umu.sePublications
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
Weather Simulation Uncertainty Estimation using Bayesian Hierarchical Model
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
Novia University of Applied Sciences, Vaasa, Finland.
Group of Atmospheric Science, Division of Space Technology, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology.
Show others and affiliations
2019 (English)In: Journal of Applied Meteorology and Climatology, ISSN 1558-8424, E-ISSN 1558-8432, Vol. 58, no 3, p. 585-603Article in journal (Refereed) Published
Abstract [en]

Estimates of the uncertainty of model output fields (e.g. 2-meter temperature, surface radiation fluxes or wind speed) are of great value to the weather and climate communities. The traditional approach for the uncertainty estimation is to conduct an ensemble of simulations where the model configuration is perturbed, and/or different models are considered. This procedure is very computationally expensive and may not be feasible in particular for higher resolution experiments. In this paper a new method based on Bayesian Hierarchical Models (BHM) that requires just one model run is proposed. It is applied to the Weather Research and Forecasting (WRF) model’s 2-meter temperature in the Botnia-Atlantica region in Scandinavia for a 10-day period in the winter and summer seasons. For both seasons, the estimated uncertainty using the BHM is found to be comparable to that obtained from an ensemble of experiments in which different Planetary Boundary Layer (PBL) schemes are employed. While WRF-BHM is not capable of generating the full set of products obtained from an ensemble of simulations, it can be used to extract commonly used diagnostics including the uncertainty estimation which is the focus of this work. The methodology proposed here is fully general and can easily be extended to any other output variable and numerical model.

Place, publisher, year, edition, pages
American Meteorological Society, 2019. Vol. 58, no 3, p. 585-603
Keywords [en]
WRF, Uncertainty, Bayesian Hierarchical Model, Matérn Covariance, Planetary Boundary Layer, Botnia-Atlantica
National Category
Probability Theory and Statistics Meteorology and Atmospheric Sciences
Research subject
Mathematical Statistics; Meteorology
Identifiers
URN: urn:nbn:se:umu:diva-155617DOI: 10.1175/JAMC-D-18-0018.1ISI: 000460652900002OAI: oai:DiVA.org:umu-155617DiVA, id: diva2:1282423
Projects
WindCoEAvailable from: 2019-01-24 Created: 2019-01-24 Last updated: 2019-04-08Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full text

Authority records BETA

Wang, Jianfeng

Search in DiVA

By author/editor
Wang, JianfengYu, Jun
By organisation
Department of Mathematics and Mathematical Statistics
In the same journal
Journal of Applied Meteorology and Climatology
Probability Theory and StatisticsMeteorology and Atmospheric Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 81 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