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1. The 2023 Atlantic Hurricane Season: An Above-Normal Season despite Strong El Nino Conditions

Author

Klotzbach, Philip J., Jones, Jhordanne J., Wood, Kimberly M., Bell, Michael M., Blake, Eric S., Bowen, Steven G., Caron, Louis-Philippe, Chavas, Daniel R., Collins, Jennifer M., Gibney, Ethan J., Schreck, Carl J., III, and Truchelut, Ryan E.

Subjects
Seasons -- Forecasts and trends -- 2023 AD, Hurricane forecasting -- Research -- 2023 AD, Meteorological research -- 2023 AD, Market trend/market analysis, Business, Earth sciences
Abstract

The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991-2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Nino event. El Nino events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August-October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Nino. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind-evaporation-SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Nino events. The concentrated warmth in the eastern tropical Pacific in August- October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific. SIGNIFICANCE STATEMENT: The 2023 Atlantic hurricane season produced above-normal activity despite strong El Nino conditions. The season had 20 named storms, along with 7 hurricanes and 3 major hurricanes. Normally, El Nino decreases Atlantic hurricane activity due to increases in vertical wind shear. In 2023, vertical wind shear was below average, likely driven by the record warm tropical Atlantic and Caribbean Sea surface temperatures which led to tropical circulation patterns that were considerably different from the atmospheric flow typically observed during El Nino events. This manuscript also uses a state-of-the-art climate model to investigate the impacts of Atlantic and Pacific SST configurations on Atlantic vertical wind shear patterns. KEYWORDS: El Nino; Hurricanes/ typhoons; Tropical cyclones; Atmospheric waves; Seasonal forecasting, 1. Introduction The 2023 North Atlantic (hereafter Atlantic) hurricane season was classified as above normal based on NOAA's definition, (1) with 20 named storms [e.g., a tropical cyclone (TC) or [...]

Published
2024
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2. Structural Forecasting for Short-term Tropical Cyclone Intensity Guidance

Author

McNeely, Trey, Khokhlov, Pavel, Dalmasso, Niccolo, Wood, Kimberly M., and Lee, Ann B.

Subjects
Statistics - Applications
Abstract

Because geostationary satellite (Geo) imagery provides a high temporal resolution window into tropical cyclone (TC) behavior, we investigate the viability of its application to short-term probabilistic forecasts of TC convective structure to subsequently predict TC intensity. Here, we present a prototype model which is trained solely on two inputs: Geo infrared imagery leading up to the synoptic time of interest and intensity estimates up to 6 hours prior to that time. To estimate future TC structure, we compute cloud-top temperature radial profiles from infrared imagery and then simulate the evolution of an ensemble of those profiles over the subsequent 12 hours by applying a Deep Autoregressive Generative Model (PixelSNAIL). To forecast TC intensities at hours 6 and 12, we input operational intensity estimates up to the current time (0 h) and simulated future radial profiles up to +12 h into a ``nowcasting'' convolutional neural network. We limit our inputs to demonstrate the viability of our approach and to enable quantification of value added by the observed and simulated future radial profiles beyond operational intensity estimates alone. Our prototype model achieves a marginally higher error than the National Hurricane Center's official forecasts despite excluding environmental factors, such as vertical wind shear and sea surface temperature. We also demonstrate that it is possible to reasonably predict short-term evolution of TC convective structure via radial profiles from Geo infrared imagery, resulting in interpretable structural forecasts that may be valuable for TC operational guidance.

Published
2022
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3. Detecting Distributional Differences in Labeled Sequence Data with Application to Tropical Cyclone Satellite Imagery

Author

McNeely, Trey, Vincent, Galen, Wood, Kimberly M., Izbicki, Rafael, and Lee, Ann B.

Subjects
Statistics - Applications, Statistics - Methodology, Statistics - Machine Learning
Abstract

Our goal is to quantify whether, and if so how, spatio-temporal patterns in tropical cyclone (TC) satellite imagery signal an upcoming rapid intensity change event. To address this question, we propose a new nonparametric test of association between a time series of images and a series of binary event labels. We ask whether there is a difference in distribution between (dependent but identically distributed) 24-h sequences of images preceding an event versus a non-event. By rewriting the statistical test as a regression problem, we leverage neural networks to infer modes of structural evolution of TC convection that are representative of the lead-up to rapid intensity change events. Dependencies between nearby sequences are handled by a bootstrap procedure that estimates the marginal distribution of the label series. We prove that type I error control is guaranteed as long as the distribution of the label series is well-estimated, which is made easier by the extensive historical data for binary TC event labels. We show empirical evidence that our proposed method identifies archetypes of infrared imagery associated with elevated rapid intensification risk, typically marked by deep or deepening core convection over time. Such results provide a foundation for improved forecasts of rapid intensification., Comment: 27 pages, 11 figures

Published
2022

4. Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones

Author

McNeely, Trey, Vincent, Galen, Izbicki, Rafael, Wood, Kimberly M., and Lee, Ann B.

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning, Statistics - Applications
Abstract

Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weather prediction, statistical models) to produce forecasts every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. While high-capacity machine learning methods are well suited for prediction problems with complex sequence data, extracting interpretable scientific information with such methods is difficult. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC convective structure leading up to the rapid intensification of a storm, hence providing forecasters and scientists with key insight into TC behavior., Comment: 7 pages, 4 figures, Tackling Climate Change with Machine Learning: workshop at NeurIPS 2021

Published
2021

5. Spatio-temporal methods for estimating subsurface ocean thermal response to tropical cyclones

Author

Hu, Addison J., Kuusela, Mikael, Lee, Ann B., Giglio, Donata, and Wood, Kimberly M.

Subjects
Statistics - Applications
Abstract

Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and for an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatio-temporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multi-step procedure, which (1) characterizes the upper ocean seasonal shift during the TC season; (2) models the variability in the temperature observations; (3) fits a thin plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side., Comment: 39 pages, 14 figures; supplement and code at https://github.com/huisaddison/tc-ocean-methods

Published
2020

6. Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning

Author

McNeely, Trey, Dalmasso, Niccolò, Wood, Kimberly M., and Lee, Ann B.

Subjects
Computer Science - Machine Learning, Statistics - Applications
Abstract

Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. The human in-the-loop pipeline requires that any forecasting guidance must be easily digestible by TC experts if it is to be adopted at operational centers like the National Hurricane Center. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does: a powerful tool for monitoring high-dimensional time series of key physically relevant predictors and the means to understand how the predictors relate to one another and to short-term intensity changes., Comment: To appear in the Tackling Climate Change with Machine Learning workshop at NeurIPS 2020 (Proposals Track) 3 pages, 1 figure

Published
2020

7. Unlocking GOES: A Statistical Framework for Quantifying the Evolution of Convective Structure in Tropical Cyclones

Author

McNeely, Trey, Lee, Ann B., Wood, Kimberly M., and Hammerling, Dorit

Subjects
Statistics - Applications
Abstract

Tropical cyclones (TCs) rank among the most costly natural disasters in the United States, and accurate forecasts of track and intensity are critical for emergency response. Intensity guidance has improved steadily but slowly, as processes which drive intensity change are not fully understood. Because most TCs develop far from land-based observing networks, geostationary satellite imagery is critical to monitor these storms. However, these complex data can be challenging to analyze in real time, and off-the-shelf machine learning algorithms have limited applicability on this front due to their ``black box'' structure. This study presents analytic tools that quantify convective structure patterns in infrared satellite imagery for over-ocean TCs, yielding lower-dimensional but rich representations that support analysis and visualization of how these patterns evolve during rapid intensity change. The proposed ORB feature suite targets the global Organization, Radial structure, and Bulk morphology of TCs. By combining ORB and empirical orthogonal functions, we arrive at an interpretable and rich representation of convective structure patterns that serve as inputs to machine learning methods. This study uses the logistic lasso, a penalized generalized linear model, to relate predictors to rapid intensity change. Using ORB alone, binary classifiers identifying the presence (versus absence) of such intensity change events can achieve accuracy comparable to classifiers using environmental predictors alone, with a combined predictor set improving classification accuracy in some settings. More complex nonlinear machine learning methods did not perform better than the linear logistic lasso model for current data., Comment: 19 pages, 14 figures, Submitted to the Journal of Applied Meteorology and Climatology

Published
2019
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11. The Tropics

Author

Diamond, Howard J., primary, Schreck, Carl J., additional, Allgood, Adam, additional, Becker, Emily J., additional, Blake, Eric S., additional, Bringas, Francis G., additional, Camargo, Suzana J., additional, Chen, Lin, additional, Coelho, Caio A.S., additional, Fauchereau, Nicolas, additional, Fogarty, Chris, additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Harnos, Daniel S., additional, He, Qiong, additional, Hu, Zeng-Zhen, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, Kumar, Arun, additional, L’Heureux, Michelle, additional, Landsea, Chris W., additional, Lin, I-I., additional, Lorrey, Andrew M., additional, Luo, Jing-Jia, additional, Magee, Andrew D., additional, Pasch, Richard J., additional, Pezza, Alexandre B., additional, Rosencrans, Matthew, additional, Rozkošný, Jozef, additional, Trewin, Blair C., additional, Truchelut, Ryan E., additional, Wang, Bin, additional, Wang, Hui, additional, and Wood, Kimberly M., additional

Published
2023
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12. Introduction

