Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
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
Resource type
Text
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

Open Access in DiVA

fulltext(803 kB)423 downloads
File information
File name FULLTEXT01.pdfFile size 803 kBChecksum SHA-512
2764a6f7b81e3f0baf9fdb7610737cc9058269c52771472eeb9aad2a9367bb523682bac9e59e0b628cf311b55f31466bd0e7b8df94ec4b25832048f943938a26
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Berg, PeterBosshard, ThomasYang, Wei

Search in DiVA

By author/editor
Berg, PeterBosshard, ThomasYang, Wei
By organisation
Hydrology
In the same journal
Climate
Oceanography, Hydrology and Water Resources

Search outside of DiVA

GoogleGoogle Scholar
Total: 423 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 379 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf