AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus GmbHGöttingen, Germany10.5194/amt-8-4055-2015Development and characterisation of a state-of-the-art GOME-2
formaldehyde air-mass factor algorithmHewsonW.BarkleyM. P.mpb14@le.ac.ukGonzalez AbadG.https://orcid.org/0000-0002-8090-6480BöschH.KurosuT.SpurrR.TilstraL. G.https://orcid.org/0000-0003-1282-6582EOS Group, Department of Physics and Astronomy, University of Leicester, Leicester, UKAtomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USANASA Jet Propulsion Laboratory, Pasadena, CA, USART Solutions Inc, Cambridge, MA, USARoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsM. P. Barkley (mpb14@le.ac.uk)5October20158104055407411August201427January201529August201511September2015This 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://amt.copernicus.org/articles/8/4055/2015/amt-8-4055-2015.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/8/4055/2015/amt-8-4055-2015.pdf
Space-borne observations of formaldehyde (HCHO) are frequently used to
derive surface emissions of isoprene, an important biogenic volatile
organic compound. The conversion of retrieved HCHO slant column
concentrations from satellite line-of-sight measurements to vertical
columns is determined through application of an air mass factor (AMF),
accounting for instrument viewing geometry, radiative transfer, and
vertical profile of the absorber in the atmosphere. This step in the
trace gas retrieval is subject to large errors. This work presents the
AMF algorithm in use at the University of Leicester (UoL), which
introduces scene-specific variables into a per-observation full
radiative transfer AMF calculation, including increasing spatial
resolution of key environmental parameter databases, input variable
area weighting, instrument-specific scattering weight calculation, and
inclusion of an ozone vertical profile climatology. Application of
these updates to HCHO slant columns from the GOME-2 instrument is
shown to typically adjust the AMF by ±20 %, compared to a
reference algorithm without these advanced parameterisations.
On average the GOME-2 AMFs increase by
4 %, with over 70 % of locations having an AMF of 0–20 % larger than originally,
largely resulting from the use of the latest GOME-2 reflectance product.
Furthermore, the new UoL algorithm also incorporates a full radiative
transfer error calculation for each scene to help characterise AMF
uncertainties. Global median AMF errors are typically 50–60 %, and
are driven by uncertainties in the HCHO profile shape and its vertical distribution relative to clouds and aerosols.
If uncertainty on the a priori HCHO profile is relatively small (< 10 %) then
the median AMF total error decreases to about 30–40 %.
Introduction
Formaldehyde (HCHO) is produced in the atmosphere from the oxidation of a
wide range of volatile organic compounds (VOCs), emitted from human
activities, vegetation and biomass burning . Direct
HCHO emissions from vegetation and industry are additional minor sources. The
main sinks of HCHO are photolysis and reaction with the hydroxyl
radical (OH), which give it a short atmospheric lifetime of only a few hours,
thus making it an important tracer of localised active photochemistry and a
useful proxy for determining underlying surface VOC emissions. In particular,
there has been widespread use of satellite measurements of HCHO integrated
columns to constrain the emissions of isoprene, the dominant biogenic VOC
(BVOC) emitted from terrestrial vegetation and a high HCHO yield precursor,
at both regional and global scales
e.g. .
However, reducing uncertainties associated with inferred (or top-down)
emission estimates depends critically on the accuracy of the retrieved HCHO
column observations .
Tropospheric vertical HCHO columns have been retrieved by a number of
groups from solar backscatter instruments such as GOME
,
SCIAMACHY , OMI
and GOME-2 . This process typically
involves three stages. First, HCHO slant columns along the instrument
line of sight are obtained via the spectral fitting of trace gas
absorption cross sections to observed UV radiance measurements
(typically in the wavelength range ∼ 325–360 nm). Second, observed
HCHO column residual biases (e.g. due to ozone interference) over the
remote Pacific Ocean are then removed using a standard reference
sector correction e.g. . Lastly,
the slant columns are divided by an air mass factor (AMF) to produce
geophysical HCHO vertical columns (independent of the satellite
viewing geometry), which are then re-normalised using the HCHO
background field from a chemical transport model. Reported final
errors on gridded monthly mean vertical columns are approximately
20–60 % , depending on
the instrument and averaging method.
Over the oceans and regions with low HCHO, the vertical column error
is mainly influenced by the slant column fitting error, whereas over
continental enhancements, the errors associated with the AMF become
more relevant. Given the primary use of HCHO columns is to constrain
surface VOC emissions, it is therefore important to fully characterise
the AMF and its error for each individual instrument and retrieval
. The AMF represents observational sensitivity
along the light path, relative to the vertical, accounting for the
atmospheric and measurement state . It is generally
computed by a multiple-scattering radiative transfer model, using a
priori information on aerosols, clouds, the HCHO vertical profile and
surface reflectance, with the uncertainty of each influencing the
final AMF error. Past studies, which have examined the HCHO AMF
sensitivity to these parameters, show the approximate errors associated
with aerosols are 20–50 %, clouds 20–30 %, and surface reflectance
20 % see
e.g. . AMF errors
arising from the HCHO profile vary depending on its relative vertical
distribution to aerosols and clouds, but are of the order of 20–40 %
. The HCHO profile is also subject
to chemistry transport model (CTM) errors, such as choice of BVOC
emission inventory or chemical reaction scheme, which affect its
accuracy .
There is, therefore, a pressing need to improve AMF calculations and
reduce uncertainties wherever possible. Accordingly, this paper
details a new algorithm, which attempts to improve the accuracy of
HCHO AMFs by performing scene-specific full-radiative transfer
calculations and through more advanced treatment of the input a priori
information. Furthermore, the algorithm includes a full radiative
transfer error calculation for each observation, to help quantify AMF
uncertainties and their corresponding spatial and temporal variation.
The new AMF algorithm is applied to retrieved GOME-2 HCHO slant
columns, to determine its subsequent impact on the tropospheric HCHO
vertical columns.
The paper is structured as follows. Sections 2 and 3 provide an
overview and a brief review of contemporary UV–Vis AMF calculations,
respectively. Section 4 describes the default University of Leicester
(UoL) GOME-2 AMF scheme, which establishes a reference to assess
subsequent AMF updates. Section 5 outlines the major updates to the
UoL AMF algorithm and assesses their subsequent impact. An assessment of
AMF errors is presented in Sect. 6. The paper concludes with a short
summary.
Calculation of UV–Vis AMFs
Comparison of three different contemporary HCHO AMF calculations. Readers are referred to cited references for full details.
The air mass factor for a given observation is defined as the ratio of
the trace gas slant column density to its vertical column density. In
a non-scattering atmosphere, the satellite viewing geometry dictates
the light path and hence a geometrical air mass factor (AMFG) can be
calculated by
AMFG=1cosθSZA+1cosθVZA,
where θSZA and θVZA are the
solar-zenith and viewing-zenith angles, respectively. In the real
atmosphere, Rayleigh scattering and scattering from aerosols and
clouds strongly influence the photon path length. To account for
these effects, current UV–Vis trace gas retrievals typically calculate
AMFs using the approach of , which decouples
atmospheric scattering from the trace gas vertical profile, via
AMF=AMFG∫0∞w(z)S(z)dz,
where w(z) are scattering weights that represent the sensitivity of
the backscattered radiance to the absorber abundance at each altitude,
and S(z) is a normalised shape factor that describes the trace gas
vertical distribution. The scattering weights are defined as
w(z)=-1AMFGα(z)αe∂(lnI)∂τ,
where α(z) is the absorption cross section, αe is the effective absorption cross section,
(a weighted average over the tropospheric column), and ∂τ is the incremental optical depth.
