Introduction
Predicting and simulating air quality remains a challenge in heavily polluted
regions (Wang et al., 2014; Ding et al., 2016). Chemical data assimilation
(DA), which combines observations and model simulations, is recognized as one
effective method to improve air quality forecasts. It has been widely used to
assimilate aerosol measurements from both ground-based and spaceborne
platforms, including surface PM10 observations (Jiang et al., 2013;
Pagowski et al., 2014), surface PM2.5 observations (Li et al., 2013;
Zhang et al., 2016), lidar observations (Yumimoto et al., 2007, 2008),
aerosol optical depth products from AERONET (the AErosol RObotic NETwork)
(Schutgens et al., 2010a, b, 2012), and various satellites (Sekiyama et
al., 2010; Liu et al., 2011; Dai et al., 2014). These studies indicate that
assimilating observations can substantially improve the spatiotemporal
variations of aerosol in the simulation and forecasts.
Aerosols are not only primarily emitted; a larger portion of them is also
formed secondarily through reactions with several gaseous-phase precursors and
oxidants in the atmosphere (Huang et al., 2014; Nie et al., 2014; Xie et al.,
2015). So, observations of trace gases are also useful in assimilating data
for aerosol simulations and forecasts. Efforts to assimilate
atmospheric-composition observations – like O3, SO2, NO,
NO2, CO, and NH3 – have also been made. For example, Elbern et
al. (1997, 2000, 2007) and Elbern and Schmidt (1999, 2001) developed a 4D-VAR
(four-dimensional variational) system to assimilate surface measurements of
O3, SO2, NO, and NO2 to improve air quality forecasts
with the joint adjustment of initial conditions (ICs) and emission rates.
Later, van Loon et al. (2000) assimilated O3 in the transport
chemistry model LOTOS, based on an ensemble Kalman filter (EnKF). Heemink and
Segers (2002) attempted to reconstruct NOx and volatile
organic compound (VOC) emissions for O3 forecasting by assimilating
O3. Carmichael et al. (2003, 2008a, b) developed 4D-VAR and EnKF
systems to assimilate O3 and NO2 to improve ICs and emission
sources for O3 forecasting. Hakami et al. (2005) constrained black
carbon (BC) emissions during the Asian Pacific Regional Aerosol
Characterization Experiment. Henze et al. (2007, 2009) estimated
SOx, NOx, and NH3 emissions based on
a 4D-VAR method by assimilating surface sulfate and nitrate aerosol
observations. Other studies have estimated the NOx (van
der A et al., 2006, 2017; Mijling and van der A, 2012; Mijling et al., 2009,
2013; Ding et al., 2015) and SO2 emissions (van der et A al., 2017)
based on an extended Kalman filter by assimilating SO2 and
NO2 retrievals from SCIAMACHY (SCanning Imaging Absorption
spectroMeter for Atmospheric CHartographY) and OMI (Ozone Monitoring
Instrument). Barbu et al. (2009) applied an EnKF to optimize the emissions
and conversion rates using surface measurements of SO2 and sulfate.
McLinden et al. (2016) constrained SO2 emissions using space-based
observations.
In recent years, severe haze pollution episodes have begun to occur more
frequently in China, especially in the megacity clusters of eastern China
(e.g., Parrish and Zhu, 2009; Sun et al., 2015; Zhang et al., 2015). Thus,
regional haze, especially when accompanied by extremely high PM2.5
concentrations, has drawn significant research interest. However, there are
large uncertainties involved in the numerical prediction of atmospheric
aerosols. During severe haze pollution episodes, air quality models often
underestimate the extreme peak mass concentration of particulate matter (Wang
et al., 2014). Previous studies have revealed that the assimilation of
atmospheric-composition observations can improve air quality forecasts by
constraining the uncertainties of both the chemical ICs and emissions (Tang
et al., 2010, 2011, 2013, 2016; Miyazaki and Eskes, 2013; Miyazaki et al.,
2012, 2014). Peng et al. (2017) demonstrated that significant improvements in
forecasting PM2.5 can be achieved via the joint adjustment of ICs and
source emissions using an EnKF to assimilate surface PM2.5 observations.
In 2013, China launched an atmospheric environmental monitoring system that
provides real-time and online atmospheric chemical observations, including
PM10, PM2.5, SO2, NO2, O3, and CO
(http://113.108.142.147:20035/emcpublish/, last access: 26 November
2018). This dataset provides an opportunity to improve air quality forecasts
using DA. However, such fruitful observations are less used in air quality
forecasting even though a large discrepancy exists between the forecasts and
observations. But it is now possible to estimate the impact on forecast
improvement of simultaneously assimilating various surface observations.
Thus, we developed an EnKF system that can simultaneously assimilate surface
measurements of PM10, PM2.5, SO2, NO2, O3,
and CO to correct WRF-Chem (Weather Research and Forecasting model with
Chemistry) forecasts using the Goddard Chemistry Aerosol Radiation and
Transport (GOCART) aerosol scheme. As an extension to Peng et al. (2017), the
impact of simultaneously assimilating various surface aerosol and chemical
observations was investigated.
Sections 2 and 3 briefly describe the DA system and observations used in this
study, respectively. The experimental design is introduced in Sect. 4.
Finally, the assimilation results are presented in Sect. 5, before a brief
summary in Sect. 6.
WRF-Chem model configurations in this study.
