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

improvement of the operational wave ensemble compared to the deterministic run, the GWES still suffers from shortcomings that limit its skill, especially associated with systematic errors that vary with forecast time and location. The GWES currently uses the conservative arithmetic ensemble mean (EM), as shown in Eq. (1) : (1) EM = 1 n ∑ i = 1 n p i , where n is the number of ensemble members and p i is the state of the i th ensemble member. The major advantage of the conservative approach is that

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

numerous processes that contribute to TC intensification. Many of these processes are inherently thermodynamic (such as latent heat flux from high sea surface temperatures; Kaplan and DeMaria 2003 ) and not well represented by the dynamic weather models used in operational forecasts ( Kotroni and Lagouvardos 2004 ; Klemp 2006 ; Mercer et al. 2013 ). Kinematic factors relevant to TC intensity change are frequently noisy (e.g., 200-hPa divergence; Leroux 2016 ) or require sufficient vertical

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

and a mechanism for microphysical charge transfer and cloud-scale charge separation, generating large electrical potential differences. An increasing rate of total lightning flashes in a storm is often a good indicator of an intensifying convective updraft (e.g., Schultz et al. 2011 ). In severe weather warning operations, an operational forecaster is confronted with far more data than can be manually analyzed. Automated methods can help forecasters manage data overload and can provide insights

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

et al. (2014 , 2018) developed an operational algorithm called ProbSevere, which uses naïve Bayes to forecast any severe weather for a given storm. Their predictors are derived from radar, satellite, and lightning data, as well as NSE variables from the Rapid Refresh model. ProbSevere has run in the HWT for several years, receiving favorable feedback from forecasters. It has improved upon the median lead time of NWS tornado and severe-thunderstorm warnings but at the cost of a decrease in CSI

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

) produced from the second-generation global medium-range ensemble reforecast dataset ( Hamill et al. 2013 ). This a retrospective weather forecast dataset generated with the currently operational NCEP GEFS, available at . The daily precipitation ensemble reforecasts considered both the control forecast and the 10 perturbed forecasts issued at 0000 UTC at 1.5-, 3.5-, and 5.5-day leads. A lead time of 1.5 days matches up the observation of day

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

selected due to its relative simplicity and to match operational Day 1 products issued by the Weather Prediction Center (WPC). The remainder of this paper is organized as follows: section 2 details the methods and datasets used herein, section 3 describes the results and presents two case studies for analysis, section 4 summarizes and discusses important findings, and section 5 concludes the paper and outlines avenues for future work. 2. Methods a. Datasets Forecast data from the SREF and HREFv

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Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

( Ball et al. 2017 ) to severe weather prediction ( McGovern et al. 2017 ). However, until recently, far fewer AI applications were developed to operationally exploit environmental satellite data, or to enhance other operational activities such as NWP, data assimilation, nowcasting, forecasting, and extreme weather prediction. AI is increasingly being considered for these applications, with promising results. However, as outlined in the section “Similarities between AI and conventional

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

due to the increasing societal impact of weather. As a result, there is a demand for consistent, comprehensive, and consolidated warnings, nowcasts, and forecasts. Weather products must combine space-based, air-based, and surface-based data sources at increasing spatial, vertical, and temporal resolutions and with improved timeliness, especially for nowcasting and short-range forecasting applications. Operational forecasting space and time horizons are expanding to range from very short

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