urban air quality and regional haze weather forecast in yangtze...
TRANSCRIPT
Urban air quality and regional haze weather forecast in Yangtze River
Delta of China
Wang Tijian Jiang Fei Deng Junjun Shen YiSchool of Atmospheric Science,Nanjing University
Fu Qinyan Wang QianShanghai Environmental Monitoring Center
Zhang Danning Fu Yin Xu JinahuaNanjing Environmental Monitoring Center
Dec.12,2012, Geneva
Outline
Air pollution in Yangtze River Delta
Urban air quality and regional haze weather forecast model
Air quality forecast in urban
Haze weather forecast in YRD
Summary
Yangtze River Delta(YRD)
Located on the western coast of the Pacific Ocean and covers an area of about 99,600 km2, with a population of 75 million.
Regional haze
Photochemical smog
Acid rain
Dust storm
PM10
0
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PM10
Heavy haze case 2008.10.27-29
Dust storm occurred in Mar.20,2010
Continuous air pollution for more than 10 days
chemical weather forecasting (CWF) modelsin the word
On line model: SKIRON/Dust, WRF-Chem, ENVIROHIRLAM, TAPM
Off line model:ALADINCAMx, CAMx-AMWFG, EURAD-RIU, FARM, LOTOSEUROS, MATCH, MM5-CAMx, MM5-CHIMERE,MM5/WRF-CMAQ, MOCAGE,NAME, OPANA,RCG, SILAM, THORRegAEMS, NAQPS, PATH,TAQM
Urban air quality forecast system
Display and Release to Website
WRF Preprocessing System
WRF-Chem Model
Products Output
Emission Inventory
Meteorological initial and Boundary conditions
Chemical initial and Boundary conditions
Previous forecast results3-D grid chemical concentrations
Automatic Download GFS Data
Emission Preprocessing Model
Weather processes simulation
Transport and diffusion
Dry and wet deposition
Chemical reactions and aerosol process
Air Pollution Index
Hourly surface concentrations distribution, SO2, NO2, O3, PM10
Hourly concentrations at selected sites
WRF-CHEM
Regional haze weather forecast system
RegAEMS
Different aerosol schemes in RegAEMS
SO42-, NO3
-:ISORROPIA( Nenes,1998 )EC,OCSOA: SORGAM(Schell,2001)Sea salt: Monahan(1986)Dust: Gillette(1988)
ext scat abs ag sp apb b b b b b= + + + +
/ ext
Visibility scheme
K bν =
( )( )4 4 4 32
2
3 ( )
4 100.6 0.175 10
extb f RH NH SO NH NO
Organics Soot SoilCoarsemass NO
= • + +
+ + ++ +
(K is 3.912)
Haze level classfication
PM2.5>75μg/m3
RH<0.8
Visibility<10km
Super heavy haze:Visibility<2km
Heavy haze:2km≤Visibility<4km
Medium haze:4km≤Visibility<7km
Light haze:7km≤Visibility<10km
Air quality and haze weather forecast step
Today Tomorrow The day after tomorrow
00:00 20:00 12:00 12:00
Spin up Day one forecast ……
……
……
16 24 24
48 hours forecast with 16 hours spin up
Model settings for Shanghai forecastHorizontal grid: 4 nested domains
Domain 1: 88*75, 81kmDomain 2: 85*70, 27kmDomain 3: 76*67, 9 kmDomain 4: 88*73, 3 km
Vertical level: 24 sigma level
Model top: 100hpa
Gas phase mechanism: RACM
Aerosol: MADE/SORGAM
Anthro. Emi.: INTEX-B + Shanghai emission inventory
Natrual Emi.: calculated online by Guenther scheme
Point source in Shanghai
CO NOx SO2
VOC PM2.5 PM10
Area source in Shanghai
CO NOx SO2
VOC PM2.5 PM10
Line source in Shanghai
CO NOx SO2
VOC PM2.5 PM10
SO2 hourly concentration
各月份预报的平均情况预报平均 相关性 平均偏差
时段 观测平均24-hour 48-hour 24-hour 48-hour 24-hour 48-hour
5月 34.20 41.17 43.22 0.16 0.21 7.00 9.97 6月 31.20 34.86 32.31 0.34 0.32 4.29 1.70 7月 24.71 40.31 36.71 0.21 0.08 15.40 11.45 8月 21.01 35.25 41.45 0.16 0.19 14.46 20.30 9月 18.15 55.67 49.41 0.09 0.05 37.57 31.51 10月 28.11 60.25 61.94 0.19 0.17 32.04 33.63 11月 39.78 48.47 45.12 0.18 0.24 12.99 12.21 12月 60.43 51.32 60.06 0.27 0.20 -6.17 0.46 1月 48.99 45.29 48.64 0.19 0.21 -3.55 -0.35 2月 28.22 36.01 39.16 0.20 0.18 7.70 10.96 3月 41.93 33.52 33.12 0.17 0.24 -8.37 -8.71 平均 34.25 43.83 44.65 0.20 0.19 10.31 11.20
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40.0
5月 6月 7月 8月 9月 10月 11月 12月 1月 2月 3月
相关
性
平均
偏差
偏差
相关性
Before emi updated
After emi updated
NO2 hourly concentration
各月份预报的平均情况预报平均 相关性 平均偏差
时段 观测平均24-hour 48-hour 24-hour 48-hour 24-hour 48-hour
