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Xin-Zhong Liang
,
Drew Gower
,
Jennifer A. Kennedy
,
Melissa Kenney
,
Michael C. Maddox
,
Michael Gerst
,
Guillermo Balboa
,
Talon Becker
,
Ximing Cai
,
Roger Elmore
,
Wei Gao
,
Yufeng He
,
Kang Liang
,
Shane Lotton
,
Leena Malayil
,
Megan L. Matthews
,
Alison M. Meadow
,
Christopher M. U. Neale
,
Greg Newman
,
Amy Rebecca Sapkota
,
Sanghoon Shin
,
Jonathan Straube
,
Chao Sun
,
You Wu
,
Yun Yang
, and
Xuesong Zhang

Abstract

Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

Open access
Walter Dabberdt
,
Darrel Baumgardner
,
Robert Bornstein
,
Gregory Carmichael
,
Richard Clark
,
Jeffrey Collett
,
Harindra Fernando
,
Efi Foufoula-Georgiou
,
Dev Niyogi
,
Mohan Ramamurthy
,
Alan Robock
, and
Julie Winkler
Open access
Zied Ben Bouallègue
,
Mariana C A Clare
,
Linus Magnusson
,
Estibaliz Gascón
,
Michael Maier-Gerber
,
Martin Janoušek
,
Mark Rodwell
,
Florian Pinault
,
Jesper S Dramsch
,
Simon T K Lang
,
Baudouin Raoult
,
Florence Rabier
,
Matthieu Chevallier
,
Irina Sandu
,
Peter Dueben
,
Matthew Chantry
, and
Florian Pappenberger

Abstract

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incremental progress in traditional numerical weather prediction (NWP) known as the “quiet revolution” of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made from increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allow forecasts that require much lower computational costs and that are highly-competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training.

Open access
Gabor Vali
and
Russell C. Schnell

Abstract

An overview is given of the path of research that led from asking how hailstones originate to the discovery that ice nucleation can be initiated by bacteria and other microorganisms at temperatures as high as −2°C. The major steps along that path were finding exceptionally effective ice nucleators in soils of high content of decayed vegetative matter, then in decaying tree leaves, then in plankton-laden ocean water. Eventually, it was shown that Pseudomonas syringae bacteria were responsible for the most of the observed activity. That identification coincided with the demonstration that the same bacteria cause frost damage on plants. Ice nucleation by bacteria meant an unexpected turn in the understanding of ice nucleation and of ice formation in the atmosphere. Subsequent research confirmed the unique effectiveness of ice nucleating particles of biological origin, referred to as bio-INPs, so that bio-INPs are now considered to be important elements of lower-tropospheric cloud processes. Nonetheless, some of the questions which originally motivated the research are still unresolved, so that revisiting the early work may be helpful to current endeavors. Part 1 of this manuscript summarizes how the discovery progressed. Part 2, (Schnell and Vali, 2024; SV24) shows the relationship between bio-INPs in soils and in precipitation with climate, and other findings. The online Supplemental Material contains a bibliography of recent work about bio-INPs.

Open access
Russell C. Schnell
and
Gabor Vali

Abstract

In Part 1 (Vali and Schnell, 2024; VS24) we described the discoveries we and our associates made in the 1960s and 1970s about biological ice nucleators (bio-INPs). Bio-INPs are far more effective than mineral INPs at temperatures above −10°C. The bio-INPs were found in decayed vegetation and in ocean water, then bacteria were identified as being the most active source for this remarkable activity. In this Part 2, we recount how, within a few years, the worldwide distribution of bio-INP sources was shown to correlate with climate zones, as was the abundance of INPs in precipitation. Oceanic sources were further studied and the presence of bio-INPs in fog diagnosed. The potential for release of bio-INPs from to the atmosphere was demonstrated. Bacterial INPs were found to play a crucial role in a plant’s frost resistance. These and other early developments of biological INPs are described. A bibliography of related recent literature is presented in the online Part 1 Supplemental Material.

