Articles | Volume 28, issue 22
https://doi.org/10.5194/hess-28-4903-2024
https://doi.org/10.5194/hess-28-4903-2024
Research article
 | 
18 Nov 2024
Research article |  | 18 Nov 2024

Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5

Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, and Richard E. Turner
Publisher's note: the paper was adjusted on 23 April 2025. The adjustments included the typo "chloropeth", the incorrect formatting of the matrix K, the text of the caption of table C2, as well as incorrect years given in the captions of tables 4, C1, and C2.

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Cited articles

Ahmed, K., Iqbal, Z., Khan, N., Rasheed, B., Nawaz, N., Malik, I., and Noor, M.: Quantitative assessment of precipitation changes under CMIP5 RCP scenarios over the northern sub-Himalayan region of Pakistan, Environ. Dev. Sustain., 22, 7831–7845, 2020. a, b
Andermann, C., Bonnet, S., and Gloaguen, R.: Evaluation of precipitation data sets along the Himalayan front, Geochem. Geophy. Geosy., 12, Q07023, https://doi.org/10.1029/2011GC003513, 2011. a, b, c
Anders, A. M., Roe, G. H., Hallet, B., Montgomery, D. R., Finnegan, N. J., and Putkonen, J.: Spatial patterns of precipitation and topography in the Himalaya, Special Paper of the Geological Society of America, 398, 39–53, 2006. a
Andersson, T. R., Bruinsma, W. P., Markou, S., Requeima, J., Coca-Castro, A., Vaughan, A., Ellis, A.-L., Lazzara, M. A., Jones, D., Hosking, S., and Turner, R. E.: Environmental sensor placement with convolutional Gaussian neural processes, Environmental Data Science, 2, e32, https://doi.org/10.1017/eds.2023.22, 2023. a, b
Bannister, D., Orr, A., Jain, S. K., Holman, I. P., Momblanch, A., Phillips, T., Adeloye, A. J., Snapir, B., Waine, T. W., Hosking, J. S., and Allen-Sader, C.: Bias correction of high-resolution regional climate model precipitation output gives the best estimates of precipitation in Himalayan catchments, J. Geophys. Res.-Atmos., 124, 14220–14239, https://doi.org/10.1029/2019JD030804, 2019. a, b, c, d, e, f, g, h
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Short summary
This work aims to improve the understanding of precipitation patterns in High-mountain Asia, a crucial water source for around 1.9 billion people. Through a novel machine learning method, we generate high-resolution precipitation predictions, including the likelihoods of floods and droughts. Compared to state-of-the-art methods, our method is simpler to implement and more suitable for small datasets. The method also shows accuracy comparable to or better than existing benchmark datasets.
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