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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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Laura D. Riihimaki, Connor Flynn, Allison McComiskey, Dan Lubin, Yann Blanchard, J. Christine Chiu, Graham Feingold, Daniel R. Feldman, Jake J. Gristey, Christian Herrera, Gary Hodges, Evgueni Kassianov, Samuel E. LeBlanc, Alexander Marshak, Joseph J. Michalsky, Peter Pilewskie, Sebastian Schmidt, Ryan C. Scott, Yolanda Shea, Kurtis Thome, Richard Wagener, and Bruce Wielicki

Abstract

Industry advances have greatly reduced the cost and size of ground-based shortwave (SW) sensors for the ultraviolet, visible, and near-infrared spectral ranges that make up the solar spectrum, while simultaneously increasing their ruggedness, reliability, and calibration accuracy needed for outdoor operation. These sensors and collocated meteorological equipment are an important part of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) User Facility, which has supported parallel integrated measurements of atmospheric and surface properties for more than two decades at fixed and mobile sites around the world. The versatile capability of these ground-based measurements includes 1) rich spectral information required for retrieving cloud and aerosol microphysical properties, such as cloud phase, cloud particle size, and aerosol size distributions, and 2) high temporal resolution needed for capturing fast evolution of cloud microphysical properties in response to rapid changes in meteorological conditions. Here we describe examples of how ARM’s spectral radiation measurements are being used to improve understanding of the complex processes governing microphysical, optical, and radiative properties of clouds and aerosol.

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Stéphane Vannitsem, John Bjørnar Bremnes, Jonathan Demaeyer, Gavin R. Evans, Jonathan Flowerdew, Stephan Hemri, Sebastian Lerch, Nigel Roberts, Susanne Theis, Aitor Atencia, Zied Ben Bouallègue, Jonas Bhend, Markus Dabernig, Lesley De Cruz, Leila Hieta, Olivier Mestre, Lionel Moret, Iris Odak Plenković, Maurice Schmeits, Maxime Taillardat, Joris Van den Bergh, Bert Van Schaeybroeck, Kirien Whan, and Jussi Ylhaisi

Abstract

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many national meteorological services (NMS), with, for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on in this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations, the necessity to preserve space–time correlation of high-dimensional corrected fields, the need to reduce the impact of model changes affecting the parameters of the corrections, the necessity for techniques to merge different types of forecasts and ensembles with different behaviors, and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues are also discussed.

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Michael Peterson

Abstract

The Geostationary Lightning Mappers (GLMs) on NOAA’s current Geostationary Operational Environmental Satellites (GOES) map the lateral development of lightning flashes across the Western Hemisphere up to 54° latitude. As staring instruments that continuously observe the Americas (GOES-16) and the Pacific Ocean (GOES-17), the GLMs resolve the spatial extent of even the rarest and most exceptional lightning flashes. GOES-16 GLM observations that include the Americas’ hotspots for the largest and longest-lasting lightning “megaflashes” are used to document where and when mesoscale lightning occurs that exceeds the largest (321 km) and longest-lasting (7.74 s) flashes that have been measured by ground-based instruments. The most exceptional GLM megaflashes in terms of extent (709 km) and duration (16.730 s) were recently recognized as global lightning extremes by the World Meteorological Organization (WMO). These world record cases beat the next-largest flash by 36 km and the next-longest-lasting flash by 1.5 s. The top GLM megaflashes between 1 January 2018 and 15 January 2020 that exceed the previous LMA records are concentrated in the central United States (most frequently along the Oklahoma–Arkansas border) and southern Brazil (Rio Grande do Sul) and Uruguay. The top North American megaflashes are most common from April through June and occur on between 4 and 14 nights per month. The top South American megaflashes are most frequent between October and January and likewise have a nocturnal preference following the diurnal cycle of mesoscale convective systems (MCSs). Potential future field programs that aim to observe extreme megaflashes should focus on these regions and seasons.

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Mark S. Kulie, Claire Pettersen, Aronne J. Merrelli, Timothy J. Wagner, Norman B. Wood, Michael Dutter, David Beachler, Todd Kluber, Robin Turner, Marian Mateling, John Lenters, Peter Blanken, Maximilian Maahn, Christopher Spence, Stefan Kneifel, Paul A. Kucera, Ali Tokay, Larry F. Bliven, David B. Wolff, and Walter A. Petersen

BAMS Capsule:

Profiling radar and ground-based in situ observations reveal the ubiquity of snowfall produced by shallow clouds, the importance of near-surface snowfall enhancement processes, and regime-dependent snow particle microphysical variability in the Northern Great Lakes Region.

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