ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-5627-2015Development of a custom OMI NO2 data product for evaluating biases in a regional chemistry transport modelKuhlmannG.gerrit.kuhlmann@my.cityu.edu.hkhttps://orcid.org/0000-0002-7021-4712LamY. F.yunflam@cityu.edu.hkhttps://orcid.org/0000-0002-5917-0907CheungH. M.HartlA.FungJ. C. H.ChanP. W.WenigM. O.School of Energy and Environment, City University of Hong Kong, Hong Kong, ChinaEmpa, Swiss Federal Laboratories for Materials Science and Technology, Dübendorf, SwitzerlandGuy Carpenter Asia-Pacific Climate Impact Centre, School of Energy and Environment, City University of Hong Kong, Hong Kong, ChinaDepartment of Mathematics, The Hong Kong University of Science & Technology, Hong Kong, ChinaHong Kong Observatory, Hong Kong, ChinaMeteorologisches Institut, Ludwig-Maximilians-Universität, Munich, GermanyG. Kuhlmann (gerrit.kuhlmann@my.cityu.edu.hk) and Y. F. Lam (yunflam@cityu.edu.hk)21May201515105627564426September20149December201420April20155May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/5627/2015/acp-15-5627-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/5627/2015/acp-15-5627-2015.pdf
In this paper, we present the custom Hong Kong NO2 retrieval
(HKOMI) for the Ozone Monitoring Instrument (OMI) on board the Aura
satellite which was used to evaluate a high-resolution chemistry
transport model (CTM) (3 km × 3 km spatial
resolution). The atmospheric chemistry transport was modelled in the
Pearl River Delta (PRD) region in southern China by the Models-3
Community Multiscale Air Quality (CMAQ) modelling system from
October 2006 to January 2007. In the HKOMI NO2 retrieval,
tropospheric air mass factors (AMFs) were recalculated using
high-resolution ancillary parameters of surface reflectance, a priori
NO2 and aerosol profiles, of which the latter two were taken
from the CMAQ simulation. We tested the influence of the ancillary
parameters on the data product using four different aerosol
parametrizations. Ground-level measurements by the PRD Regional Air
Quality Monitoring (RAQM) network were used as additional independent
measurements.
The HKOMI retrieval increases estimated tropospheric NO2
vertical column densities (VCD) by (+31 ± 38) %, when compared
to NASA's standard
product (OMNO2-SP), and improves the normalized mean bias (NMB) between
satellite and ground observations by 26 percentage points from -41 to
-15 %. The individual influences of the parameters are
(+11.4 ± 13.4) % for NO2 profiles, (+11.0 ± 20.9) %
for surface reflectance and (+6.0 ± 8.4) % for the best aerosol
parametrization. The correlation coefficient r is low between ground
and satellite observations (r= 0.35). The low r and the remaining NMB
can be explained by the low model performance and the expected differences
when comparing point measurements with area-averaged satellite observations.
The correlation between CMAQ and the RAQM network
is low (r≈ 0.3) and the model underestimates the NO2
concentrations in the northwestern model domain (Foshan and Guangzhou).
We compared the CMAQ NO2 time series of the two main plumes with
our best OMI NO2 data set
(HKOMI-4). The model overestimates
the NO2
VCDs by about 15 % in Hong Kong and Shenzhen, while
the correlation coefficient is satisfactory (r= 0.56). In Foshan
and Guangzhou, the correlation is low (r= 0.37) and the model
underestimates the VCDs strongly (NMB =-40 %). In
addition, we estimated that the OMI VCDs are also underestimated by
about 10 to 20 % in Foshan and Guangzhou because of the
influence of the model parameters on the AMFs.
In this study, we demonstrate that the HKOMI NO2 retrieval
reduces the bias of the satellite observations and how the data set
can be used to study the magnitude of NO2 concentrations in
a regional model at high spatial resolution of
3 × 3 km2. The low bias was achieved with
recalculated AMFs using updated surface reflectance, aerosol profiles
and NO2 profiles. Since unbiased concentrations are
important, for example, in air pollution studies, the results of
this paper can be very helpful in future model evaluation studies.
Introduction
Nitrogen oxides (NOx=NO+NO2) play an important role in
atmospheric chemistry. As precursors of ozone and aerosols, they are vital in
the formation of photochemical smog and acid rain
. In the troposphere, NO2 concentrations have
a high spatial and temporal variability due to their short lifetime and the
variety of sources and sinks. This spatiotemporal variability has been
studied with chemistry transport models (CTM), air quality monitoring
networks and satellite instruments
e.g..
The first satellite instrument able to detect tropospheric NO2 was
the Global Ozone Monitoring Experiment (GOME) which was launched on board the
second European Remote Sensing satellite (ERS-2) in 1995
. Successor instruments to GOME (GOME-2) are payload
on the MetOp satellites used for operational meteorology
. In 2006, the Scanning Imaging Absorption Spectrometer
for Atmospheric Cartography (SCIAMACHY) was launched on board the
ENVIronmental SATellite (ENVISAT) and in 2004,
the Ozone Monitoring Instrument (OMI) was launched on board the Aura
satellite . Since the launch of GOME, the spatial
resolution of the instruments has increased rapidly. While GOME had a smallest
ground pixel size of 40km×320km, which was suitable
for coarse global analyses, OMI has a smallest ground pixel size of
13km×24km. The higher spatial resolution makes OMI
applicable for the study of NO2 in large metropolitan areas.
The OMI NO2 standard products are the NASA standard product (OMNO2,
Version 2.1) and the Derivation
of OMI tropospheric NO2 product (DOMINO, Version 2.0)
. The NO2 retrieval
algorithms use ancillary parameters, such as surface reflectance, a priori
NO2 profiles, and aerosol and cloud information, to calculate air
mass factors (AMFs). The AMFs convert the retrieved NO2 slant column
densities (SCDs) to vertical column densities (VCDs). In the standard products,
a priori NO2 profiles are taken from global CTMs with a spatial
resolution of 2∘× 2.5∘ (OMNO2) and
2∘× 3∘ (DOMINO). Surface reflectances are taken
from an OMI climatology with a spatial resolution of
0.5∘× 0.5∘.
Since NO2 profile and surface reflectance have a large impact on the
NO2 VCD, the standard products are not directly suitable to study
VCDs on a scale below the resolution of these ancillary parameters. However,
when more accurate NO2 profiles are available, for example in model
evaluation studies, the VCDs can be corrected using scattering weights (SWs)
or averaging kernels (AKs). The correction removes the dependency on the a
priori profile and can be applied either to the OMI NO2 VCDs or the
model profiles for details see. SWs and AKs
are closely related to vertically resolved AMFs and are provided with the
standard products. AKs have been applied, for instance, by
, who validated the regional-scale air quality
model AIRPACT-3 (12 km× 12 km spatial resolution)
over the Pacific Northwest, and , who evaluated the
Comprehensive Air Quality Model (CAMx)
(10 km× 10 km) over southeastern Europe. Applying
AKs has a large influence on the magnitude of tropospheric NO2 VCDs
.
