To improve the operational air quality forecasting over China, a new
aerosol or gas-phase pollutants assimilation capability is developed within the
WRFDA system using the three-dimensional variational (3DVAR) algorithm. In this first application, the interface
for the MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme is built with the potential for flexible extension. Based
on the new WRFDA-Chem system, five experiments assimilating different
surface observations, including PM2.5, PM10, SO2, NO2, O3, and CO, are
conducted for January 2017 along with a control experiment without data assimilation (DA).
Results show that the WRFDA-Chem system evidently improves the air
quality forecasting. From the analysis aspect, the assimilation of surface
observations reduces the bias and RMSE in the initial condition (IC)
remarkably; from the forecast aspect, better forecast performances are
acquired up to 24 h, in which the experiment assimilating the six pollutants
simultaneously displays the best forecast skill overall. With respect to the
impact of the DA cycling frequency, the responses toward IC updating are found to be different among the pollutants. For PM2.5, PM10, SO2, and CO, the
forecast skills increase with the DA frequency. For O3, although
improvements are acquired at the 6 h cycling frequency, the advantage of
more frequent DA could be consumed by the disadvantages of the unbalanced
photochemistry (due to inaccurate precursor NOx/ VOC (volatile organic compound) ratios) or the changed
titration process (due to changed NO2 concentrations but not NO) from
assimilating the existing observations (only O3 and NO2, but no VOC and NO). As yet the finding is based on the 00:00 UTC forecast for this winter season only,
and O3 has strong diurnal and seasonal variations. More experiments should
be conducted to draw further conclusions. In addition, considering one
aspect (IC) in the model is corrected by DA, the deficiencies of other
aspects (e.g., chemical reactions) could be more evident. This study
explores the model deficiencies by investigating the effects of assimilating
gaseous precursors on the forecast of related aerosols. Results show that
the parameterization (uptake coefficients) in the newly added
sulfate–nitrate–ammonium (SNA)-relevant heterogeneous reactions in the model is not fully appropriate although it best simulates observed SNA aerosols
without DA; since the uptake coefficients were originally tuned under the
inaccurate gaseous precursor scenarios without DA, the biases from the two
aspects (SNA reactions and IC DA) were just compensated. In future
chemistry development, parameterizations (such as uptake coefficients) for
different gaseous precursor scenarios should be adjusted and verified with
the help of the DA technique. According to these results, DA ameliorates certain
aspects by using observations as constraints and thus provides an
opportunity to identify and diagnose the model deficiencies; it is useful
especially when the uncertainties of various aspects are mixed up and the
reaction paths are not clearly revealed. In the future, besides being used
to improve the forecast through updating IC, DA could be treated as another
approach to explore necessary developments in the model.
Introduction
Air pollution is almost inevitable for all developed (historically) and
developing (in the present day) countries. From acid rain to haze and smog, etc., air pollution significantly impacts atmospheric visibility, human
health, and climate. As one of the fastest-growing countries, China has been
suffering from the extreme haze with high particulate matter (PM)
national-wide and increasing tropospheric ozone (O3) pollution in city
clusters (Fu et al., 2019; Lu et al., 2019). To control the pollution as
well as to improve the air quality forecast, Chinese government enforced stricter air quality standards from 2012 and have deployed a monitoring
network for six “criteria” air pollutants since 2013, which includes PM2.5
and PM10 (aerosols or fine particulate matter with aerodynamic diameters of less
than 2.5 or 10 µm), SO2 (sulfur dioxide), NO2 (nitrogen dioxide), O3
(ozone), and CO (carbon monoxide). Among the six pollutants, the forecast for
aerosols (especially PM2.5) is of the greatest research interest because of the severity
of aerosol pollution and its negative effects on both health and climate.
However, it is still challenging to accurately simulate and forecast aerosols
by pure air quality models due to some issues, such as the large
uncertainties in primary and precursor emissions processes, the incomplete
understanding and parameterization of secondary inorganic or organic reactions
from precursors, and the accumulation of meteorology simulation errors. In
addition to aerosol forecast, the elevated O3 levels in city clusters over
eastern China has attracted more and more attention recently. Under these
circumstances, in the urban regions in China, which suffer from complex air
pollution with both haze and smog, the accurate forecast of air quality has
been not only a challenge for operational centers, but also a common concern
for the scientific community.
To improve the forecast skill, data assimilation (DA), a combination of
observations and numerical model output, has been widely used in meteorology
forecasting since the last century, and it was recently extended to air pollutant
forecasts. Based upon various techniques, DA is proven to be skillful at
improving the meteorology and aerosol forecasts (Bannister, 2017; McHenry et
al., 2015; Peng et al., 2018; Sandu and Chai, 2011; Schutgens et al., 2010;
Sekiyama et al., 2010; Tang et al., 2011, 2013). Focusing on
aerosol assimilation, the NCAR group conducted a series of work. Using the
three-dimensional variational (3DVAR) algorithm, Liu et al. (2011)
implemented DA on aerosol optical depth estimates within the Grid-point
Statistical Interpolation (GSI) system. Schwartz et al. (2012), Jiang et al. (2013), and Chen et al. (2019) further extended this system to assimilate
surface PM2.5 and PM10. It should be noted that the aerosols are complicated
not merely by primary emissions but also by secondary reactions with gaseous
precursors in the atmosphere (Huang et al., 2014; Nie et al., 2014; Xie et al.,
2015). However, the assimilation of aerosols along with gas-phase pollutants
is seldom investigated. Recently, it has been encouraging that an ensemble Kalman
filter (EnKF) DA system has been developed to assimilate multi-species surface
chemical observations (Peng et al., 2017), while the EnKF system may not be
the favorite choice in operational applications due to its massive
computational cost. In addition, at the Institute of Urban Meteorology
(IUM), the regional numerical weather prediction (NWP) system RMAPS-ST (adapted from WRF; RMAPS: Rapid-refresh Multi-Scale Analysis and Prediction
System) and the regional air quality model RMAPS-Chem (adapted from WRF-Chem) are applied operationally
for the weather and air quality forecast over northern China. RMAPS-ST
provides the meteorology drivers for RMAPS-Chem, and WRFDA is utilized for
the meteorology DA in RMAPS-ST (Fan et al., 2016; Yu et al., 2018). As a result,
to implement the assimilations of aerosols along with gas-phase pollutants
in the future air quality forecast operational system (e.g., the RMPAS-Chem)
and to design an efficient and unified DA platform that satisfies the
operational needs in both meteorology and air quality forecasting, this study
works on the WRFDA system with the 3DVAR algorithm. To the authors' knowledge,
this is the first attempt to assimilate hourly ground-based aerosols
simultaneously with gas-phase pollutants in the WRFDA system.
