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Margaret E. Mooney, Cathy Middlecamp, Jonathan Martin, and Steve A. Ackerman

Abstract

Advances in science literacy documented in an undergraduate course on Climate and Climate Change at the University of Wisconsin-Madison (UW) in 2020 begged the question: does the new climate knowledge translate into behavior change? Traditionally a “knowledge-action gap” has undermined educators’ efforts to galvanize actions towards mitigating climate change. Through a survey focused on carbon footprint and civic engagement and testimonials gleaned from students’ capstone elevator speeches, this study presents an encouraging update on young adults’ response to the climate crisis. By comparing responses to a similar survey distributed to UW students in another undergraduate course in 2021, we show that the course focused on Climate and Climate Change motivated behavior modifications that lighten carbon footprint to a greater degree than a traditional introductory meteorology course.

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Yi-Jie Zhu, Jennifer M. Collins, Philip J. Klotzbach, and Carl J. Schreck III

Abstract

Hurricane Ida recently became one of the strongest hurricanes to hit Louisiana on record, with an estimated landfalling maximum sustained wind of 130 kt. Although Hurricane Ida made landfall at a similar time of year and landfall location as Hurricane Katrina (2005), Ida’s postlandfall decay rate was much weaker than Hurricane Katrina. This manuscript includes a comparative analysis of pre- and post-landfall synoptic conditions for Hurricane Ida and other historical major landfalling hurricanes (Category 3+ on the Saffir-Simpson Hurricane Wind Scale) along the Gulf Coast since 1983, with a particular focus on Hurricane Katrina.

Abundant precipitation in southeastern Louisiana prior to Ida’s landfall increased soil moisture. This increased soil moisture along with extremely weak overland steering flow likely slowed the storm’s weakening rate post-landfall. Offshore environmental factors also played an important role, particularly anomalously high nearshore sea surface temperatures and weak vertical wind shear that fueled the rapid intensification of Ida just before landfall. Strong nearshore vertical wind shear weakened Hurricane Katrina before landfall, and moderate northward steering flow caused Katrina to move inland relatively quickly, aiding in its relatively fast weakening rate following landfall.

The results of this study improve our understanding of critical factors influencing the evolution of the nearshore intensity of major landfalling hurricanes in the Gulf of Mexico. This study can help facilitate forecasting and preparation for inland hazards resulting from landfalling hurricanes with nearshore intensification and weak post-landfall decay.

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Pablo Ortega, Edward W. Blockley, Morten Køltzow, François Massonnet, Irina Sandu, Gunilla Svensson, Juan C. Acosta Navarro, Gabriele Arduini, Lauriane Batté, Eric Bazile, Matthieu Chevallier, Rubén Cruz-García, Jonathan J. Day, Thierry Fichefet, Daniela Flocco, Mukesh Gupta, Kerstin Hartung, Ed Hawkins, Claudia Hinrichs, Linus Magnusson, Eduardo Moreno-Chamarro, Sergio Pérez-Montero, Leandro Ponsoni, Tido Semmler, Doug Smith, Jean Sterlin, Michael Tjernström, Ilona Välisuo, and Thomas Jung

Abstract

The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.

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Natalie J. Harvey, Luke M. Western, Helen F. Dacre, and Antonio Capponi

Abstract

Making decisions about the appropriate action to take when presented with uncertain information is difficult, particularly in an emergency response situation. Decision makers can be influenced by factors such as how information is framed, their risk sensitivity and the impact of false alarms. Uncertainty arising from limited knowledge of the current state or future outcome of an event is unavoidable when making decisions. However, there is no universally agreed method on the design and presentation of uncertainty information. The aim of this article is to demonstrate that decision theory can be applied to an ensemble of plausible realisations of a situation to build a transparent framework which can then be used to determine the optimal action by assigning losses to different decision outcomes. The optimal action is then visualized, enabling the uncertainty information to be presented in a condensed manner suitable for decision makers. The losses are adaptable depending on the hazard and the individual operational model of the decision maker. To illustrate this approach, decision theory will be applied to an ensemble of volcanic ash simulations used for the purpose of airline flight planning, focussing on the 2019 eruption of Russian volcano Raikoke. Three idealised scenarios are constructed to show the impact of different loss models on the optimal action. In all cases, applying decision theory can significantly alter the regions, and therefore potential flight tracks, identified as potentially hazardous. Thus we show that different end users would and should make different decisions when presented with the same probabilistic information based on their individual user requirements.

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Gijs de Boer, Gillian Young McCusker, Georgia Sotiropoulou, Yvette Gramlich, Jo Browse, and Jean-Christophe Raut

Abstract

Conference Title: 2nd QuIESCENT (Quantifying the Indirect Effect: from Sources to Climate Effects of Natural and Transported aerosol in the Arctic) Workshop

What: Atmospheric scientists, shared and discussed recent work to understand the complex interactions between aerosols, clouds, precipitation, radiation and dynamics at northern high latitudes, as well as recent and upcoming field campaigns to improve that understanding.

