Exposure to air pollution may cause heart damage

Radiological Society of North America

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Images from cardiac MRI native T1 mapping show that higher long-term exposure to fine particulate air pollution is associated with higher extent of myocardial fibrosis.

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Credit: Radiological Society of North America (RSNA)

OAK BROOK, Ill. – Researchers using cardiac MRI have found that long-term exposure to air pollution is associated with early signs of heart damage, according to a study that was published today in Radiology, a journal of the Radiological Society of North America (RSNA). The research indicates that fine particulate matter in the air may contribute to diffuse myocardial fibrosis, a form of scarring in the heart muscle that can precede heart failure.

Cardiovascular disease is the leading cause of death worldwide. There is a large body of evidence linking poor air quality with cardiovascular disease. However, the underlying changes in the heart resulting from air pollution exposure are unclear.

“We know that if you’re exposed to air pollution, you’re at higher risk of cardiac disease, including higher risk of having a heart attack,” said the study’s senior author Kate Hanneman, M.D., M.P.H., from the Department of Medical Imaging at the Temerty Faculty of Medicine, University of Toronto and University Health Network in Toronto. “We wanted to understand what drives this increased risk at the tissue level.”

Dr. Hanneman and colleagues used cardiac MRI, a noninvasive imaging technique, to quantify myocardial fibrosis and assess its association with long-term exposure to particles known as PM2.5. At 2.5 micrometers in diameter or less, PM2.5 particles are small enough to enter the bloodstream through the lungs. Common sources include vehicle exhaust, industrial emissions and wildfire smoke.

The researchers wanted to evaluate the effects of air pollution on both healthy people and those with heart disease, so the study group included 201 healthy controls and 493 patients with dilated cardiomyopathy, a disease that makes it more difficult for the heart to pump blood.

Higher long-term exposure to fine particulate air pollution was linked with higher levels of myocardial fibrosis in both the patients with cardiomyopathy and the controls, suggesting that myocardial fibrosis may be an underlying mechanism by which air pollution leads to cardiovascular complications. The largest effects were seen in women, smokers and patients with hypertension.

The study adds to growing evidence that air pollution is a cardiovascular risk factor, contributing to residual risk not accounted for by conventional clinical predictors such as smoking or hypertension.

“Even modest increases in air pollution levels appear to have measurable effects on the heart,” Dr. Hanneman said. “Our study suggests that air quality may play a significant role in changes to heart structure, potentially setting the stage for future cardiovascular disease.”

Knowing a patient’s long-term air pollution exposure history could help refine heart disease risk assessment and address the health inequities that air pollution contributes to both in level of exposure and effect. For instance, Dr. Hanneman said, if an individual works outside in an area with poor air quality, healthcare providers could incorporate that exposure history into heart disease risk assessment.

The air pollution exposure levels of the patients in the study were below many of the global air quality guidelines, reinforcing that there are no safe exposure limits.

“Public health measures are needed to further reduce long-term air pollution exposure,” Dr. Hanneman said. “There have been improvements in air quality over the past decade, both in Canada and the United States, but we still have a long way to go.”

In addition to illuminating the links between air pollution and myocardial fibrosis, the study highlights the important role that radiologists will play in research and clinical developments going forward.

“Medical imaging can be used as a tool to understand environmental effects on a patient’s health,” Dr. Hanneman said. “As radiologists, we have a tremendous opportunity to use imaging to identify and quantify some of the health effects of environmental exposures in various organ systems.”

Diagram shows summary of study purpose, exposure, outcome, and key results. Higher long-term exposure to ambient fine particulate air pollution is associated with greater diffuse myocardial fibrosis at cardiac MRI native T1 mapping in patients with dilated cardiomyopathy and controls with normal MRI findings. PM2.5 = fine particulate matter with 2.5-µm or smaller aerodynamic diameter.

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Radiological Society of North America (RSNA)

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“Association between Long-term Exposure to Ambient Air Pollution and Myocardial Fibrosis Assessed with Cardiac MRI.” Collaborating with Dr. Hanneman were Jacques du Plessis, M.D., Chloe DesRoche, M.D., M.Sc., Scott Delaney, Sc.D., J.D., M.P.H., Rachel C. Nethery, Ph.D., Rachel Hong, B.Sc., Paaladinesh Thavendiranathan, M.D., S.M., Heather Ross, M.D., M.H.Sc., and Felipe Castillo, M.D.