With regard to the aerosol data assimilation, the first and foremost
challenge comes from the complex components related to the aerosol scheme.
With different emphasis and applications, the aerosol scheme chosen in the
model could be different, which will lead to various choices and treatments
for the analysis variables in the DA system. For example, in the existing DA
developments, many studies used the GOCART aerosol scheme to address the
dust- or the natural-source-related events. However, the GOCART aerosol
scheme is well known to underestimate the PM concentrations due to lack of
secondary organic aerosol (SOA) formation, as well as aerosol species
related to the anthropogenic emission, such as nitrate and ammonium (McKeen
et al., 2009; Pang et al., 2018). Different from the GOCART scheme, the MOSAIC
(Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme
uses a sectional approach to represent the aerosol size distribution with
different size bins, and it takes black carbon, organic carbon, sulfate,
nitrate, ammonium, sodium, chloride, and other inorganic compounds that are
related to anthropogenic emissions into consideration. As a result, the MOSAIC
scheme shows a better performance in representing the complex PM2.5
pollution over China (Chen et al., 2016, 2019). Therefore, to
make the DA system suitable for different emphasis and applications, a
flexible aerosol assimilation capability is built within the WRFDA system in
this study, which will facilitate developments and applications for more
chemistry schemes in the future. Focusing on the air quality forecast over
China, this study mainly analyses the results of the MOSAIC aerosol scheme.
It should be mentioned that the forecast performance with DA also relies on
the air quality model itself. Due to the limited observational information
as a constraint, the DA system uses large parts of the model mechanism and
processes to derive the full analysis information (e.g., it uses total PM mass
observations to analyze all PM components). However, there are still
potential deficiencies in the model. For example, some reaction paths are
missing in the heavily polluted events in China (e.g., Y. Wang et al., 2014),
since the chemistry schemes are originally developed for relatively clean
areas and recently observed pathways have not been reflected in time in the
model. Moreover, the large uncertainties of precursor and primary emissions
could bring errors to the aerosol species partitioning and size distribution
in the model. Nevertheless, when it comes to DA, as one aspect (initial
conditions of aerosols and some precursors) in the model is corrected by
using observations as constraints, the deficiencies of other aspects, such
as the abovementioned chemical reactions, could be more evident. From this
point of view, after investigating to what extent the DA technique can help
to improve the forecast of air quality, this study further explores the
model deficiencies with the help of DA, aiming to provide helpful
indications for future model development.
In the rest of the paper, an overview of the model description,
observations, and methodology is presented in Sect. 2, followed by
evaluations of the new WRFDA-Chem system in Sect. 3. Section 4 analyzes
the DA experiments in consideration of potential issues in the model, aiming
to provide beneficial references on further model development. Conclusions
and discussions are given in Sect. 5.
Model description, observations, and methodology
In this study, the interfaced air quality model is WRF-Chem. The WRF-Chem
settings are very similar to those of Chen et al. (2016). Here, only a
summary of the model configuration and observations is provided below.
Descriptions of the most important development of this study, the WRFDA-Chem
system, are presented in Sect. 2.3.
WRF-Chem model configurations.
Aerosol schemeMOSAIC (four bins; Zaveri et al., 2008)Photolysis schemeFast-J (Wild et al.. 2000)Gas-phase chemistryCBM-Z (Zaveri and Peters, 1999)Cumulus parameterizationGrell 3-D schemeShortwave radiationGoddard Space Flight Center shortwave radiationscheme (Chou and Suarez, 1994)Long-wave radiationRRTM (Mlawer et al., 1997)MicrophysicsSingle-moment 6-class scheme (Grell and Dévényi, 2002)Land-surface model (LSM)NOAH LSM (Chen and Dudhia, 2001)Boundary-layer schemeYSU (Hong et al., 2006)Meteorology initial and boundary conditionsGFS analysis and forecast every 6 hInitial condition for chemical species11 d spinupBoundary conditions for chemical speciesAverages of midlatitude aircraft profilesDust and sea salt emissionsGOCARTThe WRF-Chem model and emissions
As in Chen et al. (2016), version 3.6.1 of the WRF-Chem model is used in
this study to simulate the aerosols and gas-phase chemistry processes. A
summary of the physical parameterizations used is given in Table 1. Details
of the WRF-Chem model have been described by Grell et al. (2005) and Fast et
al. (2006). The Carbon Bond Mechanism version Z (CBMZ) and MOSAIC schemes are used as
the gas-phase and aerosol chemical mechanisms, respectively. The relative-humidity (RH)-dependent heterogeneous reactions added by Chen et al. (2016)
are also applied in the simulations. The model computational domain covers
most of China and its surrounding regions. Figure 1 presents the horizontal
range of the domain, which contains 121×121 horizontal grids at a 40.5 km
resolution. Vertically, there are 57 levels extending from the surface to 10 hPa.
Computation domain. Dots depict surface observations with 531 stations spreading over China. The red dots indicate the observations around Beijing. The green dot indicates the IUM station.
As in Chen et al. (2019), the emission input is based on the 2010
Multi-resolution Emission Inventory for China (MEIC) (He, 2012; Lei et al.,
2011; Li et al., 2014; Zhang et al., 2009), which has already been applied in
many recent studies over China (Wang et al., 2016; L. Wang et al. 2014; Zheng et
al., 2015). The emission inventory has also been processed to match the model
grid spacing (40.5 km) from an original grid spacing of
0.25∘×0.25∘ (Chen et al., 2016).
Admittedly, the difference between the emission base year and our simulation
year and the spatiotemporal allocations may cause uncertainties in our
simulation, this emission is the only publicly available emission inventory
once the study is conducted. Meanwhile, the inhomogeneous spatial changes
and large uncertainties in seasonal allocations of the emissions made it
difficult to simply scale the original emission inventory for our study
period (Chen et al., 2019).
The dust emission is the GOCART dust emission, and the biogenic emission is
calculated online by the Gunther scheme within the WRF-Chem model. Given that the
time period of this study (January) is not the period with large fires
(crop or biomass burning), fire emission is not used in this study.
Observations
For the future application in the RMAPS-Chem operational air quality forecast
system, the WRFDA-Chem system is designed to assimilate the hourly surface
observations of six major pollutants (PM2.5, PM10, SO2, NO2, O3, and CO)
from the China National Environmental Monitoring Center (CNEMC). To verify
the capability of the system, we use the data for the whole month of January
2017. As in Chen et al. (2019), to perform statistical calculations, an
observation dataset at 531 locations (Fig. 1) is acquired by averaging all
the original observations (more than 1600 sites) that fall into the same model
grid. Meanwhile, two steps of data quality control are conducted before DA.