In the standard products, AKs and SWs depend on the OMI surface reflectance
climatology. The spatial resolution of this climatology is coarse compared to
the OMI ground pixel size. Therefore, customized NO2 products have
been developed which recalculate AKs, and thus AMFs, using high-resolution
surface reflectance products
. Furthermore, AKs and
SWs are affected by aerosols directly due to additional scattering and
absorption as well as indirectly due to their impact on
the retrieval of surface reflectance and cloud properties
. Since the ancillary parameters have a
large impact on the magnitude of the retrieved NO2 VCDs, it is
important to consider their influence, for example, in air pollution studies
where unbiased pollutant concentrations are important.
For this paper, we developed a customized OMI NO2 retrieval and
applied it to the Pearl River Delta (PRD) region in southern China. The Hong
Kong OMI (HKOMI) retrieval recalculates tropospheric AMFs and uses them with
OMNO2 SCDs to calculate VCDs. The HKOMI product was used to evaluate the
Models-3 Community Multiscale Air Quality (CMAQ) modelling system
(3 km× 3 km spatial resolution)
. The study period is October 2006 to January 2007.
Three automatic weather stations and 16 ground-level stations of the PRD
Regional Air Quality Monitoring (RAQM) network were used to validate model
and retrieval. The objective is to estimate the influence of the ancillary
parameters on the evaluation and to demonstrate some possibilities and
limitations for using satellite-based NO2 observations.
This paper is organized as follows: OMI and the retrieval of tropospheric
NO2 including the AMF calculations using the SCIATRAN radiative
transfer model are described in Sect. . The RAQM network,
the CMAQ model run and the HKOMI NO2 retrieval are described in
Sect. . The results are presented in Sect.
and discussed in Sect. . Finally,
Sect. concludes this paper.
BackgroundOzone Monitoring Instrument (OMI)
OMI is a nadir-viewing imaging spectrometer measuring Earth's reflectance
spectra in the near-ultraviolet and visible wavelength range with two
charge-coupled device (CCD) arrays. It was launched aboard NASA's EOS Aura
satellite on 15 July 2004 . The instrument provides
near-daily global coverage at an overpass time of 13:45 ± 15 min local
time (LT). Earth reflectance spectra are measured during the sunlit part of
about 14.5 sun-synchronous orbits per day. Trace gases, such as ozone
(O3), sulfur dioxide (SO2) and nitrogen dioxide
(NO2), are retrieved from the reflectance spectra as well as cloud
and aerosol properties. The measurement principle is an along-track
(push-broom) scanner with a swath width of 2600 km which is divided
into 60 pixels. The ground pixel size varies between
13 km× 24 km at nadir and
40 km× 160 km at the swath edge
. Since June 2007, OMI is affected by a row anomaly
which reduces the number of valid measurements (see
http://www.knmi.nl/omi/research/product/rowanomaly-background.php for
details).
Standard retrieval of tropospheric NO2 column densities
The OMNO2 product is the basis for the HKOMI retrieval. Therefore, we give
a brief introduction to their algorithm. The OMNO2 retrieval algorithm
has three major steps:
The total slant column densities S are obtained from the
reflectance spectra using the differential optical absorption
spectroscopy (DOAS) technique .
The stratospheric slant column densities Sstrat are
subtracted from the total column S using
a stratosphere–troposphere separation (STS) algorithm
.
The tropospheric slant column densities Strop are
converted to vertical column density Vtrop using
a tropospheric AMFs Atrop.
The AMF is defined as the ratio of slant and vertical column density
. Thus the tropospheric column density
Vtrop is calculated by
Vtrop=S-SstratAtrop=StropAtrop.
The tropospheric AMF depends on parameters such as sun position,
instrument viewing direction, surface reflectance, atmospheric scattering
properties due to air molecules, aerosols and clouds, and the
a priori NO2 profile. It is related to the vertical sensitivity
of the satellite instrument and can be computed for N vertical
layers by
Atrop=∑k=1NαkmkVk∑k=1NVk
with:
αk: an empirical temperature correction coefficient
accounting for the temperature dependency of the NO2
absorption cross-section,
mk: the differential or box air mass factor which describes
the instrument sensitivity for layer k,
Vk: the partial NO2 VCD of layer k.
The products αkmk are the scattering weights provided with the
standard product. The correction coefficient αk can be computed by
the empirical formula
αk=1-0.003K-1(Tk-Tref),
where Tk is the temperature in layer k and Tref is reference
temperature of the NO2 absorption cross-section (Tref=220K) .
The box air mass factors mk can be computed with a radiative
transfer model. The OMNO2 algorithm uses the TOMRAD radiative transfer
model . The AMF formulation used in OMNO2 is based
on a AMF formulation by . In partly cloudy
scenes, OMNO2 computes the box AMFs using the independent pixel
approximation (IPA). The approximation calculates AMFs as weighted
sums of a cloudy mkcloudy and a clear
mkclear component:
mk=w⋅mkcloudy+(1-w)⋅mkclear,
where w is the aerosol/cloud radiance fraction (CRF), that is the
fraction of measured radiation that results from clouds and aerosols
.
In OMNO2, the partial VCD Vk are taken from the Global Modeling Initiative
(GMI) CTM which combines stratospheric
chemistry described by and tropospheric
O3–NOx–hydrocarbon chemistry from the GEOS-Chem model
. It should be noted that, for an optical thin absorber,
only the relative shape of the profile nk=Vk/Vtrop is required
for the AMF calculation.
estimated tropospheric NO2 VCD uncertainties
to be 1×1015cm-2 for clear skies and up to
3×1015cm-2 for large cloud radiance fractions. In
polluted regions, the main uncertainties are the DOAS fit (10 % relative
error) and the tropospheric AMFs (20–80 %).
SCIATRAN radiative transfer model
SCIATRAN is a one-dimensional RTM which can be used for the
calculation of box AMFs . The model is
designed as a forward model for the retrieval of atmospheric
constituents from measurements of scattered light by satellite, ground
or airborne instruments. The wavelength range goes from 175 to
2380 nm which includes the ultraviolet, visible and near-infrared part of the spectrum.
SCIATRAN solves the integro-differential radiative transfer equation
using the discrete-ordinates method (DOM) in the plane-parallel or
pseudo-spherical mode to calculate box AMF profiles. Box AMFs mk
are derived from weighting functions which describe the sensitivity of
the reflectance spectrum R to a perturbation of a model parameter
Δn in layer zk. In SCIATRAN, weighting functions are
computed by a quasi-analytic approach . If Δn is a perturbation of the NO2 number density nk, the box
AMF mk is the negative weighting function .
The CMAQ model domain (D3) with MODIS
land categories in the Pearl River Delta (PRD) region grouped into
the following categories: forest (dark green), crop lands (olive),
bare land (grey), urban areas (yellow) and water (blue). The
stations of the PRD Regional Air Quality Monitoring (RAQM) network
and HKO automatic weather stations are marked by circles and
squares, respectively. The inset shows the three CMAQ model
domains which are D1, D2 and D3 from the largest to smallest.
