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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
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
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.

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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.

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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
Philip J. Klotzbach
,
Jhordanne J. Jones
,
Kimberly M. Wood
,
Michael M. Bell
,
Eric S. Blake
,
Steven G. Bowen
,
Louis-Philippe Caron
,
Daniel R. Chavas
,
Jennifer M. Collins
,
Ethan J. Gibney
,
Carl J. Schreck III
, and
Ryan E. Truchelut

Abstract

The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes and seasonal Accumulated Cyclone Energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and $3.6 billion USD in damage.

The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind-evaporation-SST feedback.

We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific.

Open access
Elena Orlova
,
Haokun Liu
,
Raphael Rossellini
,
Benjamin A. Cash
, and
Rebecca Willett

Abstract

Producing high-quality forecasts of key climate variables, such as temperature and precipitation, on subseasonal time scales has long been a gap in operational forecasting. This study explores an application of machine learning (ML) models as post-processing tools for subseasonal forecasting. Lagged numerical ensemble forecasts (i.e., an ensemble where the members have different initialization dates) and observational data, including relative humidity, pressure at sea level, and geopotential height, are incorporated into various ML methods to predict monthly average precipitation and two-meter temperature two weeks in advance for the continental United States. For regression, quantile regression, and tercile classification tasks, we consider using linear models, random forests, convolutional neural networks, and stacked models (a multi-model approach based on the prediction of the individual ML models). Unlike previous ML approaches that often use ensemble mean alone, we leverage information embedded in the ensemble forecasts to enhance prediction accuracy. Additionally, we investigate extreme event predictions that are crucial for planning and mitigation efforts. Considering ensemble members as a collection of spatial forecasts, we explore different approaches to using spatial information. Trade-offs between different approaches may be mitigated with model stacking. Our proposed models outperform standard baselines such as climatological forecasts and ensemble means. In addition, we investigate feature importance, trade-offs between using the full ensemble or only the ensemble mean, and different modes of accounting for spatial variability.

Open access
Shun-ich I. Watanabe
and
Junshi Ito

Abstract

This study evaluates a parameterization scheme for subgrid-scale (SGS) fluxes based on the scale-similarity assumption and employing a large-eddy simulation of an idealized backbuilding convective system. In this parameterization, the SGS fluxes are decomposed into the “Leonard term” which depends only on the resolved scale components, the “Reynolds term” which depends only on the SGS components, and the “cross term” which corresponds to the interaction between the resolved scale and SGS components. Assuming a linear relationship between the Leonard term and the Reynolds and cross terms, SGS fluxes are expressed as the product of an empirical coefficient and the Leonard term. The Leonard term reasonably represents the SGS flux derived by a smooth filter operation, including the counter-gradient vertical SGS transport of potential temperature, which cannot be represented by a traditional eddy-diffusivity model. The dependence of the empirical coefficient on filter width is also evaluated. This dependence is related mainly to the Reynolds term, the magnitude of which varies widely with filter width. The estimation based on the spectral decomposition of the Reynolds term explains the obtained dependence of the empirical coefficient for the vertical flux on filter width. In contrast, the variation of the empirical coefficient with filter width is not required to obtain the horizontal flux. For the parameterization of SGS fluxes in kilometer-scale models that use finite difference or volume methods, the Leonard term is expressed by the horizontal gradient of variables on a discrete grid. The Leonard term on a discrete grid also accurately represents the amplitude and spatial pattern of the SGS flux.

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John M. Peters

Abstract

The rapidly increasing resolution of global atmospheric reanalysis and climate model datasets necessitates finding methods for computing convective available potential energy (CAPE) both efficiently and accurately. To this end, this article compares two common methods for computing CAPE which conserve either energy or entropy. Inaccuracies in these computations arise from both physical and numerical errors. For instance, computing CAPE with entropy conserved results in physical errors from non-equilibrium phase transitions but minimizes numerical errors because solutions are analytic at each height. In contrast, computing CAPE with energy conserved avoids these physical errors, but accumulates numerical errors that are grid-resolution dependent because the numerical integration of a differential equation is required. Analysis of CAPE computed with large databases of soundings from the tropical Amazon and midlatitude storm environments shows that physical errors from the entropy method are typically 1-3 % as large as CAPE, which is comparable to the numerical errors from conserving energy with grid spacing of 25 m and 250 m using explicit first-order and second-order integration schemes respectively. Errors in entropy-based CAPE calculations are also insensitive to vertical grid spacing, in contrast with energy-based calculations whose error strongly scales with the grid spacing. It is shown that entropy-based methods are advantageous when intercomparing datasets with differing vertical resolution because they produce accurate and reasonably fast results that are insensitive to grid resolution. Whereas a second-order energy-based method is advantageous when analyzing data with a consistent vertical resolution because of its superior computational efficiency.

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