Systematic Errors in Weather and Climate Models: Challenges and Opportunities in Complex Coupled Modeling Systems

Santos, A. F. dos; Reynolds, Carolyn A.; Wedi, Nils; Bouallègue, Zied Ben; Caltabiano, Antonio Caetano Vaz; Casati, Barbara; Christophersen, Jonathan; Coelho, Caio A. S.; Falco, Chiara; Doyle, James D.; Fernandes, Laís G.; Forbes, R. M.; Janiga, Matthew A.; Klocke, Daniel; Magnusson, Linus; McTaggart‐Cowan, Ron; Pakdaman, Morteza; Rushley, Stephanie S.; Verhoef, Anne; Yang, Fanlin; Zängl, Günther

doi:10.1175/bams-d-23-0102.1

Cited by 3 publications

(4 citation statements)

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“…This study highlights the power of hierarchical development techniques, applied here as the progressive simplification of experimental protocols. As envisioned by Frassoni et al (2023), this framework facilitated both the identification of the error source and its mitigation. The intercomparisons used throughout the investigation further increased the likelihood that the intensity bias reduction was achieved through FIG.…”

Section: Discussionmentioning

confidence: 99%

“…Identifying the root cause of a systematic error in a complex NWP system is one research challenge; correcting it in a way that minimizes the risk of introducing additional error compensation is another. Frassoni et al (2023) recommend the use of a hierarchical system development approach for attacking such problems (Jakob 2010), which is implemented using a "hierarchy of complexity" in the current study. This strategy, combined with standard model intercomparison, provides a powerful set of tools with which to identify error sources and to constrain individual components of the system.…”

Section: Introductionmentioning

confidence: 99%

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Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

McTaggart-Cowan,

Nolan,

Aider

et al. 2024

Monthly Weather Review

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The operational Canadian Global Deterministic Prediction System suffers from a weak-intensity bias for simulated tropical cyclones. The presence of this bias is confirmed in progressively simplified experiments using a hierarchical system development technique. Within a semi-idealized, simplified-physics framework, an unexpected insensitivity to the representation of relevant physical processes leads to investigation of the model’s semi-Lagrangian dynamical core. The root cause of the weak-intensity bias is identified as excessive numerical dissipation caused by substantial off-centering in the two time-level time integration scheme used to solve the governing equations. Any (semi-)implicit semi-Lagrangian model that employs such off-centering to enhance numerical stability will be afflicted by a misalignment of the pressure gradient force in strong vortices. Although the associated drag is maximized in the tropical cyclone eyewall, the impact on storm intensity can be mitigated through an intercomparison-constrained adjustment of the model’s temporal discretization. The revised configuration is more sensitive to changes in physical parameterizations and simulated tropical cyclone intensities are improved at each step of increasing experimental complexity. Although some rebalancing of the operational system may be required to adapt to the increased effective resolution, significant reduction of the weak-intensity bias will improve the quality of Canadian guidance for global tropical cyclone forecasting.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

McTaggart-Cowan,

Nolan,

Aider

et al. 2024

Monthly Weather Review

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“…It is therefore possible to use an ensemble of deterministic forecasts to efficiently diagnose the model deficiencies that cause those biases. ECMWF pioneered work of this type (Arpe and Klinker 1986), and it continues today (Mayer et al 2022;Frassoni et al 2023). Seasonal simulations of the statistics of weather systems (e.g., mesoscale convective systems) are of climatological interest in themselves and can reveal problems with the models (e.g., Feng et al 2023;Beverley et al 2023).…”

Section: Unified Modelingmentioning

confidence: 99%

The Weather–Climate Schism

Randall,

Emanuel

2024

Bulletin of the American Meteorological Society

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The atmospheric science community includes both weather and climate scientists. These two groups interact much less than they should, particularly in the United States. The schism is widespread, and has persisted for fifty years or more. It is found in academic departments, laboratories, professional societies, and even funding agencies. Mending the schism would promote better, faster science. We sketch the history of the schism, and suggest ways to make our community whole.

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“…Previous studies have pointed out that there are potential errors in models constructed using only internet search data or climate factors [5,[30][31][32][33]. In addition, data updates from traditional government monitoring stations are often delayed by one to two weeks [28], which may delay the availability of essential data.…”

Section: Introductionmentioning

confidence: 99%

Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance

Shih,

Wu,

et al. 2024

JCM

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Background: Influenza-like illness (ILI) encompasses symptoms similar to influenza, affecting population health. Surveillance, including Google Trends (GT), offers insights into epidemic patterns. Methods: This study used multiple regression models to analyze the correlation between ILI incidents, GT keyword searches, and climate variables during influenza outbreaks. It compared the predictive capabilities of time-series and deep learning models against ILI emergency incidents. Results: The GT searches for “fever” and “cough” were significantly associated with ILI cases (p < 0.05). Temperature had a more substantial impact on ILI incidence than humidity. Among the tested models, ARIMA provided the best predictive power. Conclusions: GT and climate data can forecast ILI trends, aiding governmental decision making. Temperature is a crucial predictor, and ARIMA models excel in forecasting ILI incidences.

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Systematic Errors in Weather and Climate Models: Challenges and Opportunities in Complex Coupled Modeling Systems

Cited by 3 publications

References 0 publications

Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

Reducing a Tropical Cyclone Weak-Intensity Bias in a Global Numerical Weather Prediction System

The Weather–Climate Schism

Infodemiology of Influenza-like Illness: Utilizing Google Trends’ Big Data for Epidemic Surveillance

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