The scattering weights are computed in a similar approach for clear and cloudy conditions using a
radiative transfer model (RTM), and are a function of wavelength
(λ), surface pressure (Ps), surface albedo (A) and the
solar/viewing geometry; the shape factor is usually provided by an
offline CTM. For cloudy conditions, cloud fraction and cloud-top pressure are also inputs, usually taken from the
appropriate satellite cloud algorithm. To account for partially cloudy scenes the approach of
is commonly adopted, which assumes the total AMF
is the reflectivity-weighted average of the air mass factors for the
clear (AMFclr) and cloudy (AMFcld) pixel sub-scenes.
Calculation of accurate AMFs therefore requires each retrieval to
select the best available a priori information, and the most suitable
RTM and CTM. In the next section different approaches for calculating
the AMFs are discussed.
Comparison of four different contemporary NO2 AMF calculations. Readers are referred to cited references for full details.
Instrument(s)OMIOMIOMIGOME-2ApplicationGlobalRegionalRegionalGlobalRTMKNMI DAKLIDORT v3.6TOMRADLIDORT v3.3CTMTM4GEOS-ChemWRF-ChemMOZART v2Global 2∘× 3∘ gridNested 0.5∘× 0.67∘ gridRegional 4 km × 4 km gridGlobal 1.85∘× 1.85∘ gridA priori profileTM4GEOS-CHEMWRF-ChemMOZARTdaily profilesdaily profilesmonthly mean profilesmonthly mean profilesSurface pressureTM4 (2∘× 3∘)GEOS-Chem (0.67∘× 0.5∘)WRF-Chem (4 km × 4 km grid)MOZART (1.85∘× 1.85∘)adjusted by mean elevationadjusted by mean elevationadjusted by mean elevationadjusted by mean elevationSurface elevationDEM-3kmGMTED2010GLOBE 1 km × 1 kmGOTOPO30 1 km × 1 kmSurface albedoMODIS MCD43C2 BDRFMODIS MCD43C2 BDRFmonthly climatology16-day average16-day averagemonthly climatologyat 0.5∘× 0.5∘at 0.05∘× 0.05∘at 0.05∘× 0.05∘at 1∘× 1.25∘(λ= 440 nm)(λ= 440 nm)(λ= 342 nm)(λ= 380 and 440 nm)Temporal interpolation onlyTemporal interpolation onlyArea-weightedArea-weighted andtemporal interpolationAerosol correctionImplicit treatmentGEOS-Chem dailyImplicit treatmentImplicit treatmentusing cloud algorithmAOD profilesusing cloud algorithmusing cloud algorithm(AODλ= 438 nm)Adjusted by AERONET,MAX-DOAS and MODISPixel calculationInterpolated fromRTM calculationInterpolated fromInterpolated fromlook-up tablefor each scenelook-up tablelook-up tableCurrent AMF algorithms
While the basic method of calculation mostly remains the same for all
AMFs i.e. that of, AMF algorithms differ widely
in the temporal and spatial resolution of a priori databases, choice
of RTM, and their treatment of aerosols.
Brief summaries of state-of-the-art HCHO and analogous tropospheric
nitrogen dioxide (NO2) AMF algorithms are presented in
Tables and , respectively.
The importance of using an accurate and spatially resolved surface
reflectance product in AMF calculations has been cited as one of the
most significant factors in reducing AMF error
.
Highly reflecting surfaces increase measurement sensitivity to
boundary layer trace gases, whereas the converse is true for dull
surfaces.
Thus, an ideal albedo data set must resolve fine-scale features,
otherwise calculated AMFs will be erroneous.
For example, a MODIS 0.05∘× 0.05∘ 16-day mean
albedo product is utilised in the OMI NO2 retrieval by
, since it removes artificial NO2 features
evident when a coarser GOME 1∘× 1∘ albedo
climatology is used.
Albedo data sets currently available include: ,
, or a combination of the two,
e.g. , and the monthly
climatology derived from OMI. Until recently, a GOME-2 albedo data set was not
available; surface reflectances applied for its HCHO AMF calculations were derived from
satellite instruments with different observation times and viewing geometries
e.g. . However, there now exists an opportunity to use
scene-specific albedos consistent with GOME-2, using the new GOME-2 surface Lambertian-equivalent reflectance (LER) product developed by .
Trace gas a priori profiles are usually taken from a CTM, or
alternatively a climatology.
Retrieval groups differ in their CTM choice, whose complexity often
varies, using spatial resolutions ranging from a few km2 in regional studies to
4 × 5∘ for global use .
Monthly mean or daily profiles can be used, although the latter are
expected to provide a more detailed evolution of tropospheric
chemistry. For example, found NO2 AMF
uncertainties of about 10 % due to monthly CTM fields by comparing
against daily values calculated over the same time period. Most
advanced AMF schemes also adjust the trace gas profile according to
the mean elevation over the satellite footprint to remove biases
arising from inaccurate terrain pressure, using the surface pressure
correction devised by . Studies have shown that for
NO2 this correction can cause differences of about ±20 %
in the tropospheric column
.
Aerosol scattering and absorption can have significant impacts on HCHO
observations .
In particular, biomass burning aerosols distributed high above the
boundary layer can artificially enhance retrieved tropospheric vertical columns
by up to 50 % .
Current algorithms either explicitly correct for aerosol effects using
modelled aerosol optical depth (AOD) profiles and properties
e.g. or, alternatively,
implicitly rely on corresponding cloud algorithms to correct for their
presence e.g. .
The presence of aerosols also affects the retrieval of cloud parameters.
For example, low non-absorbing aerosol layers tend to result in an overestimation of
cloud fraction and underestimate of cloud height , whereas for
strongly absorbing aerosols located above clouds, the retrieved cloud pressure may represent
the height of the aerosol layer rather than the height of the clouds .
Uncertainties in cloud fraction and height also affect AMF accuracy.
For a cloud fraction of 0.5, estimates of HCHO AMF uncertainty can range from 30 % in the
presence of high cloud (8 km) to 50 % in low cloud conditions (< 2 km) .
Analysis of measured and modelled HCHO AMFs by
determined slightly lower biases of 10–21 % for cloud fractions 30–60 %.
For NO2, estimated AMF uncertainties of 0–30 % arising from errors in
cloud fraction and < 10 % from cloud height. Hence cloud-induced AMF errors are expected to
be in the range 20–30 % for cloud fractions typically less than 40 %,
depending on the observation conditions and cloud product accuracy.
AMFs for UV–Vis trace gas retrievals must also account for the absorption of UV radiation by ozone
. Typically a single ozone profile (e.g. US standard atmosphere) is used in the
calculation of the scattering weights, with the effects of natural ozone variability not considered.
However, in their calculation of SCIAMACHY and OMI SO2 AMFs, scaled the US total
column using coincident measurements of retrieved ozone column. They found that the magnitude of the ozone correction on SO2 AMFs
can be large (>30 %) under certain conditions, indicating that groups should evaluate this effect in their AMF algorithms.
Finally, retrievals either derive AMFs from pre-calculated look-up
tables (LUTs), or calculate an individual AMF for each
observation. AMFs derived from full RTM calculations are expected to be more accurate
since they tend to incorporate more representative a priori
information and do not suffer from potential LUT interpolation errors;
however, their calculation often requires considerable computational
expense. Additionally, AMF errors are either estimated from error LUTs
e.g. , sensitivity studies
e.g. , or in the worst case simply quoted from
relevant past studies, rather than being explicitly calculated by the
RTM for each observation over the region of interest.