Methodology
In this section, we describe the RAQM network, the CMAQ CTM and the HKOMI
retrieval. Our study period is from October 2006 to January 2007 which has
been chosen because cloud fractions in the PRD region are lowest in this
season and OMI measurements in later years are affected by the aforementioned
row anomaly.
Ground networksMeteorological observations
Meteorological observations were used for the evaluation of the simulated
meteorological fields which were used to drive the CTM. The data were
measured by three automatic weather stations in Hong Kong. The stations are
located at the Hong Kong Observatory (HKO) headquarters, the Hong Kong
International Airport (HKIA) and on Waglan Island (WGL) (see
Fig. ). HKO is in the city centre of Kowloon and
surrounded by high buildings. HKIA is located on an artificial island near
Tung Chung to the north of mountainous Lantau Island. Waglan Island is
a small island located to the east of Hong Kong Island. The island is too
small to be resolved by the model grid. The WGL station is located
56 m above sea level and used as background station by the Hong Kong
Observatory. The used meteorological parameters are hourly measurements of
temperature (T), humidity (q), sea surface pressure (p) and wind (v).
Temperature and humidity have been measured at 2 m above ground level
(a.g.l.).
Pearl River Delta regional Air Quality Monitoring network
Ground-level NO2 mixing ratios were provided by the RAQM network. The
network was established by the governments of the Guangdong province and Hong
Kong to monitor the air quality in the PRD region and has been in operation
since 30 November 2005. It consists of 16 automatic air quality
monitoring stations (see Fig. , round markers). The
network measures NO2, SO2, O3 and PM10
hourly. The monitoring network was used to validate the NO2 mixing
ratios of the OMI NO2 products and the CMAQ simulation. In the
network, NO2 is measured by chemiluminescence and DOAS technique with
an accuracy and precision of about 10 % .
CMAQ model simulation
Atmospheric chemistry was simulated with the CMAQ modelling system
version 4.7.1 . Three model domains were defined using
a Lambert conformal conic projection (Fig. ). The coarse
domain (D1) covers East Asia with a spatial resolution of
27 km× 27 km. The nested domains have grid
resolutions of 9 km× 9 km (D2) and
3 km× 3 km (D3), respectively.
Meteorological fields were provided by the Weather Research and Forecasting
(WRF) modelling system driven by NCEP Final Analysis (FNL)
data . Horizontal advection was modelled with the
mass-conserving YAMO scheme . The default vertical
advection scheme was replaced by the new advection scheme implemented in CMAQ
version 5.
The gas-phase chemistry was modelled with the Euler backward iterative solver
optimized for the Carbon Bond-05 mechanism with chlorine
. Aerosol chemistry was modelled with the
fifth-generation CMAQ aerosol model (aero5) while the impact of clouds on
deposition, mixing, photolysis, and aqueous chemistry was set by the
Asymmetrical Convective Model (ACM) cloud processor .
Three-dimensional extinction coefficients were computed by the empirical
IMPROVE formula .
The emission inventory used in this simulation was compiled by
. The inventory combines monthly anthropogenic emission from
INTEX-B with biogenic emissions from GEIA Global
Emissions Inventory Activity,, and biomass burning and ship
emissions from TRACE-P . The INTEX-B emissions were
updated with regional emissions for Hong Kong provided by the Hong Kong
Environmental Protection Department.
Model outputs used were hourly ground-level values and three-dimensional
fields of NO2 mixing ratios, aerosols and meteorological parameters
averaged from 13:00 to 15:00 LT (OMI overpass time). In addition, at each
ground station, time series of hourly meteorological parameters and
NO2 mixing ratios were extracted from the model output. The surface
pressure in the model was converted to sea level pressure using the simulated
temperature. The wind vector in the model was taken at the height of the
measurement stations to account for the elevation of the stations above
averaged surface height.
The Hong Kong OMI (HKOMI) NO2 retrieval
For the HKOMI NO2 retrieval, we recalculated tropospheric AMFs with
Eq. () with new ancillary parameters. The new AMFs were used to
compute the tropospheric VCDs with Eq. ().
For the AMF calculation, a set of ancillary parameters was compiled for each
OMI ground pixel. The parameters were taken mainly from the WRF/CMAQ
simulation. Thus, the retrieval does not depend on any other CTM model which
makes the model evaluation easier. The parameters are surface elevations,
temperature, pressure and NO2 profiles, as well as aerosol extinction
coefficients. Further ancillary parameters are cloud height and CRF, which
were taken from the OMI O2-O2 cloud product ,
and surface reflectance, which was taken from the MODIS MCD43C2 product
.
All ancillary parameters were projected to a
0.01∘× 0.01∘ (about
1 km× 1 km) longitude–latitude grid and then
averaged to each OMI ground pixel. The grid points were weighted based on the
instrument's spatial sensitivity within the pixel boundaries
, in contrast to other custom retrievals, where
each grid point was given equal weight.
We recalculated temperature correction coefficients αk(Tk),
box AMFs mk and partial VCDs Vk. The temperature correction
coefficients αk(Tk) were calculated by
Eq. () from the WRF/CMAQ output. The
box AMFs were computed with the SCIATRAN radiative transfer model. The
partial VCDs were also calculated from the WRF/CMAQ output. As an
example, two NO2 profiles are shown in
Fig. .
Surface reflectance
The surface reflectance was calculated from the MODIS MCD43C2 product. The
product is available every 8 days compiled from 16 days of data. The
spatial resolution is 0.05∘× 0.05∘. We calculated the black-sky albedo (BSA) from
the polynomial representation of the bidirectional reflectance distribution
function (BRDF) using solar zenith angle (SZA) and model parameters for MODIS
Band 3 . MODIS Band 3 has a wavelength range from 459 to
479 nm and a centre wavelength of 470 nm. This band is
closest to the DOAS fitting window used in the NO2 retrieval
(405–465 nm). Systematic errors due to the wavelength inconsistency
are expected to be small. The MODIS surface reflectance
has been used in other custom OMI NO2 products
.
Since the BSA model parameters have missing values due to cloud
contamination, filled the data gaps by applying
a series of spatial and temporal interpolations. They also reduced
measurement noise by applying a smoothing filter. In this work, we
combined their steps by using normalized convolution which is a useful
algorithm for filling missing values
. We used a three-dimensional, uniform
kernel of size 5 to fill the gaps in the model parameters. The BSA was
calculated from the model parameters for each OMI ground pixel.
Aerosols and clouds
Scattering by aerosols and clouds affects the AMF .
Aerosol scattering at or below an
NO2 layer increases the AMF, but scattering above the NO2
layer will reduce the AMF. In addition, aerosol optics affects the retrieval
of cloud properties.