Parameterization
WRF-Chem option
Aerosol scheme
Goddard Chemistry Aerosol Radiation and Transport (Chin et al., 2000, 2002)
Photolysis scheme
Fast-J (Wild et al., 2000)
Gas-phase chemistry
Regional Atmospheric Chemistry Mechanism (Stockwell et al., 1997)
Microphysics
the WRF single-moment 5-class scheme
Longwave radiation
Rapid Radiative Transfer Model longwave scheme (Mlawer et al., 1997)
Shortwave radiation
Goddard shortwave radiation scheme (Chou and Suarez, 1994)
Planetary boundary layer
Yonsei University boundary layer scheme (Hong et al., 2006)
Cumulus parameterization
Grell-3D scheme
Land surface model
NOAH (Chen and Dudhia, 2001)
Dust and sea salt emissions
Goddard Chemistry Aerosol Radiation and Transport (Chin et al., 2002)
The model domain (a) and the North China
Plain (b). Black dots are the observational sites used for
assimilation, and red stars are the observational sites used for validation.
The green frame marks the Beijing–Tianjin–Hebei region.
![]()
DA system
The DA system in this study was the same as the one used in Peng et
al. (2017). It can simultaneously analyze the chemical ICs and emissions with
the assimilation of surface PM2.5 observations. A brief summary of the
DA system is introduced here.
In every DA cycle, the ensemble emission scaling factors
λf are first calculated by the forecast model of
scaling factors MSF (see details of MSF in
Sect. 2.2). Then, the ensemble forecast emissions Ef are
calculated using the following equation:
Ei,t=λi,tEtp,(i=1,…,N),
where Etp is the prescribed anthropogenic emission. The
ensemble members of chemical fields Cf are forecasted using
WRF-Chem, forced by the forecast emissions Ef whose ICs are
previously analyzed concentration fields. Now, the background of the joint
vector, xf=Cf,λfT, has been
produced. Then, the analyzed state vector, xa=Ca,λaT, is optimized
using an ensemble square root filter (EnSRF). Finally, the assimilated
emissions Ea can be obtained using Eq. (1). It is noted that
the optimized emissions are only the results of a mathematical optimum by
utilizing observations. If the optimized emissions used in the EnSRF
experiment run with pure concentrations as state vectors are identical to the
emissions obtained from the joint EnSRF experiment run with concentrations
and emission factors (representing emissions) as state vectors, identical
results may be obtained.
WRF-Chem model
The model used to simulate the transport of aerosols and chemical species was
the WRF-Chem (Grell et al., 2005). As in Peng et al. (2017), we used version
3.6.1; the physical and chemical parameterization options are listed in
Table 1. The model computational domain covered almost the whole of China, and
the horizontal resolution was 40.5 km. Figure 1b shows our area of interest,
the North China Plain (NCP). The model included 57 vertical levels, and the
model top was 10 hPa.
The hourly prior anthropogenic emissions were based on the Multi-resolution
Emission Inventory for China (MEIC) (Li et al., 2014) for October 2010,
instead of the regional emission inventory in Asia (Zhang et al., 2009) for
the year 2006 in Peng et al. (2017). The reason we chose the MEIC-2010 was
that the total emissions are reasonable for cities over the NCP (Zheng et
al., 2015). The original resolution of the MEIC-2010 is 0.25∘×0.25∘, but it has been processed to match the model resolution
(40.5 km) (Chen et al., 2016). No time variation was added to maintain
objectivity in the prior anthropogenic emissions.
Forecast model of scaling factors
In this work, the primary sources to be optimized were the emissions of
PM10, PM2.5, SO2, NO, NH3, and CO. The sources of
NH3 were analyzed because they also impact greatly on the aerosols
distribution. Thus, the emission scaling factors
λi,tf=(λPM2.5f,λPM10f,λSO2f,λNOf,λNH3f,λCOf)
were prepared by the forecast model of scaling operator MSF
before WRF-Chem integration.
We used the same persistence forecast operator MSF to forecast
λi,tf as in Peng et al. (2017). The forecast
operator was developed by using the ensemble forecast chemical fields. Thus,
κi,t=Ci,tfCtf‾‾,(i=1,…,N)
(κi,t)inf=βκi,t-κt‾+κt‾,(i=1,…,N)λi,tp=(κi,t)infλi,tf=14λi,t-3a+λi,t-2a+λi,t-1a+λi,tp,(i=1,…,N),
where Ci,tf is the ith ensemble member of the chemical
fields at time t, and
Ctf‾=1N∑i=1NCi,tf is the ensemble mean; κi,t is the ensemble
concentration ratios and κt‾ is the ensemble mean of
κi,t with values of 1; β is the inflation factor to keep the
ensemble spreads of κi,t at a certain level; and
λi,t-1a, λi,t-2a,
and λi,t-3a are the previous assimilated
emission scaling factors. It is noted that λi,tf
are spatially varying because they are calculated by using the spatially
varying variables, the forecast chemical fields Ci,tf. Furthermore, there are very few negative values for
(κi,t)inf after inflation. A quality control procedure
is performed for (κi,t)inf before further appliance.
All these negative data were set as 0 in this work. Then
(κi,t)inf were re-centered to ensure the ensemble mean
values of (κi,t)inf were all 1. Furthermore, another
quality control procedure is performed for
λi,ta to keep them positive. Thus, all
λi,tf and λi,ta
could be positive.