The UoL GOME-2 HCHO retrievalSlant column retrieval
GOME-2 HCHO slant columns used in this work come from
. In brief, slant columns are calculated with the
DOAS method , using the QDOAS analysis package
. The cross sections of HCHO and interfering
absorbers (BrO, O3 and NO2), as well as Ring and
undersampling contributions, are fitted to GOME-2 measured line-of-sight radiances after removal of broadband absorption terms with a fifth-order polynomial. Biases in the slant columns are removed using a
reference sector method, by fitting a daily latitudinal polynomial to
measured HCHO columns over the Pacific Ocean, between 170 and 140∘ W. This latitudinal area corresponds to a region where the
only background levels of HCHO occur due to methane oxidation. The
polynomial is subtracted from all global measurements, and then AMFs are
calculated and applied to obtain vertical columns, which are then
re-normalised to expected background concentrations through the
addition of corresponding model HCHO columns
from the same Pacific region. In the UoL retrieval, the model fields
are provided by the GEOS-Chem CTM, as described in
. Details of the GEOS-Chem simulation and the
baseline University of Leicester (UoL) AMF algorithm, which explicitly
calculates an AMF for each observation, are discussed in the next
sections.
GEOS-Chem chemical transport model
The GEOS-Chem chemical transport model (version 08-03-01) is used to
simulate tropospheric chemistry at global and regional scales, and to
provide daily a priori tropospheric HCHO and AOD profiles, appropriate
to GOME-2's local overpass time (09:00–10:00). The model is driven by
meteorological fields provided by NASA's Goddard Earth Observing
System version 5 (GEOS-5) assimilation system , which are
available at a native spatial resolution of 0.5∘ latitude × 0.67∘ longitude, and with 72 vertical
pressure levels from the surface to 0.01 hPa. However, the resolution
of the GEOS-5 data is degraded accordingly to
2∘× 2.5∘ and 4∘× 5∘, to run
GEOS-Chem globally at medium and coarse spatial scales. Additionally,
over tropical South America where isoprene emissions are large and
HCHO columns high, GEOS-Chem is employed in a one-way nested grid
mode, utilising GEOS-5 default resolution to better resolve features
in this key region see. To ensure consistency,
boundary conditions for nested South America simulation are provided
by the 4∘× 5∘ model run. In each model
configuration the vertical dimensions are also degraded to 47 pressure
levels, with the lowermost layers of the model (surface ≤ 2 km)
approximated by 14 layers.
GEOS-Chem simulates tropospheric photochemistry taking into account
major chemical species (O3, NOx and VOCs) and aerosol
interactions, with a reaction scheme which consists of about 400
reactions and 80 species based on the work of
and . Relevant
input emission inventories include the MEGAN biogenic VOC database
, EDGAR anthropogenic emissions
, and the Global Fire Emissions Database v2
. Anthropogenic emissions are overwritten with more
detailed regional inventories where possible, as described in
. A detailed account of the tropical South
America simulation, including updates to the chemical and dry
deposition schemes which are applied in all simulations, can be found
in .
Baseline AMF calculation
Monthly mean GOME-2 HCHO air mass factors (AMFs) and
corresponding vertical columns (VCDs) for March and August 2007,
calculated using the UoL baseline AMF algorithm (see Sect. 4.3) and
gridded to 0.25∘× 0.25∘ using observations
with cloud fractions < 40 %.
The baseline UoL AMF calculation uses daily data from the global
GEOS-Chem 4∘× 5∘ simulation, with model quantities
sampled at the same time and location of each observation.
In this study the scattering weights and sub-scene reflectivities are
generated for each observation with the LIDORT v2.3 radiative transfer
model , following and
.
In addition to HCHO, other atmospheric profiles used within LIDORT
include GEOS-Chem AOD profiles (for mineral dust, tropospheric
sulfate, black carbon, organic carbon and sea salt), and also
US standard atmosphere O3 and NO2 profiles.
AMFs are computed at a wavelength of 340 nm, representative of the
DOAS HCHO slant column fitting region (328.5–346 nm)
, and consistent with the
Lambert equivalent reflectivity database used at
340 nm, and CTM AODs at 340 nm calculated with physical aerosol
optical properties based on the study of .
Cloud fraction and cloud-top pressure are taken from the most recent
version of the GOME-2 FRESCO+ cloud product ,
using the MERIS albedo climatology for surface
reflectivity values in the O2 A-band retrieval.
FRESCO+ does not calculate cloud optical
thickness (COT) values, thus clouds are treated as Lambertian
reflectors with an albedo of 0.8, a method consistent with other
studies e.g. .
Monthly climatological maps of the ∼ 360 nm surface albedo, taken
from the TOMS LER database (November
1978–May 1993) generated by , are re-gridded to
match the GEOS-Chem grid and used in clear-sky conditions.
Following , we account for aerosols in the AMF
calculation by representing within the LIDORT model their vertically
resolved optical properties from the GEOS-Chem simulation described in
Sect. .
In practice, height-resolved AODs are used for the aerosol extinction
(i.e. per km); for aerosol scattering the AODs are weighted by the
appropriate single-scattering albedo (SSA) of that aerosol type.
Aerosol optical properties (black and organic carbon aerosols, mineral
dust, sulfate, sea salt and water vapour) are based on the GADS
(Global Aerosol Data Set) data .
Tabulated values are calculated offline and implemented into
GEOS-Chem, as described in , with the same values used directly in the AMF
computation.
In the AMF calculation itself, a humidity of 70 % is assumed and we
use values specific to 340 nm, of the extinction efficiency, effective
radius, SSA and the first eight terms in the Legendre expansion of the
phase function (π). At 340 nm, the SSAs of the aerosols types are 0.2342 (black carbon),
0.9861 (organic carbon), 0.8394 (dust), 1.0 (sulfate), 1.0 (sea salt) and 1.0 (water cloud), respectively.
Using these default settings, scene-specific GOME-2 AMFs are
calculated for March and August 2007, months chosen to both reflect
the range of expected tropospheric HCHO concentrations, and provide a
reference for subsequent comparisons. Figure shows gridded monthly mean AMFs and HCHO
vertical columns calculated from the reference sector corrected slant
columns derived for the two selected months. Calculated AMFs are 0.55–3.68 over the ocean, and 0.61–3.68 over
land. Observed HCHO columns in March are generally low, whilst in August
seasonal enhancements are evident over southeast USA and the Amazon
rainforest, features consistent with other GOME-2 retrievals
.
Model HCHO vertical columns over the Amazon simulated by GEOS-Chem at three different spatial
resolutions (left to right: 4∘× 5∘, 2∘× 2.5∘, 0.5∘× 0.667∘).
Overlain in black are three typical orbital tracks showing the footprint of each GOME-2 observation
with cloud fraction < 40 %.
Vertical profiles of HCHO, black carbon and organic carbon (AOD) simulated by
GEOS-Chem at 4∘× 5∘ (red solid circles), and
0.5∘× 0.667∘ (blue solid circles) for two different
locations and times. The red solid squares show the monthly mean
4∘× 5∘ profile used in the AMF LUT approach discussed
in Sect. .