Clouds are typically handled by the independent pixel approximation
(Eq. ), while aerosols are only treated implicitly in the
standard and most custom NO2 products. In OMNO2, cloud pressure
and CRF are taken from the OMI O2-O2 cloud product which is
sensitive to weakly absorbing aerosols .
Aerosols are also included in the OMI surface climatology which includes
ground haze and persistent cloud features .
included aerosols from
a regional GEOS-Chem simulation. They used aerosol optical thickness
(AOT) and assumed different profiles of aerosol extinction and
absorption coefficients. They also recalculated the OMI cloud product
to remove the aerosol component.
derived an empirical relationship
between MODIS AOT and OMI CRF:
CRF=0.21⋅AOT.
The formula was derived from observations over North America which were
cloud free according to the MODIS AOT retrieval but had non-zero CRF
according to the OMI retrieval.
Averaged CMAQ NO2 and bext profiles under
“clean” and “polluted” conditions. A NO2 profiles was
categorized as polluted, if the ground number density was larger
than 4.8×1017m-3 (about
20 ppbv). A bext profile was categorized as
polluted if the ground extinction coefficient was larger than
0.4. The clean bext profile has an AOT of 0.3 while the
polluted has an AOT of 0.6. In addition, an annual GEOS-Chem
NO2 profile is shown for Hong Kong
(2∘× 2.5∘ spatial resolution).
In the HKOMI retrieval, we treated clouds as in OMNO2 using the independent pixel
approximation. In SCIATRAN, clouds were implemented as an opaque
surface at the height of the OMI cloud pressure product with a surface
reflectance of 0.8 and the box AMF was calculated with
Eq. ().
Aerosol extinction coefficients can be calculated from the CMAQ output
using the empirical IMPROVE formula . In the standard output,
CMAQ calculates only ground-level extinction coefficients. In our custom
retrieval, we implemented four different aerosol parametrizations:
No explicit aerosol treatment, i.e. aerosols were only
included implicitly through the OMI cloud product.
Aerosols were described by the LOWTRAN aerosol
parametrization which requires only very limited information about
season, aerosols type, visibility and relative humidity at four
different layers: planet boundary layer (PBL) (0–2 km),
troposphere (2–10 km), stratosphere and mesosphere. We set
the season to autumn/winter and the aerosol type to urban. The PBL
visibility was calculated from the CMAQ ground extinction
coefficients β using the definition of the meteorological
optical range (MOR) :
MOR=-log0.05β.
The visibility in the free troposphere was set to
23 km. Furthermore, we assumed that no volcanic aerosols were
in the stratosphere or mesosphere. The relative humidity in the PBL
and the free troposphere were taken from WRF. Visibility and relative
humidity were set to the nearest predefined value in the LOWTRAN
parametrization.
Vertical profiles of extinction coefficients β
were computed from CMAQ output using the IMPROVE formula. The
formula includes a constant Rayleigh extinction coefficient of 0.01
which is subtracted to obtain the aerosol extinction coefficient
βext. Since the IMPROVE formula calculates
βext at 550 nm, an Ångström exponent
α for urban aerosols is used to calculate extinction
coefficients at 435 nm. Furthermore,
a single scattering albedo ω0 of 0.82 (urban aerosol) in PBL
(below 2 km) and 0.93 in free troposphere is used to
calculate aerosol absorption coefficients βabs. The
phase function is modelled by Henyey–Greenstein parametrization
with an asymmetry factor g of 0.689 . As
examples, two βext profiles are shown in
Fig. .
Since these three parametrizations include aerosols
implicitly through the cloud product, aerosol might be counted
twice. Therefore, the CRF was corrected by
Eq. () using the AOT in CMAQ. This was done
mainly because recalculation of the OMI cloud product was outside
the scope of our study. Since this formula was derived from cloud
free observations over North America, generalization to cloudy
pixels in other regions should be considered with great
caution. Otherwise, aerosols were treated as in Case 3.
OMI NO2 data sets for the PRD region
For our study, we created six OMI NO2 data sets. The first data set
uses the OMNO2 standard product (OMNO2-SP). For the second data set (OMNO2-SW),
AMFs were recalculated using OMNO2 scattering weights and CMAQ NO2
profiles. The remaining four data sets were created with our HKOMI retrieval
for each aerosol case (HKOMI-1, -2, -3 and -4).
The number of OMI NO2 VCDs, which allows for the study of the
NO2 distribution, is limited due to two factors. First, the ground
pixel size at the end of the swath is very large and thus not suitable for
studying the local spatial distribution. Therefore, only the inner 50 out
of 60 rows were used in this study. Second, the presence of clouds
increases the retrieval uncertainty. Therefore, only ground pixels were used
with CRFs smaller than 50 %. Since the CRF is also sensitive to aerosols,
this filter criterion is likely to remove heavily polluted days as well. We
further removed all orbits that did not have valid pixels in the urbanized
area of the model domain (see Fig. ).
Data processing
The OMI NO2 data sets have been compiled in the instrument's frame of
reference (level 2). For comparison and visualization, the data sets need to
be projected to the model grid (level 3) using a suitable gridding algorithm.
This is not a trivial task, because the algorithm should conserve the
NO2 VCDs within the OMI ground pixels which are overlapping in
along-track direction. In this study, we use a new gridding algorithm which
reconstructs the spatial distribution using a parabolic spline surface
the source code was downloaded at
https://github.com/gkuhl/omi. Each orbit was projected
to a 0.01∘× 0.01∘ longitude–latitude grid. It
would be more plausible to project all data to the CMAQ model grid, but the
gridding algorithm code currently only supports longitude–latitude grids.
Since OMI has a lower spatial resolution than the CMAQ simulation, the
satellite instrument cannot resolve small features in the simulated
NO2 distribution. In addition, OMI's spatial resolution depends on
the current satellite orbit and thus changes from day to day. Therefore, we
create a processed CMAQ data set (CMAQ processed) from the model output (CMAQ
raw). The raw CMAQ VCDs were averaged for each OMI ground pixel in an orbit.
Then, the CMAQ data were projected back onto
a 0.01∘× 0.01∘ longitude–latitude grid using the
gridding algorithm . In this step, missing OMI pixels
were also removed from the CMAQ data. Using this approach, the NO2
horizontal distributions of OMI and CMAQ are directly comparable.
For each RAQM ground station location, time series of tropospheric
NO2 VCDs were computed from the six OMI data sets. To study the
additive and proportional differences, the OMI VCDs were converted to mixing
ratios using the CMAQ NO2 profile shapes. Since many stations are
located on top of buildings, the mixing ratios were calculated at the station
height using nearest-neighbour interpolation. In addition, these time series
were also computed for the processed CMAQ data set.
Two example orbits of OMI NO2 distributions for OMNO2-SP,
HKOMI-1 and HKOMI-4. The overall spatial distribution is similar but
different in details. The HKOMI-1 and HKOMI-4 data sets have larger
NO2 column densities than OMNO2-SP.