Author

Boyer, Tim, primary, Bartow-Gillies, Ellen, additional, Abida, A., additional, Ades, Melanie, additional, Adler, Robert, additional, Adusumilli, Susheel, additional, Agyakwah, W., additional, Ahmasuk, Brandon, additional, Aldeco, Laura S., additional, Alexe, Mihai, additional, Alfaro, Eric J., additional, Allan, Richard P., additional, Allgood, Adam, additional, Alves, Lincoln. M., additional, Amador, Jorge A., additional, Anderson, John, additional, Andrade, B., additional, Anneville, Orlane, additional, Aono, Yasuyuki, additional, Arguez, Anthony, additional, Arosio, Carlo, additional, Atkinson, C., additional, Augustine, John A., additional, Avalos, Grinia, additional, Azorin-Molina, Cesar, additional, Backensto, Stacia A., additional, Bader, Stephan, additional, Baez, Julian, additional, Baiman, Rebecca, additional, Ballinger, Thomas J., additional, Banwell, Alison F., additional, Bardin, M. Yu, additional, Barichivich, Jonathan, additional, Barnes, John E., additional, Barreira, Sandra, additional, Beadling, Rebecca L., additional, Beck, Hylke E., additional, Becker, Emily J., additional, Bekele, E., additional, Bellido, Guillem Martín, additional, Bellouin, Nicolas, additional, Benedetti, Angela, additional, Benestad, Rasmus, additional, Berne, Christine, additional, Berner, Logan. T., additional, Bernhard, Germar H., additional, Bhatt, Uma S., additional, Bhuiyan, A. E., additional, Bigalke, Siiri, additional, Biló, Tiago, additional, Bissolli, Peter, additional, Bjerke Jarle, W., additional, Blagrave, Kevin, additional, Blake, Eric S., additional, Blenkinsop, Stephen, additional, Blunden, Jessica, additional, Bochníček, Oliver, additional, Bock, Olivier, additional, Bodenstein, Barbara, additional, Bodin, Xavier, additional, Bosilovich, Michael, additional, Boucher, Olivier, additional, Bozkurt, Deniz, additional, Brettschneider, Brian, additional, Bringas, Francis G., additional, Bringas, Francis, additional, Buechler, Dennis, additional, Buehler, Stefan A., additional, Bukunt, Brandon, additional, Calderón, Blanca, additional, Camargo, Suzana J., additional, Campbell, Jayaka, additional, Campos, Diego, additional, Carrea, Laura, additional, Carter, Brendan R., additional, Cetinić, Ivona, additional, Chambers, Don P., additional, Chan, Duo, additional, Chandler, Elise, additional, Chang, Kai-Lan, additional, Chen, Hua, additional, Chen, Lin, additional, Cheng, Lijing, additional, Cheng, Vincent Y. S., additional, Chomiak, Leah, additional, Christiansen, Hanne H., additional, Christy, John R., additional, Chung, Eui-Seok, additional, Ciasto, Laura M., additional, Clarke, Leonardo, additional, Clem, Kyle R., additional, Clingan, Scott, additional, Coelho, Caio A.S., additional, Cohen, Judah L., additional, Coldewey-Egbers, Melanie, additional, Colwell, Steve, additional, Cooper, Owen R., additional, Cornes, Richard C., additional, Correa, Kris, additional, Costa, Felipe, additional, Covey, Curt, additional, Coy, Lawrence, additional, Créatux, Jean-François, additional, Crhova, Lenka, additional, Crimmins, Theresa, additional, Cronin, Meghan F., additional, Cropper, Thomas, additional, Crotwell, Molly, additional, Culpepper, Joshua, additional, Cunha, Ana P., additional, Cusicanqui, Diego, additional, Datta, Rajashree T., additional, Davis, Sean M., additional, De Bock, Veerle, additional, de Jeu, Richard A. M., additional, De Laat, Jos, additional, Decharme, Bertrand, additional, Degenstein, Doug, additional, Delaloye, Reynald, additional, Demircan, Mesut, additional, Derksen, Chris, additional, Deus, Ricardo, additional, Dhurmea, K. R., additional, Diamond, Howard J., additional, Dirkse, S., additional, Divine, Dmitry, additional, Dokulil, Martin T., additional, Donat, Markus G., additional, Dong, Shenfu, additional, Dorigo, Wouter A., additional, Drost Jensen, Caroline, additional, Druckenmiller, Matthew L., additional, Drumond, Paula, additional, du Plessis, Marcel, additional, Dugan, Hilary A., additional, Dulamsuren, Dashkhuu, additional, Dunmire, Devon, additional, Dunn, Robert J. H., additional, Durre, Imke, additional, Dusek, Robert, additional, Dutton, Geoff, additional, Duveiller, Gregory, additional, Ekici, Mithat, additional, Elias Chereque, Alesksandra, additional, ElKharrim, M., additional, Epstein, Howard E., additional, Espinoza, Jhan-Carlo, additional, Estilow, Thomas W., additional, Estrella, Nicole, additional, Fauchereau, Nicolas, additional, Fausto, Robert S., additional, Feely, Richard A., additional, Fenimore, Chris, additional, Fereday, David, additional, Fettweis, Xavier, additional, Fioletov, vitali E., additional, Flemming, Johannes, additional, Fogarty, Chris, additional, Fogt, Ryan L., additional, Forbes, Bruce C., additional, Foster, Michael J., additional, Franz, Bryan A., additional, Freeman, Natalie M., additional, Fricker, Helen A., additional, Frith, Stacey M., additional, Froidevaux, Lucien, additional, Frost, Gerald V. (JJ), additional, Fuhrman, Steven, additional, Füllekrug, Martin, additional, Ganter, Catherine, additional, Gao, Meng, additional, Gardner, Alex S., additional, Garforth, Judith, additional, Garg, Jay, additional, Gerland, Sebastian, additional, Gibbes, Badin, additional, Gille, Sarah T., additional, Gilson, John, additional, Gleason, Karin, additional, Gobron, Nadine, additional, Goetz, Scott J., additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Goodman, Steven, additional, Goto, Atsushi, additional, Grooß, Jens-Uwe, additional, Gruber, Alexander, additional, Gu, Guojun, additional, Guard, Charles “Chip” P., additional, Hagos, S., additional, Hahn, Sebastian, additional, Haimberger, Leopold, additional, Hall, Bradley D., additional, Hamlington, Benjamin D., additional, Hanna, Edward, additional, Hanssen-Bauer, Inger, additional, Harnos, Daniel