Top row: spatial maps of monthly mean AMF differences for
March and August 2007, relative to the default UoL AMF algorithm,
resulting from the use of atmospheric profiles from the GEOS-Chem
0.5∘× 0.67∘ nested grid simulation, as outlined in
Sect. . The AMFs are gridded on to a
0.25∘× 0.25∘ grid using observations with cloud
fractions < 40 %. Bottom row: the corresponding histograms of the
AMF differences for these two months are shown in blue. The histogram
of global AMF differences arising from the use of atmospheric profiles
from GEOS-Chem's 2∘× 2.5∘ simulation is shown in
red. Also shown are histograms resulting from the area-weighting
(green) and surface pressure correction (aqua) of the
0.5∘× 0.67∘ nested grid profiles, as discussed in
Sects. and respectively.
Note the closeness of lines detailing derivatives of the high-resolution
0.5∘× 0.67∘ grids.
Comparison of baseline AMF versus LUT AMFs
Previously, monthly averaged GEOS-Chem profiles have been
used to compute AMF LUTs for SCIAMACHY, OMI and GOME-2, as a function of location
and viewing geometry, and also surface reflectance; see e.g.
and . Whilst AMF LUTs can be calculated and applied reasonably fast,
they suffer from unavoidable interpolation errors. To quantify this error source, AMF LUT tables
were computed using the same GEOS-Chem 4∘× 5∘ model output, applied to the
GOME-2 data, and then compared to the baseline AMF algorithm detailed in Sect. .
Figure S1 in the Supplement shows the spatial maps and histogram of the AMF differences. For both March and
August of 2007, nearly 90 % of locations have AMF differences of up to ±10 %, hence
advocating the use of daily model profiles and full RTM calculations. The 1σ width
of fitted exponential functions to the histograms are 4 and 5 % for March and August, respectively. The AMF differences
are attributed to difference in HCHO and AOD vertical profiles, as discussed further in
Sect. . Similar AMF differences, due to the use of monthly versus daily profiles, were also found for NO2 by .
UoL AMF algorithm updatesOverview
To improve the UoL AMF algorithm six main updates have been applied
and evaluated.
These are: (1) assessment of different GEOS-Chem grid resolutions; (2) area weighting
of a priori inputs to match the satellite footprint;
(3) application of the terrain correction;
(4) an upgrade of the surface albedo database; (5) the HCHO and ozone
absorption cross sections within LIDORT have been changed to match
those used in the slant column retrieval, and are adjusted to account
for change of GOME-2's slit function over time and also for
temperature effects, and finally (6) the US Standard O3
vertical mixing ratios are replaced with climatology-based values and
scaled with coincident GOME-2 total column O3 observations.
The results of these improvements are as follows.
Impact of GEOS-Chem grid resolution
Low-resolution input databases can lead to inaccurate AMF calculations
due to misrepresentation of small-scale surface features, especially
over rapidly changing terrain such as land–sea boundaries and
mountainous regions .
A nominal GOME-2 pixel covers a 80 × 40 km2
footprint on the Earth's surface, considerably smaller than the
default GEOS-Chem 4∘× 5∘ simulation, as shown in
Fig. .
Reducing potential errors from this mismatch in spatial scale
requires the use of a priori information at spatial resolutions
equivalent to, or higher than, the satellite footprint.
Hence, in addition to the coarse 4∘× 5∘ simulation,
GEOS-Chem is used to generate HCHO and AOD profiles globally at
2∘× 2.5∘ and for tropical South America at
0.5∘× 0.667∘ to assess their subsequent impact on
corresponding HCHO AMFs.
Figure shows significant differences in
model HCHO column distributions over the Amazon region, between the various GEOS-Chem simulations.
The GEOS-Chem nested grid model displays more of the finer detail, compared to the medium and coarse grids,
owing to its higher horizontal resolution. The vertical distribution of model tracer species is also
affected by GEOS-Chem's resolution configuration.
Figure shows model vertical profiles of HCHO, alongside black and organic carbon AOD,
simulated by GEOS-Chem at 4∘× 5∘ and 0.5∘× 0.667∘ resolutions. Clear
differences in vertical structure are evident for HCHO and AOD between the two simulations, both within and above the
boundary layer, reflecting the simulated changes in tropospheric chemistry due to different emissions and meteorology.
For comparison, the monthly mean 4∘× 5∘ profiles used in the AMF LUT (Sect. )
are also shown in Fig. , to illustrate the differences in vertical profiles compared to the daily output.
The vertical distribution is critical in the AMFs calculation, as it influences the measurement sensitivity at a given altitude, via Eq. ().
Figure shows the spatial maps and histograms of
the AMF percentage difference (relative to the default case) resulting
from use of HCHO and AOD profiles from the high-resolution GEOS-Chem
Amazon nested grid. AMFs can vary ±20 %, with the largest
changes typically found at the edges of coarse grid cells, along
coastlines, and over mountainous regions, reflecting the ability of
the nested model to better capture HCHO spatial variations over
changing terrain. Similarly, AMF differences arising from the use of
global 2∘× 2.5∘ profile data are slightly smaller,
typically ±10 %, with the biggest differences again over grid cell
boundaries, coastlines and mountain regions. The magnitude of the AMF
differences therefore increases with higher spatial model
resolution. Hence to reduce unnecessary errors, data users focusing
on regional studies should aim to recalculate AMFs using profile
information which can resolve the spatial characteristics of their
target domain.
Impact of footprint area weighting
A pure grid cell selection algorithm (hereafter referred to as “IJ”),
which uses the observation centre coordinates to select the most
appropriate a priori data, can lead to representation errors by not
accounting for satellite pixels that overlap multiple model
grid cells. To overcome this issue an area-weighted mean value (AWM)
for each scan is calculated based on the areal proportions of
GEOS-Chem grid cells underlying the satellite footprint. The
area-weighted values of all gridded AMF inputs (surface pressure and
model profiles) are computed using a tessellation algorithm originally
developed by for GOME and SCIAMACHY operational
processing. Before calculation of average area-weighted profile
quantities, all model profiles within the satellite footprint are
first interpolated onto a common vertical pressure grid, based on the
area-weighted surface pressure, to account for pressure level
differences between neighbouring GEOS-Chem grid cells. The total AOD
is preserved by scaling the final profile accordingly.
Effect of the vertical profile pressure correction (Sect. 5.4 of main text) for a scan over the
Ecuadorian Andes (78∘ W 1∘ N); with HCHO mixing ratios
taken from a GEOS-Chem 4∘× 5∘ simulation
(solid line) along the bottom x axis and
corresponding calculated shape factor S (dotted line) on the top axis. The corrected model HCHO
profile is shifted upwards and reduced in magnitude as a result of the lower surface pressure value
on which to base the profile. Scattering weights are accordingly reduced, acting to reduce the AMF for
this scan, and subsequently increase the calculated HCHO VCD.
GOME-2 orbital tracks over the Amazon showing the effect of the pressure correction
(Sect. ) against a fully area-weighted set of GEOS-Chem 4∘× 5∘ inputs (Sect. ).
Left to right: the first two rows (area weighted mean only inputs, and pressure-corrected AWM inputs
on the second) show AMF, model surface pressure and terrain height; the bottom row details difference between these parameters for both cases.
Differences between the two tests are exclusively due to the pressure correction alone.
As such, the correction is most noticeable over mountainous terrain, causing AMF differences of about ±5 %.