Open access
M. Timofeyeva-Livezey
,
Jenna Meyers
,
Stephen Baxter
,
Margaret Hurwitz
,
James Zdrojewski
,
Keith White
,
David Ross
,
Barbara Mayes Boustead
,
Viviane Silva
,
Christopher Stachelski
,
Audra Bruschi
,
Victor Murphy
,
Andrea Bair
,
David DeWitt
,
Richard Thoman
,
Fiona Horsfall
,
Brian Brettschneider
,
Elizabeth Vickery
,
Ray Wolf
, and
Bill Ward

Abstract

National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) has been providing national, regional, and local climate services for more than 20 years. The NWS climate services building blocks consist of service provision infrastructure, partnership and outreach, discovery of user needs and requirements, and service delivery at national, regional, local, and tribal levels. To improve services, the NWS climate services program accelerated user engagement through customer surveys, workshops, and collaborations. Since 2002, the annual Climate Prediction Applications Science Workshop has developed a community of climate information producers and users through sharing of climate science applications, decision support tools, and effective communication practices. Although NWS had been producing operational climate monitoring and prediction products for several decades, the Weather Research and Forecasting Innovation Act of 2017 (US Public Law 115-25, 2017) specifically mandated that NWS deliver services at subseasonal to seasonal (S2S) time scales, including periods from two weeks to two years. Looking ahead, both the Department of Commerce (DOC) and NOAA have included climate services in their new 2022-2026 strategic plans, including DOC’s goal to address the climate crisis through mitigation, adaptation, and resilience efforts and NOAA’s initiatives to build a Climate Ready Nation (CRN). The NWS Climate Services Program supports these strategic goals and CRN initiatives through integrating climate information into Impact-based Decision Support Services, the most critical element for implementation of the NWS strategy for a Weather-Ready Nation. This includes application of state-of-the-art climate monitoring and prediction products to the most societally relevant impacts while empowering regional and local climate delivery of enhanced services.

Open access
Diana Bernstein
Open access
Travis Griggs
,
James Flynn
,
Yuxuan Wang
,
Sergio Alvarez
,
Michael Comas
, and
Paul Walter

Abstract

Photochemical modeling outputs showing high ozone concentrations over the Gulf of Mexico and Galveston Bay during ozone episodes in the Houston-Galveston-Brazoria (HGB) region have not been previously verified using in-situ observations. Such data was collected systematically, for the first time, from July-October 2021 from three boats deployed for the Galveston Offshore Ozone Observations (GO3) and Tracking Aerosol Convection Interactions ExpeRiment - Air Quality (TRACER-AQ) field campaigns. A pontoon boat and a commercial vessel operated in Galveston Bay, while another commercial vessel operated in the Gulf of Mexico offshore of Galveston. All three boats had continuously operating sampling systems that included ozone analyzers and weather stations, and the two boats operating in Galveston Bay had a ceilometer. The sampling systems operated autonomously on the two commercial boats as they traveled their daily routes. Thirty-seven ozonesondes were launched over water on forecast high ozone days in Galveston Bay and the Gulf of Mexico. During the campaigns, multiple periods of ozone exceeding 100 ppbv were observed over water in Galveston Bay and the Gulf of Mexico. These events included previously identified conditions for high ozone events in the HGB region, such as the bay/sea breeze recirculation and post-frontal environments, as well as a localized coastal high ozone event after the passing of a tropical system (Hurricane Nicholas) that was not well forecast.

Open access
Chris Vagasky
,
Ronald L. Holle
,
Martin J. Murphy
,
John A. Cramer
,
Ryan K. Said
,
Mitchell Guthrie
, and
Jesse Hietanen

Abstract

The number of cloud-to-ground (CG) flashes over the contiguous U.S. (CONUS) has been estimated to be from as small as 25 million per year to as many as 40 million. In addition, many CG flashes contact the ground in more than one place. To clarify these values, recent data from the National Lightning Detection Network (NLDN) have been examined since the network is performing well enough to make precise updates to the number of CG flashes and their associated ground contact points. The average number of CG flashes is calculated to be about 23.4 million per year over CONUS, and the average number of ground contact points is calculated as 36.8 million per year. Knowledge of these two parameters is critical to lightning protection standards, as well as better understanding of the effects of lightning on forest fire initiation, geophysical interactions, human safety, and applications that benefit from knowing that a single flash may transfer charge to ground in multiple, widely-spaced locations. Sensitivity tests to assess the effects of misclassification of CG and in-cloud (IC) lightning are also made to place bounds on these estimates; and the likely uncertainty is a few percent.

Open access
Xinru Liu
,
Hang Jie
,
Yulin Zou
,
Shengjun Liu
,
Yamin Hu
,
Shuyi Liu
,
Dangfu Yang
,
Liang Zhao
, and
Jian He

Abstract

According to HadGEM3 (CMIP6) models, anthropogenic forcing reduced the probability of 2022-like June mean precipitation by about 32% (15%) and increased 5-day rainfall extreme probability by about 1.8 (1.3) times.

Open access