Firstly, observations larger than a threshold are treated as unrealistic and
are not assimilated. Secondly, observations leading to innovations
(observations minus the model-simulated values) higher than a maximum
deviation are omitted. For PM2.5, PM10, SO2, NO2, O3, and CO, the threshold
in the first step is 500, 700, 200, 200, 200, and 20 mg m-3,
respectively; the maximum deviation in the second step is 120, 120, 60, 60, 60, and 6 mg m-3, respectively.
To verify sulfate–nitrate–ammonium partitioning, a site observation of
different chemical species is used in Sect. 4. The measurements were
performed over 14–20 January 2017, and carried out on the roof of IUM in
Beijing (green dot in Fig. 1). A detailed description of the features of
the observation, including the quality assurance and quality control, has
been given by Su et al. (2018). This study mainly uses the sulfate
(SO42-) and nitrate
(NO3-) in this dataset.
WRFDA-Chem system
In this study, an aerosol or chemical assimilation capability is built within
the version 4.0.3 of the WRFDA system with the 3DVAR algorithm. The WRFDA 3DVAR
produces the analysis through the minimization of a scalar objective
function J(x) given by
J(x)=12x-xbTB-1x-xb+12[H(x)-y]TR-1[H(x)-y],
where xb denotes the background vector, y is a vector of the
observations, and B and R represent the background and observation error
covariance matrices, respectively. The covariance matrices determine how
closely the analysis is weighted toward the background and observations. H
is the observation operator that interpolates model grid point values to
observation space and converts model-predicted variables to observed
quantities.
Generally, the implementation of WRFDA-Chem 3DVAR includes several parts:
the WRF-Chem model and surface air pollutant observation interface to WRFDA,
the addition of aerosol or chemical analysis variables, the surface air
pollutant observation operators, the update of observation errors, and the
statistics of background error covariances for chemical analysis variables.
Detailed descriptions will be presented in the following parts. It is worth
mentioning that the new WRFDA-Chem system is designed with a flexible
aerosol assimilation capability that can switch between different aerosol
schemes. Given the fact that the WRF-Chem model predicts the PM concentrations
in the forms of different prognostic variables depending on the
aerosol scheme chosen, the aerosol or chemical prognostic variables are given in the
registry file of the WRFDA-Chem instead of being specifically defined in the
code. With the help of the registry mechanism of the WRF model, the prognostic
variables in the entire DA process can be easily adjusted by modifying the
registry file. The WRFDA-Chem system has been tested with GOCART and the MOSAIC aerosol scheme, while this study focuses on the MOSAIC scheme.
Observation operators
The WRFDA-Chem is designed to assimilate six types of surface
aerosol or chemical observations, including PM2.5, PM10, SO2, NO2, O3, and CO.
For aerosol assimilation, the aerosol species in the MOSAIC scheme are
defined as black carbon (BC), organic compounds (OCs), sulfate
(SO42-), nitrate
(NO3-), ammonium
(NH4+), sodium (Na), chloride (Cl), and other
inorganic compounds (OINs). To represent the aerosol size distribution,
MOSAIC uses a sectional approach with different bins. This study uses four
size bins with aerosol diameters ranging from 0.039 to 0.1, 0.1 to 1.0,
1.0 to 2.5, and 2.5 to 10 µm. The PM2.5 total is controlled by the 24
variables in the first three bins (eight species multiplied by three bins), and the
PM10 total is controlled by the 32 variables in the four bins (eight species
multiplied by four bins). As a result, the model-simulated PM2.5 is computed by
summing the 24 variables as
yPM2.5f=ρd∑i=13BCi+OCi+SO4i+NO3i+NH4i+CLi+NAi+OINi.
The model-simulated PM10 observations are computed by summing the 32
variables as
yPM10f=ρd∑i=14BCi+OCi+SO4i+NO3i+NH4i+CLi+NAi+OINi.
Correspondingly,
yPM10–2.5f=ρd∑i=44BCi+OCi+SO4i+NO3i+NH4i+CLi+NAi+OINi,
where ρd is the dry-air density, which is used to convert the unit
of the analysis variable (µg kg-1) to the observations (µg m-3);
i denotes the bin number in the MOSAIC aerosol scheme. In the experiment
assimilating PM2.5 alone, the PM2.5 observations are used to analyze the
species in the first three bins (Eq. 2). In the experiment assimilating
PM2.5 and PM10 simultaneously, the PM2.5 observations are used to analyze
the species in the first three bins (Eq. 2), and the PM10–2.5 (PMcoarse,
hereafter) in the observations is used to analyze the species in the fourth bin
(Eq. 4). A similar approach has been adopted by Peng et al. (2018).
In the assimilation of the gas-phase pollutants, the model-simulated values
are computed by
yxf=ρd⋅MxMdair⋅Rx×103,
where x denotes the four gas-phase pollutants SO2, NO2, O3, and CO,
ρd is the dry-air density,
Mx is the relative molecular mass for the four
gas-phase pollutants, Mdair is the relative
molecular mass for dry-air, and Rx is the mixing
ratio for the four gas-phase pollutants. Since the gas-phase pollutant observations are mass concentrations in micrograms per cubic meter and the analysis
variables are mixing ratios in parts per million by volume, Eq. (5) is used for the unit
conversion.
Observation errors
Following Chen et al. (2019) and Peng et al. (2018), the observation error
covariance matrix R in Eq. (1) is estimated from measurement error
ε0 and the representativeness error εr in this study. The measurement error ε0 is
defined as ε0=1.0+0.0075⋅Mi,
where Mi denotes the observation of the six major pollutants in micrograms per cubic meter; the representativeness error εr is defined
as εr=γε0ΔxL, where γ is an adjustable parameter
scaling (set as 0.5), Δx is the grid spacing (40.5 km in
our case), and L is the radius of influence of the observation
(set to 2 km). These parameter settings are based on the sensitivity tests
by Chen et al. (2019). The total observation error (εx) is computed as εx=ε0x2+εrx2, where x denotes the six major
pollutants PM2.5, PM10, SO2, NO2, O3, and CO.
Background error covariance
To implement the aerosol or chemical DA with the MOSAIC four-bin scheme, this study
expands GEN_BE v2.0 (Descombes et al., 2015) to compute
the B matrix in Eq. (1) for the 32 chemical variables as in Eq. (3) (BC, OC,
SO42-,
NO3-, NH4+, Na, Cl,
and OIN in four bins), as well as the four gas-phase variables as in Eq. (5)
(SO2, NO2, O3, and CO). Since it is both technically and scientifically
challenging to model the cross-correlations between different
aerosol or chemical variables in a 3DVAR framework, they are not considered in
this study. We plan to introduce the cross-variable correlations with the
ensemble-variational approach in the future extension of the system. With
the updated GEN_BE v2.0, the statistics for background error
covariance, such as standard deviation, vertical and horizontal length
scales, and vertical correlations, are computed for each of the
aerosol or chemical variables. In this study, the background error covariance
is estimated using the National Meteorological Center (NMC) method (Parrish
and Derber, 1992) from 1-month WRF-Chem forecasts over January 2017.