To evaluate this method the area-weighting technique was first applied
to all three GEOS-Chem model simulations independently, and compared
to the corresponding results when the IJ method is applied to the same
model grid resolution. In all three cases, use of AWM model profiles
changes the AMFs by about ±2.5 % for about 85–95 % of locations,
i.e. only a small difference overall (see also Fig. S2). If AMFs, calculated using AWM
model profiles from GEOS-Chem's nested grid
0.5∘× 0.667∘ simulation, are then compared to AMFs
from the default UoL algorithm, the effect of the AWM is also small
and less than the effect of using the nested grid profiles alone, as
shown by the green and blue lines, respectively, in the histograms of
Fig. . Hence for GOME-2, the effect on the
AMFs from using higher-resolution model data is greater than effects
from area-weighting model quantities. This is also true globally when
both IJ and AWM model profiles from GEOS-Chem's
2∘× 2.5∘ profile are compared to the default UoL
algorithm (not shown). Nevertheless, the area-weighting of model
profiles still represents a small but important correction for those
observations straddling multiple model grid cells.
Impact of surface pressure correction
Accurate surface pressure values are a critical component in defining
the trace gas vertical distribution. presented a
modification to regional NO2 AMF calculations, to mitigate for
terrain bias in mountainous regions due to inadequate topography
representation. Accordingly, this terrain pressure correction is also
applied here for HCHO. Following the terminology of ,
the 0.0083∘× 0.0083∘ GMTED2010 Digital Elevation
Model (DEM) , is used to calculate heff, an
area-weighted effective terrain height for each GOME-2
observation. Similarly, corresponding area-weighted model values of
surface temperature (Tsurf), original surface pressure
(pCTM) and CTM terrain height (hCTM) are also computed for
each scan. To perform the correction, an effective surface pressure
peff is first derived:
peff=pCTM×TsurfTsurf+Γ×(hCTM-heff)-g/rΓ,
with Γ the adiabatic lapse rate of 6.5 K km-1, g as
gravitational acceleration at 9.8 m s-2, and r dry air gas
constant of 287 J kg-1 K-1. From this, the tops and bottoms
of the model pressure layers l are defined for peff and
pCTM, using GEOS-5's eta (η) vertical coordinate:
pCTMb(l)=ηA(l)+pCTM×ηB(l)pCTMt(l)=ηA(l+1)+pCTM×ηB(l+1)peffb(l)=ηA(l)+peff×ηB(l)pefft(l)=ηA(l+1)+peff×ηB(l+1),
where the ηA and ηB are the GEOS-5 coefficients that
define the pressure levels.
A scaling factor, to conserve mixing ratios when interpolating to the
new pressure grid, is calculated from
peffscl(l)=peffb(l)-pefft(l)pCTMb(l)-pCTMt(l).
Model HCHO profiles are then transferred to the new peff
grid, and scaled with peffscl; AOD profiles are also
interpolated to the new grid and the total column AODs preserved.
Figure details an example HCHO profile before, and
after, the application of the pressure correction, with the shape and
amount of the vertical profile changing as a function of the scaling
value derived for the new pressure grid; in this instance the AMF
decreases by about 4 %. To illustrate the effect of the pressure correction on a scan by scan
basis, individual GOME-2 orbits over the Amazon are presented in
Fig. .
To isolate the effect of the pressure correction, AMFs calculated with
area-weighted GEOS-Chem inputs from the default algorithm are shown in
the top left plot.
For these orbits, adjusting the coarse-resolution
4∘× 5∘ surface pressure grids with the high-resolution GMTED surface elevation data produces AMF differences of up
to ±5 %, mostly over areas of rapidly changing terrain (e.g. over
the Andes mountains).
However, when surface pressure correction is applied to the GEOS-Chem
0.5∘× 0.667∘ nested grid profiles, the effect is
smaller as the GEOS-5 surface pressures more accurately represent the
surface topography.
This is confirmed by the histograms shown in
Fig. , which reveals the impact area-weighting
(green line) and the subsequent pressure correction (aqua line) are
small, in comparison to the effect of using the
0.5∘× 0.667∘ nested grid profiles alone (blue
line).
Impact of new GOME-2 surface albedo product
The baseline UoL AMF algorithm uses the surface reflectance maps from the
database. Choice of surface reflectance data is critical
since it can cause 20 % changes in retrieved tropospheric HCHO and
NO2 columns . Bi-directional
distribution function (BRDF) effects associated with the surface
reflectance are less than < 5 % for NO2,
but unfortunately for HCHO cannot be assessed owing to the lack of a
BRDF product at relevant wavelengths. Given the stronger
Rayleigh scattering that occurs at UV wavelengths,
BRDF effects on the AMF will likely be smaller for HCHO than found for NO2.
To improve the UoL AMF algorithm the surface reflectance is upgraded to the
GOME-2 1.0∘× 1.0∘ database generated by
, using the mode LER data at 340 nm.
Furthermore, daily changes in surface reflectance are accounted for
using linear interpolation between months, following the approach of
, and with the area-weighting procedure described
in Sect. also applied.
Left: spatial maps of the AMF differences resulting from the use of the temporally interpolated
and area-weighted GOME-2 1∘× 1∘ 340 nm mode LER (Tilstra et al., 2014),
as discussed in Sect. 5, relative to the default UoL AMF algorithm
i.e. 100 % × (new AMF – baseline AMF)/naseline AMF.
The AMFs are gridded on to 0.25∘× 0.25∘ using observations with cloud fractions ≤40 %. Right: corresponding histograms of the AMF differences.
Compared with the TOMS data, the GOME-2 mode LER reflectances are generally higher in most regions
(see Figs. S3 and S4 in the Supplement). The overall difference between GOME-2 and TOMS is most likely due to
differences in the radiometric calibration of the two instruments, but
on a regional scale also the different orbital characteristics of each
instrument and their respective treatment of clouds may play a role. A higher albedo results in a higher AMF owing to an increased measurement sensitivity as more photons are reflected.
Consequently, AMF differences reflect these albedo changes, as shown in Fig. .
Relative to the default UoL AMF algorithm, about 80–90 % of locations
show an AMF increase of between 0 and 20 %, and about < 10 % of locations show a
decreases of up to 10 %. The overall median AMF difference is about 4 % for March and August.
These statistics are consistent if the GOME-2 min LER is used instead of the mode LER,
although localised spatial differences occur and the 1σ width of fitted exponential
probability density functions to the histograms increases from 2.7 % (min LER) to 4.1 % (mode LER) in August; in March the 1σ value is unchanged.
By comparison, if the OMI mode LER at 342 nm, which is available at 0.5∘× 0.5∘ resolution
, is used in the UoL AMF algorithm in the same manner, the AMF differences are slightly
less positively skewed, as shown in Fig. S5. Compared with the TOMS data, ocean albedos are generally higher with
the OMI product, whilst over land, albedos are also generally higher,
with some exceptions including high northern latitudes, and much
of the boreal landmass in March (Fig. S3).
Figure S4 shows that relative to the default UoL AMF algorithm, about 65–80 % of locations
show an AMF increase of up to 10 %, and about 15–25 % of locations show a
decrease of up to 10 %. The median AMF difference is about 2 %.
Use of the OMI 342 nm min LER yields similar changes in the AMF, with median differences of about 1 %.
Impact of GOME-2 cross sections
The baseline AMF implementation generates scattering weights with HCHO
using absorption spectra based on . This is
improved on by passing the HCHO and ozone
cross sections from the slant column fitting of
the GOME-2 retrieval, convolved to the current orbit's asymmetric slit
function, additionally allowing for the time-dependent slit function
degradation throughout the instrument's lifetime e.g. . Furthermore, the
HCHO and ozone cross sections are adjusted to the local temperature
profile, via cited temperature coefficients. However, the result of
this algorithm update is minor, causing a fairly uniform global
decrease in AMFs of between 0 and 2 %.
