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William Rudisill
,
Alan Rhoades
,
Zexuan Xu
, and
Daniel R. Feldman

Abstract

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

Open access
Sharanya J. Majumdar
,
David Hoffmann
,
Elizabeth E. Ebert
, and
Brian W. Golding

Abstract

University students can learn about weather warnings and contribute to a database for the World Meteorological Organization (WMO) project on Value Chain Approaches to Evaluate the End-to-End Warning Chain. The project offers students a way to understand how information about high-impact weather is created, shared, and used within a complete warning system for a selected event. Their contributions are intended to inform researchers and practitioners on what has and what has not worked well in the warning process. The students use a structured questionnaire designed to collect information on observations, forecasting, hazards, impacts, warning communications, and responses.

Two institutions took contrasting approaches to using the questionnaire. At the University of Miami, teams of meteorology undergraduates evaluated the value chain for three hurricanes. Among the issues identified were the dynamic nature of the forecasts, misinterpretations of the products, social media influences, demographic factors, and disparities in responses. The Australian Bureau of Meteorology engaged student interns in different disciplines and experience levels to evaluate and contrast the warning value chains for domestic and international events.

The students expressed enthusiasm for the exercises. Educational benefits included team collaboration, critical thinking, research and composition skills, a comprehensive view of weather events, understanding information flow, learning about new tools, and identifying gaps in practices. We encourage educators to adopt similar exercises to enable students to develop these skills, adopt value chain ideas, and contribute meaningfully to the community. The level of maintenance is low, and there is flexibility in how the exercises can be developed.

Open access
Louise Crochemore
,
Stefano Materia
,
Elisa Delpiazzo
,
Stefano Bagli
,
Andrea Borrelli
,
Francesco Bosello
,
Eva Contreras
,
Francesco Dalla Valle
,
Silvio Gualdi
,
Javier Herrero
,
Francesca Larosa
,
Rafael Lopez
,
Valerio Luzzi
,
Paolo Mazzoli
,
Andrea Montani
,
Isabel Moreno
,
Valentina Pavan
,
Ilias Pechlivanidis
,
Fausto Tomei
,
Giulia Villani
,
Christiana Photiadou
,
María José Polo
, and
Jaroslav Mysiak

Abstract

Assessing the information provided by coproduced climate services is a timely challenge, given the continuously evolving scientific knowledge and its increasing translation to address societal needs. Here, we propose a joint evaluation and verification framework to assess prototype services that provide seasonal forecast information based on the experience from the Horizon 2020 (H2020) Climate forecasts enabled knowledge services (CLARA) project. The quality and value of the forecasts generated by CLARA services were first assessed for five climate services utilizing the Copernicus Climate Change Service seasonal forecasts and responding to knowledge needs from the water resources management, agriculture, and energy production sectors. This joint forecast verification and service evaluation highlights various skills and values across physical variables, services, and sectors, as well as a need to bridge the gap between verification and user-oriented evaluation. We provide lessons learned based on the service developers’ and users’ experience and recommendations to consortia that may want to deploy such verification and evaluation exercises. Last, we formalize a framework for joint verification and evaluation in service development, following a transdisciplinary (from data purveyors to service users) and interdisciplinary chain (climate, hydrology, economics, and decision analysis).

Open access
Feimin Zhang
,
Shang Wan
,
Shuanglong Jin
, and
Hao Wang

Abstract

Data assimilation is an important approach to improve the prediction performance of near-surface wind and wind power. Based on four-dimensional variational technique, this study proposes an approach to improve near-surface wind and wind power prediction by extracting and assimilating the principal components of cabin radar radial wind observations installed at wind turbine within wind farm. The verification for a series of cases under strong and weak vertical wind shear conditions indicates that, compared to the simulations without assimilation, the predicted ultra-short term (0–4 h) mean absolute error of near-surface wind and single turbine wind power could be reduced by 0.09–1.17 m s−1 and 53–209 kW after the assimilation of radial wind directly, while by 0.33–1.38 m s−1 and 62–239 kW after the assimilation of principal components. These illustrate that assimilating the principal components of radial wind is superior to assimilating radial wind directly, and could obviously reduce prediction error.

Further investigation suggests that extracting the principal components of radial wind has marginal influences on the density and distribution of observations, but could obviously reduce the fluctuation of the observations and the correlation among the observations. The prediction improvement by assimilating the principal components of radial wind is essentially due to the assimilation of low-frequency and low-correlation information involved in the observations.

