DOI: 10.1016/j.atmosenv.2015.02.021
Scopus记录号: 2-s2.0-84924196048
论文题名: PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model
作者: Djalalova I ; , Delle Monache L ; , Wilczak J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 108 起始页码: 76
结束页码: 87
语种: 英语
英文关键词: Analog forecast
; CMAQ
; Kalman-filtering
; PM2.5
Scopus关键词: Air quality
; Errors
; Forecasting
; Iterative methods
; Kalman filters
; Processing
; Quality control
; CMAQ
; Community multi-scale air qualities
; Environmental prediction
; Kalman-filtering
; Meteorological variables
; National Oceanic and Atmospheric Administration
; Post-processing techniques
; Real time quality control
; Air filters
; air quality
; climate prediction
; error analysis
; error correction
; interpolation
; Kalman filter
; particulate matter
; seasonal variation
; air monitoring
; air quality control
; analytical error
; Article
; circadian rhythm
; community multiscale air quality
; comparative study
; controlled study
; environmental protection
; filtration
; forecasting
; information processing
; kalman filtering
; mathematical analysis
; meteorology
; observational method
; particulate matter
; prediction
; priority journal
; quality control procedures
; seasonal variation
; summer
; winter
; United States
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: A new post-processing method for surface particulate matter (PM2.5) predictions from the National Oceanic and Atmospheric Administration (NOAA) developmental air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model is described. It includes three main components:• A real-time quality control procedure for surface PM2.5 observations;• Model post-processing at each observational site using historical forecast analogs and Kalman filtering. • Spreading the forecast corrections from the observation locations to the entire gridded domain.The methodology is tested using 12 months of CMAQ forecasts of hourly PM2.5, from December 01, 2009 through November 30, 2010. The model domain covers the contiguous USA, and model data are verified against U.S. Environmental Prediction Agency AIRNow PM2.5 observations measured at 716 stations over the CMAQ domain. The model bias is found to have a strong seasonal dependency, with a large positive bias in winter and a small bias in the summer months, and also to have a strong diurnal cycle.Five different post-processing techniques are compared, including a seven-day running mean subtraction, Kalman-filtering, analogs, and combinations of analogs and Kalman filtering. The most accurate PM2.5 forecasts have been found to be produced when using historical analogs of the hourly Kalman-filtered forecasts, referred to as KFAN. The choice of meteorological variables used in the hourly analog search is also found to have a significant effect. A monthly error analysis is computed, in each case using the remaining 11 months of the data set for the analog searches. The improvement of KFAN errors over the raw CMAQ model errors ranges from 50 to 75% for MAE and from 40 to 60% for the correlation coefficient. Since the post-processing analysis is only done at the locations where observations are available, the spreading of post-processing correction information over nearby model grid points is necessary to make forecast contour maps. This spreading of information is accomplished with an eight-pass Barnes-type iterative objective analysis scheme. The final corrected CMAQ forecast over the entire domain is composed of the sum of the original CMAQ forecasts and the KFAN bias information interpolated over the entire domain, and is applied on an hourly basis. © 2015 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81846
Appears in Collections: 气候变化事实与影响
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作者单位: Cooperative Institute for Research in the Environmental Sciences (CIRES), University of Colorado at BoulderCO, United States; National Center for Atmospheric Research (NCAR), Research Applications Laboratory (RAL), Boulder, CO, United States; National Oceanic and Atmospheric Administration (NOAA), Earth Systems Research Laboratory (ESRL), Boulder, CO, United States
Recommended Citation:
Djalalova I,, Delle Monache L,, Wilczak J. PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model[J]. Atmospheric Environment,2015-01-01,108