Summary of the baseline and updated UoL AMF algorithm.
Baseline AMF algorithmUpdated AMF algorithmCTMGEOS-Chem global 4 × 5∘ gridGEOS-Chem global 2 × 2.5∘ gridA priori profileGEOS-Chem daily profilesGEOS-Chem daily profiles– selected using observation centre coordinates– area-weighted mean for observation footprintSurface pressureGEOS-Chem (4 × 5∘)GEOS-Chem (2 × 2.5∘)– adjusted by area-weighted mean elevationSurface albedo monthly climatology monthly climatology– regridded to 4∘× 5∘ (λ∼360 nm)– default 1.0∘× 1.0∘(λ= 340 nm)– area-weighted & time interpolatedSurface elevationNAGMTED2010 (0.0083 × 0.0083∘)LIDORT cross sectionsFixed OMI cross sectionOrbit-specific GOME-2LIDORT O3 profileU.S. Standard atmosphereMonthly & latitudinal TOMS v8 climatology– scaled to coincident GOME-2 total ozone observationsCloud algorithmFRESCO+FRESCO+Aerosol correctionGEOS-Chem monthly mean AOD profilesGEOS-Chem daily AOD profiles*– area-weighted mean for observation footprint
*Algorithm employs optional flag to switch between explicit versus implicit (AODs = 0.0) aerosol correction (see discussion in Sect. )
Left: spatial maps of the AMF differences resulting from the application of all AMF updates,
as discussed in Sect. , relative to the default UoL AMF baseline algorithm
(Sect. ), i.e. 100 % × (final AMF – baseline AMF)/baseline AMF.
The AMFs are gridded to a 0.25∘× 0.25∘ using observations with cloud fractions < 40 %.
Right: corresponding histograms of the AMF differences.
Impact of TOMS ozone climatology
In the baseline UoL AMF algorithm, O3 vertical mixing ratios
are fixed to a single profile representing the US standard summertime
atmosphere, thus any major O3 spatial and temporal variations
are ignored in the AMF computation. Using a fixed O3 profile
is therefore likely to introduce errors through incorrect scattering
weight values, particularly significant for weak absorbers such as
HCHO. To overcome this issue, the fixed US O3 profile is
replaced by a climatology derived from TOMS version 8 O3 data, as applied in the SCIATRAN v2.2 radiative
transfer model . The TOMS v8 climatology provides
monthly O3 VMRs in eighteen 10∘ latitude bands for 61
atmospheric levels. To account for concurrent O3 variability,
each selected TOMS v8 profile is interpolated onto the pressure grid
based on the AWM surface pressure, and then scaled to coincident
GOME-2 O3 total column measurements, provided operationally by DLR in the framework of the
EUMETSAT/O3M-SAF project . Note that a similar scaling
of the US ozone profile was also performed by in the
computation of OMI SO2 AMFs.
Results of the ozone profile substitutions are presented in Fig. S6,
which shows that whilst the magnitude of the AMF differences are
small, mostly within ±2 %, the variation is geographically
widespread. The most notable changes occur over regions of high
surface elevation (> 1500 m) where divergence between the US standard
atmosphere and TOMS v8 ozone profiles, relative to the HCHO profile
peak, are most pronounced.
Combined effect of all AMF updates
To produce an improved air mass factor calculation the updates
presented are combined in a new UoL AMF algorithm, as summarised in
Table .
In future, global processing of the GOME-2 HCHO columns (as here)
will rely on using GEOS-Chem model data at 2∘× 2.5∘ resolution, whereas studies focusing on tropical South America will
utilise output from the Amazon nested grid simulation.
Figure shows the differences of the new AMF
algorithm against the initial baseline implementation.
On a single Intel Xeon X5550 running at 2.67 GHz, per-orbit processing
time for the AMF calculations including all algorithm modifications is
between 15 and 20 min (increased from 7 to 8 min per orbit for
the baseline method), reflecting extra time spent applying pixel
tessellation routines to input grids.
Monthly mean GOME-2 total AMF errors for March (left) and August (right) 2007 calculated using Eq. ().
The AMF errors are gridded on to a 0.25∘× 0.25∘ grid using observations with
cloud fractions < 40 %.
Typically differences between the original IJ algorithm and the
updated AMF calculations are of the order of ±20 %, although the histogram of the AMF differences
shown in Fig. is positively skewed. Overall, the median AMF differences are about 4 %, and
over 70 % of locations now
have an AMF of 0–20 % larger than produced from the baseline algorithm. This
increase is driven by the use of the new GOME-2 surface reflectance product.
Other significant changes occur over mountain regions, coastlines and the grid cell
outlines of the GEOS-Chem 2∘× 2.5∘ and
4∘× 5∘ horizontal grids.
Cancellation of opposing effects from individual algorithm changes
mitigates the magnitude of the overall difference.
Besides on average increasing the AMFs,
the overall impact of the algorithm updates is therefore to mainly improve
the tropospheric vertical column retrievals over regions with rapidly
changing surface elevation and terrain properties.
AMFs from the updated UoL algorithm are now 0.64–3.82 over land and
0.73–4.64 over the oceans.
Interestingly, in August 2007 there is a significant reduction in the
AMFs over the mid-Atlantic, and over the Arabian sea, just south of
the Yemen and Oman coastlines.
These features are spatially coincident with elevated dust AODs from
GEOS-Chem, reflecting the simulated aerosol field sensitivity to the
model's spatial resolution, and its subsequent effect on the AMF.
If the aerosol are not included in the AMF algorithm (i.e. the AOD is set to zero), then the decrease
in the AMFs over these regions is not observed (see
Sect. ).
AMF error assessment
Any AMF algorithm should properly characterise its error.
Individual AMF error estimates are valuable as they provided
a more robust error characterisation of the HCHO vertical columns; this
allows correct observational uncertainty weighting when grid averaging and to properly
calculate the errors of inferred top-down VOC emissions.
Following and , the AMF total
error (σAMF) may be expressed as
σAMF2=∂AMF∂AsσAs2+∂AMF∂CFσCF2+∂AMF∂CTPσCTP2+∂AMF∂SσS2,
where σAs, σCF,
σCTP and σS are the uncertainties
associated with the surface albedo, cloud fraction, cloud-top
pressure and the HCHO shape profile, and the partial derivatives
indicate the local AMF sensitivity with respect to each parameter.
For the GOME-2 data (2007–2010) the AMF errors are explicitly
calculated for each observation (using the updated algorithm) through
assigning appropriate uncertainties for σAs, σCF, σCTP
and σS, and then by applying these uncertainties to determine the local AMF
sensitivity, i.e. by generating partial derivatives of the radiance
fields with respect to these sources of model error using LIDORT.
Systematic values of σCF=0.05 and σCTP=60 hPa are
used for cloud parameter uncertainties .
The GOME-2 reflectance product has an associated uncertainty for each grid cell
which is used for σAs. Figure S4 shows the geographical distribution
of σAs for March and August; uncertainties are typically largest at high latitudes and also over the Sahara. Based on comparisons
to TOMS, GOME-1 and OMI LER data, the GOME-2 surface LER product is estimated to be accurate within 0.01 for
the UV wavelength bands .