When: 30 March – 1 April, 2022

Where: Tromsø, Norway

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Ryan M. May, Kevin H. Goebbert, Jonathan E. Thielen, John R. Leeman, M. Drew Camron, Zachary Bruick, Eric C. Bruning, Russell P. Manser, Sean C. Arms, and Patrick T. Marsh

Abstract

MetPy is an open-source, Python-based package for meteorology, providing domain specific functionality built extensively on top of the robust scientific Python software stack, which includes libraries like NumPy, SciPy, Matplotlib, and xarray. The goal of the project is to bring the weather analysis capabilities of GEMPAK (and similar software tools) into a modern computing paradigm. MetPy strives to employ best practices in its development, including software tests, continuous integration, and automated publishing of web-based documentation. As such, MetPy represents a sustainable, long-term project that fills a need for the meteorological community. MetPy’s development is substantially driven by its user community, both through feedback on a variety of open, public forums like Stack Overflow, and through code contributions facilitated by the GitHub collaborative software development platform.

MetPy has recently seen the release of version 1.0, with robust functionality for analyzing and visualizing meteorological datasets. While previous versions of MetPy have already seen extensive use, the 1.0 release represents a significant milestone in terms of completeness and a commitment to long-term support for the programming interfaces. This article provides an overview of MetPy’s suite of capabilities, including its use of labeled arrays and physical unit information as its core data model, unit-aware calculations, cross-sections, Skew-T and GEMPAK-like plotting, station model plots, and support for parsing a variety of meteorological data formats. The general roadmap for future planned development for MetPy is also discussed.

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Louis W. Uccellini, Richard W. Spinrad, Dorothy M. Koch, Craig N. McLean, and William M. Lapenta

Abstract

NOAA has launched the Earth Prediction Innovation Center (EPIC) in partnership with the Weather Enterprise (academia, government, and industry) to bring the power of distributed innovation to bear on one of the greatest challenges of our time. EPIC provides a collaborative framework building upon the community-driven Unified Forecast System (UFS) to accelerate innovative improvements to the Nation’s forecast system in order to save lives, protect property, and enhance the economy. This article describes NOAA’s strategic, tactical and organizational approaches to utilize EPIC to transform the world-leading U.S. national forecast systems into an even stronger and more effective community-based, computationally-advanced Earth prediction system to meet the expanding and pressing needs of national and international societies.

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Agniv Sengupta, Bohar Singh, Mike DeFlorio, Colin Raymond, Andrew W. Robertson, Xubin Zeng, Duane E. Waliser, and Jeanine Jones
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Bart Geerts and Robert M. Rauber

Abstract

This essay is intended to provide stakeholders and news outlets with a plain-language summary of orographic cloud seeding research, new capabilities, and prospects. Specifically, we address the question of whether a widely-practiced type of weather modification, glaciogenic seeding of orographic clouds throughout the cold season, can produce an economically-useful increase in precipitation over a catchment-scale area. Our objective is to clarify current scientific understanding of how cloud seeding may affect precipitation, in terms that are more accessible than in the peer-reviewed literature. Public confidence that cloud seeding “works” is generally high in regions with operational seeding, notwithstanding decades of scientific reports indicating that the changes in precipitation are uncertain. Randomized seeding experiments have a solid statistical foundation and focus on the outcome, but, in light of the small seeding signal and the naturally noisy nature of precipitation, they generally require too many cases to be affordable, and therefore are discouraged. A complementary method, physical evaluation, examines changes in cloud and precipitation processes when seeding material is injected, and yields insights into the most suitable ambient conditions. Recent physical evaluations have established a robust, well-documented scientific basis for glaciogenic seeding of cold-season orographic clouds to enhance precipitation. The challenge of seeding impact assessment remains, but evidence is provided that, thanks to recent significant progress in observational and computational capabilities, the research community is finally on track to be able to provide stakeholders with guidance on the likely quantitative precipitation impact of cloud seeding in their region. We recommend further process-level evaluations combined with highly-resolved, well-constrained numerical simulations of seasonal cloud seeding.

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Sid-Ahmed Boukabara and Ross N. Hoffmanb

Abstract

The Advanced Systems Performance Evaluation tool for NOAA (ASPEN), is developed to help support designing and evaluating existing and planned observing systems in terms of comparative assessment, tradeoffs analysis, and design optimization studies. ASPEN is a dynamic tool that rapidly assesses the benefit and cost effectiveness of environmental data obtained from any set of observing systems: whether ground-based or space-based, whether an individual sensor or a collection of sensors. The ASPEN assessed cost effectiveness accounts for the level of ability to measure the environment, the cost(s) associated with acquiring these measurements, and the degree of usefulness of these measurements to users and applications. It computes both the use benefit, measured as a requirements-satisfaction metric, and the cost effectiveness (equal to the benefit to cost ratio). ASPEN provides a uniform interface to compare the performance of different observing systems and to capture the requirements and priorities of applications. This interface describes the environment in terms of geophysical observables and their attributes. A prototype implementation of ASPEN is described and demonstrated in this study to assess the benefits of several observing systems for a range of applications. ASPEN could be extended to other types of studies such as assessing the cost effectiveness of commercial data, to applications in all the NOAA mission service areas, and ultimately to societal application areas and thereby become a valuable addition to the observing systems assessment toolbox.

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