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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.
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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: 2019-11-19Bibliographically approved
In thesis
1. Enhanced block sparse signal recovery and bayesian hierarchical models with applications
Open this publication in new window or tab >>Enhanced block sparse signal recovery and bayesian hierarchical models with applications
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is carried out within two projects ‘Statistical modelling and intelligentdata sampling in Magnetic resonance imaging (MRI) and positron-emission tomography(PET) measurements for cancer therapy assessment’ and ‘WindCoE -Nordic Wind Energy Center’ during my PhD study. It mainly focuses on applicationsof Bayesian hierarchical models (BHMs) and theoretical developments ofcompressive sensing (CS). Under the first project, Paper I improves the quantityestimation of MRI parametric imaging by utilizing inherent dependent structure inthe image through BHMs; Paper III constructs a theoretically unbiased and asymptoticallynormal estimator of sparsity of a sparsified MR image by using a BHM;Paper IV extends block sparsity estimation from real-valued signal recovery tocomplex-valued signal recovery. It also demonstrates the importance of accuratelyestimating the block sparsity through a sensitivity analysis; Paper V proposes anew measure, i.e. q-ratio block constrained minimal singular value, of measurementmatrix for block sparse signal recovery. An algorithm for computing thisnew measure is also presented. In the second project, Paper II estimates the uncertaintyof Weather Research and Forecasting (WRF) model’s daily-mean 2-metertemperature in a cold region by using a BHM. It is a computationally cheaper andfaster alternative to traditional ensemble approach. In summary, this thesis makessignificant contributions in improving and optimizing the estimation proceduresof parameters of interest in MRI and WRF in practice, and developing the novelestimators and measure under the framework of CS in theory.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2019. p. 35
Series
Research report in mathematical statistics, ISSN 1653-0829 ; 69
Keywords
Magnetic resonance imaging, Bayesian hierarchical models, Weather Research and Forecasting, Compressive sensing, Block sparsity, Multivariate isotropic symmetric a-stable distribution, q-ratio block constrained minimal singular value
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:umu:diva-165285 (URN)978-91-7855-148-4 (ISBN)
Public defence
2019-12-17, N450, Naturvetarhuset, Umeå University, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2019-11-26 Created: 2019-11-19 Last updated: 2019-11-26Bibliographically approved

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Wang, Jianfeng

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