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José Vicencio, Roberto Rondanelli, Diego Campos, Raúl Valenzuela, René Garreaud, Alejandra Reyes, Rodrigo Padilla, Ricardo Abarca, Camilo Barahona, Rodrigo Delgado, and Gabriela Nicora

Abstract

In late May 2019, at least seven tornadoes were reported within a 24-h period in southern Chile (western South America, 36°–38°S), including EF1 and EF2 events causing substantial damage to infrastructure, dozens of injuries, and one fatality. Despite anecdotal evidence and chronicles of similar historical events, the threat from tornadoes in Chile was regarded with skepticism until the 2019 outbreak. Herein, we describe the synoptic-scale features instrumental in the development of these tornadic storms, including an extended southwest–northeast trough along the South Pacific, with a large postfrontal instability area. Tornadic storms appear to be embedded in a modestly unstable environment (positive convective available potential energy but less than 1,000 J kg−1) and strong low- and midlevel wind shear, with high near-surface storm-relative helicity values (close to −200 m2 s−2), clearly differing from the Great Plains tornadoes in North America (with highly unstable environments) but resembling cold-season tornadoes previously observed in the midlatitudes of North America, Australia, and Europe. Reanalyzing rainfall and lightning data from the last 10 years, we found that tornadic storms in our region occur associated with locally extreme values of both CAPE and low-level wind shear, where a combination of the two in a low-level vorticity generation parameter appears as a simple first-order discriminant between tornadic and nontornadic environments. Future research should thoroughly examine historical events worldwide to assemble a database of high-shear, low-CAPE midlatitude storms and help improve our understanding of these storms’ underlying physics.

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Jessica Spaccio, Arthur DeGaetano, and Nolan Doesken
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Minghua Zheng, Luca Delle Monache, Xingren Wu, F. Martin Ralph, Bruce Cornuelle, Vijay Tallapragada, Jennifer S. Haase, Anna M. Wilson, Matthew Mazloff, Aneesh Subramanian, and Forest Cannon

Abstract

Conventional observations of atmospheric rivers (ARs) over the northeastern Pacific Ocean are sparse. Satellite radiances are affected by the presence of clouds and heavy precipitation, which impact their distribution in the lower atmosphere and in precipitating areas. The goal of this study is to document a data gap in existing observations of ARs in the northeastern Pacific, and to investigate how a targeted field campaign called AR Reconnaissance (AR Recon) can effectively fill this gap. When reconnaissance data are excluded, there is a gap in AR regions from near the surface to the middle troposphere (below 450 hPa), where most water vapor and its transport are concentrated. All-sky microwave radiances provide data within the AR object, but their quality is degraded near the AR core and its leading edge, due to the existence of thick clouds and precipitation. AR Recon samples ARs and surrounding areas to improve downstream precipitation forecasts over the western United States. This study demonstrates that despite the apparently extensive swaths of modern satellite radiances, which are critical to estimate large-scale flow, the data collected during 15 AR Recon cases in 2016, 2018, and 2019 supply about 99% of humidity, 78% of temperature, and 45% of wind observations in the critical maximum water vapor transport layer from the ocean surface to 700 hPa in ARs. The high-vertical-resolution dropsonde observations in the lower atmosphere over the northeastern Pacific Ocean can significantly improve the sampling of low-level jets transporting water vapor to high-impact precipitation events in the western United States.

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Cenlin He, Olivia Clifton, Emmi Felker-Quinn, S. Ryan Fulgham, Julieta F. Juncosa Calahorrano, Danica Lombardozzi, Gemma Purser, Mj Riches, Rebecca Schwantes, Wenfu Tang, Benjamin Poulter, and Allison L. Steiner

Abstract

Interactions between air pollution and terrestrial ecosystems play an important role in the Earth system. However, process-based knowledge of air pollution–terrestrial ecosystem interactions is limited, hindering accurate quantification of how changes in tropospheric chemistry, biogeochemical cycling, and climate affect air quality and its impact on humans and ecosystems. Here we summarize current challenges and future directions for advancing the understanding of air pollution–ecosystem interactions by synthesizing discussions from a multidisciplinary group of scientists at a recent Integrated Land Ecosystem–Atmosphere Processes Study (iLEAPS) early-career workshop. Specifically, we discuss the important elements of air pollution–terrestrial ecosystem interactions, including vegetation and soil uptake and emissions of air pollutants and precursors, in-canopy chemistry, and the roles of human activities, fires, and meteorology. We highlight the need for a coordinated network of measurements of long-term chemical fluxes and related meteorological and ecological quantities with expanded geographic and ecosystem representation, data standardization and curation to reduce uncertainty and enhance observational syntheses, integrated multiscale observational and modeling capabilities, collaboration across scientific disciplines and geographic regions, and active involvement by stakeholders and policymakers. Such an enhanced network will continue to facilitate the process-level understanding and thus predictive ability of interactions between air pollution and terrestrial ecosystems and impacts on local-to-global climate and human health.

