Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
https://doi.org/10.5194/gmd-14-6355-2021
Development and technical paper
 | 
22 Oct 2021
Development and technical paper |  | 22 Oct 2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi

Related authors

Fully Coupled High-Resolution Atmosphere-Ocean-Wave Simulations of Hurricane Henri (2021): Implications for Offshore Load Assessments
Chunyong Jung, Pengfei Xue, Chenfu Huang, William Pringle, Mrinal Biswas, Geeta Nain, and Jiali Wang
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-47,https://doi.org/10.5194/wes-2025-47, 2025
Preprint under review for WES
Short summary
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025,https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Evaluation of a High-Resolution Regional Climate Simulation for Surface and Hub-height Wind Climatology over North America
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., https://doi.org/10.5194/wes-2025-13,https://doi.org/10.5194/wes-2025-13, 2025
Preprint under review for WES
Short summary
Performance of wind assessment datasets in United States coastal areas
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115,https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript under review for WES
Short summary
A conditional approach for joint estimation of wind speed and direction under future climates
Qiuyi Wu, Julie Bessac, Whitney Huang, Jiali Wang, and Rao Kotamarthi
Adv. Stat. Clim. Meteorol. Oceanogr., 8, 205–224, https://doi.org/10.5194/ascmo-8-205-2022,https://doi.org/10.5194/ascmo-8-205-2022, 2022
Short summary

Related subject area

Climate and Earth system modeling
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025,https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025,https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
The ensemble consistency test: from CESM to MPAS and beyond
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025,https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025,https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025,https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary

Cited articles

Abeykoon, V., Liu, Z., Kettimuthu, R., Fox, G., and Foster, I.: Scientific image restoration anywhere, in: 2019 IEEE/ACM 1st Annual Workshop on Large-scale Experiment-in-the-Loop Computing (XLOOP), Denver, Colorado, November 2019, IEEE, 8–13, 2019. a
Barrett, A. I., Wellmann, C., Seifert, A., Hoose, C., Vogel, B., and Kunz, M.: One step at a time: How model time step significantly affects convection-permitting simulations, J. Adv. Model. Earth Sy., 11, 641–658, 2019. a
Bretherton, C. S. and Khairoutdinov, M. F.: Convective self-aggregation feedbacks in near-global cloud-resolving simulations of an aquaplanet, J. Adv. Model. Earth Sy., 7, 1765–1787, 2015. a
Chang, W., Stein, M. L., Wang, J., Kotamarthi, V. R., and Moyer, E. J.: Changes in spatiotemporal precipitation patterns in changing climate conditions, J. Climate, 29, 8355–8376, 2016. a
Chang, W., Wang, J., Marohnic, J., Kotamarthi, V. R., and Moyer, E. J.: Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking, Clim. Dynam., 55, 175–192, 2020. a
Download
Short summary
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.
Share