In this study, the ensemble forecast chemical fields of PM25, PM10,
SO2, NO, NH3, and CO of the previous assimilation cycle are
respectively used to calculate the emission scaling factors
(λPM2.5f,λPM10f,λSO2f,λNOf,λNH3f,λCOf). Previous works (Peng et al.,
2015, 2017) showed that reasonable results can be obtained when the ensemble
spread of the emission scaling factors range from 0.1 to 1. In order to keep
the ensemble spread of the scaling factors at this level in most model area,
β is chosen as 1.3, 1.4, 1.3, 1.2, 1.2, and 1.4 for the ensemble
concentration ratios of P2.5, P10, SO2, NO, NH3, and
CO, respectively, in Eq. (3).
Then, the sources Ei,tf = (EPM2.5f,
EPM10f, ESO2f,
ENOf, ENH3f,
ECOf) are calculated using Eq. (1).
From the perspective of PM2.5 emissions, these include the unspeciated
primary sources of PM2.5 EPM2.5, sulfate
ESO4, and nitrate ENO3. We updated
EPM2.5, ESO4, and ENO3 (including the
nuclei and accumulation modes) following Peng et al. (2017).
DA algorithm
The assimilation algorithm employed was the EnSRF proposed by Whitaker and
Hamill (2002). The EnKF proposed by Evensen (1994) needs perturbations of
observations in practice. Compared to the original EnKF, the EnSRF obviates
the need to perturb the observations and avoids additional sampling errors
introduced by perturbing observations.
We used the same EnSRF as in Schwartz et al. (2012, 2014). The ensemble
member was chosen as 50. The localization radius was chosen as 607.5 km, so
EnSRF analysis increments were forced to zero 607.5 km away from an
observation (Gaspari and Cohn, 1999). The posterior (after assimilation)
multiplicative inflation factor was chosen as 1.2 for all the concentration
analysis.
State vectors in the data assimilation system.
Observations
PM2.5
PM10-2.5
SO2
NO2
CO
O3
Mass concentration
P25, S, OC1, OC2 BC1, BC2, D1, D2, S1, S2
P10, D3, D4, D5 S3, S4,
SO2
NO, NO2
CO
O3
Scaling factors
λPM2.5,λNH3
λPM10
λSO2
λNO
λCO
–
State variables
The DA system provides joint analysis of ICs and emissions following Peng et
al. (2017). Among them, 16 WRF-Chem/GOCART aerosol variables are included as
the state variables. Furthermore, chemical species,such as SO2,
NO2, and O3 are also included because they are the most
important gas-phase precursors or oxidants of the secondary inorganic
aerosols. CO is also assimilated because CO is an important tracer of
combustion sources, as well as a precursor of O3 beyond NO2
(Parrish et al., 1991). The state variables of the emission scaling factors
are λ=(λPM2.5,λPM10,λSO2,λNO,λNH3λCO).
Similar to weak-coupling DA, the DA system simultaneously updates both the
ICs and the emissions, but with no cross-variable update, in order to avoid
the effects of spurious multivariate correlations in the background error
covariance that may develop due to the limited ensemble size and errors in
both the model and observations (Miyazaki et al., 2012).
For the PM2.5 observations, the observation operator is expressed as
(Schwartz et al., 2012)
yPM2.5f=ρdP2.5+1.375S+1.8OC1+OC2+BC1+BC2+D1+0.286D2+S1+0.942S2,
where ρd is the dry-air density; P25 is the fine
unspeciated aerosol contributions; S represents sulfate; OC1
and OC2 are hydrophobic and hydrophilic organic carbon,
respectively; BC1 and BC2 are hydrophobic and hydrophilic black
carbon, respectively; D1 and D2 are dusts with effective radii
of 0.5 and 1.4 µm, respectively; and S1 and S2 are sea
salts with effective radii of 0.3 and 1.0 µm, respectively. In
fact, PM2.5 observations were only used to analyze P2.5, S,
OC1, OC2 BC1, BC2, D1, D2, S1, S2,
and λPM2.5. Since we had no NH3
observations, PM2.5 observations were also used to analyze
λNH3 (see Table 2). For other control variables,
PM2.5 observations were not allowed to alter them.
For the PM10 observations, the PM10 observation operator is
expressed as (Jiang et al., 2013)
yPM10f=ρdP10+P2.5+1.375S+1.8OC1+OC2+BC1+BC2+D1+0.286D2+D3+0.87D4+S1+0.942S2+S3.
Thus,
yPM10-2.5f=ρdP10+D3+0.87D4+S3,
meaning that, in the assimilation experiments, we did not use the PM10
observations directly. In Eqs. (13) and (14), P10 denotes the
coarse-mode unspeciated aerosol contributions; D3 and D4 are dusts
with effective radii of 2.4 and 4.5 µm, respectively; and S3
is sea salt with an effective radius of 3.25 µm. We used the
PM10-2.5 observations (the differences between the PM10
observations and the PM2.5 observations,
yPM10-2.5o=yPM10o-yPM10o)
to analyze P10, D3, D4, S3, and
λPM10. In addition, PM10-2.5 observations
were used to analyze D5 and S4, since they are coarse-mode mineral
dust and sea salt aerosols. PM10-2.5 observations were not allowed to
impact other control variables.
Moreover, as shown in Table 2, SO2 observations were used to analyze
the SO2 concentration and λSO2. NO2
observations were used to estimate the NO, NO2 concentration and
λNO. CO observations were used to analyze the CO
concentration and λCO. And finally, O3
observations were only used to analyze the O3 concentration.