Radiology is edited by Linda Moy, M.D., New York University, New York, N.Y., and owned and published by the Radiological Society of North America, Inc. (https://pubs.rsna.org/journal/radiology)

RSNA is an association of radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Illinois. (RSNA.org)

For patient-friendly information on cardiac MRI, visit RadiologyInfo.org.

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Journal

Radiology

Subject of Research

People

Article Title

Association between Long-term Exposure to Ambient Air Pollution and Myocardial Fibrosis Assessed with Cardiac MRI

Article Publication Date

1-Jul-2025

 

Research from the University of Kansas suppresses coronavirus by targeting Mac1

University of Kansas

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LAWRENCE — A new study published in mBio details the vulnerability of coronaviruses to inhibitors of a small protein domain called Mac1, or the “macrodomain,” found in all coronaviruses such as SARS-CoV-2 and MERS-CoV.

The findings point toward potential antiviral therapies to combat future coronavirus pandemics and confirm the importance of Mac1 to the viability of the virus.

“The macrodomain is critical for the virus’s ability to cause disease,” said Anthony Fehr, associate professor of molecular biosciences at the University of Kansas, who led the research. “We’ve known for a long time, based on our work and that of others, this gene is really important for the virus. Several groups, including ours, have started efforts to develop antivirals against it. But until recently, there hadn’t been any proven compounds that could target this gene and affect the virus, at least in cell culture.”

In the new paper, Fehr’s KU lab reports developing molecules that bind to the Mac1 protein and inhibit coronavirus replication in cell cultures derived from mice and human lung tissue. This work builds on recent experiments in the Fehr lab that produced the first molecules effective against Mac1.

“This paper is similar to our earlier work, though with a completely different set of inhibitors,” Fehr said. “But this study had some cool twists and turns. One key development was the discovery of our top compound — we called it ‘4B.’ It looked very promising in how it fit into the Mac1 binding pocket, and its IC50 — the amount of drug needed to reduce viral activity by half — was significantly lower than all of our other compounds, meaning it should be a better inhibitor.”

Yet when the group first tested 4B in antiviral assays, it didn’t actually work.

“It showed no effect,” Fehr said. “Based on its biochemical properties, we believe it wasn’t able to cross the cellular membrane. Cell membranes are greasy, hydrophobic barriers that keep things inside cells but also prevent certain molecules from entering — especially charged molecules, like those with acid or base groups attached. Compound 4B had a notable acid group, which likely prevented it from getting into cells.”

To address this, the group modified the compound to improve its ability to enter coronavirus-infected cells.

“We converted the acid into an ester, using a couple of different types of modifications,” Fehr said. “Once we did that, we started to see robust antiviral activity. This was the ‘aha!’ moment. We were finally able to make the compound cell-permeable and functional in cell culture. That was a big step — taking a molecule that had strong activity in vitro, modifying it and showing it could now work in cells against the actual virus.”

Fehr credited many of his collaborators for the breakthrough on Mac1 targets.

“I have to give credit to co-authors Dana Ferraris, chemistry professor at McDaniel College in Westminster, Maryland; Lari Lehtiö, a biochemistry professor at Oulu University in Finland; and KU core facility directors Anuradha Roy and David Johnson. Each of these individuals and their labs put a lot of work into this paper,” Fehr said. “And of course the majority of the credit goes to the first author, Jessica J. Pfannenstiel, a graduate student in the KU Department of Molecular Biosciences, as she produced almost all the antiviral data and identified the drug-resistant mutations.”

While coronaviruses can develop resistance to the experimental KU inhibitors, it does so at a “fitness cost” — meaning future generations with more resistance are otherwise weakened and don’t fare well in mice. This mechanism suggests these inhibitors, if further developed into an antiviral, could render coronavirus harmless.

For this to happen, Fehr said more work is needed to refine the promising molecules.

“Mouse models are important, and before we can test our inhibitors in mice we need to improve their potency and stability so that they will survive the harsh environment of a living organism,” he said. His group is continuing to work towards these goals.

The KU researcher gave credit to funding from the National Institutes of Health and KU’s Chemical Biology of Infectious Disease program, led by KU faculty member Scott Hefty, for the breakthroughs.

