Open AccessOzone ensemble forecasts: 2. A Kalman filter predictor bias correction
Author(s) -
Delle Monache Luca,
Nipen Thomas,
Deng Xingxiu,
Zhou Yongmei,
Stull Roland
Publication year - 2006
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2005jd006311
Subject(s) - kalman filter , cmaq , ensemble kalman filter , mean squared error , statistics , data assimilation , meteorology , environmental science , econometrics , consensus forecast , ensemble average , forecast skill , ensemble forecasting , mathematics , climatology , air quality index , extended kalman filter , physics , geology
The Kalman filter (KF) is a recursive algorithm to estimate a signal from noisy measurements. In this study it is tested in predictor mode, to postprocess ozone forecasts to remove systematic errors. The recent past forecasts and observations are used by the KF to estimate the future bias. This bias correction is calculated separately for, and applied to, 12 different air quality (AQ) forecasts for the period 11–15 August 2004, over five monitoring stations in the Lower Fraser Valley, British Columbia, Canada, a population center in a complex coastal mountain setting. The 12 AQ forecasts are obtained by driving an AQ Model (CMAQ) with two mesoscale meteorological models (each run at two resolutions) and for three emission scenarios (Delle Monache et al., 2006). From the 12 KF AQ forecasts an ensemble mean is calculated (EK). This ensemble mean is also KF bias corrected, resulting in a high‐quality estimate (KEK) of the short‐term (1‐ to 2‐day) ozone forecast. The Kalman filter predictor bias‐corrected ensemble forecasts have better forecast skill than the raw forecasts for the locations and days used here. The corrected forecasts are improved for correlation, gross error, root mean square error, and unpaired peak prediction accuracy. KEK is the best and EK is the second best forecast overall when compared with the other 12 forecasts. The reason for the success of EK and KEK is that both the systematic and unsystematic errors are reduced, the first by Kalman filtering and the second by ensemble averaging.