Observations and errors
The surface chemical observations used in this study were obtained from the
Ministry of Ecology and Environment of China. Altogether, there were 876
observational sites over the model domain (Fig. 1). At most sites, one
measurement was selected randomly for the assimilation experiment on a
0.1∘×0.1∘ grid. Altogether, 355 stations were kept
for the model domain, where 133 assimilation stations were located on the NCP
and 40 stations were located in the Beijing–Tianjin–Hebei (BTH) region.
Other stations were used for verification purposes: 167 independent stations
were located on the NCP, and 47 in the BTH region.
The observation error covariance matrix R included measurement
errors and representation errors. We assumed that R is a diagonal
matrix (without observation correlation).
Following Elbern et al. (2007), the measurement error ε0 is
defined as
ε0=a+b×Π0,
where Π0 represents the measurements for PM2.5, PM10-2.5,
SO2, NO2, CO, or O3 (units: µg m-3).
Values of a=1.5 and b=0.0075 were chosen for PM2.5, PM10-2.5,
SO2, and NO2. For CO, a=10 and b=0.0075.
The representativeness error is defined as
εr=rε0Δx/L,
where r=0.5, Δx=40.5 km (the model resolution), and L=3 km due
to the lack of the information of the station type (Elbern et al., 2007).
Finally, the total error (εt) is defined as
εt=ε02+εr2.
In order to ensure data reliability, the observations were subjected to
quality control before DA. Data values larger than a certain threshold were
classified as unrealistic and were not assimilated. The threshold values were
chosen as 700, 800, 300, 300, 400, and 4000 µg m-3 for
PM2.5, PM10-2.5, SO2, NO2, O3, and CO,
respectively. In addition, observations leading to innovations exceeding a
certain value were also omitted. These threshold values were chosen as
70 µg m-3 for PM2.5, PM10-2.5, SO2,
NO2, and O3. Also, 1500 µg m-3 was chosen for
CO.
Time series of prior ensemble mean RMSE (blue line) and total spread
(black line) for PM2.5, PM10, SO2, NO2, CO, and
O3 concentrations aggregated over all observations over the
Beijing–Tianjin–Hebei region. Units for all these variables:
µg m-3.
![]()
Experimental design
The DA experiment followed that of Peng et al. (2017), in which the
assimilation of pure surface PM2.5 measurements with the EnKF was
performed to correct finer aerosol variables and associated emissions. The
experiment focused on an extreme haze event that occurred in October 2014
over northern China.
The 50-member ensemble spin-up forecasts were first performed from 1 to
4 October 2014 using the perturbed meteorological ICs, lateral boundary
conditions (LBCs), and emissions. The perturbed meteorological ICs and LBCs
are created by adding Gaussian random noise (Torn et al., 2006) to the
temperature, water vapor, velocity, geopotential height, and dry surface
pressure fields of the products of the National Centers for Environmental
Prediction Global Forecast System (GFS) by WRFDA. The perturbed emissions
were generated also by adding Gaussian random noise with a standard deviation
of 10 % of the corresponding anthropogenic emissions. The aerosol ICs
were zero, and the aerosol LBCs were idealized profiles embedded within the
WRF-Chem model; neither of them was perturbed (Peng et al., 2017).
Then, the observed PM10, PM2.5, SO2, NO2,
O3, and CO data starting from 5 to 16 October were assimilated hourly
to adjust the ICs and the corresponding emissions. The ICs were the subject of analysis
of the previous DA cycle. The meteorological LBCs were perturbed. The
anthropogenic emissions – EPM2.5, EPM10,
ESO2, ENO, ENH3, ECO,
ESO4, and ENO3 – are calculated by
using the forecast emission scaling factors. Other species, such as the
organic compounds Eorg and elemental compounds
EBC, are perturbed by adding Gaussian random noise. Since the
emissions are calculated by Eq. (1), their background uncertainties and the
spatial correlations are completely dependent on those of the corresponding
emission factors. The forecast scaling factors are calculated by
Eqs. (2)–(5). And no other perturbations are added to the scaling factors;
no other correlations are assumed for the scaling factors.
After that, two sets of 72 h forecasts were performed, each at 00:00 UTC
from 6 to 15 October 2014, with hourly forecasting outputs for the
assimilation experiment. These two sets of forecasting experiments were
conducted using the ensemble mean of the concentration analysis as the ICs.
One set of the experiments was forced by the optimized emissions (denoted as
fcICsEs), and the other was forced by the prescribed anthropogenic emissions
(denoted as fcICs). The aim was to use the difference between the fcICsEs and
fcICs to indicate the impact of the optimized emissions.
Moreover, we also ran a control experiment. The ICs were based on the
ensemble mean of the spin-up forecasts at 00:00 UTC on 5 October 2014. The
emissions were the prescribed emissions.
Comparison with observations of the surface PM2.5 mass
concentrations in the Beijing–Tianjin–Hebei region from the control
experiment, the assimilation experiment, and the first-day forecast, over all
analysis times from 6 to 16 October 2014. Units: µg m-3.
