A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia

The accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this...

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Published in:Water Resources Research
Main Authors: Mei, Yiwen, Maggioni, Viviana, Houser, Paul, Xue, Yuan, Rouf, Tasnuva
Format: Article in Journal/Newspaper
Language:unknown
Published: NASA EOSDIS Land Processes DAAC 2020
Subjects:
High Mountain Asia
predictor selection
topographic correction
random forest
precipitation downscaling
Natural Resources and Environment
Science
Merra
The Cryosphere
Online Access:https://hdl.handle.net/2027.42/163571
https://doi.org/10.1029/2020WR027472
id ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/163571
record_format openpolar
institution Open Polar
collection University of Michigan: Deep Blue
op_collection_id ftumdeepblue
language unknown
topic High Mountain Asia
predictor selection
topographic correction
random forest
precipitation downscaling
Natural Resources and Environment
Science
spellingShingle High Mountain Asia
predictor selection
topographic correction
random forest
precipitation downscaling
Natural Resources and Environment
Science
Mei, Yiwen
Maggioni, Viviana
Houser, Paul
Xue, Yuan
Rouf, Tasnuva
A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
topic_facet High Mountain Asia
predictor selection
topographic correction
random forest
precipitation downscaling
Natural Resources and Environment
Science
description The accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground‐ and satellite‐based observations. Results suggest improvements with respect to the original resolution MERRA‐2 precipitation product and comparable performance with gauge‐adjusted satellite precipitation products.Key PointsThis is the first use of recursive feature elimination in predictor selection for spatial downscaling of precipitationRandom forest classification is applied to create high‐resolution precipitation mask to identify whether the pixels are rainy or notValidation is performed against ground‐based precipitation observations and remote sensing precipitation products over High Mountain Asia Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/163571/2/wrcr24938.pdf http://deepblue.lib.umich.edu/bitstream/2027.42/163571/1/wrcr24938_am.pdf
format Article in Journal/Newspaper
author Mei, Yiwen
Maggioni, Viviana
Houser, Paul
Xue, Yuan
Rouf, Tasnuva
author_facet Mei, Yiwen
Maggioni, Viviana
Houser, Paul
Xue, Yuan
Rouf, Tasnuva
author_sort Mei, Yiwen
title A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
title_short A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
title_full A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
title_fullStr A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
title_full_unstemmed A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia
title_sort nonparametric statistical technique for spatial downscaling of precipitation over high mountain asia
publisher NASA EOSDIS Land Processes DAAC
publishDate 2020
url https://hdl.handle.net/2027.42/163571
https://doi.org/10.1029/2020WR027472
long_lat ENVELOPE(12.615,12.615,65.816,65.816)
geographic Merra
geographic_facet Merra
genre The Cryosphere
genre_facet The Cryosphere
op_relation Mei, Yiwen; Maggioni, Viviana; Houser, Paul; Xue, Yuan; Rouf, Tasnuva (2020). "A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia." Water Resources Research 56(11): n/a-n/a.
0043-1397
1944-7973
https://hdl.handle.net/2027.42/163571
doi:10.1029/2020WR027472
Water Resources Research
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spelling ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/163571 2023-08-20T04:10:08+02:00 A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia Mei, Yiwen Maggioni, Viviana Houser, Paul Xue, Yuan Rouf, Tasnuva 2020-11 application/pdf https://hdl.handle.net/2027.42/163571 https://doi.org/10.1029/2020WR027472 unknown NASA EOSDIS Land Processes DAAC Wiley Periodicals, Inc. Mei, Yiwen; Maggioni, Viviana; Houser, Paul; Xue, Yuan; Rouf, Tasnuva (2020). "A Nonparametric Statistical Technique for Spatial Downscaling of Precipitation Over High Mountain Asia." Water Resources Research 56(11): n/a-n/a. 0043-1397 1944-7973 https://hdl.handle.net/2027.42/163571 doi:10.1029/2020WR027472 Water Resources Research Ruiz‐Arias, J. A., Alsamanra, H., Tovar‐Pescador, J., & Pozo‐Vázquez, D. ( 2010 ). Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions. Energy Conversion and Management, 51 ( 5 ), 881 – 893. https://doi.org/10.1016/j.enconman.2009.11.024 Ibarra‐Berastegi, G., Saénz, J., Ezcurra, A., Elías, A., Diaz Argandoña, J., & Errasti, I. ( 2011 ). Downscaling of surface moisture flux and precipitation in the Ebro Valley (Spain) using analogues and analogues followed by random forests and multiple linear regression. Hydrology and Earth System Sciences, 15 ( 6 ), 1895 – 1907. https://doi.org/10.5194/hess‐15‐1895‐2011 Joyce, R. J., Janowiak, J. E., Arkin, P. A., & Xie, P. ( 2004 ). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5 ( 3 ), 487 – 503. https://doi.org/10.1175/1525‐7541(2004)005<0487:camtpg>2.0.co;2 Lawrence, M. G. ( 2005 ). The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications. Bulletin of American Meteorological Society, 86 ( 2 ), 225 – 234. https://doi.org/10.1175/bams‐86‐2‐225 Ma, Y., Hong, Y., Chen, Y., Yang, Y., Tang, G., Yao, Y., Long, D., Li, C., Han, Z., & Liu, R. ( 2018 ). Performance of optimally merged multisatellite precipitation products using the dynamic Bayesian model averaging scheme over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 123, 814 – 834. https://doi.org/10.1002/2017JD026648 Ma, Y., Yang, Y., Han, Z., Tang, G., Maguire, L., Chu, Z., & Hong, Y. ( 2018 ). Comprehensive evaluation of ensemble multi‐satellite precipitation dataset using the dynamic Bayesian model averaging scheme over the Tibetan Plateau. Journal of Hydrology, 556, 634 – 644. https://doi.org/10.1016/j.jhydrol.2017.11.050 Ma, Z., He, K., Tan, X., Xu, J., Fang, W., He, Y., & Hong, Y. ( 2018 ). Comparisons of spatially downscaling TMPA and IMERG over the Tibetan Plateau. Remote Sensing, 10 ( 12 ), 1883. https://doi.org/10.3390/RS10121883 Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., & Thiele‐Eich, I. ( 2010 ). Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48, RG3003. https://doi.org/10.1029/2009RG000314 Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., & Finkelnburg, R. ( 2013 ). Precipitation seasonality and variability over the Tibetan Plateau as resolved by the High Asia Reanalysis. Journal of Climate, 27 ( 5 ), 1910 – 1927. https://doi.org/10.1175/jcli‐d‐13‐00282.1 Naghibi, S. A., & Pourghasemi, H. R. ( 2015 ). A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resources Management, 29 ( 14 ), 5217 – 5236. https://doi.org/10.1007/s11269‐015‐1114‐8 Reichle, R. H., Liu, Q., Koster, R. D., Draper, C. S., Mahanama, S. P. P., & Partyka, G. S. ( 2017 ). Land surface precipitation in MERRA‐2. Journal of Climate, 30 ( 5 ), 1643 – 1664. https://doi.org/10.1175/jcli‐d‐16‐0570.1 Rouf, T., Mei, Y., Maggioni, V., Houser, P., & Noonan, M. ( 2019 ). A physically‐based downscaling technique for a set of atmospheric variables. Journal of Hydrometeorology, 21 ( 1 ), 93 – 108. https://doi.org/10.1175/jhm‐d‐19‐0109.1 Roebber, P. J. ( 2009 ). Visualizing multiple measures of forecast quality. Weather and Forecasting, 24 ( 2 ), 601 – 608. https://doi.org/10.1175/2008waf2222159.1 Ruiz‐Arias, J. A., Cebecauer, T., Tovar‐Pescador, J., & Šúri, M. ( 2010 ). Spatial disaggregation of satellite‐derived irradiance using a high‐resolution digital elevation model. Solar Energy, 84 ( 9 ), 1644 – 1657. https://doi.org/10.1016/j.solener.2010.06.002 Saha, S., Moorthi, S., Pan, H.‐L., Wu, X., Wang, J., Nadiga, S., Tripp, P., Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R., Gayno, G., Wang, J., Hou, Y.‐T., Chuang, H.‐y., Juang, H.‐M. H., Sela, J., Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J., Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A., Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.‐K., Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou, C.‐Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge, G., & Goldberg, M. ( 2010 ). The NCEP climate forecast system reanalysis. Bulletin of American Meteorological Society, 91 ( 8 ), 1015 – 1058. https://doi.org/10.1175/2010bams3001.1 Schaaf, C., & Wang, Z. ( 2015 ). 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Computational Geosciences, 81, 1 – 11. https://doi.org/10.1016/j.cageo.2015.04.007 IndexNoFollow High Mountain Asia predictor selection topographic correction random forest precipitation downscaling Natural Resources and Environment Science Article 2020 ftumdeepblue https://doi.org/10.1029/2020WR02747210.1175/bams‐86‐2‐22510.1175/2008waf2222159.110.1029/2000JD90071910.1023/a:101093340432410.5067/MODIS/MOD13Q1.00610.5067/MODIS/MYD13Q1.00610.1198/tast.2009.08199 2023-07-31T20:59:10Z The accurate representation of the local‐scale variability of precipitation plays an important role in understanding the hydrological cycle and land‐atmosphere interactions in the High Mountain Asia region. Therefore, the development of hyper‐resolution precipitation data is of urgent need. In this study, we propose a statistical framework to downscale the Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2) precipitation product using the random forest classification and regression algorithm. A set of variables representing atmospheric, geographic, and vegetation cover information are selected as model predictors, based on a recursive feature elimination method. The downscaled precipitation product is validated in terms of magnitude and variability against a set of ground‐ and satellite‐based observations. Results suggest improvements with respect to the original resolution MERRA‐2 precipitation product and comparable performance with gauge‐adjusted satellite precipitation products.Key PointsThis is the first use of recursive feature elimination in predictor selection for spatial downscaling of precipitationRandom forest classification is applied to create high‐resolution precipitation mask to identify whether the pixels are rainy or notValidation is performed against ground‐based precipitation observations and remote sensing precipitation products over High Mountain Asia Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/163571/2/wrcr24938.pdf http://deepblue.lib.umich.edu/bitstream/2027.42/163571/1/wrcr24938_am.pdf Article in Journal/Newspaper The Cryosphere University of Michigan: Deep Blue Merra ENVELOPE(12.615,12.615,65.816,65.816) Water Resources Research 56 11

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