S., additional, Harris, Ian, additional, He, Qiong, additional, Heim, Richard R., additional, Hellström, Sverker, additional, Hemming, Deborah L., additional, Hendricks, Stefan, additional, Hicks, J., additional, Hidalgo, Hugo G., additional, Hirschi, Martin, additional, Ho, Shu-peng (Ben), additional, Hobbs, W., additional, Holmes, Robert M., additional, Holzworth, Robert, additional, Hrbáček, Filip, additional, Hu, Guojie, additional, Hu, Zeng-Zhen, additional, Huang, Boyin, additional, Huang, Hongjie, additional, Hurst, Dale F., additional, Ialongo, Iolanda, additional, Inness, Antje, additional, Isaksen, Ketil, additional, Ishii, Masayoshi, additional, Jadra, Gerardo, additional, Jevrejeva, Svetlana, additional, John, Viju O., additional, Johns, W., additional, Johnsen, Bjørn, additional, Johnson, Bryan, additional, Johnson, Gregory C., additional, Jones, Philip D., additional, Jones, Timothy, additional, Josey, Simon A., additional, Jumaux, G., additional, Junod, Robert, additional, Kääb, Andreas, additional, Kabidi, K., additional, Kaiser, Johannes W., additional, Kaler, Robb S.A., additional, Kaleschke, Lars, additional, Kaufmann, Viktor, additional, Kazemi, Amin Fazl, additional, Keller, Linda M., additional, Kellerer-Pirklbauer, Andreas, additional, Kendon, Mike, additional, Kennedy, John, additional, Kent, Elizabeth C., additional, Kerr, Kenneth, additional, Khan, Valentina, additional, Khiem, Mai Van, additional, Kidd, Richard, additional, Kim, Mi Ju, additional, Kim, Seong-Joong, additional, Kipling, Zak, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, Koppa, Akash, additional, Korshunova, Natalia N., additional, Kraemer, Benjamin M., additional, Kramarova, Natalya A., additional, Kruger, A. C., additional, Kruger, Andries, additional, Kumar, Arun, additional, L’Heureux, Michelle, additional, La Fuente, Sofia, additional, Laas, Alo, additional, Labe, Zachary M., additional, Lader, Rick, additional, Lakatos, Mónika, additional, Lakkala, Kaisa, additional, Lam, Hoang Phuc, additional, Lan, Xin, additional, Landschützer, Peter, additional, Landsea, Chris W., additional, Lang, Timothy, additional, Lankhorst, Matthias, additional, Lantz, Kathleen O., additional, Lara, Mark J., additional, Lavado-Casimiro, Waldo, additional, Lavers, David A., additional, Lazzara, Matthew A., additional, Leblanc, Thierry, additional, Lee, Tsz-Cheung, additional, Leibensperger, Eric M., additional, Lennard, Chris, additional, Leuliette, Eric, additional, Leung, Kinson H. Y., additional, Lieser, Jan L., additional, Likso, Tanja, additional, Lin, I-I., additional, Lindsey, Jackie, additional, Liu, Yakun, additional, Locarnini, Ricardo, additional, Loeb, Norman G., additional, Loomis, Bryant D., additional, Lorrey, Andrew M., additional, Loyola, Diego, additional, Lu, Rui, additional, Lumpkin, Rick, additional, Luo, Jing-Jia, additional, Luojus, Kari, additional, Lyman, John M., additional, Maberly, Stephen C., additional, Macander, Matthew J., additional, MacFerrin, Michael, additional, MacGilchrist, Graeme A., additional, MacLennan, Michelle L., additional, Madelon, Remi, additional, Magee, Andrew D., additional, Magnin, Florence, additional, Mamen, Jostein, additional, Mankoff, Ken D., additional, Manney, Gloria L., additional, Marcinonienė, Izolda, additional, Marengo, Jose A., additional, Marjan, Mohammadi, additional, Martínez, Ana E., additional, Massom, Robert A., additional, Matsuzaki, Shin-Ichiro, additional, May, Linda, additional, Mayer, Michael, additional, Mazloff, Matthew R., additional, McAfee, Stephanie A., additional, McBride, C., additional, McCabe, Matthew F., additional, McClelland, James W., additional, McPhaden, Michael J., additional, Mcvicar, Tim R., additional, Mears, Carl A., additional, Meier, Walter N., additional, Mekonnen, A., additional, Menzel, Annette, additional, Merchant, Christopher J., additional, Merrifield, Mark A., additional, Meyer, Michael F., additional, Meyers, Tristan, additional, Mikolajczyk, David E., additional, Miller, John B., additional, Miralles, Diego G., additional, Misevicius, Noelia, additional, Mishonov, Alexey, additional, Mitchum, Gary T., additional, Moat, Ben I., additional, Moesinger, Leander, additional, Moise, Aurel, additional, Molina-Carpio, Jorge, additional, Monet, Ghislaine, additional, Montzka, Stephan A., additional, Moon, Twila A., additional, Moore, G. W. K., additional, Mora, Natali, additional, Morán, Johnny, additional, Morehen, Claire, additional, Morice, Colin, additional, Mostafa, A. E., additional, Mote, Thomas L., additional, Mrekaj, Ivan, additional, Mudryk, Lawrence, additional, Mühle, Jens, additional, Müller, Rolf, additional, Nance, David, additional, Nash, Eric R., additional, Nerem, R. Steven, additional, Newman, Paul A., additional, Nicolas, Julien P., additional, Nieto, Juan J., additional, Noetzli, Jeannette, additional, Noll, Ben, additional, Norton, Taylor, additional, Nyland, Kelsey E., additional, O’Keefe, John, additional, Ochwat, Naomi, additional, Oikawa, Yoshinori, additional, Okunaka, Yuka, additional, Osborn, Timothy J., additional, Overland, James E., additional, Park, Taejin, additional, Parrington, Mark, additional, Parrish, Julia K., additional, Pasch, Richard J., additional, Pascual Ramírez, Reynaldo, additional, Pellet, Cécile, additional, Pelto, Mauri S., additional, Perčec Tadić, Melita, additional, Perovich, Donald K., additional, Petersen, Guðrún Nína, additional, Petersen, Kyle, additional, Petropavlovskikh, Irina, additional, Petty, Alek, additional, Pezza, Alexandre B., additional, Pezzi, Luciano P., additional, Phillips, Coda, additional, Phoenix, Gareth