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Lars van Galen, Oscar Hartogensis, Imme Benedict, and Gert-Jan Steeneveld

Abstract

We report on renewing the undergraduate course about synoptic meteorology and weather forecasting at Wageningen University (The Netherlands) to meet the current-day requirements of operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning and personal safety, especially in the case of weather extremes. Numerical weather prediction models (NWP) have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for 21st century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

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Marina Baldissera Pacchetti, Jillian Schacher, Suraje Dessai, Marta Bruno Soares, Rob Lawlor, and Joseph Daron
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James H. Ruppert Jr., Steven E. Koch, Xingchao Chen, Yu Du, Anton Seimon, Y. Qiang Sun, Junhong Wei, and Lance F. Bosart

Abstract

Over the course of his career, Fuqing Zhang drew vital new insights into the dynamics of meteorologically significant mesoscale gravity waves (MGWs), including their generation by unbalanced jet streaks, their interaction with fronts and organized precipitation, and their importance in midlatitude weather and predictability. Zhang was the first to deeply examine “spontaneous balance adjustment” – the process by which MGWs are continuously emitted as baroclinic growth drives the upper-level flow out of balance. Through his pioneering numerical model investigation of the large-amplitude MGW event of 4 January 1994, he additionally demonstrated the critical role of MGW–moist convection interaction in wave amplification.

Zhang’s curiosity-turned-passion in atmospheric science covered a vast range of topics and led to the birth of new branches of research in mesoscale meteorology and numerical weather prediction. Yet, it was his earliest studies into midlatitude MGWs and their significant impacts on hazardous weather that first inspired him. Such MGWs serve as the focus of this review, wherein we seek to pay tribute to his groundbreaking contributions, review our current understanding, and highlight critical open science issues. Chief among such issues is the nature of MGW amplification through feedback with moist convection, which continues to elude our understanding. The pressing nature of this subject is underscored by the continued failure of operational numerical forecast models to adequately predict most large-amplitude MGW events. Further research into such issues therefore presents a valuable opportunity to improve the understanding and forecasting of this high-impact weather phenomenon, and in turn to preserve the spirit of Zhang’s dedication to this subject.

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Michael Steele, Hajo Eicken, Uma Bhatt, Peter Bieniek, Ed Blanchard-Wrigglesworth, Helen Wiggins, Betsy Turner-Bogren, Lawrence Hamilton, Joseph Little, François Massonnet, Walter N. Meier, James Overland, Mark Serreze, Julienne Stroeve, John Walsh, and Muyin Wang
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Douglas J Parker, Alan M Blyth, Steven J. Woolnough, Andrew J. Dougill, Caroline L. Bain, Estelle de Coning, Mariane Diop-Kane, Andre Kamga Foamouhoue, Benjamin Lamptey, Ousmane Ndiaye, Paolo Ruti, Elijah A. Adefisan, Leonard K Amekudzi, Philip Antwi-Agyei, Cathryn E. Birch, Carlo Cafaro, Hamish Carr, Benard Chanzu, Samantha J. Clarke, Helen Coskeran, Sylvester K. Danuor, Felipe M. de Andrade, Kone Diakaria, Cheikh Dione, Cheikh Abdoulahat Diop, Jennifer K. Fletcher, Amadou T Gaye, James L. Groves, Masilin Gudoshava, Andrew J. Hartley, Linda C. Hirons, Ishiyaku Ibrahim, Tamora D. James, Kamoru A. Lawal, John H Marsham, J N Mutemi, Emmanuel Chilekwu Okogbue, Eniola Olaniyan, J. B. Omotosho, Joseph Portuphy, Alexander J. Roberts, Juliane Schwendike, Zewdu T. Segele, Thorwald H.M. Stein, Andrea L Taylor, Christopher M Taylor, Tanya A. Warnaars, Stuart Webster, Beth J. Woodhams, and Lorraine Youds

Abstract

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps.

Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment.