Background error standard deviations of aerosol species of the (a) first size bin, (b) second size bin, (c) third size bin, (d) fourth size bin, and (e) gas pollutants. The units for the x axis are micrograms per cubic meter for
(a–d) and parts per million for (e). The left y axis denotes the model level, and the
right y axis denotes the vertical height (units: km).
Following the analyses based on GEN_BE v2.0 (Descombes et
al., 2015), Fig. 2 presents the background error standard deviations of
each species at different vertical levels. For the aerosols in the first
three size bins (Fig. 2a–c), although the standard deviation errors vary
across the species, the errors of NO3-,
SO42-,
NH4+, OC, and OIN are generally larger
than those of the others (BC, Cl, and Na) in the three size bins. These
results are consistent with the finding in Chen et al. (2019), which
allows inorganic compounds (NO3-,
SO42-,
NH4+), OC, and OIN to be adjusted more corresponding to their larger background errors. For the aerosols in the
fourth size bin (Fig. 2d), the errors are unreasonably much smaller than in the first three bins due to model deficiency. Under these circumstances, to
obtain a reasonably larger adjustment for the aerosols in the fourth size bin, it
might be necessary to enlarge their background errors in the DA procedure. As for
the gaseous pollutants (Fig. 2e), CO has the largest background errors in
the middle and lower layers, followed by O3, SO2, and NO2.
For the background error horizontal correlation length scales, the results
are similar to those in Liu et al. (2011) (figure omitted). The length scales
of aerosols are comparable in most of the species, which generally span from
1.5 to 2.5 times the grid spacing, while the aerosol species Na shows a
smaller horizontal length scale than all the other species. For the
background error vertical correlations (figure omitted), the results are
similar to those in Descombes et al. (2015), in which the vertical correlations
are larger in the lower levels (where they are emitted) in most of the
species. According to Descombes et al. (2015), the reactions with species
emitted near the surface might create these strong correlations in the lower
model levels.
Detailed setting of six experiments and their purposes.
To seek for the best forecast performance, six experiments were conducted
for January 2017 in this study: NODA, PM1, PM2, ALL,
ALL_3h, and ALL_1h (detailed in Table 2). NODA
is the control experiment without any data assimilation. The design of PM1,
PM2, and ALL is to investigate the assimilation impacts of PM2.5, PMcoarse,
and gas-phase pollutants (SO2, NO2, O3, CO) step by step.
The NODA experiment initialized a new WRF-Chem forecast every 6 h between
00:00 UTC, 20 December 2016, and 18:00 UTC, 31 January 2017, in which the
aerosol or chemical fields were simply carried over from cycle to cycle, and
the meteorological initial condition or boundary conditions were updated from
GFS (Global Forecast System) data every 6 h. The first 10 d were treated as the spinup period,
and only simulations in January were used in the following analyses. The
PM1, PM2, and ALL experiments updated the chemical initial condition (IC) using the WRFDA-Chem
system every 6 h starting from 00:00 UTC, 1 January. The background of the
first cycle was obtained from the NODA experiment, and all subsequent cycles
were derived from the 6 h forecast of the previous cycle. The only
difference between PM1, PM2, and ALL experiments is that PM1 only
assimilated PM2.5 observations; PM2 assimilated PM2.5 and PMcoarse
(PM10–2.5) simultaneously; ALL assimilated PM2.5, PM10–2.5, SO2, NO2, O3,
and CO together.
In view of the cycling frequency being an important aspect in the DA strategy,
especially for 3DVAR, two more experiments that assimilate all the six major
pollutants with a 3 and 1 h cycling frequency were conducted (experiments ALL_3h and ALL_1h). To investigate
the forecast improvements, a 24 h forecast was initialized for all the
experiments at 00:00 UTC of each day.
Averaged bias (colored bar, left y axis) and RMSE (hollow bar, right
y axis) of the analysis at 00:00 UTC over 1–31 January 2017 for (a) PM2.5, (b)
PM10, (c)SO2, (d)NO2, (e)O3, and (f) CO in different experiments, verified
against the surface observations of 531 stations in China. The blue, red,
green, and gray shaded bars denote the bias of the experiments NODA, PM1, PM2, and ALL, respectively; the corresponding hollow bars denote the RMSE of these
experiments. Units of the y axis are micrograms per cubic meter in (a–e) and milligrams per cubic meter in (f).
Performance of the WRFDA-Chem systemImpact on analyses
To evaluate the performance of the WRFDA-Chem system, the impact on analyses
is firstly investigated. Figure 3 presents the domain-averaged bias and
root-mean-square-error (RMSE) of the analysis at 00:00 UTC over 1–31 January 2017. For PM2.5 (Fig. 3a), the NODA experiment displays a general
overestimation of 36.60 µg m-3, along with a large RMSE of 70.41 µg m-3. After DA, in the PM1, PM2, and ALL experiments, the bias of
PM2.5 drops to 5.62, 5.19, and 5.98 µg m-3, respectively; the RMSE drops to 22.10, 22.84, and 23.15 µg m-3, respectively.
Averaged PMcoarse (PM10–2.5; units: µg m-3) at 00:00 UTC
over 1–31 January 2017 in (a) observation and the four experiments (b) NODA,
(c) PM1, (d) PM2, (e) ALL, and (f) averaged bias (units: µg m-3)
for PMcoarse in different experiments as a function of forecast range (the
blue, red, green, and gray lines denote the results of experiment NODA, PM1,
PM2, and ALL, respectively), verified against the surface observations of 531
stations in China. The numbers at the top of each panel denote the average
PMcoarse concentrations over 531 stations (units: µg m-3).
In the analyses of PM10, it is noted that the PM1 experiment has a larger
bias than the NODA run (Fig. 3b). To explain this phenomenon, Fig. 4
presents the monthly mean difference between PM10 and PM2.5 (PM10 minus
PM2.5, PMcoarse) in the analysis. In the observation, PMcoarse generally
increases from south to north, reaching above 50 µg m-3 over
northern China (Fig. 4a). However, PMcoarse in the NODA experiment (with
an average of 5.47 µg m-3) is much smaller than that in the
observation (with an average of 39.13 µg m-3). This result suggests
that the WRF-Chem model failed to reasonably represent PMcoarse, which
is actually the fourth bin of the aerosol species in the MOSAIC scheme.