K., additional, Pierson, Don, additional, Pinto, Izidine, additional, Pires, Vanda, additional, Pitts, Michael, additional, Po-Chedley, Stephen, additional, Pogliotti, Paolo, additional, Poinar, Kristin, additional, Polvani, Lorenzo, additional, Preimesberger, Wolfgang, additional, Price, Colin, additional, Pulkkanen, Merja, additional, Purkey, Sarah G., additional, Qiu, Bo, additional, Quisbert, Kenny, additional, Quispe, Willy R., additional, Rajeevan, M., additional, Ramos, Andrea M., additional, Randel, William J., additional, Rantanen, Mika, additional, Raphael, Marilyn N., additional, Reagan, James, additional, Recalde, Cristina, additional, Reid, Phillip, additional, Rémy, Samuel, additional, Reyes Kohler, Alejandra J., additional, Ricciardulli, Lucrezia, additional, Richardson, Andrew D., additional, Ricker, Robert, additional, Robinson, David A., additional, Robjhon, M., additional, Rocha, Willy, additional, Rodell, Matthew, additional, Rodriguez Guisado, Esteban, additional, Rodriguez-Fernandez, Nemesio, additional, Romanovsky, Vladimir E., additional, Ronchail, Josyane, additional, Rosencrans, Matthew, additional, Rosenlof, Karen H., additional, Rösner, Benjamin, additional, Rösner, Henrieke, additional, Rozanov, Alexei, additional, Rozkošný, Jozef, additional, Rubek, Frans, additional, Rusanovskaya, Olga O., additional, Rutishauser, This, additional, Sabeerali, C. T., additional, Salinas, Roberto, additional, Sánchez-Lugo, Ahira, additional, Santee, Michelle L., additional, Santini, Marcelo, additional, Sato, Katsunari, additional, Sawaengphokhai, Parnchai, additional, Sayouri, A., additional, Scambos, Theodore, additional, Schenzinger, Verena, additional, Schimanke, Semjon, additional, Schlegel, Robert W., additional, Schmid, Claudia, additional, Schmid, Martin, additional, Schneider, Udo, additional, Schreck, Carl J., additional, Schultz, Cristina, additional, _, _, additional, Segele, Z. T., additional, Sensoy, Serhat, additional, Serbin, Shawn P., additional, Serreze, Mark C., additional, Setiawan, Amsari Mudzakir, additional, Sezaki, Fumi, additional, Sharma, Sapna, additional, Sharp, Jonathan D., additional, Sheffield, Gay, additional, Shi, Jia-Rui, additional, Shi, Lei, additional, Shiklomanov, Alexander I., additional, Shiklomanov, Nikolay I., additional, Shimaraeva, Svetlana V., additional, Shukla, R., additional, Siegel, David A., additional, Silow, Eugene A., additional, Sima, F., additional, Simmons, Adrian J., additional, Smeed, David A., additional, Smith, Adam, additional, Smith, Matthew M., additional, Smith, Sharon L., additional, Soden, Brian J., additional, Sofieva, Viktoria, additional, Souza, Everaldo, additional, Sparks, Tim H., additional, Spence-Hemmings, Jacqueline, additional, Spencer, Robert G. M., additional, Spillane, Sandra, additional, Sreejith, O. P., additional, Srivastava, A. K., additional, Stackhouse, Paul W., additional, Stammerjohn, Sharon, additional, Stauffer, Ryan, additional, Steinbrecht, Wolfgang, additional, Steiner, Andrea K., additional, Stella, Jose L., additional, Stephenson, Tannecia S., additional, Stradiotti, Pietro, additional, Strahan, Susan E., additional, Streletskiy, Dmitry A., additional, Surendran, Divya E., additional, Suslova, Anya, additional, Svendby, Tove, additional, Sweet, William, additional, Takahashi, Kiyotoshi, additional, Takemura, Kazuto, additional, Tank, Suzanne E., additional, Taylor, Michael A., additional, Tedesco, Marco, additional, Thackeray, Stephen J., additional, Thiaw, W. M., additional, Thibert, Emmanuel, additional, Thoman, Richard L., additional, Thompson, Andrew F., additional, Thompson, Philip R., additional, Tian-Kunze, Xiangshan, additional, Timmermans, Mary-Louise, additional, Timofeyev, Maxim A., additional, Tobin, Skie, additional, Tømmervik, Hans, additional, Tourpali, Kleareti, additional, Trescilo, Lidia, additional, Tretiakov, Mikhail, additional, Trewin, Blair C., additional, Triñanes, Joaquin A., additional, Trotman, Adrian, additional, Truchelut, Ryan E., additional, Trusel, Luke D., additional, Tye, Mari R., additional, van der A, Ronald, additional, van der Schalie, Robin, additional, van der Schrier, Gerard, additional, Van Hemert, Caroline, additional, Van Meerbeeck, Cedric J., additional, van vliet, Arnold J.H., additional, Vazife, Ahad, additional, Verburg, Piet, additional, Vernier, Jean-Paul, additional, Vimont, Isaac J., additional, Virts, Katrina, additional, Vivero, Sebastián, additional, Volkov, Denis L., additional, Vömel, Holger, additional, Vose, Russell S., additional, Walker, Donald (Skip) A., additional, Walsh, John E., additional, Wang, Bin, additional, Wang, Hui, additional, Wang, Muyin, additional, Wang, Ray H. J., additional, Wang, Xinyue, additional, Wanninkhof, Rik, additional, Warnock, Taran, additional, Weber, Mark, additional, Webster, Melinda, additional, Wehrlé, Adrian, additional, Wen, Caihong, additional, Westberry, Toby K., additional, Widlansky, Matthew J., additional, Wiese, David N., additional, Wild, Jeannette D., additional, Wille, Jonathan D., additional, Willems, An, additional, Willett, Kate M., additional, Williams, Earle, additional, Willis, J., additional, Wong, Takmeng, additional, Wood, Kimberly M., additional, Woolway, Richard Iestyn, additional, Xie, Ping-Ping, additional, Yang, Daqing, additional, Yin, Xungang, additional, Yin, Ziqi, additional, Zeng, Zhenzhong, additional, Zhang, Huai-min, additional, Zhang, Li, additional, Zhang, Peiqun, additional, Zhao, Lin, additional, Zhou, Xinjia, additional, Zhu, Zhiwei, additional, Ziemke, Jerry R., additional, Ziese, Markus, additional, Zolkos, Scott, additional, Zotta, Ruxandra M., additional, Zou, Cheng-Zhi, additional, Allen, Jessicca, additional, Camper, Amy V., additional, Haley, Bridgette O., additional, Hammer, Gregory, additional, Love-Brotak, S. Elizabeth, additional, Ohlmann, Laura, additional, Noguchi, Lukas, additional, Riddle, Deborah B., additional, and Veasey, Sara W., additional