Restricted access
Garik Gutman
,
Roger Pielke Sr
,
Richard Anthes
,
Pinhas Alpert
,
Alexander Baklanov
,
Svante Bodin
,
Alexander Khain
, and
Simon Krichak

Abstract

On March 5, 2023, Professor Lev Gutman would have been 100 years old. This article describes Professor Gutman’s legacy in the field of dynamic mesoscale meteorology and numerical weather prediction. Gutman developed his career as a mathematician and meteorologist in the Soviet Union, where he built a school of specialists in mesoscale meteorology during the 1950s through the 1970s. He primarily worked on analytical methods to solve complex nonlinear problems, such as the structure of sea breezes, mountain-valley circulations, and thermal convection over heated terrain. Gutman pioneered the development of theories of cumulus clouds, tornados, and other atmospheric phenomena. In the 1960s, he carried out numerous research investigations on these topics with his doctoral students and collaborators at High-Altitude Geophysical Institute in Nalchik in the northern Caucasus and later at the Siberian scientific center near Novosibirsk. Gutman compiled the results from these studies into a monograph titled “Introduction to the Nonlinear Theory of Mesoscale Meteorological Processes”, which was published in Russian in 1969, and later translated into English, Chinese, and Japanese. This monograph became a major textbook for specialists in mesoscale meteorology, remaining relevant to this day. After Prof. Gutman immigrated to Israel in 1978, his collaborations expanded to include Israeli and western scientists from Europe and the United States. Gutman did not receive the recognition he deserved due to the political realities of the time. His book and his seminal analytical solutions should still be useful for early career scientists in mesoscale meteorology and atmospheric dynamics.

Open access
Hans Burchard
,
Matthew Alford
,
Manita Chouksey
,
Giovanni Dematteis
,
Carsten Eden
,
Isabelle Giddy
,
Knut Klingbeil
,
Arnaud Le Boyer
,
Dirk Olbers
,
Julie Pietrzak
,
Friederike Pollmann
,
Kurt Polzin
,
Fabien Roquet
,
Pablo Sebastia Saez
,
Sebastiaan Swart
,
Lars Umlauf
,
Gunnar Voet
, and
Bethan Wynne-Cattanach
Open access
Andrew C. Winters
,
Nick P. Bassill
,
John R. Gyakum
, and
Justin R. Minder

Abstract

The St. Lawrence River Valley experiences a variety of precipitation types (p-types) during the cold season, such as rain, freezing rain, ice pellets, and snow. These varied precipitation types exert considerable impacts on aviation, road transportation, power generation and distribution, and winter recreation, and are shaped by diverse multiscale processes that interact with the region’s complex topography. This study utilizes ERA5 reanalysis data, a surface cyclone climatology, and hourly station observations from Montréal, Québec and Burlington, VT, during October–April 2000–2018 to investigate the spectrum of synoptic-scale weather regimes that induce cold season precipitation across the St. Lawrence River Valley. In particular, k-means clustering and self-organizing maps (SOMs) are used to classify cyclone tracks passing near the St. Lawrence River Valley, and their accompanying thermodynamic profiles, into a set of event types that include a U.S. East Coast track, a Central U.S. track, and two Canadian clipper tracks. Composite analyses are subsequently performed to reveal the synoptic-scale environments and the characteristic p-types that most frequently accompany each event type. GEFSv12 reforecasts are then used to examine the relative predictability of cyclone characteristics and the local thermodynamic profile associated with each event type at 0–5-day forecast lead times. The analysis suggests that forecasted cyclones near the St. Lawrence River Valley develop too quickly and are located left-of-track relative to the reanalysis on average, which has implications for forecasts of the local thermodynamic profile and p-type across the region when the temperature is near 0°C.

Restricted access
Yuting Yang
,
Xiaopeng Cui
,
Ying Li
,
Lijun Huang
, and
Jia Tian

Abstract

The northeast cold vortex (NECV) is an essential system in the northeast region (NER) of China. Understanding the moisture source and associated transport characteristics of NECV rainstorms is the key to the knowledge of its mechanisms. In this study, we focus on two NECV rainstorm centers during the warm season (May–September) from 2008 to 2013. The Flexible Particle (FLEXPART) model and quantitative contribution analysis method are applied to reveal the moisture sources and their quantitative contribution. The results demonstrate that for the northern NECV rainstorm center (R1), Northeast Asia (35.66%) and east-central China and its coastal regions (29.14%) make prominent moisture contributions, followed by R1 (11.37%), whereas east-central China and its coastal regions (45.16%), the southern NECV rainstorm center itself (R2, 17.90%), and the northwest Pacific (10.24%) principally contribute to R2. Moisture uptake in Northeast Asia differs between R1 and R2, which could serve as one of the vital indicators to judge where the NECV rainstorm falls in NER. Moisture from the Arabian Sea, the Bay of Bengal, and the South China Sea suffers massive en route loss, although these sources’ contribution and uptake are positively correlated with the intensity and scale of NECV rainstorms in the two centers. There exists intermonth and geographical variability in NECV rainstorms when the main moisture source region contributes the most. Regulated by the atmospheric circulation and the East Asian summer monsoon, the particle trajectories and source contributions of NECV rainstorms vary from month to month. Sources’ contribution also turns out to be diverse in the overall warm season.

