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Seasonality properties of four statistical-downscaling methods in central Sweden
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Air and Water Science. (Hydrologi)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Air and Water Science. (Hydrologi)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Air and Water Science. (Hydrologi)
2007 (English)In: Journal of Theoretical and Applied Climatology, ISSN 0177-798X, E-ISSN 1434-4483, Vol. 87, no 1-4, 123-137 p.Article in journal (Refereed) Published
Abstract [en]

Daily precipitation in northern Europe has different statistical properties depending on season. In this study, four statistical downscaling methods were evaluated in terms of their ability to capture statistical properties of daily precipitation in different seasons. Two of the methods were analogue downscaling methods; one using principal component analysis (PCA) and one using gradients in the pressure field (Teweles-Wobus scores, TWS) to select the analogues in the predictor field. The other two methods were conditional-probability methods; one using classification of weather patterns (MOFRBC) and the other using a regression method conditioning a stochastic weather generator (SDSM). The two analogue methods were used as benchmark methods. The study was performed on seven precipitation stations in south-central Sweden and the large-scale predictor was gridded mean-sea-level pressure over Northern Europe. The four methods were trained and calibrated on 25 years of data (1961–1978, 1994–2000) and validated on 15 years (1979–1993). Temporal and spatial limitations were imposed on the methods to find the optimum predictor settings for the downscaling. The quality measures used for evaluating the downscaling methods were the residuals of a number of key statistical properties, and the ranked probability scores (RPS) for precipitation and maximum length of dry and wet spells. The results showed that (1) the MOFRBC and SDSM outperformed the other methods for the RPS, (2) the statistical properties for the analogue methods were better during winter and autumn; for SDSM and TWS during spring; and for MOFRBC during summer, (3) larger predictor areas were needed for summer and autumn precipitation than winter and spring, and (4) no method could well capture the difference between dry and wet summers.

Place, publisher, year, edition, pages
2007. Vol. 87, no 1-4, 123-137 p.
National Category
Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:uu:diva-93486DOI: 10.1007/s00704-005-0223-3ISI: 000241581000008OAI: oai:DiVA.org:uu-93486DiVA: diva2:166975
Available from: 2005-09-09 Created: 2005-09-09 Last updated: 2017-12-14Bibliographically approved
In thesis
1. Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation: Comparison of Methods and Climate Regions
Open this publication in new window or tab >>Statistical Downscaling of Precipitation from Large-scale Atmospheric Circulation: Comparison of Methods and Climate Regions
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Statistisk nedskalning av nederbörd från storskalig atmosfärscirkulation : Jämförelse mellan metoder och klimatregioner
Abstract [en]

A global climate change may have large impacts on water resources on regional and global scales. General circulation models (GCMs) are the most used tools to evaluate climate-change scenarios on a global scale. They are, however, insufficiently describing the effects at the local scale. This thesis evaluates different approaches of statistical downscaling of precipitation from large-scale circulation variables, both concerning the method performance and the optimum choice of predictor variables.

The analogue downscaling method (AM) was found to work well as “benchmark” method in comparison to more complicated methods. AM was implemented using principal component analysis (PCA) and Teweles-Wobus Scores (TWS). Statistical properties of daily and monthly precipitation on a catchment in south-central Sweden, as well as daily precipitation in three catchments in China were acceptably downscaled.

A regression method conditioning a weather generator (SDSM) as well as a fuzzy-rule based circulation-pattern classification method conditioning a stochastical precipitation model (MOFRBC) gave good results when applied on Swedish and Chinese catchments. Statistical downscaling with MOFRBC from GMC (HADAM3P) output improved the statistical properties as well as the intra-annual variation of precipitation.

The studies show that temporal and areal settings of the predictor are important factors concerning the success of precipitation modelling. The MOFRCB and SDSM are generally performing better than the AM, and the best choice of method is depending on the purpose of the study. MOFRBC applied on output from a GCM future scenario indicates that the large-scale circulation will not be significantly affected. Adding humidity flux as predictor indicated an increased intensity both in extreme events and daily amounts in central and northern Sweden.

Publisher
64 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 93
Keyword
Hydrology, Statistical, Downscaling, Precipitation, Large-scale circulation, PCA, TWS, Weather pattern, Regression, Weather generator, Hydrologi
National Category
Oceanography, Hydrology, Water Resources
Identifiers
urn:nbn:se:uu:diva-5937 (URN)91-554-6344-4 (ISBN)
Public defence
2005-09-30, Axel Hambergssalen, Geocentrum, Villavägen 16, Uppsala, 10:00
Opponent
Supervisors
Available from: 2005-09-09 Created: 2005-09-09Bibliographically approved

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Halldin, SvenXu, Chong-Yu

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