Published
2023
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17. Evaluation of Experimental High-Resolution Model Forecasts of Tropical Cyclone Precipitation Using Object-Based Metrics.

Author

Stackhouse, Shakira D., Zick, Stephanie E., Matyas, Corene J., Wood, Kimberly M., Hazelton, Andrew T., and Alaka Jr., Ghassan J.

Subjects
CYCLONE forecasting, TROPICAL cyclones, HURRICANE forecasting, METEOROLOGICAL research, WEATHER forecasting, WIND forecasting
Abstract

Tropical cyclone (TC) precipitation poses serious hazards including freshwater flooding. High-resolution hurricane models predict the location and intensity of TC rainfall, which can influence local evacuation and preparedness policies. This study evaluates 0–72-h precipitation forecasts from two experimental models, the Hurricane Analysis and Forecast System (HAFS) model and the basin-scale Hurricane Weather Research and Forecasting (HWRF-B) Model, for 2020 North Atlantic landfalling TCs. We use an object-based method that quantifies the shape and size of the forecast and observed precipitation. Precipitation objects are then compared for light, moderate, and heavy precipitation using spatial metrics (e.g., area, perimeter, elongation). Results show that both models forecast precipitation that is too connected, too close to the TC center, and too enclosed around the TC center. Collectively, these spatial biases suggest that the model forecasts are too intense even though there is a negative intensity bias for both models, indicating there may be an inconsistency between the precipitation configuration and the maximum sustained winds in the model forecasts. The HAFS model struggles with forecasting stratiform versus convective precipitation and with the representation of lighter (stratiform) precipitation during the first 6 h after initialization. No such spinup issues are seen in the HWRF-B forecasts, which instead exhibit systematic biases at all lead times and systematic issues across all rain-rate thresholds. Future work will investigate spinup issues in the HAFS model forecast and how the microphysics parameterization affects the representation of precipitation in both models. [ABSTRACT FROM AUTHOR]

Published
2023
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20. The Tropics

Author

Diamond, Howard J., primary, Schreck, Carl J., additional, Allgood, Adam, additional, Becker, Emily J., additional, Blake, Eric S., additional, Bringas, Francis G., additional, Camargo, Suzana J., additional, Chen, Lin, additional, Coelho, Caio A. S., additional, Fauchereau, Nicolas, additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Halpert, Michael S., additional, He, Qiong, additional, Hu, Zeng-Zhen, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, Kumar, Arun, additional, Landsea, Chris W., additional, L’Heureux, Michelle, additional, Lin, I.-I., additional, Lorrey, Andrew M., additional, Luo, Jing-Jia, additional, Magee, Andrew D., additional, Pasch, Richard J., additional, Pezza, Alexandre B., additional, Rosencrans, Matthew, additional, Trewin, Blair C., additional, Truchelut, Ryan E., additional, Wang, Bin, additional, Wang, Hui, additional, Wood, Kimberly M., additional, and Woolley, John-Mark, additional

Published
2022
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21. A Hyperactive End to the Atlantic Hurricane Season October–November 2020

Author

Barcelona Supercomputing Center, Klotzbach, Philip J., Wood, Kimberly M., Bell, Michael M., Blake, Eric S., Bowen, Steven G., Caron, Louis-Philippe, Collins, Jennifer M., Gibney, Ethan J., Schreck III, Carl J., Truchelut, Ryan E., Barcelona Supercomputing Center, Klotzbach, Philip J., Wood, Kimberly M., Bell, Michael M., Blake, Eric S., Bowen, Steven G., Caron, Louis-Philippe, Collins, Jennifer M., Gibney, Ethan J., Schreck III, Carl J., and Truchelut, Ryan E.