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Markus Petters

Abstract

Student-centered active learning pedagogies improve learning outcomes and increase the engagement of underrepresented groups. Implementing such pedagogies requires interactive tools for students to manipulate inputs and use the outputs to construct knowledge. This work introduces interactive worksheets for teaching about atmospheric aerosol and cloud physics and describes the toolchain to create and deliver the content. The material is appropriate for upper-level undergraduate and graduate instruction with pedagogy based on process-oriented guided inquiry learning. Students playfully interact with physical relationships and atmospheric models. Two examples are the interaction with an aerosol–cloud parcel model for simulating the early stage of cloud formation and the interaction with the Bowen model for simulating the formation of rain by coalescence. Photos, text, figures, and software associated with the project are free to be shared and free to be adapted. In addition to focusing on discipline-based learning objectives, the worksheets emphasize interacting with real-world data and practicing graph comprehension. Hosting the content in the cloud ensures reliable and scalable delivery to any device with a browser and Internet access. The worksheets are designed to be used in a student-centered active learning classroom but can also be used in an online setting.

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David Mayers and Christopher Ruf

Abstract

MTrack is an automated algorithm that determines the center location (latitude and longitude) of a tropical cyclone from a scalar wind field derived from satellite observations. Accurate storm centers are useful for operational forecasting of tropical cyclones and for their reanalysis (e.g., research on storm surge modeling). Currently, storm center fixes have significantly larger errors for weak, disorganized storms. The MTrack algorithm presented here improves storm centers in some of those cases. It is also automated and, therefore, less subjective than manual fixes made by forecasters. The MTrack algorithm, which was originally designed to work with CYGNSS wind speed measurements, is applied to SMAP winds for the first time. The average difference between MTrack and Best Track storm center locations is 21, 36, and 46 km for major hurricanes, category 1–2 hurricanes, and tropical storms, respectively. MTrack is shown to operate successfully when a storm is only partially sampled by the observing satellite and when the eye of the storm is not resolved.

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Sharanya J. Majumdar, Juanzhen Sun, Brian Golding, Paul Joe, Jimy Dudhia, Olivier Caumont, Krushna Chandra Gouda, Peter Steinle, Béatrice Vincendon, Jianjie Wang, and Nusrat Yussouf

Abstract

Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.

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Pao-Liang Chang, Jian Zhang, Yu-Shuang Tang, Lin Tang, Pin-Fang Lin, Carrie Langston, Brian Kaney, Chia-Rong Chen, and Kenneth Howard

Abstract

Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.

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C. D. Hewitt, F. Guglielmo, S. Joussaume, J. Bessembinder, I. Christel, F. J. Doblas-Reyes, V. Djurdjevic, N. Garrett, E. Kjellström, A. Krzic, M. Máñez Costa, and A. L. St. Clair

Abstract

Climate observations, research, and models are used extensively to help understand key processes underlying changes to the climate on a range of time scales from months to decades, and to investigate and describe possible longer-term future climates. The knowledge generated serves as a scientific basis for climate services that are provided with the aim of tailoring information for decision-makers and policy-makers. Climate models and climate services are crucial elements for supporting policy and other societal actions to mitigate and adapt to climate change, and for making society better prepared and more resilient to climate-related risks. We present recommendations for future research topics for climate modeling and for climate services. These recommendations were produced by a group of experts in climate modeling and climate services, selected based on their individual leadership roles or participation in international activities. The recommendations were reached through extensive analysis, consideration and discussion of current and desired research capabilities, and wider engagement and refinement of the recommendations was achieved through a targeted workshop of initial recommendations and an open meeting at the European Geosciences Union General Assembly. The findings emphasize how research and innovation activities in the fields of climate modeling and climate services can contribute to improving climate knowledge and information with saliency for users in order to enhance capacity to transition to a sustainable and resilient society. The findings are relevant worldwide but are deliberately intended to influence the European Commission’s next major multi-annual framework program of research and innovation over the period 2021–27.

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Philip J. Klotzbach, Carl J. Schreck III, Gilbert P. Compo, Steven G. Bowen, Ethan J. Gibney, Eric C. J. Oliver, and Michael M. Bell

Abstract

The 1933 Atlantic hurricane season was extremely active, with 20 named storms and 11 hurricanes including 6 major (category 3+; 1-min maximum sustained winds ≥96 kt) hurricanes occurring. The 1933 hurricane season also generated the most accumulated cyclone energy (an integrated metric that accounts for frequency, intensity, and duration) of any Atlantic hurricane season on record. A total of 8 hurricanes tracked through the Caribbean in 1933—the most on record. In addition, two category 3 hurricanes made landfall in the United States just 23 h apart: the Treasure Coast hurricane in southeast Florida followed by the Cuba–Brownsville hurricane in south Texas. This manuscript examines large-scale atmospheric and oceanic conditions that likely led to such an active hurricane season. Extremely weak vertical wind shear was prevalent over both the Caribbean and the tropical Atlantic throughout the peak months of the hurricane season, likely in part due to a weak-to-moderate La Niña event. These favorable dynamic conditions, combined with above-normal tropical Atlantic sea surface temperatures, created a very conducive environment for hurricane formation and intensification. The Madden–Julian oscillation was relatively active during the summer and fall of 1933, providing subseasonal conditions that were quite favorable for tropical cyclogenesis during mid- to late August and late September to early October. The current early June and August statistical models used by Colorado State University would have predicted a very active 1933 hurricane season. A better understanding of these extremely active historical Atlantic hurricane seasons may aid in anticipation of future hyperactive seasons.

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