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Luz Calvo, Isadora Christel, Marta Terrado, Fernando Cucchietti, and Mario Pérez-Montoro

Abstract

The visual communication of climate information is one of the cornerstones of climate services. It often requires the translation of multidimensional data to visual channels by combining colors, distances, angles, and glyph sizes. However, visualizations including too many layers of complexity can hinder decision-making processes by limiting the cognitive capacity of users, therefore affecting their attention, recognition, and working memory. Methodologies grounded on the fields of user-centered design, user interaction and cognitive psychology, which are based on the needs of the users, have a lot to contribute to the climate data visualization field. Here, we apply these methodologies to the redesign of an existing climate service tool tailored to the wind energy sector. We quantify the effect of the redesign on the users’ experience performing typical daily tasks, using both quantitative and qualitative indicators that include response time, success ratios, eye-tracking measures, user perceived effort and comments among others. Changes in the visual encoding of uncertainty and the use of interactive elements in the redesigned tool reduced the users’ response time by half, significantly improved success ratios, and eased decision making by filtering non-relevant information. Our results show that the application of user-centered design, interaction, and cognitive aspects to the design of climate information visualizations reduces the cognitive load of users during tasks performance, thus improving user experience. These aspects are key to successfully communicating climate information in a clearer and more accessible way, making it more understandable for both technical and non-technical audiences.

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Graciela B. Raga, Luis A. Ladino, Darrel Baumgardner, Carolina Ramirez-Romero, Fernanda Córdoba, Harry Alvarez-Ospina, Daniel Rosas, Talib Amador, Javier Miranda, Irma Rosas, Alejandro Jaramillo, Jacqueline Yakobi-Hancock, Jong Sung Kim, Leticia Martínez, Eva Salinas, and Bernardo Figueroa

Abstract

Biomass burning (BB) emissions and African dust (AD) are often associated with poor regional air quality, particularly in the tropics. The Yucatan Peninsula is a fairly pristine site due to predominant trade winds, but occasionally BB and AD plumes severely degrade its air quality. The African Dust And Biomass Burning Over Yucatan (ADABBOY) project (January 2017–August 2018) was conducted in the Yucatan Peninsula to characterize physical and biological properties of particulate pollution at remote seaside and urban sites. The 18-month-long project quantified the large interannual variability in frequency and spatial extent of BB and AD plumes. Remote and urban sites experienced air quality degradation under the influence of these plumes, with up to 200% and 300% increases in coarse particle mass under BB and AD influence, respectively. ADABBOY is the first project to systematically characterize elemental composition of airborne particles as a function of these sources and identify bioaerosol over Yucatan. Bacteria, actinobacteria (both continental and marine), and fungi propagules vary seasonally and interannually and revealed the presence of very different species and genera associated with different sources. A novel contribution of ADABBOY was the determination of the ice-nucleating abilities of particles emitted by different sources within an undersampled region of the world. BB particles were found to be inefficient ice-nucleating particles at temperatures warmer than −20°C, whereas both AD and background marine aerosol activated ice-nucleating particles below −10°C.