Evaluation study
For our evaluation study, we have six OMI data sets, two CMAQ data sets as well
as time series of these data sets at each RAQM station and the measurements of
the RAQM stations. Furthermore, we have simulated and measured meteorological
parameters at three ground stations. In this paper, we performed the
following analyses with these data sets:
First, we compared the six OMI data sets (level 2) with each other to identify the impact
of the ancillary parameters on the tropospheric NO2 VCDs. We
calculated normalized mean bias (NMB) and coefficient of variance (CV) for
all valid OMI VCDs in the data sets. Since high NO2 values are
particularly important for air pollution studies, we also calculated NMB and
CV for the 10 % highest VCDs based on HKOMI-3. Furthermore, we used
a standard set of typical ancillary parameters (see
Table ) to obtain a better understanding of the
differences between the data sets.
Second, we compared the WRF/CMAQ time series with the ground network measurements using
index of agreement (IOA), Pearson's correlation coefficient r, root mean
square error (RMSE), mean bias (MB), CV and NMB (Table ). In our analysis, we concentrated on two large urban areas which
have the highest NO2 mixing ratios. The first area consists of the
Hong Kong area and the Shenzhen prefecture (HKSZ) and the second area
consists of the Foshan and Guangzhou prefectures (FSGZ). HKSZ includes the
RAQM stations Liyuan, Tap Mun, Tung Chung and Tsuen Wan. FSGZ includes the
RAQM stations Huijingcheng, Luhu Park, Shunde Dangxioa and Wanqingsha.
Third, we validated the OMI data sets (level 3) with ground measurements. The
validation with point measurements is challenging, because OMI measures the
mean value within the ground pixels
(≥ 13 km× 24 km). Since NO2 has
a high spatial variability, area average and point measurement may not agree
well – in particular in an urban area with complex NO2 sources and
sinks. In order to get an idea of the expected deviations, we compared the
time series of the processed CMAQ data set, which have been averaged to the
OMI ground pixels, with the raw CMAQ data set at the 16 stations. Furthermore,
if the VCDs are converted to ground values with the CMAQ profile shapes, the
validation depends on the modelled NO2 profiles which can have large
uncertainties. This was studied by comparing different NO2 profiles.
Finally, OMI and CMAQ data sets were compared to evaluate the regional
CTM with the OMI NO2 products. We studied the spatial distribution of tropospheric
NO2 VCDs in all data sets. Furthermore, time series were analysed for the two urban
areas (HKSZ and FSGZ).
Four-month mean distribution of (a) raw and
(b) processed CMAQ NO2 VCDs, (c) OMNO2-SP
VCDs and (d) the difference to CMAQ, (e) HKOMI-4
VCDs and (f) the difference to CMAQ.
Standard set of ancillary parameters used in the AMF sensitivity study.
Ancillary parameterValuesolar zenith angle (SZA)48∘viewing zenith angle (VZA)0∘ (nadir)relative azimuth angle (RAA)180∘terrain height0.0 kmsurface reflectance0.05aerosol/cloud radiance fraction0.00temperature and pressure profilesWRF averageNO2 and aerosol profilesCMAQ averages (see Fig. )ResultsOMNO2 and HKOMI NO2 retrieval
In this section, we look at the differences between the six OMI
NO2 data sets. After cloud filtering, 56 days with satellite
observations were available covering about 50 % of all days in our study
period. Figure shows two examples of tropospheric
NO2 distributions (level 2). The spatial distributions are similar
between the OMNO2-SP, HKOMI-1 and HKOMI-4 data sets with some differences
between individual pixels. On the other hand, NO2 magnitudes are
quite different between the data sets. Figure c and e shows
the averaged NO2 distribution for whole study period. The averaged
OMNO2-SP data set has a mean value of 0.45×1016cm-2 and
a maximum value of 3.46×1016cm-2, while the HKOMI-4
data set has a mean value of 0.59×1016cm-2 and a maximum
value of 5.04×1016cm-2. The HKOMI-2 data set has the
largest mean (0.70×1016cm-2) and maximum value
(5.50×1016cm-2).
Table shows NMB and CV between the
data sets for all and the 10 % highest VCDs. Since we compared data sets
which only differ by one ancillary parameter, the comparison shows the
influence of each parameter. CMAQ NO2 profiles and MODIS surface
reflectance increase VCDs by about 10 %. When aerosols were included, VCDs
increased between 6 and 24 % depending on the parametrization. If a priori
profiles and aerosols are replaced, the increase of the VCDs is larger for
the 10 % largest VCDs. The HKOMI-4 data set is 31.0 ± 34.0 %
larger than the OMNO2-SP data set.
(a) The averaged MODIS black-sky albedo (BSA) and
(b) the differences between low- and high-resolution BSAs.
Error measures used to compare observations and model.
〈⋅〉 is the mean value and σx and σy are standard deviations.
Index of agreement IOA=1-∑i=1N(xi-yi)2∑i=1N(|xi-〈y〉|+|yi-〈y〉|)2Pearson's correlation coefficientr=1n-1∑i=1Nyi-〈y〉σyxi-〈x〉σxRoot mean square errorRMSE=1N∑i=1N(xi-yi)2Coefficient of variation of the RMSECV=RMSE〈y〉Mean biasMB=1N∑i=1N(xi-yi)Normalized mean biasNMB=MB〈y〉
Ground-level NO2 mixing ratios averaged for
October 2006 to January 2007: (a) CMAQ simulation and
(b) RAQM network measurements. The values between stations
have been estimated by linear interpolation.
Difference between OMI NO2 data sets (level 2) due to different ancillary parameters.
Data setCompared toAll VCDs 10 % highest VCDs Different ancillary parameterNMBaCVaNMBaCVaOMNO2-SWOMNO2-SP+11.413.4+13.214.3a-priori NO2 profileHKOMI-1OMNO2-SW+11.020.9+11.319.4surface reflectancebHKOMI-2HKOMI-1+24.124.1+29.929.9aerosols (case 2)HKOMI-3HKOMI-1+8.19.3+12.212.5aerosols (case 3)HKOMI-4HKOMI-1+6.08.4+9.811.2aerosols (case 4)HKOMI-4OMNO2-SP+31.034.0+38.340.0all
a In percent, b difference are also due to other difference between the OMNO2 and HKOMI retrieval, e.g. temperature profiles.
In order to assess the differences better, we looked at the influence of the
ancillary parameters using typical values. Figure shows a
“clean” and “polluted” NO2 profiles from CMAQ and a profile from
GEOS-Chem (2∘× 2.5∘ spatial resolution) for Hong
Kong . The polluted CMAQ NO2 profile has an
AMF of 0.82 and is about 3 % smaller than the clean profile
(AMF = 0.84). The GEOS-Chem profile has an AMF of 1.19 which is 41 %
larger than the clean and 45 % larger than the polluted CMAQ
profile. To determine the variance of the AMFs, we calculated the AMFs using
all CMAQ profiles. In this sample, the largest AMF is 1.64, due to an
elevated NO2 layer in the upper troposphere, and the smallest AMF is
0.75, due to a heavily polluted ground layer. The sample mean is 0.89 with a
standard deviation of 0.08 (about 9 % of the mean).