“This program has been crucial in supporting multiple groups working on inhibitors for viruses, parasites and bacteria,” Fehr said. “It provides core facilities and resources, like the high-throughput screening lab led by Anuradha Roy, that enable robust assay development and compound testing. These tools position us well to rapidly respond to potential future coronavirus outbreaks by identifying promising inhibitors. While drug development is a long process, KU is equipped to quickly and effectively begin the process of identifying novel antimicrobial compounds.”

Fehr said that beyond drug discovery, involvement in COBRE helps faculty gain expertise that improves their ability to evaluate national antimicrobial research, strengthening the broader scientific community.

“As a university, our primary role is to provide knowledge and develop leaders — faculty, students and postdocs — with expertise to advance infectious disease research nationwide,” Fehr said.

Other KU contributors include Daniel Cluff, undergraduate researcher; Nathaniel Schemmel, undergraduate researcher; Joseph O’Connor, graduate research assistant; Pradtahna Saenjamsai, graduate research assistant; Srivatsan Parthasarathy, postdoctoral researcher; Mei Feng, formulation scientist at the Biopharmaceutical Innovation and Optimization Center; and Michael Hageman, Valentino J. Stella Distinguished Professor of Pharmaceutical Chemistry.

Other collaborators include undergraduate researchers Lavinia Sherrill, Iain Colquhoun, Gabrielle Cadoux and Devyn Thorne of McDaniel College in Westminster, Maryland; and research scientists Men Thi Hoai Duong, and Johan Pääkkönen of Oulu University in Finland.

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DOI

10.1128/mbio.03865-24 

 UK

WHY DEI?!

Racism and sexism are “alarmingly normalized” in the NHS

Evidence of impact of discrimination and inequalities is clear; what is needed now is action from government and NHS institutions, says the BMJ Commission on the Future of the NHS

BMJ Group

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Racism and sexism are “alarmingly normalised” within the structures and person-to-person interactions across the NHS, and the NHS has delayed acknowledging and learning from the evidence, says a report from the BMJ Commission on the Future of the NHS, published in The BMJ today.

There is an ethical imperative for the government and NHS institutions to act now, it concludes.

On a wider scale, discrimination and inequities related to protected characteristics, such as race and ethnicity, sex and gender, age, disability, sexuality, religion and belief have a major impact on the health of the public the NHS serves—and on staff wellbeing—says the report. 

Discrimination and inequities contribute to increased risk of physical and mental health conditions, limit access to care, shape negative experiences of illness and encounters with services, and lead to worse overall health outcomes, including mortality, it highlights.

As well as having a major impact on population health, discrimination and inequity also have huge financial consequences for the UK economy. Every year health inequity leads to productivity losses of £31-£33bn, lost taxes, and increased welfare payments of £20-£32bn, as well as direct healthcare costs of at least £5.5bn, it points out. 

After reviewing the evidence, the report makes recommendations for the UK government, healthcare leaders, the Care Quality Commission (CQC) and equivalent regulators, and the NHS on tackling discrimination and inequities across the NHS to enhance the experience of patients and staff and improve health outcomes. 

For the UK government:

• Hold NHS leaders responsible for achieving the ambitions outlined in the NHS equality, diversity, and inclusion improvement plan.
• Implement the independent Messenger report on inclusive leadership in full.
• Give the NHS Race and Health Observatory (RHO) statutory responsibility for producing equity based impact assessments of new NHS policies and programmes and make it the main repository for all matters related to race and ethnicity in the NHS, including the Workforce Race Equality Standards.
• Mandate national research and health bodies to establish equality standards (especially concerning race, ethnicity, sex, and gender) in all research grants, studies, and approvals of drugs, medical devices, and technologies.
• Ensure that biases in existing advanced technology and artificial intelligence are identified and corrected, preventing the introduction of new biases that discriminate against patients.

For the Care Quality Commission (CQC) and equivalent regulators:

• Add an explicit inspection criterion for staff wellbeing to identify and tackle racism, sexism, and other forms of discrimination.
• Hold leaders and organisations accountable for failures in addressing discrimination.

