Evaluation of High Mountain Asia - Land Data Assimilation System (version 1) from 2003 to 2016, Part I: A hyper-resolution terrestrial modeling system. Xue, Y., Houser, P. R., Maggioni, V., Mei, Y., Kumar, S. V., & Yoon, Y. Journal of Geophysical Research: Atmospheres, n/a(n/a):e2020JD034131. _eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JD034131![Evaluation of High Mountain Asia - Land Data Assimilation System (version 1) from 2003 to 2016, Part I: A hyper-resolution terrestrial modeling system [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This first paper of the two-part series focuses on demonstrating the accuracy of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° ( ∼ 1-km) and 0.25° ( ∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations. This article is protected by copyright. All rights reserved.
@article{xue_evaluation_nodate,
title = {Evaluation of {High} {Mountain} {Asia} - {Land} {Data} {Assimilation} {System} (version 1) from 2003 to 2016, {Part} {I}: {A} hyper-resolution terrestrial modeling system},
volume = {n/a},
issn = {2169-8996},
shorttitle = {Evaluation of {High} {Mountain} {Asia} - {Land} {Data} {Assimilation} {System} (version 1) from 2003 to 2016, {Part} {I}},
url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2020JD034131},
doi = {https://doi.org/10.1029/2020JD034131},
abstract = {This first paper of the two-part series focuses on demonstrating the accuracy of a hyper-resolution, offline terrestrial modeling system used for the High Mountain Asia (HMA) region. To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° ( ∼ 1-km) and 0.25° ( ∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9\% relative to coarse-resolution estimates. The model forced by downscaled forcings in its entirety yields the highest skill in model output states as well as precipitation, which improves the skill obtained by coarse-resolution estimates by 7\%. These findings, on one hand, corroborate the importance of employing the hyper-resolution versus coarse-resolution modeling in areas characterized by complex terrain. On the other hand, by evaluating four sets of model simulations forced with different precipitation products, this study emphasizes the importance of accurate hyper-resolution precipitation products to drive model simulations. This article is protected by copyright. All rights reserved.},
language = {en},
number = {n/a},
urldate = {2021-04-11},
journal = {Journal of Geophysical Research: Atmospheres},
author = {Xue, Yuan and Houser, Paul R. and Maggioni, Viviana and Mei, Yiwen and Kumar, Sujay V. and Yoon, Yeosang},
note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020JD034131},
keywords = {Downscaling, High Mountain Asia, Hyper-resolution modeling, Noah-MP},
pages = {e2020JD034131},
}
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To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° ( ∼ 1-km) and 0.25° ( ∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9% relative to coarse-resolution estimates. 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To this end, this study systematically evaluates four sets of model simulations at point scale, basin scale, and domain scale obtained from different spatial resolutions including 0.01° ( ∼ 1-km) and 0.25° ( ∼ 25-km). The assessment is conducted via comparisons against ground-based observations and satellite-derived reference products. The key variables of interest include surface net shortwave radiation, surface net longwave radiation, skin temperature, near-surface soil temperature, snow depth, snow water equivalent, and total runoff. In the evaluation against ground-based measurements, the superiority of the 0.01° estimates are mostly demonstrated across relatively complex terrain. Specifically, hyper-resolution modeling improves the skill in meteorological forcing estimates (except precipitation) by 9\\% relative to coarse-resolution estimates. 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