Quantification of the profile uncertainty σS is difficult
to assess, since the HCHO vertical distribution is influenced by many
complex processes (e.g. transport, chemistry and boundary layer
height).
Hence in the case of the HCHO profile shape, the error and local
sensitivity are estimated using a poor man's approach by perturbing the HCHO profiles below and
two model layers above the simulated HCHO peak by +25 %, whilst
layers above these are decreased by -25 %.
The 25 % uncertainty is based on the study of , who compared
various GEOS-Chem simulations of HCHO to aircraft observations over Guyana and surrounding areas.
These profile uncertainties are higher than those determined by for the US, reflecting significant modelling errors for tropical latitudes (i.e. high VOC emission and low NOx conditions).
Modifying the HCHO profile in this way also provides a partial
assessment of AMF uncertainties due to the presence of aerosols and clouds, since
their relative vertical distribution has changed.
However, without precise information on the aerosol distribution and
optical properties it is extremely difficult to accurately quantify
aerosol-induced errors; simply adjusting the GEOS-Chem aerosol
profiles only provides a limited insight into this error source
e.g. .
Monthly mean GOME-2 component albedo, cloud fraction, cloud-top pressure and CTM HCHO profile AMF errors for March (left) and August (right) 2007.
Errors are gridded on to a 0.25∘× 0.25∘ grid using observations with
cloud fractions < 40 %.
2007–2010 time series over southeast USA (top) and tropical South America (bottom), showing monthly
median AMFs calculated with and without aerosols (solid black and blue lines, respectively), and
monthly median total AMF error (red solid line; calculated with aerosols present).
The dashed red line shows the contribution to the total AMF error from
uncertainty in the HCHO profile shape.
Figures and show total and
individual component errors respectively, revealing that AMF uncertainty
varies considerably both in magnitude and distribution.
The greatest source of AMF uncertainty is associated with the
HCHO profile shape, with median errors of the order of 50 %.
HCHO profile errors are particularly large where low-lying cloud occurs,
e.g. off the west coast of North and South America in August, owing to cloud
albedo and shielding effects. In such cases the large AMF errors are more attributable to the
sensitivity of the HCHO vertical distribution relative to the cloud layer, rather than uncertainties in the HCHO profile shape alone. A further AMF calculation, in which the reverse 25 % scaling to the a
priori HCHO profile was also performed, resulted in similar but more
widespread errors. Over regions where model HCHO can be simulated reasonably well
e.g. over the US;, it is likely that AMF uncertainties from the
profile shape will be less. To account for this, a further calculation in which the HCHO profile was scaled
in a similar manner by 10 % was also conducted. The resulting median AMF error from the profile shape
decreased to about 30–40 %. Thus for profile errors of 10–25 %, the likely AMF error will be 30–50 %,
and will decrease accordingly as the accuracy of the HCHO profile is increased.
In comparison to the HCHO shape error, average uncertainties due to cloud-top pressure and
cloud fraction are both about 10 %, whilst those associated the
surface albedo are about 5 %. Median AMF total errors are therefore approximately 50–60 %,
consistent with those found previously for the SCIAMACHY and OMI
instruments by . However, for individual observations GOME-2 AMF errors can
range from 5 to 600 % depending on the immediate local conditions.
If the 10 % scaling is applied to the HCHO profile in the error calculation
(instead of the default 25 %), then the median total AMF error drops to about 30–40 %.
The calculated AMF uncertainties for GOME-2 presented here are slightly larger than those
determined for GOME over the US by , who found AMF biases in the range 16–24 %
through comparison of measured and modelled AMFs. This is partly due to this work's global
analysis which must take into account larger model uncertainties over tropical latitudes.
However, found that clouds were the dominant source of error in the AMF (10–21 %
bias for cloud fractions ranging 30–60 %). also confirmed that
uncertainties in cloud fraction produced the biggest changes in SCIAMACHY and OMI AMFs
over tropical South America. Hence to provide an alternative estimate of the errors due
to cloud fraction and height, the change in the GOME-2 AMFs were calculated after systematically introducing errors of
+0.1 in cloud fraction (after being cloud filtered using its original value) and -60 hPa in cloud-top height
as done similarly in. Figure S7 shows the change in AMFs for March 2007. Increasing the
cloud fraction typically changes the AMFs by only ±5 % for over 95 % of locations,
whereas simulating a higher altitude cloud top (i.e. lower pressure) results in a median
decrease of 5 %. Hence the impact of clouds on the GOME-2 appears relatively small,
and hints towards requiring external validation with aircraft measurements to clarify its true magnitude.
An additional source of uncertainty is from the inconsistency of the different surface reflectivity and topography
fields used in the GOME-2 AMF and FRESCO+ cloud algorithms. The latter uses the MERIS black-sky
albedo (BSA) climatology and the GTOPO30 topography data
downgraded to 0.25∘× 0.25∘.
Here the preferred option is to use the GOME-2 surface reflectivity of , as it is
consistent with the radiometric calibration of the instrument itself, and also the viewing geometry,
time and wavelength of the GOME-2 HCHO retrieval. It is theoretically possible to scale the MERIS BSA to 335 nm using the GOME-1 reflectances derived by ,
using the approach outlined in , although in reality it is acknowledged that the 335 nm
reflectivity suffers significantly from instrument degradation .
Nevertheless, MERIS 335 nm BSA maps were constructed via
MERISAs(335nm)=MERISAs(412nm)×GOMEAs(335nm)GOMEAs(416nm)
and compared to the TOMS, OMI and GOME-2 reflectivities (all remapped to a 1∘× 1∘ grid).
Overall, the GOME-2 340 nm reflectivities agree marginally better with the scaled MERIS 335 nm BSA than the TOMS or OMI data
(not shown), supporting their implementation in the AMF algorithm.
Similarly, it is also preferable to use the higher resolution GMTED2010 topography, as it will give a more accurate surface pressure correction than the more coarse GTOPO30 topography.
Hence a reprocessing of the FRESCO+ algorithm with the GOME-2 reflectance product and GMTED2010 is a priority to remove these inconsistencies.
Figure shows the seasonal variability of the AMF and
its error over two key regions: the southeast USA and tropical South
America.
In general, the monthly median AMFs show little variation over 2007–2010
for either region.
For both regions, the total AMF error is dominated by the uncertainty
associated with the a priori HCHO profiles.
AMF errors over tropical South America also do not vary significantly,
owing to copious biogenic emissions from the rainforest sustaining
high levels of HCHO all year round.
In contrast, the AMF errors over the southeast USA have a
distinct seasonal pattern, with low AMF errors in winter when biogenic
emissions and HCHO levels are a minimum, and high AMF errors in
summer, when HCHO concentrations peak due to significant isoprene
emissions .
Thus, any top-down estimates of isoprene emissions over North America
are likely to be compromised by large AMF errors in the months of
highest emissions. However, it is likely that HCHO profile errors
over the southeast USA are in reality smaller than those calculated (which uses the globally
applied 25 % HCHO profile scaling in the AMF error calculation), for the reasons discussed above.
Examination of other regions (not shown), also confirms that any
variance in the AMF errors is predominantly driven by biogenic
emission seasonality influencing the HCHO profile shape
(see e.g. Fig. S2 of ).
Aerosol effect on AMF errors
In their assessment of HCHO AMF uncertainty,
conducted an extensive investigation into AMF sensitivity to AOD
over the Amazon region for both SCIAMACHY and OMI HCHO AMFs.
