Articles | Volume 18, issue 21
https://doi.org/10.5194/gmd-18-8313-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-8313-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
06 Nov 2025
Methods for assessment of models |
| 06 Nov 2025
| 06 Nov 2025
Bias correcting regional scale Earth system model projections: novel approach using empirical mode decomposition
Arkaprabha Ganguli
CORRESPONDING AUTHOR
Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
Jeremy Feinstein
CORRESPONDING AUTHOR
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Ibraheem Raji
Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, IL, USA
Akintomide Akinsanola
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Department of Earth and Environmental Sciences, University of Illinois Chicago, Chicago, IL, USA
Connor Aghili
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Chunyong Jung
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
Jordan Branham
Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, USA
Tom Wall
Decision and Infrastructure Sciences, Argonne National Laboratory, Lemont, IL, USA
Whitney Huang
School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA
Rao Kotamarthi
Environmental Science Division, Argonne National Laboratory, Lemont, IL, USA
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EGUsphere, 10.5194/egusphere-2025-1805, 10.5194/egusphere-2025-1805, 2025
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We use a high-resolution regional climate model to better understand hurricanes in the North Atlantic over the past 20 years. The model closely matches observed storm frequency and captures stronger storms more accurately than traditional datasets. It also shows better performance in areas with limited data, like the Caribbean. These results can help improve local storm preparedness and planning for critical infrastructure.
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Wind Energ. Sci. Discuss., 10.5194/wes-2025-47, 10.5194/wes-2025-47, 2025
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This study introduces a system that combines weather, ocean, and wave models to better understand their interactions during tropical storms and their impact on offshore structures like wind turbines. Tested using Hurricane Henri (2021), the system improves storm predictions by including how waves and ocean cooling affect storm strength and wind patterns. The results show this approach helps assess risks to offshore infrastructure during severe weather, making it more accurate and reliable.
Kyle Peco, Jiali Wang, Chunyong Jung, Gökhan Sever, Lindsay Sheridan, Jeremy Feinstein, Rao Kotamarthi, Caroline Draxl, Ethan Young, Avi Purkayastha, and Andrew Kumler
Wind Energ. Sci. Discuss., 10.5194/wes-2025-13, 10.5194/wes-2025-13, 2025
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Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, 10.5194/ascmo-8-205-2022, 10.5194/ascmo-8-205-2022, 2022
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We study wind conditions and their potential future changes across the U.S. via a statistical conditional framework. We conclude that changes between historical and future wind directions are small, but wind speeds are generally weakened in the projected period, with some locations being intensified. Moreover, winter wind speeds are projected to decrease in the northwest, Colorado, and the northern Great Plains (GP), while summer wind speeds over the southern GP slightly increase in the future.
William J. Shaw, Larry K. Berg, Mithu Debnath, Georgios Deskos, Caroline Draxl, Virendra P. Ghate, Charlotte B. Hasager, Rao Kotamarthi, Jeffrey D. Mirocha, Paytsar Muradyan, William J. Pringle, David D. Turner, and James M. Wilczak
Wind Energ. Sci., 7, 2307–2334, 10.5194/wes-7-2307-2022, 10.5194/wes-7-2307-2022, 2022
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This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, 10.5194/wes-7-1153-2022, 10.5194/wes-7-1153-2022, 2022
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Romit Maulik, Vishwas Rao, Jiali Wang, Gianmarco Mengaldo, Emil Constantinescu, Bethany Lusch, Prasanna Balaprakash, Ian Foster, and Rao Kotamarthi
Geosci. Model Dev., 15, 3433–3445, 10.5194/gmd-15-3433-2022, 10.5194/gmd-15-3433-2022, 2022
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In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, 10.5194/gmd-14-6355-2021, 10.5194/gmd-14-6355-2021, 2021
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Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Jaydeep Singh, Narendra Singh, Narendra Ojha, Amit Sharma, Andrea Pozzer, Nadimpally Kiran Kumar, Kunjukrishnapillai Rajeev, Sachin S. Gunthe, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 1427–1443, 10.5194/gmd-14-1427-2021, 10.5194/gmd-14-1427-2021, 2021
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Atmospheric models often have limitations in simulating the geographically complex and climatically important central Himalayan region. In this direction, we have performed regional modeling at high resolutions to improve the simulation of meteorology and dynamics through a better representation of the topography. The study has implications for further model applications to investigate the effects of anthropogenic pressure over the Himalaya.
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Short summary
This study introduces a timescale-aware bias-correction framework to enhance Earth system model assessments, vital for the geoscience community. By decomposing model outputs into oscillatory components, we preserve critical information across various timescales, ensuring more reliable projections. This improved reliability supports strategic decisions in sectors such as agriculture, water resources, and disaster preparedness.
This study introduces a timescale-aware bias-correction framework to enhance Earth system model...