Abstract

The active 2020 Atlantic hurricane season produced 30 named storms, 14 hurricanes, and 7 major hurricanes (category 3+ on the Saffir–Simpson hurricane wind scale). Though the season was active overall, the final two months (October–November) raised 2020 into the upper echelon of Atlantic hurricane activity for integrated metrics such as accumulated cyclone energy (ACE). This study focuses on October–November 2020, when 7 named storms, 6 hurricanes, and 5 major hurricanes formed and produced ACE of 74 × 104 kt2 (1 kt ≈ 0.51 m s−1). Since 1950, October–November 2020 ranks tied for third for named storms, first for hurricanes and major hurricanes, and second for ACE. Six named storms also underwent rapid intensification (≥30 kt intensification in ≤24 h) in October–November 2020—the most on record. This manuscript includes a climatological analysis of October–November tropical cyclones (TCs) and their primary formation regions. In 2020, anomalously low wind shear in the western Caribbean and Gulf of Mexico, likely driven by a moderate-intensity La Niña event and anomalously high sea surface temperatures (SSTs) in the Caribbean, provided dynamic and thermodynamic conditions that were much more conducive than normal for late-season TC formation and rapid intensification. This study also highlights October–November 2020 landfalls, including Hurricanes Delta and Zeta in Louisiana and in Mexico and Hurricanes Eta and Iota in Nicaragua. The active late season in the Caribbean would have been anticipated by a statistical model using the July–September-averaged ENSO longitude index and Atlantic warm pool SSTs as predictors., We would like to acknowledge the three anonymous reviewers and the editor, Chris Landsea, for helpful comments that significantly improved the manuscript. P. Klotzbach thanks the G. Unger Vetlesen Foundation for financial support that helped fund this research. K. Wood was supported by National Science Foundation Award AGS-2011812. M. Bell was supported by Office of Naval Research Award N000141613033. E. Gibney’s research was supported by NOAA’s Science Collaboration Program and administered by UCAR’s Cooperative Programs for the Advancement of Earth System Science (CPAESS) under Awards NA16NWS4620043 and NA18NWS4620043B. C. Schreck was supported by NOAA through the Cooperative Institute for Satellite Earth System Studies under Cooperative Agreement NA19NES4320002. We thank Christina Patricola for providing the ENSO longitude index and Chris Landsea for providing Fig. SB1., Peer Reviewed, Postprint (published version)

Published
2022

24. The Interactive Teaching Network: Distance Learning Applications for Inservice and Preservice Teacher Preparation in Attention Deficit Hyperactivity Disorder.

Author

McLaughlin, Phillip J., Bender, William N., and Wood, Kimberly M.

Abstract

Describes the distance-learning staff-development opportunities produced through an initial project on attention deficit hyperactivity disorder (AD/HD) at the University of Georgia, the effects of the technology involved, and the opportunities that have evolved from programs created. Addresses selection of distance learning technology. (Author/CR)

Published
1997

26. The Tropics

Author

Diamond, Howard J., primary, Schreck, Carl J., additional, Becker, Emily J., additional, Bell, Gerald D., additional, Blake, Eric S., additional, Bond, Stephanie, additional, Bringas, Francis G., additional, Camargo, Suzana J., additional, Chen, Lin, additional, Coelho, Caio A. S., additional, Domingues, Ricardo, additional, Goldenberg, Stanley B., additional, Goni, Gustavo, additional, Fauchereau, Nicolas, additional, Halpert, Michael S., additional, He, Qiong, additional, Klotzbach, Philip J., additional, Knaff, John A., additional, L'Heureux, Michelle, additional, Landsea, Chris W., additional, Lin, I.-I., additional, Lorrey, Andrew M., additional, Luo, Jing-Jia, additional, MacRitchie, Kyle, additional, Magee, Andrew D., additional, Noll, Ben, additional, Pasch, Richard J., additional, Pezza, Alexandre B., additional, Rosencrans, Matthew, additional, Tippet, Michael K., additional, Trewin, Blair C., additional, Truchelut, Ryan E., additional, Wang, Bin, additional, Wang, Hui, additional, Wood, Kimberly M., additional, Woolley, John-Mark, additional, and Young, Steven H., additional

Published
2021
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27. Factors affecting the 2019 Atlantic hurricane season and the role of the Indian Ocean dipole

Author

Barcelona Supercomputing Center, Wood, Kimberly M., Klotzbach, Philip J., Collins, Jennifer M., Caron, Louis‐Philippe, Truchelut, Ryan E., Schreck, Carl J., Barcelona Supercomputing Center, Wood, Kimberly M., Klotzbach, Philip J., Collins, Jennifer M., Caron, Louis‐Philippe, Truchelut, Ryan E., and Schreck, Carl J.

