We present the High-Elective Resolution Modelling Emission System version 3
(HERMESv3), an open source, parallel and stand-alone multi-scale atmospheric
emission modelling framework that computes gaseous and aerosol emissions for
use in atmospheric chemistry models. HERMESv3 is coded in Python and consists
of a global_regional module and a bottom_up module
that can be either combined or executed separately. In this contribution
(Part 1) we describe the global_regional module, a customizable
emission processing system that calculates emissions from different sources,
regions and pollutants on a user-specified global or regional grid. The user
can flexibly define combinations of existing up-to-date global and regional
emission inventories and apply country-specific scaling factors and masks.
Each emission inventory is individually processed using user-defined
vertical, temporal and speciation profiles that allow obtaining emission
outputs compatible with multiple chemical mechanisms (e.g. Carbon-Bond 05).
The selection and combination of emission inventories and databases is done
through detailed configuration files providing the user with a widely
applicable framework for designing, choosing and adjusting the emission
modelling experiment without modifying the HERMESv3 source code. The
generated emission fields have been successfully tested in different
atmospheric chemistry models (i.e. CMAQ, WRF-Chem and NMMB-MONARCH) at
multiple spatial and temporal resolutions. In a companion article (Part 2;
Guevara et al., 2019) we describe the
bottom_up module, which estimates emissions at the source level
(e.g. road link) combining state-of-the-art bottom–up methods with local
activity and emission factors.
Introduction
Emission inputs of trace gases and aerosols play a key role in the
performance of atmospheric chemistry models for air quality research and
forecasting applications. Depending on the purpose of the application, an
atmospheric chemistry model may be applied at global, regional or local
(urban) scales. Similarly, the level of coverage and detail required for the
emission input data will vary according to the type of study and modelling
scale (e.g. Borge et al., 2014).
For global and regional modelling, emissions are typically estimated at
country level (combining national statistics and technology-dependent
emission factors) and then disaggregated using spatial proxies such as
population density and land use. Different global and regional emission
inventories are continuously being developed and made publicly available by
research groups and international programs such as the Global Emissions
Initiative (GEIA) (Frost et al., 2013). These inventories usually report total annuals per primary pollutant
and source sector distributed over a rectangular grid at resolutions ranging
from 1∘ by 1∘ to 0.1∘ by 0.1∘. The practical
use of these inventories suffers from several problems. On the one hand, the
reporting format is not directly compatible with the emission input
requirements of atmospheric chemistry models as these typically ingest hourly
and chemical species-based emissions over other grid projections and
resolutions using specific file formats and conventions. On the other hand,
there are substantial discrepancies in the total emissions, sectorial
emission shares, spatial distribution and pollutant sources considered
between the available inventories and therefore in their respective behaviour
when used in atmospheric chemistry models (e.g. Granier et al., 2011;
Trombetti et al., 2018; Saikawa et al., 2017). While having independent
emission datasets instead of only one totally harmonized inventory is crucial
from a science perspective, having the capacity to combine them and apply
adjustment factors in a flexible and transparent way can be also of
importance for air quality modelling studies. All in all, the incorporation
of emission data into atmospheric chemistry models usually implies laborious
programming in order to combine, adjust and adapt the original inventories to
the model requirements.
Global and regional inventories are too imprecise for urban-scale modelling
applications (e.g. Timmermans et al., 2013). Emission and activity factors
lack specificity for the local conditions of interest (e.g. Guevara et al.,
2014), and the spatial proxies used to allocate the emissions are of poor
resolution and may not apply to certain emission processes (e.g.
López-Aparicio et al., 2017). These inventories are, for example, limited
when it comes to predicting and assessing the impact of emission reduction measures
upon local air quality such as the change in speed limits (e.g. Baldasano et
al., 2010) or the penetration of new vehicle technologies (e.g. Soret et al.,
2014). Consequently, working at the urban scale requires dedicated local
emission inventories combining activity data collected at a fine spatial
scale (e.g. point source, road links, household) with bottom–up detailed
emission algorithms that represent the different factors influencing the
emission processes (e.g. vehicle speed, outdoor temperature).