Under these circumstances, when the assimilation of PM2.5 tries to reduce its
evident overestimation (Fig. 3a), components in the first three bins (within
2.5 µm) of PM10 decrease dramatically. Meanwhile, since the simulated
PMcoarse is too small, the PM10 variates are eventually dominated by the
adjustment of PM2.5. As a result, the assimilation of PM2.5 causes a large
negative bias in the PM10 analysis (Fig. 3b). Correspondingly, compared to
the NODA run, PMcoarse in the PM1 experiment shows no significant
changes (only a slight decrease) in the analysis (Fig. 4b and c) and also
in the forecast (Fig. 4f).
To overcome this issue, several adjustments have been adapted in the PM10
assimilation: instead of using the PM10 observations directly, PMcoarse
is used to analyze the species in the fourth bin (Eq. 4); to reflect the
large uncertainty of the simulated PMcoarse and to appropriately weight the model and observation errors, the background error covariance of PMcoarse (species in the fourth bin) is arbitrarily inflated (inflation
factor 1 is normally used and 90 is selected after tuning). By these means,
after assimilating the PM10 observations, the PM2 and ALL experiments
show similar distributions in PMcoarse (Fig. 4d–e, with an average
of 34.58 and 34.68 µg m-3) as in the observation
(with an average of 39.13 µg m-3). Correspondingly, compared to the
NODA experiment, evident improvements for PM10 analysis appear in the PM2
and ALL experiments, in which the bias and RMSE drops noticeably (Fig. 3b).
Overall, the DA experiments show strong contributions to the analyses of
PM2.5 and PM10, suggesting that the WRFDA-Chem system works effectively in
updating the initial conditions.
As for the analyses of gaseous pollutants (Fig. 3c–f), large improvements
can be seen in the ALL experiment by further assimilating SO2, NO2, O3, and
CO. Compared to the PM2 experiment, although the bias and RMSE for PM2.5 and
PM10 in the ALL experiment are slightly larger, the bias for the four
gaseous pollutants decreases from 4.74, -4.59, 4.92, and -8.31 mg m-3 (PM2 experiment) to
-1.68, -1.25, -0.31, and
-0.18 mg m-3 (ALL experiment), respectively, and the corresponding RMSE
drops from 37.87, 15.39, 21.04, and 1.11 mg m-3 (PM2 experiment) to 23.85, 9.70, 8.62, and 0.43 mg m-3 (ALL
experiment). In general, by assimilating all the six major pollutants, the
ALL experiment displays the largest improvement in the analyses of gaseous
pollutants among all the experiments, along with a comparable improvement in
the analyses of the aerosols.
Vertical profile of the analysis at 00:00 UTC over 1–31 January 2017
for (a) PM2.5, (b) PM10, (c)SO2, (d)NO2, (e)O3, and (f) CO in different
experiments, averaged over the 531 surface stations in China. The blue, red,
green, and gray lines denote the results of experiment NODA, PM1, PM2, and
ALL, respectively. Units of the y axis are micrograms per cubic meter in (a–e) and milligrams per cubic meter in (f).
Due to the lack of vertical information within the observations, the common
mathematical solution to use the surface total mass observations to analyze
multiple 3-D fields variables is to utilize prior information in the
background. As shown in Fig. 5, based on vertical correlations specified in
the background error covariance, the observation impact spreads to a certain
height, even though the analysis variables used in the observation operator
(Eqs. 2–5) are only at the lowest model level. It is also noted that
observations contribute differently to the analysis variables. Corresponding
to the strong overestimation of PM2.5 (Fig. 3a), all the three DA
experiments (PM1, PM2, and ALL) tend to reduce the PM2.5 below 6 km;
corresponding to the distinct underestimation for CO (Fig. 3f), the
experiment assimilating CO (ALL experiment) increases the value below 9 km.
Relatively small analysis increments are shown in the other three gas
pollutants (SO2, NO2, and O3).
Averaged bias (units: µg m-3), RMSE (units: µg m-3), and correlation for (a) PM2.5 and (b) PM10 in different
experiments as a function of forecast range, verified against the surface
observations of 531 stations in China. The blue, red, green, and gray lines
denote the results of experiment NODA, PM1, PM2, and ALL, respectively.
Forecast improvements
After illustrating the effect of WRFDA-Chem on the analyses, this section
further investigates the forecast performances based on the new analyses. A
24 h forecast is performed at each 00:00 UTC from 1 to 31 January 2017. The
forecast error statistics, including bias, RMSE, and correlation, are
computed by verifying them against the surface observations at 531 stations over
China.
Averaged bias (units: µg m-3), RMSE (units: µg m-3), and correlation over forecast hours 0–24 h for different
variables and different experiments. The statistics for gas-phase pollutants
in PM1 and PM2 experiments are very close to the results in the NODA
experiment and thus are left blank in the table.
Same as Fig. 6, but for the forecast of (a)SO2, (b)NO2, (c)O3
(units: µg m-3), and (d) CO (units: mg m-3).
As shown in Fig. 6, the model performs relatively poorly in the forecast of
aerosols without DA. For PM2.5, the average bias, RMSE, and correlation over
0–24 h are 31.17, 88.99, and 0.41 µg m-3,
respectively (Table 3). As expected, all the DA experiments evidently improve the
forecasts. Along with the forecast range, distinct improvements in bias, RMSE, and correlation last from 0 to 24 h. For example, in the PM1
experiment, the average improvement percentages (over 0–24 h) for bias, RMSE, and correlation reach up to 71.8 %, 39.4 %, and 43.9 %, respectively.
It is also noted that the PM2.5 observation is the dominant data source in
improving PM2.5 forecast. As for PM10, distinct improvements in RMSE and
correlation can be seen from 0 to 24 h. Especially after assimilating PMcoarse (PM10–2.5 in PM2 and ALL experiments), the averaged improvement
percentage for RMSE and correlation reach up to about 27.0 % and
55.5 %. For bias, since the statistics are averaged over the 531 stations,
the offset of large positive and negative bias at different stations leads
to the small averaged bias in the NODA run (see the spatial distribution of
bias at the individual site in Sect. S1 of the Supplement).
Considering that the DA experiments show distinct improvements in RMSE and
correlation, WRFDA-Chem still provides a generally positive contribution to
the PM10 forecast.
Figure 7 presents the averaged forecast error statistics for SO2, NO2, O3,
and CO with respect to the forecast range. In PM1 and PM2 experiments that do
not assimilate the gas-phase observations, no significant changes appear in
the forecasts of the gaseous pollutants compared to the NODA run; after
assimilating the gas-phase observations, the ALL experiment shows evident
improvements in all the four gaseous pollutants, in which the improvements
for SO2, NO2, and O3 are more significant in 0–10 h, and the improvements
for CO last up to 24 h. According to the numbers shown in Table 3, for SO2,
NO2, O3, and CO, the average bias (RMSE) in the ALL experiment decreases by
43.3 %, 42.2 %, 73.9 %, and 74.0 % (13.4 %, 5.3 %, 11.3 %, and
33.7 %), compared to the NODA run, and the average correlation increases
by 37.9 %, 8.3 %, 41.4 %, and 103.5 %, respectively. It is worth
noting that the WRFDA-Chem system has a positive impact on the forecast of
NO2 and O3 by merely analyzing the IC. Since NO2 and O3 are related to
complex photochemical reaction processes, the assimilation of NO2 and O3
usually does not work as well as other gas-phase pollutants for the forecast
aspect, even with both emission and IC analyzed (Peng et al., 2018). As a result, the aerosol or chemical assimilation based on WRFDA-Chem could not only
contribute to the conventional aerosol forecasts in operational applications
but also provide valuable help in the emerging study demands for gaseous
pollutants, especially O3.