Abstract

The 2019 Atlantic hurricane season exhibited above‐average Accumulated Cyclone Energy—60% of which was produced by Hurricanes Dorian and Lorenzo. Most tropical cyclone (TC) activity was concentrated in a ~6‐week period from late August to early October. During the early part of the season, relatively TC‐unfavorable conditions persisted in the main development region (MDR). The MDR environment became largely favorable in September, followed by an abrupt shift back to less conducive conditions in October coincident with a strongly positive Indian Ocean Dipole (IOD). The IOD produced an El Niño‐like teleconnection pattern observed through 200‐hPa velocity potential anomalies. In the subtropical Atlantic, above‐average sea surface temperatures persisted for much of the season, which may have contributed to increased activity at higher latitudes. Given the neutral El Niño‐Southern Oscillation conditions during the 2019 hurricane season, our study highlights the need for further analysis of IOD impacts on Atlantic TC activity., K. Wood was supported by the Office of Research and Economic Development and the Department of Geosciences at Mississippi State University. P. Klotzbach acknowledges a grant from the G. Unger Vetlesen Foundation. C. Schreck was supported by NOAA through the Cooperative Institute for Satellite Earth System Studies under Cooperative Agreement NA19NES4320002. The authors thank Dr. Chris Landsea and an anonymous reviewer for their helpful feedback, especially during the COVID-19 pandemic., Peer Reviewed, Postprint (published version)

Published
2020

28. The Tropics

Author

Baxter, Stephen, additional, Bell, Gerald D, additional, Blake, Eric S, additional, Bringas, Francis G, additional, Camargo, Suzana J, additional, Chen, Lin, additional, Coelho, Caio A. S, additional, Domingues, Ricardo, additional, Goldenberg, Stanley B, additional, Goni, Gustavo, additional, Fauchereau, Nicolas, additional, Halpert, Michael S, additional, He, Qiong, additional, Klotzbach, Philip J, additional, Knaff, John A, additional, L'Heureux, Michelle, additional, Landsea, Chris W, additional, Lin, I.-I, additional, Lorrey, Andrew M, additional, Luo, Jing-Jia, additional, Magee, Andrew D, additional, Pasch, Richard J, additional, Pearce, Petra R, additional, Pezza, Alexandre B, additional, Rosencrans, Matthew, additional, Trewin, Blair C, additional, Truchelut, Ryan E, additional, Wang, Bin, additional, Wang, H, additional, Wood, Kimberly M, additional, and Woolley, John-Mark, additional

Published
2020
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31. The Extratropical Transition of Tropical Cyclones. Part I: Cyclonic Evolution and Direct Impacts

Author

2319283, Evans, Clark, Wood, Kimberly M., Aberson, Sim D., Archambault, Heather M., Milrad, Shawn M., Bosart, Lance F., et al., 2319283, Evans, Clark, Wood, Kimberly M., Aberson, Sim D., Archambault, Heather M., Milrad, Shawn M., Bosart, Lance F., and et al.

Abstract

Extratropical transition (ET) is the process by which a tropical cyclone, upon encountering a baroclinic environment and reduced sea surface temperature at higher latitudes, transforms into an extratropical cyclone. This process is influenced by, and influences, phenomena from the tropics to the midlatitudes and from the meso- to the planetary scales to extents that vary between individual events. Motivated in part by recent high-impact and/or extensively observed events such as North Atlantic Hurricane Sandy in 2012 and western North Pacific Typhoon Sinlaku in 2008, this review details advances in understanding and predicting ET since the publication of an earlier review in 2003. Methods for diagnosing ET in reanalysis, observational, and model-forecast datasets are discussed. New climatologies for the eastern North Pacific and southwest Indian Oceans are presented alongside updates to western North Pacific and North Atlantic Ocean climatologies. Advances in understanding and, in some cases, modeling the direct impacts of ET-related wind, waves, and precipitation are noted. Improved understanding of structural evolution throughout the transformation stage of ET fostered in large part by novel aircraft observations collected in several recent ET events is highlighted. Predictive skill for operational and numerical model ET-related forecasts is discussed along with environmental factors influencing posttransition cyclone structure and evolution. Operational ET forecast and analysis practices and challenges are detailed. In particular, some challenges of effective hazard communication for the evolving threats posed by a tropical cyclone during and after transition are introduced. This review concludes with recommendations for future work to further improve understanding, forecasts, and hazard communication.

Published
2017

32. The Extratropical Transition of Tropical Cyclones. Part I: Cyclone Evolution and Direct Impacts

Author

Evans, Clark, primary, Wood, Kimberly M., additional, Aberson, Sim D., additional, Archambault, Heather M., additional, Milrad, Shawn M., additional, Bosart, Lance F., additional, Corbosiero, Kristen L., additional, Davis, Christopher A., additional, Dias Pinto, João R., additional, Doyle, James, additional, Fogarty, Chris, additional, Galarneau, Thomas J., additional, Grams, Christian M., additional, Griffin, Kyle S., additional, Gyakum, John, additional, Hart, Robert E., additional, Kitabatake, Naoko, additional, Lentink, Hilke S., additional, McTaggart-Cowan, Ron, additional, Perrie, William, additional, Quinting, Julian F. D., additional, Reynolds, Carolyn A., additional, Riemer, Michael, additional, Ritchie, Elizabeth A., additional, Sun, Yujuan, additional, and Zhang, Fuqing, additional

Published
2017
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