Restricted access
Paola Salio
,
Hernán Bechis
,
Bruno Z. Ribeiro
,
Ernani de Lima Nascimento
,
Vito Galligani
,
Fernando Garcia
,
Lucas Alvarenga
,
Maria de los Milagros Alvarezs Imaz
,
Daiana Marlene Baissac
,
María Florencia Barle
,
Cristian Bastías-Curivil
,
Marcos Benedicto
,
Maite Cancelada
,
Izabelly Carvalho da Costa
,
Daniela D’Amen
,
Ramon de Elia
,
David Eduardo Diaz
,
Anthony Duarte Páez
,
Sergio González
,
Vitor Goede
,
Julián Goñi
,
Agustín Granato
,
Murilo Machado Lopes
,
Matias Mederos
,
Matias Menalled
,
Romina Mezher
,
Eduardo José Mingo Vega
,
María Gabriela Nicora
,
Lucía Pini
,
Roberto Rondanelli
,
Juan Jose Ruiz
,
Nestor Santayana
,
Laís Santos
,
Guilherme Schild
,
Inés Simone
,
Raul Valenzuela
,
Yasmin Romina Velazquez
,
Luciano Vidal
, and
Constanza Inés Villagrán Asiares

Abstract

Despite southern South America being recognized as a hotspot for deep convective storms, little is known about the socioenvironmental impacts of high-impact weather (HIW) events. Although there have been past efforts to collect severe weather reports in the region, they have been highly fragmented among and within countries, sharing no common protocol, and limited to a particular phenomenon, a very specific region, or a short period of time. There is a pressing need for a more comprehensive understanding of the present risks linked to HIW events, specifically deep convective storms, on a global scale as well as their variability and potential future evolution in the context of climate change. A database of high-quality and systematic HIW reports and associated socioenvironmental impacts is essential to understand the regional atmospheric conditions leading to hazardous weather, to quantify its predictability, and to build robust early warning systems. To tackle this problem and following successful initiatives in other regions of the world, researchers, national weather service members, and weather enthusiasts from Argentina, Brazil, Chile, Paraguay, and Uruguay have embarked on a multinational collaboration to generate a standardized database of reports of HIW events principally associated with convective storms and their socioenvironmental impacts in South America. The goal of this paper is to describe this unprecedented initiative over the region, to summarize first results, and to discuss the potential applications of this collaboration.

Open access
Jorge L. García-Franco
,
Chia-Ying Lee
,
Suzana J. Camargo
,
Michael K. Tippett
,
Neljon G. Emlaw
,
Daehyun Kim
,
Young-Kwon Lim
, and
Andrea Molod

Abstract

This paper analyzes the climatology, prediction skill, and predictability of tropical cyclones (TCs) in NASA’s Global Earth Observing System Subseasonal to Seasonal (GEOS-S2S) forecast system version 2. GEOS reasonably simulates the number and spatial distribution of TCs compared to observations except in the Atlantic where the model simulates too few TCs due to low genesis rates in the Caribbean Sea and Gulf of Mexico. The environmental conditions, diagnosed through a genesis potential index, do not clearly explain model biases in the genesis rates, especially in the Atlantic. At the storm-scale, GEOS reforecasts replicate several key aspects of the thermodynamic and dynamic structure of observed TCs, such as a warm core and the secondary circulation. The model, however, fails to simulate an off-center eyewall when evaluating vertical velocity, precipitation and moisture. The analysis of prediction skill of TC genesis and occurrence shows that GEOS has comparable skill to other global models in WMO S2S archive and that its skill could be further improved by increasing the ensemble size. After calibration, GEOS forecasts are skillful in the Western North Pacific and Southern Indian Ocean up to 20 days in advance. A model-based predictability analysis demonstrates the importance of the Madden-Julian Oscillation (MJO) as a source of predictability of TC occurrence beyond the 14 day lead-time. Forecasts initialized under strong MJO conditions show evidence of predictability beyond week 3. However, due to model biases in the forecast distribution there are notable gaps between MJO-related prediction skill and predictability which require further study.

Restricted access