Their series of tests included calculating AMFs with no aerosol
correction, arbitrary AOD scaling, and redistribution of black
(BC) and organic (OC) carbon to various heights above the boundary layer dependent on
AMF peak layer AOD residing in the boundary layer.
Results from this work showed that HCHO AMFs were only significantly
affected (in a range of 10–50 %) when BC and OC were
distributed high above the boundary layer to approximately 5 km –
an extreme case of localised high aerosol loading.
For a basic indication of aerosol errors in this work we therefore
include a brief investigation on aerosol effects on our GOME-2 AMFs.
Testing of aerosol effects are limited to BC only, given the
sensitivity of HCHO AMFs to the species found in and
. To this end, scans were identified whose
a priori GEOS-Chem BC AOD profile peaked within 2 km of the Earth's surface. In such cases, the BC
AOD profile had its layer values
increased between the surface and 5 km to its maximum value for that scan, and the local sensitivity
(i.e. ∂AMF/∂AOD) was then calculated using LIDORT.
We calculate the new AMF error through inclusion of
an extra term to Eq. (), via
σAMF2=…+∂AMF∂AODσAOD2
with σAOD assigned a value of 20 %. Scans with BC AOD profile peak above 2 km were
assigned a default error of 20 %.
Estimated mean AMF error due to BC for the two tested months are
plotted in Fig. S8, displaying maximum values in the range of 30–70 %, showing consistency
with values reported in .
Increased BC AMF error values exhibit a very similar spatial pattern
to HCHO profile errors in Fig. , suggesting that the
relative distribution of the two components is key for understanding
the aerosol AMF error source. It should be noted that whilst aerosols
may potentially create the largest uncertainty in the AMF calculation,
their effects are often localised and that on continental scales, errors due to uncertainties in
cloud parameters and the HCHO profile shape will likely be larger.
Implicit aerosol correction
The UoL baseline and updated AMF algorithms have focused on using an explicit aerosol
correction through inclusion of aerosol optical depth profiles and properties in the
LIDORT radiative transfer calculations. This is based on the optimistic assumption
that the GEOS-Chem simulation of aerosols is correct, and that the presence of aerosol
is not fully accounted for via the FRESCO+ cloud algorithm, through the independent
pixel approximation . Despite evidence that the clouds can be
effective for implicitly correcting for the presence of aerosols , a
large number of studies have still opted for an explicit aerosol correction in their AMF
calculations see e.g. .
Aerosols complexly affect both the retrieval of cloud parameters and subsequent computation of tropospheric AMFs,
depending on the aerosol type (scattering versus absorbing) and its relative vertical distribution to cloud
height and the trace gas profile see e.g. Figure 5 ofand discussion therein.
For example, showed that explicit aerosol corrections can overestimate OMI NO2 AMFs
over South America by 20–40 %, for cloud-top pressures less than 800 hPa, but where cloud radiance
fractions were less than 0.3 or effective cloud-top pressure greater than 800 hPa, the difference between
tropospheric AMFs calculated using implicit and explicit aerosol parameters were about 6 % for more than 70 % of OMI pixels.
Left: spatial maps of the AMF differences which exceed ±5 %, resulting from the implicit aerosol correction test using the final updated AMF algorithm,
as discussed in Sect. , i.e. 100 % × (AMF without AODs – AMF with AODs)/AMF with AODs.
The AMFs are gridded to a 0.25∘× 0.25∘ using observations with cloud fractions < 40 %.
Right: corresponding histograms of the AMF differences.
For these reasons, the updated UoL AMF algorithm was re-run with the explicit aerosol correction turned off
(AODs set to zero). Figure shows the AMF differences, relative to the updated UoL AMF
algorithm with the explicit aerosol correction applied – i.e. (without AOD – with AOD)/(with AOD). For about 90 % of
locations the change in the AMFs is less than ±5 %, but for a relatively small number of areas (< 1 %)
the changes in the AMF can be over ±30 %. Including aerosol parameters from GEOS-Chem reduces the AMFs over dusty
regions (e.g. the Sahara and tropical North Atlantic) and decreases AMFs over biomass burning regions
(e.g. the southeast Amazon in August), results consistent with the study of Martin et al. (2003).
Figures and S9 show both sets of AMFs over 2007–2010 for four key regions: the
southeast USA (-2.5 % median difference), the Amazon (<-0.2 %), central Africa (+2.9 %)
and Europe (< 1 %).
In general, the AMF temporal variation, either with or without the inclusion of aerosol optical
depths, is similar in all cases, and the differences generally small. Note that these findings also correlate
with the implicit AMF aerosol correction sensitivity tests in , where aerosol corrections
were shown to impart only a moderate effect on HCHO vertical columns over areas of large BVOC emissions (< 15 %),
with larger effects noted for regions and time periods containing significant quantities of desert dust, and biomass burning.
Where these aerosol types and conditions are prevalent, the AMFs will likely be compromised, which may affect
subsequent top-down VOC emission estimates. Clearly a much more detailed global study, following a similar
approach to , is warranted to resolve simultaneous aerosol and cloud effects on HCHO AMFs.
This goal will be the key focus of future algorithm development. Until then, the implicit versus explicit aerosol
correction will remain as an optional algorithm flag. This will allow assessment of the different AMFs,
and subsequent HCHO vertical columns, against in situ validation data (e.g. MAX-DOAS measurements).
Summary
This work has presented and evaluated a new University of Leicester
algorithm to compute HCHO AMFs for the GOME-2 instrument. The most
novel aspects of the new algorithm are the area weighting of improved
a priori information over the satellite footprint, to more accurately
represent the local surface conditions and atmospheric state, and the
full radiative transfer calculation of the AMF and its error for each
GOME-2 observation.
Compared to an earlier UoL AMF code, the new
algorithm typically changes calculated AMFs by up to ±20 %, but on average the AMFs are increased by
4 %, with over 70 % of locations having an AMF of 0–20 % larger than originally. This AMF increase largely
comes from updating to the latest GOME-2 reflectance product.
Other significant changes mostly occur over coastal and mountain regions, and the model
cell boundaries of the GEOS-Chem horizontal grids. Another large impact
on the AMFs arises from using HCHO profiles from a high-resolution
GEOS-Chem 0.5∘× 0.667∘ nested grid simulation in
preference to those from coarser global simulations. Furthermore, it
is found that (a) the largest AMF error component is also associated
with the HCHO profile shape and its vertical distribution relative to
cloud height and aerosol profiles, and (b) seasonal variations in the total
AMF error are driven by seasonal changes in the HCHO profile
distribution. These results therefore highlight the critical
importance of accurate and high-resolution profiles within the GOME-2
AMF calculation, or for that matter, any other HCHO retrieval. In
addition, users of HCHO data products should be fully aware of
seasonal shifts in the AMF error, and the likely impact on any
inferred top-down emission estimates.
Ongoing efforts are being conducted to validate and develop a
full-error analysis of the UoL GOME-2 HCHO tropospheric column
product, to provide confidence in its use for inversion studies of
surface VOC emissions. Further algorithm refinement to potentially
improve retrievals in the presence of aerosols and over snow-covered
surfaces are also being investigated.
The Supplement related to this article is available online at doi:10.5194/amt-8-4055-2015-supplement.
Acknowledgements
This work was supported by the UK National Centre for Earth
Observation (NCEO) and the UK Natural Environment Research Council
(NERC) (grants NE/G523763/1, NE/GE013810/2 and NE/D001471).
Edited by: J. Tamminen
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