Species
Experiment
Mean observed value
Mean simulated value
Bias
RMSE
CORR
PM2.5
Control
114.8
80.7
-34.1
92.1
0.740
Analysis
119.9
5.1
51.5
0.891
fcICsEs24
121.2
6.5
77.8
0.736
fcICs24
123.1
8.3
75.1
0.748
PM10
Control
174.6
96.9
-77.7
134.6
0.691
Analysis
169.0
-5.6
63.4
0.890
fcICsEs24
162.7
-11.9
98.7
0.716
fcICs24
164.3
-10.3
95.9
0.726
SO2
Control
33.0
81.1
48.1
66.6
0.088
Analysis
41.1
8.1
27.9
0.540
fcICsEs24
62.0
29.0
51.2
0.120
fcICs24
75.7
42.7
65.8
0.038
NO2
Control
56.4
78.8
22.4
39.7
0.545
Analysis
48.0
-8.3
31.7
0.557
fcICsEs24
71.8
15.4
46.2
0.408
fcICs24
82.8
26.4
55.5
0.414
CO
Control
1318.0
752.3
-565.7
962.7
0.354
Analysis
1157.5
-160.4
618.9
0.705
fcICsEs24
1418.4
100.4
805.1
0.476
fcICs24
1448.2
130.2
838.2
0.439
O3
Control
57.5
26.5
-31.0
50.8
0.463
Analysis
59.6
2.1
31.1
0.753
fcICsEs24
63.5
6.0
49.0
0.460
fcICs24
58.98
1.5
50.5
0.478
Results
Ensemble performance
We begin by assessing the ensemble performance for the DA system. Figure 2
shows the time series of the prior total spreads and the prior
root-mean-square errors (RMSEs) for PM2.5, PM10, and the four trace
gases calculated against all observations in the BTH region. It shows that
the magnitudes of the total spreads were close to the RMSEs, indicating that
the DA system was well calibrated (Houtekamer et al., 2005).
Figure 3 shows the area-averaged time series extracted from the ensemble
spread of the six emission scaling factors
(λPM2.5f,
λPM10f,
λSO2f,
λNOf,
λNH3f, and
λCOf) in the BTH region. It shows that
the ensemble spread of all the scaling factors were very stable throughout
the ∼10-day experiment period, which indicates that MSF
can generate stable artificial data to generate the ensemble emissions. The
value of the emission scaling factors ranged from 0.2 to 0.6, indicating that
the uncertainty of the assimilated emissions was about
20 %–60 %.
Time series of the area-averaged ensemble spread for the emission
scaling factors over the Beijing–Tianjin–Hebei region.
![]()
Time series of the hourly pollutant concentrations in the
Beijing–Tianjin–Hebei (BTH) region obtained from observations (red line),
the control run (black line), the analysis (pink line), the first-day
forecast from fcICsEs (fcICsEs24, blue line), and the first-day forecast from
fcICs (fcICs24, blue line). The observations were obtained from the 47
independent sites in the BTH region. The modeled time series were
interpolated to the 47 independent sites using the spatial bilinear
interpolator method. The shaded backgrounds indicate the distribution of the
observations, where the top edge represented the 90th percentile and the
bottom edge the 10th percentile. Units: µg m-3.
![]()
Forecast improvements
In order to evaluate the overall performance of the DA system, time series of
the hourly pollutant concentrations from the control run, the analysis, and
the first-day forecast of the two forecasting experiments were compared with
the independent observations in the BTH region (Fig. 4). Furthermore, model
evaluation statistics (Table 3) were calculated against independent
observations from 6 to 16 October 2014. In addition, biases and RMSEs were
presented as a function of forecast range for the control, analysis, and
forecast experiments (Figs. 5–7).
The control run did not perform very well, although it was able to capture
the synoptic variability and reproduce the overall pollutant levels when
there was a severe haze event. The statistics show that there were larger
systematic biases and RMSEs and a smaller correlation coefficient (CORR) for
the control (see Table 3). The biases were -34.1, -77.7, -565.7, and
-31 µg m-3 for PM2.5, PM10, CO, and O3,
respectively, from 6 to 16 October – about 29.7 %, 44.5 %,
42.9 %, and 53.9 % lower than the corresponding observed
concentrations. During the severe haze episode from 8 to 10 October in
particular, when observed PM2.5 were larger than
200 µg m-3, the biases reached -90.5, -143.1, -911.8,
and -39.1µg m-3, respectively – about 44.4 %,
51.9 %, 49.2 % and 55.7 % lower than the corresponding observed
concentrations, suggesting a significant systematic underestimation of the
WRF-Chem simulation. Additionally, a significant overestimation of
48.1 µg m-3 was obtained for SO2 – about 145.8 %
higher than the observed concentrations. As for the NO2 simulation,
WRF-Chem was able to realistically describe the diurnal and synoptic
evolution of NO2 concentrations. The model bias was
22.4 µg m-3, which was about 39.7 % higher than the
observed NO2. These results were similar to the simulations of Chen
et al. (2016). Most of the WRF-Chem settings used here were the same as those
used in Chen et al. (2016), except that they used CBMZ (Carbon Bond
Mechanism, version Z) and MOSAIC (Model for Simulating Aerosol Interactions
and Chemistry) as the gas-phase and aerosol chemical mechanisms.
Bias of surface PM2.5, PM10, SO2, NO2, CO,
and O3 as a function of forecast range calculated against all the
independent observations over the Beijing–Tianjin–Hebei region shown in
Fig. 1. The 72 h forecasts were performed at each 00:00 UTC from 6 to
14 October 2014, and the statistics were computed from 6 to 14 October. Units:
µg m-3.