Figure a shows the MODIS surface reflectance for a SZA of
48∘. On average, the MODIS reflectance is 0.01 ± 0.02 smaller
than the OMI reflectance (not shown). The BSA is smaller than the OMI
climatology over land but larger over water. In order to identify the
improvement due to the higher spatial resolution, we averaged the BSA to the
lower spatial resolution used in the standard product
(0.5∘× 0.5∘) and subtracted the high-resolution BSA
used in our custom product (Fig. b). The distribution shows
areas with large differences (up to ±0.03) along the urbanized and likely
polluted coastline which would result in large biases (up to ±20 %
for a reflectance of 0.05) in the AMF calculation.
Table shows the AMFs for the different aerosol cases
using the clean and polluted profiles (Fig. ).
Cases 3 and 4 are equal because the CRF is zero in the test parameters. The
Case 2 AMFs are 19 % smaller than Case 1 for the clean and 57 %
smaller for the polluted aerosol profile. If Case 3 is compared to
Case 1, the AMF is reduced between 8 and 14 %
(Table ). In Case 2, the CMAQ ground-level extinction
coefficient is used as constant coefficient in the PBL (0–2 km). The
βext profiles show that using the ground-level extinction
coefficient is reasonable for clean but not for polluted profiles
(Fig. ), because βext is overestimated above
1 km which reduces the AMF because these artificial aerosols are
shading the NO2 layer below.
The AMFs for different aerosol treatment cases for the standard parameters and profiles (Fig. ).
The results of the WRF validation with the automatic weather stations are
tabulated in the Supplement (Table S1). The IOA between observed and
simulated sea level pressure is high (IOA = 0.99) at all stations.
Simulated temperature and humidity also have high agreement with the
observations. The model slightly underestimates temperature and humidity at
HKO and HKIA, while it slightly overestimates temperature and humidity at
WGL. The agreement between modelled and observed wind speed is highest at WGL
(IOA = 0.84), which is located on a remote island, and lower at HKIA
(IOA = 0.68) and HKO (IOA = 0.57), which are located in complex
terrain.
The results of the CMAQ validation with the RAQM network are tabulated in the
Supplement (Table S2). For the 16 stations, the IOAs have an average of
0.52 and vary between 0.29 (Tap Mun) and 0.75 (Tsuen Wan). The average ground
mixing ratios are shown in Fig. . In the
measurements, two major plumes can be identified in HKSZ and in FSGZ. In the
simulations, the FSGZ plume is much less pronounced than in the measurements,
which can be seen in the averaged mean bias, which is -17.6 ppbv
(-44 %) for the four stations in FSGZ. The averaged mean bias is
-0.0 ppbv (-5 %) for the stations in HKSZ.
OMI validation with ground measurements
The comparison between OMI data sets and RAQM stations is impacted by the area
averaging effect. The expected error measures were computed by comparing raw
and processed CMAQ data sets (Table ). The
expected correlation coefficient is very low (r= 0.25) at HKSZ and
good (r= 0.67) at FSGZ. The expected NMBs are -16 and -12 % at
HKSZ and FSGZ, respectively.
The difference between raw and processed CMAQ data due to the spatial resolution of the satellite instrument (area averaging error).
To study additive and proportional differences between satellite and ground
observations, the OMI VCDs were converted to ground-level mixing ratios using
the CMAQ NO2 profiles. The mean conversion factor (V0/(Δz0Vtrop)) is (1.47±0.47)×10-3m-1. The
conversion factor of the clean profiles is about 3 % smaller than of
the polluted profile. The GEOS-Chem profile has a conversion factor of
3.92×10-3m-1 which is more than twice the factor
computed for the CMAQ profiles. As a result, ground-level mixing ratios would
be much larger if the GEOS-Chem profile were to be used for the conversion. The
ground mixing ratio map is shown in Fig. for
29 January 2007 using two different gridding algorithms. The algorithm which
uses parabolic splines does not show discontinuities at the pixel boundaries
and has slightly larger VCDs (Fig. b). The
NO2 variability below the OMI pixel size is caused by the variability
of the CMAQ profile shapes showing the large impact of the model.
OMI ground mixing ratios from orbit number 13513 on
29 January 2007 comparing a (a) “standard” and
(b) newly developed gridding algorithm
. The discontinuous map created by the
standard algorithm is difficult to interpret while the new
algorithms makes an analysis of the spatial distribution easier.
Table shows the statistical measures for the
comparison between the six OMI data sets and the ground network. Statistical
measures for all 16 stations are tabulated in the Supplement (Table S3)
showing that the measures vary considerably between the stations. The
averaged correlation coefficients are similar with no large difference
between the data sets. At FSGZ, r is smaller than the expected value, while
at HKSZ, r is close to the quite small expected value
(Table ). At HKSZ, the HKOMI data sets have a
slightly larger correlation coefficient than the two OMNO2 data sets. The
data sets largely differ in the mean biases. The bias is largest for the
OMNO2-SP and smallest for HKOMI-2 data set. The bias is closest to the
expected bias for the HKOMI-3 and -4 data sets. OMNO2-SP and -SW underestimate
NO2 mixing ratios while HKOMI-2 overestimates the mixing ratios.
OMI evaluation with the RAQM network.
Data setIOArMBaNMBbRMSEaCVbHong Kong and Shenzhen OMNO2-SP0.31+0.19-17.5-5423.473OMNO2-SW0.34+0.10-13.0-4021.667HKOMI-10.43+0.24-8.8-2720.664HKOMI-20.52+0.25-0.7-221.968HKOMI-30.44+0.20-6.3-2020.764HKOMI-40.45+0.24-7.4-2320.162Foshan and Guangzhou OMNO2-SP0.43+0.47-14.3-4820.971OMNO2-SW0.46+0.45-13.1-4420.268HKOMI-10.48+0.41-10.7-3619.265HKOMI-20.58+0.42-5.2-1817.860HKOMI-30.50+0.40-8.5-2918.462HKOMI-40.53+0.46-8.7-2917.960All stations OMNO2-SP0.40+0.35-10.7-4118.773OMNO2-SW0.42+0.29-8.2-3218.271HKOMI-10.46+0.31-5.6-2218.271HKOMI-20.53+0.32+0.7+319.676HKOMI-30.47+0.29-3.3-1318.672HKOMI-40.50+0.35-3.9-1517.668
a In ppbv; b in
percent.
NO2 mean values (in ppbv) at the RAQM network stations for CMAQ, network and OMI data sets.
StationsCMAQCMAQRAQMOMNO2-SPOMNO2-SWHKOMI-1HKOMI-2HKOMI-3HKOMI-4(raw)(processed)HKSZ29.325.132.914.919.323.431.625.924.8FSGZ13.712.029.515.216.518.924.421.020.8All stations18.615.725.314.516.919.425.621.621.0CMAQ evaluation with OMI NO2 data sets
The modelled NO2 VCDs were evaluated with the OMI NO2
data sets. Figure a and b show the difference between the
raw and the processed CMAQ data set. In the processed data set, the spatial
NO2 distribution is smoothed below the size of the OMI ground pixel.
