A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature

Handle URI:
http://hdl.handle.net/10754/561083
Title:
A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature
Authors:
Jha, Sanjeev Kumar; Mariethoz, Gregoire; Evans, Jason; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Sharma, Ashish
Abstract:
A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference dataset indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local scale estimates of precipitation and temperature from General Circulation Models. This article is protected by copyright. All rights reserved.
KAUST Department:
Biological and Environmental Sciences and Engineering (BESE) Division
Citation:
A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature 2015:n/a Water Resources Research
Journal:
Water Resources Research
Issue Date:
21-Jul-2015
DOI:
10.1002/2014WR016729
Type:
Article
ISSN:
00431397
Additional Links:
http://doi.wiley.com/10.1002/2014WR016729
Appears in Collections:
Articles; Biological and Environmental Sciences and Engineering (BESE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorJha, Sanjeev Kumaren
dc.contributor.authorMariethoz, Gregoireen
dc.contributor.authorEvans, Jasonen
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorSharma, Ashishen
dc.date.accessioned2015-07-27T12:25:20Zen
dc.date.available2015-07-27T12:25:20Zen
dc.date.issued2015-07-21en
dc.identifier.citationA space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature 2015:n/a Water Resources Researchen
dc.identifier.issn00431397en
dc.identifier.doi10.1002/2014WR016729en
dc.identifier.urihttp://hdl.handle.net/10754/561083en
dc.description.abstractA geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here, the training image consists of daily rainfall and temperature outputs from the Weather Research and Forecasting (WRF) model at 50 km and 10 km resolution for a twenty year period ranging from 1985 to 2004. The data are used to predict downscaled climate variables for the year 2005. The result, for each downscaled pixel, is daily time series of precipitation and temperature that are spatially dependent. Comparison of predicted precipitation and temperature against a reference dataset indicates that both the seasonal average climate response together with the temporal variability are well reproduced. The explicit inclusion of time dependence is explored by considering the climate properties of the previous day as an additional variable. Comparison of simulations with and without inclusion of time dependence shows that the temporal dependence only slightly improves the daily prediction because the temporal variability is already well represented in the conditioning data. Overall, the study shows that the multiple-point geostatistics approach is an efficient tool to be used for statistical downscaling to obtain local scale estimates of precipitation and temperature from General Circulation Models. This article is protected by copyright. All rights reserved.en
dc.relation.urlhttp://doi.wiley.com/10.1002/2014WR016729en
dc.rightsThis is the peer reviewed version of the following article: Jha, S. K., Mariethoz, G., Evans, J., McCabe, M. F. and Sharma, A. (2015), A space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperature. Water Resour. Res.. Accepted Author Manuscript. doi:10.1002/2014WR016729, which has been published in final form at http://doi.wiley.com/10.1002/2014WR016729. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.en
dc.titleA space and time scale-dependent nonlinear geostatistical approach for downscaling daily precipitation and temperatureen
dc.typeArticleen
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Divisionen
dc.identifier.journalWater Resources Researchen
dc.eprint.versionPost-printen
dc.contributor.institutionSchool of Civil and Environmental Engineering; University of New South Wales; Sydney Australiaen
dc.contributor.institutionExtended Hydrological Prediction Section, Bureau of Meteorology, Canberra, Australiaen
dc.contributor.institutionClimate Change Research Center, University of New South Wales; Sydney Australiaen
dc.contributor.institutionUniversity of Lausanne, Institute of Earth Surface Dynamics, Lausanne, Switzerlanden
kaust.authorMcCabe, Matthewen
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