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Sharon Stammerjohn, Ted A. Scambos, Susheel Adusumilli, Sandra Barreira, Germar H. Bernhard, Deniz Bozkurt, Seth M. Bushinsky, Kyle R. Clem, Steve Colwell, Lawrence Coy, Jos De Laat, Marcel D. du Plessis, Ryan L. Fogt, Annie Foppert, Helen Amanda Fricker, Alex S. Gardner, Sarah T. Gille, Tessa Gorte, Bryan Johnson, Eric Keenan, Daemon Kennett, Linda M. Keller, Natalya A. Kramarova, Kaisa Lakkala, Matthew A. Lazzara, Jan T. M. Lenaerts, Jan L. Lieser, Zhi Li, Hongxing Liu, Craig S. Long, Michael MacFerrin, Michelle L. Maclennan, Robert A. Massom, David Mikolajczyk, Lynn Montgomery, Thomas L. Mote, Eric R. Nash, Paul A. Newman, Irina Petropavlovskikh, Michael Pitts, Phillip Reid, Steven R. Rintoul, Michelle L. Santee, Elizabeth H. Shadwick, Alessandro Silvano, Scott Stierle, Susan Strahan, Adrienne J. Sutton, Sebastiaan Swart, Veronica Tamsitt, Bronte Tilbrook, Lei Wang, Nancy L. Williams, and Xiaojun Yuan
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Matthew L. Druckenmiller, Twila A. Moon, Richard L. Thoman, Thomas J. Ballinger, Logan T. Berner, Germar H. Bernhard, Uma S. Bhatt, Jarle W. Bjerke, Jason E. Box, R. Brown, John Cappelen, Hanne H. Christiansen, B. Decharme, C. Derksen, Dmitry Divine, D. S. Drozdov, A. Elias Chereque, Howard E. Epstein, L. M. Farquharson, Sinead L. Farrell, Robert S. Fausto, Xavier Fettweis, Vitali E. Fioletov, Bruce C. Forbes, Gerald V. Frost, Emily Gargulinski, Sebastian Gerland, Scott J. Goetz, Z. Grabinski, Jens-Uwe Grooß, Christian Haas, Edward Hanna, Inger Hanssen-Bauer, Stefan Hendricks, Robert M. Holmes, Iolanda Ialongo, K. Isaksen, Piyush Jain, Bjørn Johnsen, L. Kaleschke, A. L. Kholodov, Seong-Joong Kim, Niels J. Korsgaard, Zachary Labe, Kaisa Lakkala, Mark J. Lara, Bryant Loomis, K. Luojus, Matthew J. Macander, G. V. Malkova, Kenneth D. Mankoff, Gloria L. Manney, James W. McClelland, Walter N. Meier, Thomas Mote, L. Mudryk, Rolf Müller, K. E. Nyland, James E. Overland, T. Park, Olga Pavlova, Don Perovich, Alek Petty, Gareth K. Phoenix, Martha K. Raynolds, C. H. Reijmer, Jacqueline Richter-Menge, Robert Ricker, Vladimir E. Romanovsky, Lindsay Scott, Hazel Shapiro, Alexander I. Shiklomanov, Nikolai I. Shiklomanov, C. J. P. P. Smeets, Sharon L. Smith, Amber Soja, Robert G. M. Spencer, Sandy Starkweather, Dimitri A. Streletskiy, Anya Suslova, Tove Svendby, Suzanne E. Tank, Marco Tedesco, X. Tian-Kunze, Mary-Louise Timmermans, Hans Tømmervik, Mikhail Tretiakov, Mark Tschudi, Sofia Vakhutinsky, Dirk van As, R. S. W. van de Wal, Sander Veraverbeke, Donald A. Walker, John E. Walsh, Muyin Wang, Melinda Webster, Øyvind Winton, K. Wood, Alison York, and Robert Ziel
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Josh Welty and Xubin Zeng

Abstract

Snowmelt is an essential process for the health and sustenance of numerous communities and ecosystems across the globe, though it also presents potential hazards when ablation processes are exceedingly rapid. Using 4-km daily snow water equivalent, temperature, and precipitation data for three decades (1988–2017), here we provide a broad characterization of extreme snowmelt episodes over the conterminous United States in terms of magnitude, timing, and coincident synoptic weather patterns. Larger-magnitude extreme snowmelt events usually coincide with minimal precipitation and elevated temperatures. However, certain regions, particularly mountainous regions and the northeastern United States, exhibit greater likelihood of extreme snowmelt events during pronounced rain-on-snow events. During snowmelt extremes, snowmelt rate often exceeds precipitation in many regions. Meteorological patterns and associated water vapor transport most directly connected to extreme events over different regions are classified via a machine-learning technique. Over the 30-yr study period, there is a weakly increasing trend in the frequency of extremes, though this does not necessarily signify an increase in snowmelt magnitudes.

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