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Model Consistent Pseudo-Observations of Precipitation and Their Use for Bias Correcting Regional Climate Models
SMHI, Research Department, Hydrology.
SMHI, Research Department, Hydrology.
SMHI, Research Department, Hydrology.
2015 (English)In: Climate, E-ISSN 2225-1154, Vol. 3, no 1, p. 118-132Article in journal (Refereed) Published
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Abstract [en]

Lack of suitable observational data makes bias correction of high space and time resolution regional climate models (RCM) problematic. We present a method to construct pseudo-observational precipitation data by merging a large scale constrained RCM reanalysis downscaling simulation with coarse time and space resolution observations. The large scale constraint synchronizes the inner domain solution to the driving reanalysis model, such that the simulated weather is similar to observations on a monthly time scale. Monthly biases for each single month are corrected to the corresponding month of the observational data, and applied to the finer temporal resolution of the RCM. A low-pass filter is applied to the correction factors to retain the small spatial scale information of the RCM. The method is applied to a 12.5 km RCM simulation and proven successful in producing a reliable pseudo-observational data set. Furthermore, the constructed data set is applied as reference in a quantile mapping bias correction, and is proven skillful in retaining small scale information of the RCM, while still correcting the large scale spatial bias. The proposed method allows bias correction of high resolution model simulations without changing the fine scale spatial features, i.e., retaining the very information required by many impact models.

Place, publisher, year, edition, pages
2015. Vol. 3, no 1, p. 118-132
National Category
Oceanography, Hydrology and Water Resources
Research subject
Hydrology
Identifiers
URN: urn:nbn:se:smhi:diva-1962DOI: 10.3390/cli3010118ISI: 000357804500001OAI: oai:DiVA.org:smhi-1962DiVA, id: diva2:923376
Available from: 2016-04-26 Created: 2016-03-03 Last updated: 2020-12-01Bibliographically approved

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Berg, PeterBosshard, ThomasYang, Wei

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