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22 results on '"Mansfield, Laura"'

1. Generative Diffusion-based Downscaling for Climate

Author

Watt, Robbie A. and Mansfield, Laura A.

Subjects
Physics - Atmospheric and Oceanic Physics, Computer Science - Machine Learning
Abstract

Downscaling, or super-resolution, provides decision-makers with detailed, high-resolution information about the potential risks and impacts of climate change, based on climate model output. Machine learning algorithms are proving themselves to be efficient and accurate approaches to downscaling. Here, we show how a generative, diffusion-based approach to downscaling gives accurate downscaled results. We focus on an idealised setting where we recover ERA5 at $0.25\degree$~resolution from coarse grained version at $2\degree$~resolution. The diffusion-based method provides superior accuracy compared to a standard U-Net, particularly at the fine scales, as highlighted by a spectral decomposition. Additionally, the generative approach provides users with a probability distribution which can be used for risk assessment. This research highlights the potential of diffusion-based downscaling techniques in providing reliable and detailed climate predictions.

Published
2024

13. Supplementary Figure S2 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Author

Williams, Christopher J.M., primary, Elliott, Faye, primary, Sapanara, Nancy, primary, Aghaei, Faranak, primary, Zhang, Liping, primary, Muranyi, Andrea, primary, Yan, Dongyao, primary, Bai, Isaac, primary, Zhao, Zuo, primary, Shires, Michael, primary, Wood, Henry M., primary, Richman, Susan D., primary, Hemmings, Gemma, primary, Hale, Michael, primary, Bottomley, Daniel, primary, Galvin, Leanne, primary, Cartlidge, Caroline, primary, Dance, Sarah, primary, Bacon, Chris M., primary, Mansfield, Laura, primary, Young-Zvandasara, Kathe, primary, Sudan, Ajay, primary, Lambert, Katy, primary, Bibby, Irena, primary, Coupland, Sarah E., primary, Montazeri, Amir, primary, Kipling, Natalie, primary, Hughes, Kathryn, primary, Cross, Simon S., primary, Dewdney, Alice, primary, Pheasey, Leanne, primary, Leng, Cathryn, primary, Gochera, Tatenda, primary, Mangham, D. Chas, primary, Saunders, Mark, primary, Pritchard, Martin, primary, Stott, Helen, primary, Mukherjee, Abhik, primary, Ilyas, Mohammad, primary, Silverman, Rafael, primary, Hyland, Georgina, primary, Sculthorpe, Declan, primary, Thornton, Kirsty, primary, Gould, Imogen, primary, O'Callaghan, Ann, primary, Brown, Nicholas, primary, Turnbull, Samantha, primary, Shaw, Lisa, primary, Seymour, Matthew T., primary, West, Nicholas P., primary, Seligmann, Jenny F., primary, Singh, Shalini, primary, Shanmugam, Kandavel, primary, and Quirke, Philip, primary

Published
2023
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14. Supplementary Table S3 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Author

Williams, Christopher J.M., primary, Elliott, Faye, primary, Sapanara, Nancy, primary, Aghaei, Faranak, primary, Zhang, Liping, primary, Muranyi, Andrea, primary, Yan, Dongyao, primary, Bai, Isaac, primary, Zhao, Zuo, primary, Shires, Michael, primary, Wood, Henry M., primary, Richman, Susan D., primary, Hemmings, Gemma, primary, Hale, Michael, primary, Bottomley, Daniel, primary, Galvin, Leanne, primary, Cartlidge, Caroline, primary, Dance, Sarah, primary, Bacon, Chris M., primary, Mansfield, Laura, primary, Young-Zvandasara, Kathe, primary, Sudan, Ajay, primary, Lambert, Katy, primary, Bibby, Irena, primary, Coupland, Sarah E., primary, Montazeri, Amir, primary, Kipling, Natalie, primary, Hughes, Kathryn, primary, Cross, Simon S., primary, Dewdney, Alice, primary, Pheasey, Leanne, primary, Leng, Cathryn, primary, Gochera, Tatenda, primary, Mangham, D. Chas, primary, Saunders, Mark, primary, Pritchard, Martin, primary, Stott, Helen, primary, Mukherjee, Abhik, primary, Ilyas, Mohammad, primary, Silverman, Rafael, primary, Hyland, Georgina, primary, Sculthorpe, Declan, primary, Thornton, Kirsty, primary, Gould, Imogen, primary, O'Callaghan, Ann, primary, Brown, Nicholas, primary, Turnbull, Samantha, primary, Shaw, Lisa, primary, Seymour, Matthew T., primary, West, Nicholas P., primary, Seligmann, Jenny F., primary, Singh, Shalini, primary, Shanmugam, Kandavel, primary, and Quirke, Philip, primary