Therefore, CMAQ (processed) and OMI data sets can be directly compared without
artefacts due to different spatial resolutions.
Table shows the mean values at RAQM stations for CMAQ
and OMI data sets. If the model performance were good, CMAQ (raw) should
agree well with RAQM and CMAQ (processed) should agree well with the OMI
data sets. The former were already compared in
Sect. showing a small bias at HKSZ and a large
bias at FSGZ. The results are similar between CMAQ (processed) and HKOMI-4
having similar mean values at HKSZ but a large bias at FSGZ.
The NO2 distribution of the HKOMI-4 data set has one plume at FSGZ and
a second plume at HKSZ (Fig. e). In addition, the OMI
data set has increased NO2 near the northern edge of the model domain
and at the Pearl River in the western part of the domain. The differences
between CMAQ and OMI are shown in Fig. d and f. The
modelled VCDs are underestimated at FSGZ and overestimated at HKSZ. The form
of the plume at HKSZ differs between OMI and CMAQ. In CMAQ, the plume has an
elliptic shape with a strong, southwestern outflow. In the OMI data sets,
this feature is less distinct.
The results of the time series analysis are shown in
Table . The correlation coefficient is smaller at FSGZ
than at HKSZ. At FSGZ, CMAQ VCDs are much smaller than the HKOMI-4 VCDs
(NMB =-40 %). This bias is close to the bias between CMAQ and RAQM
(-44 %). On the other hand, CMAQ VCDs are larger than the OMI VCDs
(+15 %) at HKSZ.
Evaluation of the time series of CMAQ with OMI NO2 VCDs in the two areas marked in Fig. .
Data setOMI MeanaIOArMBaNMBbFoshan and Guangzhou (area) OMNO2-SP2.50.570.37-0.5-18OMNO2-SW2.80.570.39-0.7-26HKOMI-13.10.550.35-1.1-35HKOMI-24.00.510.35-2.0-49HKOMI-33.50.560.43-1.5-42HKOMI-43.40.540.37-1.3-40Hong Kong and Shenzhen (area) OMNO2-SP1.70.510.57+1.2+73OMNO2-SW2.20.640.59+0.8+37HKOMI-12.40.650.51+0.6+24HKOMI-23.20.660.45-0.2-8HKOMI-32.70.750.58+0.2+9HKOMI-42.60.710.56+0.4+15
a In 1016moleculescm-2; b in percent.
DiscussionsInfluence of ancillary parameters
Ancillary parameters have a large influence on the retrieval of tropospheric
NO2 VCDs. In our study, their influence on the AMFs is similar to
findings for other retrieval algorithms
e.g.. A direct
comparison is difficult because the influence depends on regional factors.
For example, we found a very strong gradient in surface reflectance along the
coastline in the PRD region which has a large influence on tropospheric
NO2 VCDs. In other regions, the variability of surface reflectance
can be smaller, influencing VCDs less.
In our retrieval, CMAQ NO2 profiles were used which have a quite
different shape compared to GEOS-Chem profiles (Fig. ).
Since NO2 profile measurements were not available for the PRD region,
the modelled profiles were not validated in this study. As a consequence, AMF
uncertainties due to NO2 profiles are difficult to quantify. However,
it can be argued that the regional CMAQ CTM provides more accurate vertical
distributions than the global model, because of higher spatial resolution and
more detailed PBL, vertical advection and diffusion schemes. On the other
hand, different schemes exist for regional CTM and these might result in
different NO2 profiles e.g.. Therefore,
the validation of model profiles is important to better estimate the
uncertainties for model evaluation with satellite observations. In this
study, the CMAQ NO2 profiles have a strong vertical gradient in the
PBL which is expected for strong emissions near the surface. The qualitative
shape of the profile agrees reasonably with measured and simulated urban
NO2 profiles used in other studies
e.g..
Aerosols have a large influence on the retrieved VCDs. In the HKOMI
retrieval, good agreement with ground measurements was found when calculating
βext profiles and reducing CRF based on AOT in the model
(Case 4). βext calculation and CRF reduction are based on
empirical formulas which have the advantage that they are easy to use.
However, the IMPROVE formula was not developed to calculate
βext profiles in an urban environment which can result in
uncertainties. An improved IMPROVE formula is available which we consider to
use in an updated version of our retrieval . Alternatively,
optical properties can be calculated by Mie theory. The aerosol information
was not validated with ground- or satellite observations which is also
planned to be implemented in an updated version. Nevertheless, the shape of
the aerosol profiles is in reasonable agreement with LIDAR measurements in
Hong Kong . The empirical cloud correction formula is also
very simple and, as mentioned before, should be improved in future. In
conclusion, the impact of aerosols and clouds is an important factor for
unbiased retrieval of tropospheric NO2 VCDs. Unfortunately, a
complete treatment of aerosols and clouds is complex and often not feasible
for air quality model evaluations.
The OMI surface reflectance climatology provides scene reflectance, which
includes both the surface and the presence of boundary layer haze or
aerosols, and minimum reflectance, which is the lowest retrieved reflectance
value . Since scattering by weakly absorbing
aerosols at or below an NO2 layer increases AMFs, it has been argued
that scene reflectance should be used for satellite-based trace gas
retrieval, if no information about aerosols is available
. Furthermore, the minimum reflectance can be
underestimated due to ground- and cloud shading or darkening by rainfall. For
these reasons, scene reflectance is used in the standard product
. However, if an NO2 layer is mixed with
highly absorbing aerosols, the AMF is not increased but decreased
. In the HKOMI retrieval, we have highly absorbing
(ω0=0.82) urban aerosols and thus AMFs are decreased in the
presence of aerosols. As a result, if scene reflectance is used for an urban
area, NO2 VCDs can be strongly underestimated.
Since ancillary parameters have a large influence on the VCDs, it is
important to quantify their uncertainties. This is often difficult because
vertical profiles are required which are rarely available. In the view of
model evaluation, it is also helpful if surface reflectance, aerosols and
clouds are provided independently, because it makes it possible to use modelled aerosol
profiles in the retrieval.
WRF/CMAQ validation with ground measurements
The evaluation of the meteorological fields shows good agreement between
model and observations within the expected limitations due to the model
resolution. The agreement is lower for the wind fields due to the impact of
local topography. Unfortunately, no meteorological data for the whole PRD
region were available. However, due to the high agreement in Hong Kong,
similar model performance is expected in the complete model domain. The
meteorological fields are sufficient to provide input for the chemistry
transport simulations.
The agreement between CMAQ and RAQM network is low for various reasons.