For the NHS:

• Collect and report transparent, accurate, disaggregated data on race, ethnicity, sex, and gender in all organisations.
• Prioritise equitable research, with financial support from government research funders like the National Institute for Health and Care Research (NIHR) and UK Research and Innovation (UKRI), and collaboration with charitable research funders. Withhold funding where these principles are not met.
• Implement the starkest findings of research on inequitable clinical care and ringfence funding to support improvements.
• Set national standards for diversity and inclusion to produce culture change.
• Provide statutory protection for whistleblowers and update the NHS England long term workforce plan to include the role of freedom to speak up guardians.
• Make evidence based training focused on reducing bias and discrimination (including intersectionality training which explores how various aspects of a person's identity, like race, gender, and class, intersect to create unique experiences of privilege and oppression) readily available, emphasising improved interactions with diverse colleagues, cultural safety, and cultural competency. Encourage professionals to develop awareness of clinical interactions and reflect on personal and systemic bias.

For healthcare leaders:

• Improve working conditions by facilitating flexible and remote working and reducing bullying, harassment, and discrimination.
• Model anti-racist and anti-sexist behaviour to encourage similar conduct in employees.
• Monitor staff diversity and track inequity across roles and career trajectories.

The six expert authors conclude: “NHS leaders and the public must recognise that prioritising health equity is a proved strategic investment that leads to good patient outcomes, and better retention and recruitment rates of staff. It is also an ethical and legal imperative.”

Equity in healthcare is about acknowledging that different needs require different responses with varying resources, and that inequity involves multiple characteristics in many cases. Services should be codesigned with those who struggle the most to access care and have the poorest outcomes to reduce inequality in health outcomes, they argue.

They add: “Inaction represents an unacceptable choice that increases harms to patients and costs in terms of increased staff absences, sickness, resignations, and reduced productivity. 

“The evidence and policy options are abundantly clear. Political and institutional leaders must urgently choose to prioritise the elimination of these avoidable, unhealthy, and costly injustices, or face the consequences of a disaffected NHS workforce, and widening inequalities in health outcomes in the general population.

“The recommendations we make, if implemented, will go a long way to make the NHS a happier and healthier place.”

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Journal

The BMJ

DOI

10.1136/bmj.r1334 

Method of Research

Commentary/editorial

Subject of Research

People

Article Title

NHS must tackle racism and sexism for the benefit of patients, staff, and society

Article Publication Date

1-Jul-2025

COI Statement

We have read and understood BMJ policy on declaration of interests and have the following interests to declare: CN and YC are board members of the Race and Health Observatory and JSB isco-chair of its mental health advisory group. KW receivesfunding from the Wellcome Trust.

 

Harnessing big data for apple breeding: Genomic models to meet climate challenges

Nanjing Agricultural University The Academy of Science

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Relative contribution of different model components estimated for eleven traits. A, Average proportions of phenotypic variance related to genotypic (g) and genomic (G) effects, their interactions (×) with the vector of environments (E), the enviromic effects (W), the interaction effects G × W, as well as the residual effect extracted from the statistical genomic prediction model fits. The relationship matrices for the different effects in the statistical genomic prediction models were constructed using the G-BLUP approach or, where indicated, the Gaussian kernel (GK) or Deep kernel (DK). The statistical genomic prediction models were compared with a model based on phenotypic data (Phenotypic). Error bars correspond to standard deviation around the mean. B, Average proportions of phenotypic variance related to genomic (G), additive (A), and dominance (D) effects, their interactions (×) with the vector of environments (E), and the residual effect extracted from the statistical genomic prediction model fits. The model structures G and G + D were additionally extended with the fixed effect of inbreeding (inb). The relationship matrices for the different effects were based on G-BLUP. Error bars correspond to standard deviation around the mean. The results for the benchmark model G are the same as shown in A. C Relative contribution of the SNP, PC, weather, and soil feature streams estimated using SHAP for the deep learning genomic prediction model. Error bars correspond to standard deviation around the mean.

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Credit: Horticulture Research

In the face of climate change, apple breeding programs need innovative ways to select cultivars that can thrive under diverse conditions. A recent study demonstrates how integrating genomic data with environmental factors such as weather and soil can drastically improve the selection process. By using multi-environmental genomic prediction models, including deep learning, researchers have enhanced the accuracy of predictions for key apple traits. These advancements provide a promising tool for breeding apple varieties that are not only high-yielding but also adaptable to the fluctuating climate, offering a potential breakthrough in ensuring future food security.