Published
2023
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15. Data from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Author

Williams, Christopher J.M., primary, Elliott, Faye, primary, Sapanara, Nancy, primary, Aghaei, Faranak, primary, Zhang, Liping, primary, Muranyi, Andrea, primary, Yan, Dongyao, primary, Bai, Isaac, primary, Zhao, Zuo, primary, Shires, Michael, primary, Wood, Henry M., primary, Richman, Susan D., primary, Hemmings, Gemma, primary, Hale, Michael, primary, Bottomley, Daniel, primary, Galvin, Leanne, primary, Cartlidge, Caroline, primary, Dance, Sarah, primary, Bacon, Chris M., primary, Mansfield, Laura, primary, Young-Zvandasara, Kathe, primary, Sudan, Ajay, primary, Lambert, Katy, primary, Bibby, Irena, primary, Coupland, Sarah E., primary, Montazeri, Amir, primary, Kipling, Natalie, primary, Hughes, Kathryn, primary, Cross, Simon S., primary, Dewdney, Alice, primary, Pheasey, Leanne, primary, Leng, Cathryn, primary, Gochera, Tatenda, primary, Mangham, D. Chas, primary, Saunders, Mark, primary, Pritchard, Martin, primary, Stott, Helen, primary, Mukherjee, Abhik, primary, Ilyas, Mohammad, primary, Silverman, Rafael, primary, Hyland, Georgina, primary, Sculthorpe, Declan, primary, Thornton, Kirsty, primary, Gould, Imogen, primary, O'Callaghan, Ann, primary, Brown, Nicholas, primary, Turnbull, Samantha, primary, Shaw, Lisa, primary, Seymour, Matthew T., primary, West, Nicholas P., primary, Seligmann, Jenny F., primary, Singh, Shalini, primary, Shanmugam, Kandavel, primary, and Quirke, Philip, primary

Published
2023
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16. Supplementary Methods S1 from Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Author

Williams, Christopher J.M., primary, Elliott, Faye, primary, Sapanara, Nancy, primary, Aghaei, Faranak, primary, Zhang, Liping, primary, Muranyi, Andrea, primary, Yan, Dongyao, primary, Bai, Isaac, primary, Zhao, Zuo, primary, Shires, Michael, primary, Wood, Henry M., primary, Richman, Susan D., primary, Hemmings, Gemma, primary, Hale, Michael, primary, Bottomley, Daniel, primary, Galvin, Leanne, primary, Cartlidge, Caroline, primary, Dance, Sarah, primary, Bacon, Chris M., primary, Mansfield, Laura, primary, Young-Zvandasara, Kathe, primary, Sudan, Ajay, primary, Lambert, Katy, primary, Bibby, Irena, primary, Coupland, Sarah E., primary, Montazeri, Amir, primary, Kipling, Natalie, primary, Hughes, Kathryn, primary, Cross, Simon S., primary, Dewdney, Alice, primary, Pheasey, Leanne, primary, Leng, Cathryn, primary, Gochera, Tatenda, primary, Mangham, D. Chas, primary, Saunders, Mark, primary, Pritchard, Martin, primary, Stott, Helen, primary, Mukherjee, Abhik, primary, Ilyas, Mohammad, primary, Silverman, Rafael, primary, Hyland, Georgina, primary, Sculthorpe, Declan, primary, Thornton, Kirsty, primary, Gould, Imogen, primary, O'Callaghan, Ann, primary, Brown, Nicholas, primary, Turnbull, Samantha, primary, Shaw, Lisa, primary, Seymour, Matthew T., primary, West, Nicholas P., primary, Seligmann, Jenny F., primary, Singh, Shalini, primary, Shanmugam, Kandavel, primary, and Quirke, Philip, primary