First, the point measurement may not be representative for the grid cell
because of the influence of local sources and sinks as well as local
topography and station height. Second, the differences can arise from the
model due to inaccurate wind fields, limited parametrization of the chemistry
and, in particular, insufficient knowledge about strength and distribution of
emissions. The model performance is similar to the result by
who also evaluated CMAQ in the PRD region using a similar emission inventory.
The low model performance can largely be explained by problems with the
emission inventory. For example, the low IOA at Tap Mun is the result of
several peaks in the simulated time series which were not found in the
measurements. The peaks were traced back to NOx emissions at the Dapend
Peninsula about 15 km east of Tap Mun. As a second example, the large
bias between model and observations at FSGZ is mainly due to underestimated
NOx emissions in this region. The smaller bias in HKSZ is thought to be
the result of the updated emission inventory with more accurate information
for Hong Kong. It should be noted that updated emission inventories exist for
the PRD region which would improve the model performance. However, our
objective was not an accurate CMAQ simulation but a model evaluation with
satellite observations. For this application, the used emissions inventory
was found to be very useful.
OMI validation with ground measurements
The OMI data sets were validated with the RAQM network. The HKOMI retrieval
does not change the correlation coefficient considerably compared to the
standard products, while they are generally increased in other custom products
e.g.. The non-existent
improvement has two main reasons. First, the low CTM performance can result
in random errors impacting ancillary parameters and conversion factors.
Second, ground-based point values were compared with satellite-based
area-averaged values in an urban area with high NO2 variability. The
expected discrepancies between ground- and satellite-based observations were
estimated using CMAQ NO2 distributions showing that the expected
correlation coefficients were quite small reducing possible improvements.
On the other hand, the HKOMI retrieval significantly reduces the bias between
ground- and satellite-based observations. However, systematic errors in the
parameters can still cause biases in the data sets. For example, if the model
underestimates emissions, NO2 and βext profiles might
be described better by the polluted than the clean profiles
(Fig. ). As a consequence, the AMFs are reduced by about
9 % and the conversion factor by about 3 %. Therefore, NO2
mixing ratios would be underestimated by 10 to 20 % which can partly
explain the large NMB at FSGZ.
Besides these limitations, the HKOMI retrieval shows the possibility for
unbiased NO2 observations using satellite instruments. Of the four
aerosol cases, HKOMI-4 performed best with the highest correlation
coefficient and the smallest NMB. Furthermore, HKOMI-4 makes the most
reasonable assumptions, although very simple, about aerosols and clouds.
Correlation coefficients and mean biases are expected to improve further with
better CTM performance.
Model evaluation and further applications
The model evaluation with OMI NO2 data sets demonstrated that it is
possible to study the spatial distribution and the magnitude of NO2
VCDs with satellite observations. The biases between CMAQ and HKOMI-4 are
consistent with the bias found between CMAQ and RAQM giving us further
confidence that the HKOMI-4 retrieval results in smaller systematic errors.
Small systematic errors are important for various applications.
In air quality studies, satellite observations can be used to obtain ground-level NO2 concentrations e.g.. To estimate the
impact on human health, these concentrations need to be unbiased because
otherwise the air quality is misinterpreted. This is particular important in
polluted areas, where NO2 VCDs can be biased due to the presence of
absorbing aerosols. Our retrieval reduces this bias in particular for high
NO2 values making it more suitable for satellite-based air quality
studies in urban areas.
Satellite observations can also be used for estimating NOx emissions by
inverse modelling e.g.. Systematic errors in
the satellite observations can cause biases in the derived emission inventory
e.g.. To reduce this problem, derived
emissions by iteratively updating the a priori NO2 profiles used in
satellite retrieval. Their method reduces differences between emissions
derived from GOME-2 and OMI. However, we and other showed that NO2
VCDs can still be biased due to surface reflectance, aerosols and clouds.
Therefore, we think it is necessary to also update AKs in the inverse method
and to characterize the spatiotemporal distribution of systematic errors in
the satellite retrieval. The latter was done, for example, for the retrieval
of carbon dioxide .
Conclusions
In this paper, we evaluated biases in a regional CTM
(3 km× 3 km spatial resolution) with ground- and
satellite-based NO2 observations. Atmospheric chemistry was simulated
with the CMAQ modelling system and ground measurements were taken from the
RAQM network. Six OMI NO2 data sets were compiled with NASA's standard
retrieval (OMNO2-SP and -SW) and our custom retrieval (HKOMI-1 to -4). In the
OMNO2-SW data set, a priori NO2 profiles were replaced by CMAQ
profiles using the scattering weights provided with the product. In the HKOMI
retrieval, we recalculated tropospheric AMFs using updated ancillary
parameters of a priori NO2 profiles, surface reflectance and aerosol
profiles. The HKOMI data sets differ in how aerosols were implemented in the
retrieval.
The updated ancillary parameters increased tropospheric NO2 VCDs by
(+11.4±13.4) % (a priori NO2 profiles), (+11.0±20.9) % (surface reflectance) and (+6.0±8.4) % (HKOMI-4
aerosol parametrization). As a result, the normalized mean bias (NMB) between
satellite and ground observations was significantly reduced from -41 %
(OMNO2-SP) to -15 % (HKOMI-4).
The remaining biases can be explained by CMAQ model biases, resulting from
the smoothed model terrain, the finite layer structure, emission errors etc.,
and the area averaging effect, i.e. OMI's inability to resolve complex
structures due to its ground pixel size. If only the a priori profiles are
replaced in the standard product, which is recommended for model evaluation
studies, the NMB is only reduced to -32 %.
Since ancillary parameters have such a strong influence on the VCDs, the
parameters need to be well known to obtain accurate NO2 VCDs and to
estimate their uncertainties. In our study, NO2 and aerosol profile
observations were not available making it difficult to estimate uncertainties
and their impact on the AMF calculations. Future studies are necessary to
address these limitations.
The CTM performance was low mainly due to the underestimated emissions
causing a large bias in Foshan and Guangzhou (NMB =-40 %).
However, the results the model evaluation with the RAQM network and the OMI
NO2 data sets were consistent. We also estimated that our custom
retrieval could still underestimate VCDs by 10 to 20 % in some areas
which is important to remember when validating a model with satellite
products. In general, we expect an improved HKOMI product with better CTM
simulation.
To conclude, our study demonstrates that the data sets created by the HKOMI
retrieval are suitable for the evaluation of the spatial distributions and
the magnitudes of NO2 concentrations in the model. We showed that
a retrieval, which updates not only a priori NO2 profiles, reduces
the biases in urban areas. Since the ancillary parameters have a large impact
on the retrieval, tools need to be developed to keep track of their influence
on the final product. Such tools could improve CTM evaluations with satellite
observations.
The Supplement related to this article is available online at doi:10.5194/acp-15-5627-2015-supplement.
Acknowledgements
The work described in this paper is partly funded by the Guy
Carpenter Asia-Pacific Climate Impact Centre (project no. 9360126),
a grant from the Research Grant Council of Hong Kong (project
no. 102912) and the start-up grant from City University of Hong Kong
(project no. 7200296). Edited by: A. B. Guenther
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