , 2018 ; Roberts et al. 2019 ; Clark et al. 2018 ; Schwartz et al. 2015 , 2019 ); by increasing the capacity to run and store models for research and operations (e.g., Hamill and Whitaker 2006 ; Kain et al. 2010 ; Hamill et al

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

last decade ( Brooks and Correia 2018 ). During this time, the amount of data available to forecasters has exploded—including dual-polarization radar observations, high-resolution satellite observations, and forecasts from convection-allowing models (CAM). However, none of these datasets explicitly resolves tornadoes, so they must still be translated into useful information by forecasters, which can lead to cognitive overload ( Wilson et al. 2017 ). This problem can be alleviated by explicit

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

1. Introduction The U.S. National Centers for Environmental Prediction (NCEP) have produced atmospheric forecasts using ensembles since 1992 and wave ensembles since 2005. Kalnay (2003) describes the two main advantages of using ensemble forecasts: the ensemble members tend to smooth out uncertain components, which lead to better skill than single deterministic forecasts; and the spread of the ensemble members provides an estimation of the uncertainty. The mean of the ensemble members is

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

1. Introduction Tropical cyclones (TCs) can cause numerous societal hazards, including storm surge, high winds, and flooding from excessive rainfall. Although these hazards are primarily associated with coastal locations, the strongest TCs often produce significant impacts far inland from the coast, particularly with regard to flooding. Prior knowledge of TC intensity is essential for resource deployment ahead of a landfalling system, yet TC intensity forecasts remain challenging due to the

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

image classification and segmentation ( LeCun et al. 2015 ; He et al. 2016 ; Liu and Deng 2015 ). DL models are increasingly being applied to complex problems in Earth science and meteorology, including probabilistic hail forecasting, cloud classification, predicting algal blooms, tropical-cyclone-track forecasts, and severe weather detection ( Gagne et al. 2017 ; Giffard-Roisin et al. 2020 ; Lagerquist et al. 2020 ; Lee et al. 2004 ; Recknagel et al. 1997 ; McGovern et al. 2017 ). This paper

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

Machine learning can improve handling large volumes of observations, modeling, analysis, and forecasting of the environment by increasing the speed and accuracy of computations, but success requires great care in designing and training the machine learning models. The purpose of this article is to provide evidence, based on specific examples, mostly remote sensing and NWP examples, that AI has tremendous potential for being used successfully in meteorology and for transforming the exploitation

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