![]()
After the assimilation of surface observations, the time series of the hourly
pollutant concentrations from the analysis showed much better agreement with
observations than those from the control. The magnitudes of the bias and the
RMSEs decreased, and the CORRs increased for all six species. The biases were
5.1, -5.6, 8.1, -8.3, -160.4, and 2.1 µg m-3 for
PM2.5, PM10, SO2, NO2, CO, and O3,
respectively – about 4.4 %, -3.2 %, 24.5 %, -14.7 %,
-12.17 %, and 3.7 % of the corresponding observed concentrations,
indicating that the analysis fields were very close to the observations. The
RMSEs were 51.5, 63.4, 27.9, 31.7, 618.9, and 31.1 µg m-3,
respectively – about 44.1 %, 52.9 %, 58.1 %, 20.2 %,
35.7 %, and 38.78 % lower than the RMSEs of the control run. The CORRs
reached 0.891, 0.890, 0.540, 0.557, 0.705, and 0.753, respectively. These
statistics indicate that the DA system was able to adjust the chemical ICs
efficiently.
The PM2.5, PM10, and CO concentrations from both sets of forecasting
experiments benefitted substantially from the DA procedure, as expected.
Smaller biases and RMSEs were obtained for almost the entire 72 h forecast
range (see Figs. 5–7), as compared with the control run. For the first-day
forecast in particular, the model performed almost perfectly. It faultlessly
captured the diurnal and synoptic variability of the pollutant (see Fig. 4),
in a manner that was very close to that of the analysis. The overall
biases were 6.5, -11.9, and 100.4 µg m-3 for PM2.5,
PM10, and CO, respectively, and the RMSEs were 77.8, 98.7, and
805.1 µg m-3, respectively, in fcICsEs24 (see Table 3). In
fcICs24, the biases were 8.3, -10.3, and 130.2 µg m-3,
respectively, and the RMSEs were 75.1, 95.9, and 838.2 µg m-3,
respectively (see Table 3). However, with longer-range forecasts, the impact
of DA quickly decayed. The relative reductions in RMSE mostly ranged from
30 % to 5 % for the second- and third-day forecast. From the
perspective of the impact of the assimilated emissions, fcICs performed
similarly to fcICsEs for PM2.5, PM10, and CO, indicating that ICs
play key roles in aerosol and CO forecasts during severe haze episodes, while
the impact of assimilated emissions seems negligible.
As in Fig. 5 but for RMSE. Units: µg m-3.
![]()
For the SO2 verification forecast, however, fcICsEs performed much
better than both fcICs and the control run. Smaller biases and RMSEs were
obtained for almost the entire 72 h forecast range. At nighttime in
particular (from 18 to 23 h, 42 to 47 h, and 66 to 73 h), when there was
significant systematic overestimation in the control run, both the biases and
the RMSEs in fcICsEs were about 30 % lower than those of the control run.
During the daytime (from 0 to 9 h, 24 to 33 h, and 48 to 57 h), fcICsEs
still performed slightly better, although the control run did a near-perfect
job. As for fcICs, smaller biases and RMSEs were obtained for only
the first 3 h. Then, the performance was the same as the control run,
indicating that the impact of the ICs had disappeared. These results
demonstrate the superiority of the assimilated emissions, and that the joint
adjustment of SO2 ICs and emissions is an efficient way to improve
the SO2 forecast.
The NO2 DA results for the independent sites showed really poor
performance (see Figs. 5–7). Smaller biases were gained in the daytime of
the experiment trials. At nighttime, however, when the simulated NO2
deviated considerably from the observations in the control run, the biases of
both sets of the validation forecasts became even larger. Furthermore, almost all
the RMSEs of both sets of the validation forecasts were always larger than
those of the control run.
Normalized RMSE (assimilation divided by control) for fcICsEs and
fcICs for PM2.5, PM10, SO2, and CO.
![]()
The O3 DA results were dependent on the NO2 DA results in the
daytime, due to chemical transformation. Both the biases and the RMSEs were
larger, as compared with those of the control run (see Figs. 5–7). However,
at nighttime, when there was significant systematic underestimation in the
control run, the biases in fcICsEs had very similar values to those of the
analysis. Also, the biases in fcICs ranged between the analysis and the
control run, and the RMSEs of both sets of forecasting experiments were about
10 % smaller than those of the control run. All these results indicate
that the DA system performed well at night.
Spatial distribution of the prescribed emissions (top panels) of
PM2.5 (a, d), PM10 (b, e), and
NH3 (c, f) and the corresponding time-averaged differences
between the ensemble mean analysis and the prescribed values at the lowest
model level averaged over all hours from 6 to 16 October 2014 in the NCP
region. Units for PM2.5 and PM10 emissions:
µg m-2 s-1; units for NH3 emissions:
mol km-2 h-1.
![]()
Emission optimization results
Besides improved pollutant forecasts, improved estimates of emissions were
expected from the joint DA procedure. The MEIC-2010 was constructed on the
basis of annual statistical books in which the data were often 2–3 years
older than the actual year (Chen et al., 2016). However, consistent efforts
aimed at reducing and managing anthropogenic emissions have been made over
the past decade to mitigate air pollution. Thus, there was a large
difference between the emission year and our simulation year. Furthermore, the
spatial allocations of these emissions over small spatial scales, and the
monthly allocations, will also lead to some uncertainties. Lastly, the
emissions inventory cannot fully capture the day-to-day variability or the
actual daily variations, though its differentiation in terms of working days
and weekend days, plus the daily variations, can be taken into account in
practical applications. However, in this assimilation procedure, the
differentiation in terms of working days and weekend days, plus the daily
variations, was ignored. Therefore, the prescribed anthropogenic emissions
were subject to large uncertainties.