Averaged threat score (TS) for the Air Quality Index (AQI) from AQI
level 1 to level 6 (a–f) in different experiments as a function of forecast
range, verified against the surface observations of 531 stations in China.
The blue, red, green, and gray lines denote the results of experiment NODA,
PM1, PM2, and ALL, respectively. The numbers to the right of each panel
denote the averaged TS from 0 to 24 h for different experiments.
The Air Quality Index (AQI), which is used for reporting daily air quality and
issuing alarms, is one of the service products of RMAPS-Chem operational air
quality model over northern China. Generally, AQI is classified into six
level ratings from good to hazardous: 0–50 (level 1), 51–100 (level 2),
101–150 (level 3), 151–200 (level 4), 201–300 (level 5), and 300+ (Level
6). Similar to previous studies (Kumar and Goyal, 2011; Tao et al., 2015;
Zheng et al., 2014), AQI is calculated for the six major pollutants. The
pollutant with the highest AQI level is deemed to be the “main pollutant” and
its AQI determines the overall AQI level. Accordingly, the accurate forecast
of AQI requires the overall good performances of the six pollutants. To
reflect the integrated DA effect of aerosols and gas-phase pollutants, the
threat score (TS), one of the most commonly used criteria in the
verification of meteorology forecasting, is used for AQI at each AQI level.
The TS for the AQI is calculated by
TSi=HiHi+Mi+Fi,
where H, M, and F denote the times of the hits, the misses, and the false
alarms in the forecast of AQI and i denotes the AQI levels from 1 to 6. As a result, the TS is acquired at each AQI level ranging from 0 to 1, and the
higher (lower) TS represents the better (worse) forecast performance.
As shown in Fig. 8, in the beginning of the forecast, DA experiments (PM1,
PM2, and ALL) increase the TS remarkably at all AQI levels, and it then
gradually decreases (quickly drops) with the forecast range at AQI levels 2–6
(AQI level 1). Nevertheless, for the polluted situations with AQI levels
3–6, evident improvements can be seen from 0 to 24 h in all the DA
experiments, in which the average TS increases from 0.19, 0.09, 0.16, and
0.19 (NODA experiment) to about 0.27, 0.16, 0.27, and 0.26 (DA experiments),
respectively. For heavily polluted situations with AQI levels 5–6 (Fig. 8e–f), compared to the PM1 case, TS experiences a further increase in the
PM2 and ALL experiments after assimilating PMcoarse (PM10–2.5). This
result indicates that for heavily polluted events during this period
(January 2017), PM2.5 and PM10 could be the main pollutant that
contributes the most to the AQI.
In general, the new WRFDA-Chem evidently improves the aerosol or chemical
forecasting. Based on the assimilation of the six major pollutants, the
chemical ICs are improved distinctly and a better forecast performance is
acquired up to 24 h. Among the different experiments, the ALL experiment
displays the best forecast error statistics for most of the major pollutants along with the highest TS for AQI. In the following operational
applications, it is recommended to assimilate the six major pollutants
simultaneously, which will help to obtain better analyses and forecast skills
on the whole.
Same as Fig. 3, but for the experiments of NODA,
ALL_6h, ALL-3h, and ALL_1h, respectively. Units of
the y axis are micrograms per cubic meter in (a–e) and milligram per cubic meter in (f).
Averaged bias (units: µg m-3), RMSE (units: µg m-3), and correlation for (a) PM2.5 and (b) PM10 in different
experiments as a function of forecast range, verified against the surface
observations of 531 stations in China. The blue, red, green, and gray lines
denote the results of experiment NODA, ALL_6h,
ALL_3h, and ALL_1h, respectively.
Response to DA cycling frequency
Cycling frequency is an important aspect in the DA strategy. However, the
responses toward IC updating could be different among the pollutants. To
work out this issue and to provide helpful references for future
applications, DA experiments with different cycling frequencies were
analyzed in this section.
Same as Fig. 10, but for the forecast of (a)SO2, (b)NO2, (c)O3
(units: µg m-3), and (d) CO (units: mg m-3).
Same as Fig. 8, but for the experiments NODA,
ALL_6h, ALL-3h, and ALL_1h, respectively.
Figure 9 shows the domain-averaged bias and RMSE of the analysis as in Fig. 3, but for experiments with different DA frequencies (ALL_6h,
ALL_3h, and ALL_1h; the ALL_6h
is the ALL experiment in Table 2). Except for O3, most of the variables
display a gradual improvement with the increase in cycling frequency. For
example, from the NODA run to the 6 h cycling experiment and then to the 3
and 1 h cycling experiment, the bias (RMSE) for PM2.5 gradually decreases from 36.60 µg m-3 (70.41 µg m-3) to 5.98 µg m-3
(23.15 µg m-3) and then to 5.41 µg m-3 (21.32 µg m-3) and 4.30 µg m-3 (18.54 µg m-3). Similar
results also exist for the bias for SO2, NO2, and CO, as well as for the RMSE for
PM10, SO2, and CO. In accordance with the gradual improvements in the
analyses, the forecast skills increase with the cycling frequency in most of
the variables except for O3 (Figs. 10–11). Especially for the forecasts of
aerosols, evident gradual improvements can be seen from 0 to 24 h. From the
6 h cycling experiment to the 3 and 1 h cycling experiment, the
averaged decrease percentage of RMSE for PM2.5 (PM10) enlarges from
38.76 % to 41.27 % and 44.21 % (27.31 % to 30.17 % and 32.97 %);
the average increased percentage of correlation for PM2.5 (PM10) enlarges
from 42.82 % to 49.51 % and 55.58 % (57.71 % to 66.39 % and
74.89 %). To further investigate the integrated DA effect of aerosols and
gas-phase pollutants under a different cycling frequency, the TS for AQI is
shown in Fig. 12. The forecast of air quality is improved step by step with
the increase in cycling frequency. On AQI levels 2–6, the TS for the
ALL_1h experiment is situated above the ALL_3h
experiment most of the time and is followed by the ALL_6h
experiment. These results indicate that frequent IC updating is helpful
to further improve the forecast for most of the pollutants.