Traditional apple breeding methods often fail to account for the complexities of genotype-by-environment interactions, which are crucial in selecting the best cultivars for varying climates. To overcome this limitation, the latest research combines phenotypic, genomic, and environmental data into sophisticated multi-environmental prediction models. These models are designed to predict how apple cultivars will perform across different environments, a process that has historically been difficult due to the vast diversity of environmental conditions. This new approach leverages both statistical and deep learning methods to process complex datasets, offering a more robust and accurate model for future breeding efforts.

Published (DOI: 10.1093/hr/uhae319) in Horticulture Research (November 2024), this study from Agroscope, ETH Zurich, and collaborators presents a breakthrough in apple breeding by applying multi-environmental genomic prediction. The research explores the integration of genomic data with environmental variables such as soil conditions and weather patterns, providing a new method for predicting key apple traits. By incorporating genotype-by-environment interactions into the prediction models, the study paves the way for selecting apple cultivars that are better suited to different climates, ultimately helping breeders respond to the challenges posed by climate change.

The study utilized the apple REFPOP, a comprehensive genetic population, to examine how different models predict 11 important traits in apples, including harvest date, fruit weight, and acidity. Researchers employed a range of prediction techniques, from the standard G-BLUP method to more advanced deep learning models, assessing the impact of genotype-by-environment interactions. The results showed that incorporating environmental factors such as weather and soil significantly improved the prediction accuracy for most traits, particularly when the G-BLUP model was enriched with environmental data.

The study also highlighted the power of deep learning models, which outperformed traditional methods for traits with complex genetic architectures, such as harvest date and titratable acidity. For these traits, deep learning models improved predictive ability by up to 0.10, providing a clear advantage in precision. The findings emphasize that as climate variability becomes more pronounced, integrating both genomic and environmental data using advanced machine learning approaches will be key to breeding apple cultivars that can adapt to future environmental challenges.

“By combining genomic data with environmental factors, we are opening a new frontier in apple breeding,” said Dr. Michaela Jung, the lead researcher from Agroscope. “The ability to predict how different apple cultivars will perform under various environmental conditions will give breeders a powerful tool to select varieties that are not only high-yielding but also climate-resilient. Deep learning models, in particular, have shown immense potential in refining these predictions, offering a promising solution for adapting apple breeding to the challenges of a changing climate.”

This study provides a roadmap for apple breeders to develop cultivars that are more resilient to climate change, ensuring stable production despite fluctuating environmental conditions. The integration of genomic and environmental data in breeding programs will enable the selection of apple varieties that are better suited for different regions and climates, improving both yield and quality. Moreover, the application of deep learning models in multi-environmental genomic prediction can be extended to other crops, offering a broader solution for global food security. By harnessing big data and advanced algorithms, this approach could revolutionize the way crops are bred, making them more adaptable to the future.

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References

DOI

10.1093/hr/uhae319

Original Source URL

https://doi.org/10.1093/hr/uhae319

Funding information

C.Q.-T. was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 847585 – RESPONSE. This study was partially funded by the FOAG project ‘Apfelzukunft dank Züchtung’ (2020/17/AZZ).

About Horticulture Research

Horticulture Research is an open access journal of Nanjing Agricultural University and ranked number one in the Horticulture category of the Journal Citation Reports ™ from Clarivate, 2023. The journal is committed to publishing original research articles, reviews, perspectives, comments, correspondence articles and letters to the editor related to all major horticultural plants and disciplines, including biotechnology, breeding, cellular and molecular biology, evolution, genetics, inter-species interactions, physiology, and the origination and domestication of crops.

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Journal

Horticulture Research

DOI

10.1093/hr/uhae319 

Subject of Research

Not applicable

Article Title

Integrative multi-environmental genomic prediction in apple

 

Residential segregation and lung cancer risk in African American adults

JAMA Network Open

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About The Study: 

The results of this study suggest that structural racism embedded in neighborhood conditions contributes to lung cancer development and provides evidence for policymakers and public health leaders working to reduce disparities. 

Corresponding Author: To contact the corresponding author, Loretta Erhunmwunsee, MD, email LorettaE@coh.org.

