A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction

Handle URI:
http://hdl.handle.net/10754/552148
Title:
A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction
Authors:
Ershadi, Ali; McCabe, Matthew ( 0000-0002-1279-5272 ) ; Evans, Jason P.; Mariethoz, Gregoire; Kavetski, Dmitri
Abstract:
The influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.
KAUST Department:
Water Desalination and Reuse Research Center (WDRC)
Citation:
A Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction 2013, 49 (5):2343 Water Resources Research
Publisher:
Wiley-Blackwell
Journal:
Water Resources Research
Issue Date:
May-2013
DOI:
10.1002/wrcr.20231
Type:
Article
ISSN:
00431397
Additional Links:
http://doi.wiley.com/10.1002/wrcr.20231
Appears in Collections:
Articles; Water Desalination and Reuse Research Center (WDRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorErshadi, Alien
dc.contributor.authorMcCabe, Matthewen
dc.contributor.authorEvans, Jason P.en
dc.contributor.authorMariethoz, Gregoireen
dc.contributor.authorKavetski, Dmitrien
dc.date.accessioned2015-05-04T16:13:59Zen
dc.date.available2015-05-04T16:13:59Zen
dc.date.issued2013-05en
dc.identifier.citationA Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model prediction 2013, 49 (5):2343 Water Resources Researchen
dc.identifier.issn00431397en
dc.identifier.doi10.1002/wrcr.20231en
dc.identifier.urihttp://hdl.handle.net/10754/552148en
dc.description.abstractThe influence of uncertainty in land surface temperature, air temperature, and wind speed on the estimation of sensible heat flux is analyzed using a Bayesian inference technique applied to the Surface Energy Balance System (SEBS) model. The Bayesian approach allows for an explicit quantification of the uncertainties in input variables: a source of error generally ignored in surface heat flux estimation. An application using field measurements from the Soil Moisture Experiment 2002 is presented. The spatial variability of selected input meteorological variables in a multitower site is used to formulate the prior estimates for the sampling uncertainties, and the likelihood function is formulated assuming Gaussian errors in the SEBS model. Land surface temperature, air temperature, and wind speed were estimated by sampling their posterior distribution using a Markov chain Monte Carlo algorithm. Results verify that Bayesian-inferred air temperature and wind speed were generally consistent with those observed at the towers, suggesting that local observations of these variables were spatially representative. Uncertainties in the land surface temperature appear to have the strongest effect on the estimated sensible heat flux, with Bayesian-inferred values differing by up to ±5°C from the observed data. These differences suggest that the footprint of the in situ measured land surface temperature is not representative of the larger-scale variability. As such, these measurements should be used with caution in the calculation of surface heat fluxes and highlight the importance of capturing the spatial variability in the land surface temperature: particularly, for remote sensing retrieval algorithms that use this variable for flux estimation.en
dc.publisherWiley-Blackwellen
dc.relation.urlhttp://doi.wiley.com/10.1002/wrcr.20231en
dc.rightsArchived with thanks to Water Resources Researchen
dc.titleA Bayesian analysis of sensible heat flux estimation: Quantifying uncertainty in meteorological forcing to improve model predictionen
dc.typeArticleen
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)en
dc.identifier.journalWater Resources Researchen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionSchool of Civil & Environmental Engineering; University of New South Wales; Sydney New South Wales Australiaen
dc.contributor.institutionClimate Change Research Centre, University of New South Wales; Sydney New South Wales Australiaen
dc.contributor.institutionSchool of Civil & Environmental Engineering; University of New South Wales; Sydney New South Wales Australiaen
dc.contributor.institutionSchool of Civil, Environmental and Mining Engineering; University of Adelaide; South Australia Australiaen
kaust.authorMcCabe, Matthewen
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