However, the analysis and 24 h forecast of O3 become worse under higher
cycling frequencies for this winter season (Figs. 9e and 11c). Given that the
analysis is at 00:00 UTC, the worsening of the analysis in the experiments with higher DA
frequencies (1 h, 3 h) could be mainly due to the unfavorable changes in the
1 h and 3 h forecasts period (starting from 23:00 and 21:00 UTC), which is different
from the situation in the 6 h cycling experiment. As for the forecasts, the
24 h performances starting from 00:00 UTC show complex changes along with the
forecast range: compared to the 6 h cycling experiment, the biases in the
experiments with higher DA frequencies decrease at 09:00–14:00 UTC but increase
for other hours. The RMSE and correlations in the experiments with higher DA
frequencies become worse at most of the hours (Fig. 11c). It should be
mentioned that O3 is a relatively short-lived chemical reactive species and
takes part in highly complex and photochemical reactions in association with
NOx and volatile organic compounds (VOCs) (Peng et al., 2018; Lu et al., 2019). From this perspective, the
performances of O3 could also rely on the photochemistry and the NOx
titration, in addition to the IC. Although the winter month (January 2017)
is investigated here when ozone photochemistry is relatively weak compared
to other seasons, the photochemistry and the NOx titration still play their
roles. Accordingly, when the assimilation of NO2 changes the NO2
concentration and leaves the NO and VOC unadjusted due to the absence of NO
and VOC measurements, two results might occur: firstly, the NO2/ VOC ratio, which determines the photochemical reactions, and even the regime might be
changed (O3 production or loss direction might change); secondly, the NOx
titration process might be changed due to the NO2 concentration updates (but
no change in NO). Considering that the relevant NOx–VOC–O3 reactions take place
quickly, changing the O3 concentration in a short period, the advantage of
IC DA could compete with the disadvantages of the disordered photochemistry
(inaccurate NO2/ VOC ratios) or the changed titration (adjusted NO2
concentrations but not NO) resulting from the DA. Under these circumstances,
the more frequently the O3 and NO2 were assimilated, the more
incompatibilities could be brought into the related photochemical or titration
reactions, resulting in the model performing worse in the O3 forecasts under
higher cycling frequencies. It is noted that these statistics were only for
the analysis at 00:00 UTC and the 24 h forecast starting from 00:00 UTC for winter
season. Since O3 has strong diurnal and seasonal variations, more
experiments and statistics at different times of the day and different seasons
of the year should be conducted in the future.
According to the results above, it is better to assimilate PM2.5, PM10, SO2,
and CO every 1 h and assimilate O3 and NO2 every 6 h in future
applications, given the fact that the 6 h cycling experiment performs the
best in the O3 forecasting (Fig. 11c) and displays no significant
differences in the NO2 forecasting with experiments under higher cycling
frequencies (Fig. 11b). It could also be helpful to assimilate the VOC along
with O3 and NO2 after there are corresponding observations.
Indications on further model development
A higher forecast skill relies not only on a better working of DA but also on a better performance of the forecast model. To further improve the forecast
skill, a crucial task is to understand the deficiencies in the model, while
the challenge in chemistry model diagnostic is that uncertainties from
various aspects are mixed up in the model simulations, and the situation
becomes even more complex when the reaction path is not yet revealed by the laboratory. However, with the help of DA, as one aspect (IC) in the model is
corrected by using observations as constraints, the deficiencies of other
aspects (e.g., chemical reactions) could be more evident, and thus there
could be a better chance of diagnosing the deficiencies in the model.
Specifically, sulfate–nitrate–ammonium (SNA) are the predominant inorganic
aerosol species that contribute up to 50 % of total PM2.5 in heavily
polluted events in northern China (Y. Wang et al., 2014). In addition to the
normal pathways in the MOSAIC scheme, we added SO2–NO2–NO3-related heterogeneous reactions for the high relative-humidity case in WRF-Chem (Chen et
al., 2016), which greatly improved the underestimated SNA simulations. Since
the newly added reactions are calculated on both the concentration of
precursors (SO2, NO2-NO3) and the uptake coefficients in the model, after DA
corrected the concentrations of the precursors (one aspect), the impacts of
the uptake coefficients could be more evident (the other aspect, not corrected). Ideally, if the newly added reactions depict the heterogeneous
reaction processes properly, a forecast improvement for the aerosols could be
expected by assimilating their gaseous precursors. Based on this notion,
this section verifies the forecast of two specific aerosol species – sulfate
(SO42-) and nitrate
(NO3-) – against a size-resolved
particle observation over Beijing IUM station (in view of the assimilated
SO2 and NO2 being the corresponding gaseous precursors of these aerosol
species), aiming to explore the deficiencies in the uptake coefficients in
the newly added heterogeneous reactions and to provide beneficial
indications for future model development.
Time series of (a) sulfate and (b) nitrate over 14–20 January,
verified against the size-resolved particle observation at IUM station
(units: µg m-3). The gray, blue, and red lines denote the
observation and the results of experiments PM2 and ALL, respectively. The
numbers to the right of each panel denote the averaged RMSE over 14–20 January
for different experiments.
Figure 13 presents the time series of sulfate and nitrate over Beijing IUM
station. In the ALL experiment, after assimilating both the PM
concentrations and the gaseous precursors (SO2, NO2), the forecasts of
sulfate and nitrate become even worse than the PM2 experiment, which only
assimilates the PM concentrations. In the ALL experiment, sulfate
experiences a decrease, accompanied by the average RMSE grows from 4.32 to
4.88 µg m-3; nitrate shows an increase, accompanied by the
average RMSE grows from 8.74 to 10.12 µg m-3. However, compared to
the PM2 experiment, the precursors (SO2 and NO2) are indeed improved. Figure 14 displays the analysis statistics of SO2 and NO2 in the ALL experiment
around the Beijing area (red dots in Fig. 1) on 16 January, the period with the
largest changes of sulfate and nitrate (Fig. 13). To correct the
overestimated SO2 (underestimated NO2) in the background, the DA reduces
(enhances) the model value in the ALL experiment, making it closer to the
observations.
It should be mentioned that the heterogeneous reactions are added by using
the sulfate–nitrate–ammonium observations as constraints to tune the
“observation-best-matched” uptake coefficients under the scenario without
DA, in which the precursor concentrations are from a pure model and thus not very
accurate. To best match the observation, when gaseous precursors are
overestimated (underestimated) in the model, the uptake coefficient is tuned
to a low-biased (high-biased) value. As a result, such a coefficient may no
longer be suited for the cases with DA. For instance, after DA reduces the
overestimated SO2, the uptake coefficient is still relatively low and thus
the reaction from SO2 to sulfate will stay at a low rate (with both a low
value of SO2 and a low reaction coefficient). A similar result also exists for
the reaction from NO2 to nitrate. From this perspective, the negative
effects on sulfate and nitrate in the ALL experiment may not be hard to
understand (Fig. 13). Therefore, in future chemistry development, it is
necessary to develop more appropriate coefficients for different gaseous
precursor scenarios, in which more constraints, such as precursor and
species concentrations, should be provided with the help of the DA technique.