To access the embargoed study: Visit our For The Media website at this link https://media.jamanetwork.com/

(doi:10.1001/jamanetworkopen.2025.18481)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

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Embed this link to provide your readers free access to the full-text article 

http://jamanetwork.com/journals/jamanetworkopen/fullarticle/10.1001/jamanetworkopen.2025.18481?utm_source=For_The_Media&utm_medium=referral&utm_campaign=ftm_links&utm_term=070125

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication. 

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Journal

JAMA Network Open

 

Extending classical CNOP method for deep-learning atmospheric and oceanic forecasting

New approach reveals when and where input uncertainty matters most

Institute of Atmospheric Physics, Chinese Academy of Sciences

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The sea temperature in the South China Sea has a significant impact on the regional marine ecosystem as well as the high-impact ocean-atmosphere events. Therefore, accurate forecasting of ocean temperature is crucial. This study presents a method that aims to enhance the forecast skill of deep learning forecasting models by optimizing the deployment strategy of limited observational resources.

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Credit: Ziqing Zu

In recent years, deep learning methods have been increasingly applied in atmospheric and oceanic forecasting, showing superior forecast skills. Unlike time-stepping numerical models, deep learning forecasting models (DLMs) typically adopt a “multi-time-slice input” structure. This structure breaks the deterministic causality in the time dimension that exists in the numerical models. In this case, the forecast errors in DLMs should be attributed to all input slices, rather than any single one. This fundamental difference limits the applicability of the classical conditional nonlinear optimal perturbation (CNOP) method, as CNOP is defined at a single time slice, specifically, the initial time.

Recently, researchers have extended the CNOP method in the time dimension and proposed the CNOP-DL method. Designed specifically for DLMs with multi-time-slice inputs, CNOP-DL includes perturbations across multiple times of the inputs, revealing the sensitivity of forecast errors to input errors in both time and space dimensions. The new method is published in Advances in Atmospheric Sciences.

 

“CNOP-DL is useful in the targeted observation studies, as it allows us to identify not only where, but also when additional observations should be deployed to reduce the input errors, ultimately to significantly mitigate the forecast errors,” said Dr. Ziqing Zu from National Marine Environmental Forecasting Center of China, the lead author of the study. “This is especially valuable for improving the forecasts of rapidly developing systems such as typhoons and mesoscale eddies, where observational resources are often limited.”

 

To demonstrate the utility of the method, they applied CNOP-DL to a case study of sea surface temperature (SST) forecasting in the South China Sea. The CNOP-DL included six time slices in the time dimension. Therefore, the optimal time can be identified according to the temporal structure of the perturbation energies. Furthermore, the results revealed that forecast errors are more sensitive to the time of the input perturbations than to the location. In other words, determining when to deploy additional observations can be more critical than determining where.

 

“In conventional targeted observation studies, the focus is typically on identifying the optimal locations for targeted observations at the initial time. By extending CNOP in the time dimension, CNOP-DL can identify which time steps in the inputs are more critical, thereby broadening the scope of conventional targeted observation studies.” said Professor Mu Mu from Fudan University, the corresponding author of the study. “By highlighting the importance of time sensitivity, CNOP-DL holds the potential for guiding practical field campaigns that optimize both spatial and temporal deployment of observational platforms such as moored buoys, gliders, and research vessels.”

 

CNOP-DL is also useful in predictability studies. The authors demonstrate that there are significant differences between CNOP-DL and CNOP, and that CNOP-DL can lead to larger forecast errors, thereby providing a more accurate estimate of the upper bound of forecast uncertainty. This is because, essentially, CNOP searches for the optimal solution within a subset of the CNOP-DL space; thus, CNOP can be regarded as a special case of CNOP-DL.

 

Next, the authors plan to calculate CNOP-DL for a lot of forecast cases, and then conduct composite analyses of CNOP-DL results. By identifying common patterns in sensitive regions and key time windows, they aim to design an optimal observational network in the South China Sea, particularly for moored buoy arrays. Such a system could provide valuable observations to improve significantly operational forecasts, using limited observational resources.

 

Other contributors include Jiangjiang Xia from Key Laboratory of Regional Climate-Environment for Temperate East Asia at CAS IAP in Beijing, China and Qiang Wang from College of Oceanography, Hohai University in Nanjing, China.

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Journal

Advances in Atmospheric Sciences

DOI

10.1007/s00376-025-4297-9 

Article Publication Date

25-Jul-2025