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title Selection of bias correction models for improving the daily PM10 forecasts of WRF-EURAD in Porto, Portugal
authors Mok, KM; Miranda, AI; Yuen, KV; Hoi, KI; Monteiro, A; Ribeiro, I
author full name Mok, K. M.; Miranda, A. I.; Yuen, K. V.; Hoi, K. I.; Monteiro, A.; Ribeiro, I.
title Selection of bias correction models for improving the daily PM10 forecasts of WRF-EURAD in Porto, Portugal
nationality internacional
language English
document type Article
author keywords Bayesian; Bias correction; Model selection; PM10; Porto
abstract The techniques of bias correction are commonly used for improving the performance of deterministic air quality forecasting systems. One issue not addressed in previous studies is how to select systematically and objectively the best correction model from a pool of candidates. In this study, a method that could evaluate the probabilities of all model candidates based on a set of training data is proposed to select the most accurate and robust model by finding the one with the maximum probability. The Bayesian method was applied to select bias correction models at 12 monitoring stations for improving the forecasts of daily averaged PM10 concentrations given by the deterministic air quality forecasting system WRF-EURAD in Porto, Portugal. At each station, 4095 (=2(12)-1) correction model candidates were systematically formed by adopting different linear combinations of 12 input variables. Selection of the best model was processed based on one year of monitoring and WRF-EURAD data. Based on the 2012 data, the selected model at each station was found to have significantly higher probability than the other candidates, and it is also much simpler than the full model. These selected models were then used to correct the raw forecasts by WRF-EURAD in the following year. The corrected forecasts show significant improvement on the performance indicators (RMSE by 35.8%, R by 58.5%, MFB by 68%, EDR by 38.3%, FAR by 51.8%, CSI by 30.8%) over the raw outputs of WRF-EURAD, confirming the success of the proposed technique. (C) 2017 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
author address [Mok, K. M.; Yuen, K. V.; Hoi, K. I.] Univ Macau, Dept Civil & Environm Engn, Ave Univ, Taipa, Macao, Peoples R China; [Miranda, A. I.; Monteiro, A.; Ribeiro, I.] Univ Aveiro, Dept Environm & Planning, Aveiro, Portugal; [Miranda, A. I.; Monteiro, A.; Ribeiro, I.] Univ Aveiro, CESAM, Aveiro, Portugal; [Ribeiro, I.] Julich Res Ctr, Inst Energy & Climate Res, Troposphere IEK 8, Julich, Germany
reprint address Hoi, KI (reprint author), Univ Macau, Dept Civil & Environm Engn, Ave Univ, Taipa, Macao, Peoples R China.
e-mail address KIHOI@umac.mo
orcid number Miranda, Ana/0000-0001-5807-5820; Monteiro, Alexandra/0000-0001-8182-3380
funding agency and grant number research committee of University of Macau [MYRG2014-00038-FST]; Macau Science and Technology Development Fund [079/2013/A3]
funding text This study was sponsored by the research committee of University of Macau under grant no: MYRG2014-00038-FST and the Macau Science and Technology Development Fund under grant no: 079/2013/A3. The Agencia Portuguesa do Ambiente is thanked for supplying the air quality data of Porto, Portugal.
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cited reference count 48
publisher city BUCA
issn 1309-1042
29-character source abbreviation ATMOS POLLUT RES
iso source abbreviation Atmos. Pollut. Res.
publication date JUL
year published 2017
volume 8
issue 4
beginning page 628
ending page 639
digital object identifier (doi) 10.1016/j.apr.2016.12.010
subject category 12
document delivery number Environmental Sciences
unique article identifier Environmental Sciences & Ecology
CESAM authors