As in Fig. 8 but for SO2 (a, d),
NO (b, e), and CO (c, f). Units for SO2, NO, and CO
emissions: mol km-2 h-1.
![]()
Figures 8 and 9 display the spatial distribution of the prescribed emission
rates and the differences between the analysis and the prescribed emission
rates of PM2.5, PM10, NH3, SO2, NO, and CO averaged
over all hours from 6 to 16 October 2014 in the NCP region. The assimilated
emission rates of PM2.5, SO2, NO, and CO were lower than the
prescribed emissions on the whole. In the BTH region especially, the
differences reached -0.02 µg m-2 s-1, -2.9, -8.8,
and -24.65 mol km-2 h-1, which was a reduction of about
10 %–20 % of the prescribed emissions. For PM10 emissions, the
assimilated values were very close to the prescribed ones, indicating that
the prescribed PM10 emissions had small uncertainties for the NCP
region. For NH3 emissions, the assimilated values were a little
larger than the prescribed emissions in large industrial cities like Beijing,
Tianjin, Baoding, Xingtai, Handan, and Taiyuan. However, they were smaller
than the prescribed emissions in agricultural regions, especially in Shandong
and Henan provinces. However, in the BTH region, the assimilated
NH3 emissions were very close to the prescribed emissions on the
whole.
Figure 10 shows the time series of the emission scaling factors and the
emissions. As concluded in Peng et al. (2017), the forecast emission scaling
factors changed with the analyzed emission scaling factors due to the use of
the time-smoothing operator. Furthermore, although the prescribed emissions were
constant when designing the assimilation experiment, the analyzed emission
scaling factors showed obvious variation with time, as did the analyzed
emissions. For the assimilated SO2 and NO emissions in particular,
the diurnal variations were perfect. In addition, the difference between the
assimilated emissions and the prescribed emissions were consistent with those
in Figs. 8 and 9. The assimilated emissions of PM2.5, SO2, NO,
and CO were apparently lower than the corresponding prescribed emissions,
whereas the values of the assimilated emissions of PM10 and NH3
were very close to their corresponding prescribed emissions.
In order to investigate the impact of optimized emissions on chemical
simulations, a simulation (fcEs) using the optimized emissions was performed
from 5 to 16 October 2014. Just as in the control run, the ICs were the ensemble
mean of the spin-up forecasts at 00:00 UTC on 5 October 2014. Thus the
difference between the fcEs and the control run is the anthropogenic
emissions. The results showed that the fcEs performed very similarly to the
control run on the whole in the BTH region. For PM2.5, PM10, and CO,
the values of the fcEs were a little smaller than those of the control run,
which were consistent with the difference of the anthropogenic emissions. For
SO2 and NO2, fcEs performed much better than the control run
most of the time, though significant systematic overestimation still existed
during the nighttime. For O3, minor improvements were also gained due
to the better simulation in fcEs for NO2.
Hourly area-averaged time series extracted from the analyzed
emission scaling factors (black line), the forecast emission scaling factors
(green dashed line), the analyzed emissions (blue line), and the prescribed
emissions (blue dashed line) in the Beijing–Tianjin–Hebei region. Units for
PM2.5 and PM10 emissions: µg m-2 s-1; units for
NH3, SO2, NO, and CO emissions: mol km-2 h-1.
![]()
NO2 and O3 time series of the hourly pollutant
concentrations in the Pearl River delta region (PRD; 21–24∘ N,
112.5–115∘ E) obtained from observations (referred to as “obs”,
red line), the control run (referred to as “ct”, black line), the analysis
(referred to as “an”, pink line), the first-day forecast from fcICsEs
(referred to as “fcICs24”, blue line), and the first-day forecast from
fcICs (referred to as “fcICs24”, blue line). The bias and RMSEs of surface
NO2 and O3 as a function of forecast range calculated against
all the independent observations (34 sites) over the PRD region. Units:
µg m-3.
![]()
Discussion
From the results presented above, it is clear that improvements were achieved
for almost all the 72 h verification forecasts using the optimized ICs and
emissions for PM2.5, PM10, SO2, and CO concentrations in the
BTH region. However, the 72 h NO2 verification forecasts performed
much worse than the control run, due to the assimilation. Plus, the 72 h
O3 verification forecasts performed worse than the control run during
the daytime, due to the worse performance of the NO2 forecasts,
although they did perform better at night. However, relatively favorable
NO2 and O3 forecast results were gained for the Yangtze River
delta and Pearl River delta (PRD) regions (see Fig. 11). In the PRD region,
during the daytime, the three NO2 forecasts (i.e., the control run,
the fcICsEs, and the fcICs) performed similarly and had relatively small
biases and RMSEs. At nighttime, when there was significant systematic
overestimation in the control run, the biases and the RMSEs in fcICsEs were
much smaller than those in the control run. For the O3 72 h
verification forecasts, fcICsEs performed much better than the control run,
except for the first 8 h. Also, fcICs improved the O3 forecasts to
some extent in the 9–72 h forecast range. These results indicate that
DA is still an effective way to improve NO2 and O3 forecasts.