5月 54.39 49.09 52.37 0.33 0.34 -3.86 -1.26 6月 48.48 44.92 48.20 0.41 0.33 -3.08 3.44 7月 40.47 51.12 52.61 0.33 0.21 10.14 11.59 8月 32.28 45.28 50.51 0.22 0.42 14.41 19.25 9月 39.21 47.72 48.86 0.21 0.00 7.92 9.28 10月 56.85 50.59 54.13 0.39 0.42 -6.20 -2.67 11月 61.00 49.52 49.80 0.31 0.25 -12.32 -10.52 12月 67.15 52.45 56.69 0.24 0.23 -12.49 -8.57 1月 57.89 47.64 51.58 0.23 0.25 -10.17 -6.23 2月 43.23 44.98 46.40 0.28 0.21 1.74 3.22 3月 51.78 44.49 46.14 0.29 0.30 -7.15 -5.39 平均 50.25 47.98 50.66 0.29 0.27 -1.91 1.10
0.00
0.05
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0.35
0.40
‐10.0
‐5.0
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35.0
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5月 6月 7月 8月 9月 10月 11月 12月 1月 2月 3月
相关
性
平均
偏差
偏差
相关性
PM10 hourly concentration
各月份预报的平均情况预报平均 相关性 平均偏差
时段 观测平均24-hour 48-hour 24-hour 48-hour 24-hour 48-hour
5月 81.91 60.69 65.73 0.11 0.14 -25.44 -22.56
6月 90.35 52.37 52.45 0.35 0.33 -35.92 -34.47
7月 56.85 66.68 75.29 0.17 0.10 9.35 18.06
8月 56.44 63.88 83.46 0.16 0.20 8.66 26.96
9月 51.35 68.93 68.42 0.06 -0.01 17.53 18.32
10月 92.98 81.70 83.90 0.25 0.26 -10.97 -8.83
11月 84.00 91.05 88.75 0.30 0.33 5.03 9.38
12月 119.61 108.57 128.66 0.27 0.28 -7.56 10.53
1月 90.67 95.93 107.80 0.31 0.37 5.46 17.23
2月 56.54 82.43 91.77 0.09 0.09 25.71 35.07
3月 87.16 81.76 86.09 0.10 0.13 -5.23 -0.73
平均 78.90 77.63 84.76 0.20 0.20 -1.22 6.27
0.00
0.05
0.10
0.15
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0.25
0.30
0.35
0.40
‐35.0
‐25.0
‐15.0
‐5.0
5.0
15.0
25.0
35.0
5月 6月 7月 8月 9月 10月 11月 12月 1月 2月 3月 相关
性
平均
偏差
偏差
相关性
Statistics on API prediction
‐15
‐10
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May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr
Deviation
Accuracy & Correlatio
n
Month
Accuracy
Correlation
Deviation
Air quality forecast for 2nd Youngth Olympic game in Nanjing in 2014
DOMAIN 1: 88×75, 81 kmDOMAIN 2: 85×70, 27 kmDOMAIN 3: 70×64, 9 kmDOMAIN 4: 55×61, 3 km
Area source Point source
NOxNOx
PM10PM10
SO2SO2
Meteorological variables in Nanjing during Oct., 2007
Observed and predicted concentrations of PM10, SO2 and NO2 in Nanjing during Oct., 2007
Paved road dustConstruction dust
Biomass burning dustSoil dust
Haze weather forecast in YRD using RegAEMS
Regional Center:(32.06°N, 119.86°E)
Vertical level:Vertical: 10 levels
Horizontal Grid Size:DOMAIN 1: 88×75, 81 kmDOMAIN 2: 85×70, 27 kmDOMAIN 3: 70×64, 9 km
Prediction Period:2009.1.1—2009.12.31
Observed vs. predicted meteorological variables
Daily concentrations of PM10 and PM2.5 in YRD during 2009
Hourly visibility from observation and prediction in YRD during 2009
Daily RH, PM2.5, Visibilityand Haze level forecast
Heavy haze case prediction
Backward trajectory, fire spot and biomass emission
117E 118E 119E 120E 121E 122E
30N
31N
32N
33N
34N
Nanjing
Hefei
Shanghai
Hangzhou
Biomass burning
Fire spot
RegAEMS
10月28日18:00~20:00
Data Assimilation in dust prediction
Directly analyze 3D aerosol mass concentration with a one-step procedure of variational minimization within the GSI
Do NOT apply any assumption about vertical shape and relative weight of individual species.
14 WRF/Chem-GOCART 3D aerosol mass concentration as analysis variables
need background error covariance statistics for each aerosol species
Use CRTM as the AOD observation operator, including both forwardand Jacobian models
In short, no much difference from 3DVAR DA for meteorological obs.
38
J(x) =12
(x − xb )T B−1(x − xb ) +12
[y − H(x)]T R−1[y − H(x)]
OMB/OMA of MODIS AOD
40
41
SummaryWRF-CHEM and RegAEMS are useful tools for urban quality and regional haze forecast.
To improve the spatial resolution of emission inventory, especially emissions from fugitive dust, transportation and biomass burning in Yangtze River Delta region, to improve the performance of model prediction.
To include urban canopy model in the meteorological model to improve the performance of meteorology prediction.
To improve the model performance of forecast on heavy air pollution case using data assimilation and satellite spot, such as dust storm, and biomass burning.