Accordingly, further improvements for aerosol forecast could be expected by
assimilating their gaseous precursors.
According to the results above, the DA technique provides an opportunity to
identify and diagnose the deficiencies in the model. By correcting the
precursor concentrations through DA (one aspect), the deficiency of the
uptake coefficients for the SNA heterogeneous reactions (the other aspect, not corrected) is revealed. In the future, besides being used to
improve the forecast skill through updating the IC, DA could be used as
another approach to reveal the necessary developments in the model.
Averaged scatterplot of (a, c) observation versus background and
(b, d) observation versus analysis for (a, b)SO2 and (c, d)NO2 around
the Beijing area (red dots in Fig. 1) on 16 January. The numbers above the panels denote the accumulated numbers of the observations used around the Beijing area
on 16 January (16:00, 16:06, 16:12, and 16:18 UTC).
Conclusions and discussions
To improve the operational air quality forecasting over China, a flexible
aerosol and gas-phase pollutants assimilation capability that can switch
between different aerosol schemes is developed based on the WRFDA system
with the 3DVAR algorithm. This flexibility is designed to address the complexity
of current aerosol schemes and to facilitate future chemistry developments.
In this first application, the assimilation capability of surface
observations of six major pollutants, including PM2.5, PM10, SO2, NO2, O3,
and CO, is built with the MOSAIC aerosol scheme.
Before application in the operational air quality model, capability of the
WRFDA-Chem system is verified in terms of analysis and forecast
performances. Using the updated system, five DA experiments (assimilating
different combinations of pollutants in various frequencies) were conducted
for January 2017, along with a control experiment without DA. Results
show that the WRFDA-Chem system evidently improves the forecast of
aerosols and gas-phase pollutants. From the aspect of analysis, the
assimilation of different atmospheric-composition observation reduces the
bias and RMSE in the IC remarkably (i.e., by about 68 %, 61 %, and
30 %–60 % in the RMSE for PM2.5, PM10, and gas-phase pollutants); from the
aspect of forecast skill, better performances are acquired up to 24 h
with about 10 %–40 % (30 %–50 %) improvements in the RMSE (correlation) for
different pollutants. Among the different experiments, the one assimilating all
the six pollutants displays the best forecast error statistics for most of
the pollutants, along with the highest TS for AQI. In future applications, to
obtain a better analysis and forecast skill in general, it is recommended to
assimilate the six major pollutants simultaneously.
As the cycling frequency is an important aspect in the DA strategy, DA
experiments with various cycling frequencies are also analyzed. Results
show that the responses toward IC updating are different among the
pollutants. For PM2.5, PM10, SO2, and CO, the forecast skills increase with
the DA frequency; for O3, compared to a better performance at the 6 h
cycling frequency, its analysis at 00:00 UTC and the following 24 h forecast
become generally worse under higher cycling frequencies for this winter
season, although the biases did decrease at 09:00–14:00 UTC in the 24 h forecast.
Considering that the relevant NOx–VOC–O3 reaction system changes the NO2/O3
concentration in a short period, the advantage of IC DA could compete with
the disadvantages of the disordered photochemistry (inaccurate NO2/ VOC
ratios) or the changed titration (adjusted NO2 concentrations but not NO)
resulting from the DA. In future applications, it is better to assimilate
PM2.5, PM10, SO2, and CO every 1 h. For the frequency of O3 and NO2
assimilation, every 6 h is the best in this winter season in our study.
Since O3 has strong diurnal and seasonal variations, more experiments and
statistics at different times of the day and different seasons of the year
should be conducted in the future. Also, it might be helpful to assimilate
NO / VOC simultaneously with O3 and NO2 after there are corresponding
measurements.
By investigating the effect of assimilating gaseous precursors on the
forecast of related aerosols, the deficiencies in the WRF-Chem model are
further revealed. The uptake coefficients for sulfate–nitrate–ammonium
heterogeneous reactions in the model are found to be not appropriate in
the applications with gaseous precursor (SO2 and NO2) assimilations, since
they were originally tuned under the gaseous precursor scenarios without DA
and the biases from the two aspects (SNA reactions and IC DA) were just
compensated. In future chemistry development, it is necessary to develop
appropriate coefficients for different gaseous precursor scenarios, in which
more constraints, such as precursor and species concentrations, should be
provided with the help of the DA technique.
As for the significantly underestimated PMcoarse in the model, the results
might relate to the missing emissions under current situations. Different
from the United States or European countries, where national emission
inventories are provided and updated frequently by the government (e.g., the US
National Emission Inventory NEI 05-08-11-14-17), the publicly available
emission inventories for China are mainly established by several scientific
research groups. As a result, the uncertainties of the publicly available
emission inventories in China are relatively large compared with others
(US, European countries), and it is a known problem that the fugitive dust
emissions over the whole of China is still lacking, which might cause the
underestimated PMcoarse simulation in the model.
Due to the flexible aerosol assimilation capability of the
WRFDA-Chem system, the development of other aerosol schemes targeting different
regions in Asia is underway. In the next step, a study will focus on
assimilating chemical observations from different observing platforms, such
as satellite aerosol optical depth (AOD) observations, which contain more information over the areas
with sparse surface observations. In addition, more advanced DA techniques,
such as 4DVAR and Hybrid DA, could be taken into consideration in further
developing the aerosol or chemical DA system.
Code and data availability
The data used in the figures and the developed WRFDA-Chem codes are
available from Wei Sun upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-9311-2020-supplement.
Author contributions
WS and ZL conducted the development of the DA system. ZL, DC, WS, and MC designed the research; WS performed experiments and analyzed results; PZ provided PM
species observations, and WS and DC wrote the paper with contributions from
all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This work was supported by the National Key R&D Program on Monitoring,
Early Warning and Prevention of Major Natural Disasters under grant no. 2017YFC1501406, and the Basic R&D special fund for central-level,
scientific research institutes (IUMKYSZHJ201701, IUMKY201807) of China. NCAR
is sponsored by the US National Science Foundation.
Financial support
This research has been supported by the National Key R&D Program on Monitoring, Early Warning and Prevention of Major Natural Disasters (grant no. 2017YFC1501406) and the Basic R&D special fund for central-level, scientific research institutes of China (grant no. IUMKYSZHJ201701, IUMKY201807).
Review statement
This paper was edited by Chul Han Song and reviewed by two anonymous referees.
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