Regarding the failure to improve the NO2 and O3 forecasts in
the BTH region, there are three likely factors. Certainly, NO2
and O3 forecasts in other areas are also facing similar challenges.
Firstly, there are still some limitations for the EnKF method. EnKF
assimilation is influenced greatly by model errors and observation errors.
There are many sources of uncertainties in air quality forecasts that were not
directly considered in this study (such as chemical schemes and
parameterizations, meteorology, and emissions). And it is very difficult to
accurately evaluate the uncertainties of models, though the covariance
inflation technique was simply applied for all state variables to roughly
compensate for model errors. Therefore, we can only obtain suboptimal results
through EnKF assimilation. Furthermore, short-lived chemical reactive
species, such as NO2 and O3, undergo highly complex
nonlinear photochemical reactions, even on timescales of hours, such that the
forecast accuracy is largely dependent on the chemical process as well as the
physical transportation process, the ICs, and the emissions. However, those
complex photochemical reaction processes are not precisely described in
current chemical mechanisms, e.g., heterogeneous reactions (Yang et al.,
2015), the photolysis of nitrous acid and ClNO2 during daytime (Zhang
et al., 2017), and so on. Therefore, on the one hand, there are still large
uncertainties for NO2 and O3 forecasts, while, on the other
hand, it is very difficult for NO2 and O3 DA to accurately
estimate the model errors with a limited ensemble size. Thus, NO2 and
O3 assimilations do not perform well (Elbern et al., 2007; Tang et
al., 2016). However, for SO2 and CO, which are representative of
long-lived chemical reactive species, the chemical reaction process does not
work on timescales of hours, meaning that to some extent hourly chemical DA
has the potential to improve their forecasts. For CO in particular, due to
its inertness, we might be able to obtain high-quality ICs and emissions
through DA. The primary sources of aerosol are the dominant part of the
atmospheric aerosol concentration. So, 72 h aerosol forecasts may perform
similarly to CO, although there are large uncertainties in the chemical model.
Secondly, as stated in the above paragraph, the analysis ICs and emissions
are only a mathematical optimum under the existing conditions. In addition,
only part of the chemical ICs and emissions are involved in the DA
experiment; VOC ICs and emissions, which may greatly influence the
NO2 and O3 forecasts, were not included here because of the
absence of VOC measurements. Although we carried out two DA sensitivity
experiments to adjust the VOC ICs and emissions through the use of
NO2 or O3 measurements, we were still unable to gain improved
NO2 and O3 forecasts in the BTH region in both DA
experiments. VOC measurements are needed to reduce uncertainties of VOC ICs
and emissions. In addition, almost all available data were observed in
cities, and no observation stations were located in rural areas. Thus, the atmospheric
environmental monitoring system was still spatially heterogeneous.
Another important point is that there are still limitations to the current
chemical mechanisms used in our model, such as the treatment of model error.
NO is the primary species of NOx emissions in city areas
and reacts directly with O3 to form NO2 (NO+O3→NO2+O2). Thus, O3 concentrations may inversely correlate with
NO2 concentrations at night. Consequently, air quality models may
systematically underestimate O3 concentrations. Currently, DA can
only revise the ICs and the emissions in this work. It cannot change the
model performance, especially when there are certain uncertainties for the
meteorological simulation.
Summary
In this study, we developed an EnKF system to simultaneously assimilate
surface measurements of PM10, PM2.5, SO2, NO2,
O3, and CO via the joint adjustment of ICs and source emissions. This
system was applied to assimilate hourly pollution data while modeling an
extreme haze event that occurred in early October 2014 over northern China. In
order to evaluate the impact of DA, two sets of 72 h verification forecasts
were performed. One was conducted with the optimized ICs and emissions, and
the other with only optimized ICs and the prescribed emissions. A control
experiment without DA was also performed for comparison.
The results showed that both verification forecasts performed much better
than the control simulations for PM2.5, PM10, and CO. Obvious
improvements were achieved for almost the entire 72 h forecast range. For
the first-day forecast especially, near-perfect forecasts results were
achieved. However, with longer-range forecasts, the impact of DA quickly
decayed. In addition, the forecasts with only optimized ICs and the
prescribed emissions performed similarly to those with the optimized ICs and
emissions, indicating that ICs play key roles in PM2.5, PM10, and
CO forecasts during severe haze episodes.
Also, large improvements were achieved for SO2 forecasts with both
the optimized ICs and emissions for the whole 72 h forecast range. However,
similar improvements were achieved for SO2 forecasts with the
optimized ICs only for the first 3 h, and then the impact of the ICs
decayed quickly to zero. This demonstrates that the joint adjustment of
SO2 ICs and emissions is an efficient way to improve SO2
forecasts.
Even though we failed to improve the NO2 and O3 forecasts in
the BTH region, relatively favorable NO2 and O3 forecast
results were gained in other areas. Also, the forecasts with both the
optimized ICs and emissions performed much better than the forecasts with
only optimized ICs and the prescribed emissions. These results indicate that
there is still potential to improve NO2 and O3 forecasts via
the joint adjustment of SO2 ICs and emissions.
However, only one case was investigated in this work. Thus it is uncertain if
the conclusions about different performance of forecasts for various species
would hold in a general. Therefore, more case studies are needed to obtain
general conclusions in future works.