Published
2023
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17. Associations between AI-Assisted Tumor Amphiregulin and Epiregulin IHC and Outcomes from Anti-EGFR Therapy in the Routine Management of Metastatic Colorectal Cancer

Author

Williams, Christopher J.M., primary, Elliott, Faye, additional, Sapanara, Nancy, additional, Aghaei, Faranak, additional, Zhang, Liping, additional, Muranyi, Andrea, additional, Yan, Dongyao, additional, Bai, Isaac, additional, Zhao, Zuo, additional, Shires, Michael, additional, Wood, Henry M., additional, Richman, Susan D., additional, Hemmings, Gemma, additional, Hale, Michael, additional, Bottomley, Daniel, additional, Galvin, Leanne, additional, Cartlidge, Caroline, additional, Dance, Sarah, additional, Bacon, Chris M., additional, Mansfield, Laura, additional, Young-Zvandasara, Kathe, additional, Sudan, Ajay, additional, Lambert, Katy, additional, Bibby, Irena, additional, Coupland, Sarah E., additional, Montazeri, Amir, additional, Kipling, Natalie, additional, Hughes, Kathryn, additional, Cross, Simon S., additional, Dewdney, Alice, additional, Pheasey, Leanne, additional, Leng, Cathryn, additional, Gochera, Tatenda, additional, Mangham, D. Chas, additional, Saunders, Mark, additional, Pritchard, Martin, additional, Stott, Helen, additional, Mukherjee, Abhik, additional, Ilyas, Mohammad, additional, Silverman, Rafael, additional, Hyland, Georgina, additional, Sculthorpe, Declan, additional, Thornton, Kirsty, additional, Gould, Imogen, additional, O'Callaghan, Ann, additional, Brown, Nicholas, additional, Turnbull, Samantha, additional, Shaw, Lisa, additional, Seymour, Matthew T., additional, West, Nicholas P., additional, Seligmann, Jenny F., additional, Singh, Shalini, additional, Shanmugam, Kandavel, additional, and Quirke, Philip, additional

Published
2023
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19. Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts.

Author

Mansfield, Laura A., Gupta, Aman, Burnett, Adam C., Green, Brian, Wilka, Catherine, and Sheshadri, Aditi

Subjects
*MACHINE learning, *ATMOSPHERIC models, *CLIMATOLOGY, *CLIMATE change
Abstract

The climate model hierarchy encompasses models of varying complexity along different axes, ranging from idealized models that elegantly describe isolated mechanisms to fully coupled Earth system models that aspire to provide useable climate projections. Based on the second Model Hierarchies Workshop, which took place in 2022, we present perspectives on how this field has evolved since the first Model Hierarchies Workshop in 2016. In this period, we have witnessed a dramatic increase in the use of (a) machine learning in climate modeling and (b) climate models to estimate risks and influence decision making under climate change. Here, we discuss the implications of these growing areas of research and how we expect them to become integrated into the model hierarchies framework. Plain Language Summary: The climate modeling community often describes the variety of models used to understand and simulate climate processes as a hierarchy in complexity. Simple idealized models exist at the bottom of the hierarchy and are useful for explaining underlying physics, while fully coupled Earth system models exist at the top of the hierarchy and aim to provide useable climate projections. We present perspectives on how the model hierarchy field is evolving, focusing on two noticeable changes in recent years. Firstly, models are increasingly using machine learning, and secondly, there has been a growing interest in the usability of climate models, for instance, for estimating risks associated with climate change. Here, we discuss the implications of these growing areas of interest and how we expect them to become integrated into the model hierarchies framework. Key Points: Inspired by the 2022 Model Hierarchies workshop, we present perspectives on how the field is evolvingFirstly, we note the growing interest in the use of machine learning which presents another way to ascend or descend the model hierarchySecondly, we discuss the role of the model hierarchy for actionable climate science and decision‐making [ABSTRACT FROM AUTHOR]

Published
2023
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