In this paper and a companion paper (Guevara et al., 2019), we describe the
newly developed High-Elective Resolution Modelling Emission System version 3
(HERMESv3). HERMESv3 is a multi-scale, open-source emission modelling
framework that consists of two independent modules that can be either
combined or executed separately: (i) the global_regional module
and (ii) the bottom_up module. The global_regional
module is a customizable emission processing system that calculates emissions
from different sources, regions and pollutants on a user-specified global or
regional model grid. The user can easily define combinations of existing
global and regional emission inventories, which are individually processed
using vertical, temporal and speciation profiles, and apply regional scaling
factors and masks. The generated emission fields have been tested for
different chemical mechanisms and atmospheric chemistry models, including
CMAQ (Appel et al., 2017), WRF-Chem (Grell et al., 2005) and NMMB-MONARCH
(Badia et al., 2017) models, and can easily be adapted to other models, grids
or chemical mechanisms upon demand.
The bottom_up module is an emission model that can be used to
estimate emissions at the source level (e.g. road link, industrial facility,
crop type) and hourly level combining state-of-the-art estimation methods
with local activity and emission factors along with meteorological data. This
model covers the estimation of bottom–up emissions from point sources (e.g.
power and manufacturing industries), road transport, residential combustion
and agricultural activities (manure management, fertilizer application and
crop operations), as well as the modelling of detailed emission scenarios for
air quality planning studies. Besides the aforementioned atmospheric
chemistry models, the emission outputs of this module are also adapted for
their application with the R-LINE urban dispersion model (Snyder et al.,
2013).
We conceive HERMESv3 as a flexible multi-scale modelling framework that
allows integrating and combining different emissions estimation approaches,
so that the emission related outputs can be as detailed and specific as
possible for the different domains (global, regional or local) involved in
the corresponding application.
The development of HERMESv3 is based on the knowledge acquired from previous
versions of HERMES for Spain (Baldasano et al., 2008; Guevara et al., 2013),
Europe (Ferreira et al., 2013) and Mexico City (Guevara et al., 2017) that
have been developed at the Earth Sciences Department of the Barcelona
Supercomputing Center (BSC) during the last decade. Other existing emission
software such as HEMCO (Keller et al., 2014) and PREP-CHEM-SRC (Freitas et
al., 2011) have also been taken as a reference for the development of
HERMESv3.
In this paper (Part 1) we provide a description of the
global_regional module (herein referred to as HERMESv3_GR).
The bottom_up module is described in the companion paper (Part 2;
Guevara et al., 2019). The paper is organized as follows. Section 2
describes the processing system and its main functionalities together with
some illustrative examples of the outputs that can be generated with this
tool. Section 3 describes some of the current implementations of
HERMESv3_GR for air quality modelling. Finally, Sect. 4 presents the
main conclusions of this work.
Description of HERMESv3Overview
Figure 1 shows a schematic representation of the structure of HERMESv3_GR
along with the execution workflow. HERMESv3_GR first defines the
destination grid and selects the emission inventories (see Sect. 2.2), and
the vertical, temporal and speciation profiles based on the specifications
defined by the user in the general and emission inventory configuration files
(see Sect. 2.3 and 2.4, respectively). During the initialization process,
HERMESv3_GR automatically creates a set of auxiliary files that are
subsequently used during the emission calculation process. These auxiliary
files, including the output grid description, the time zones and the country
mask, are specific to each new working domain and are stored by default after
their creation so that they can be reused in subsequent executions. The
emissions are calculated in four steps that are applied to each pollutant
sector and species of the selected original emission inventories. These four
steps include (i) the spatial regridding from source grid to destination
grid (see Sect. 2.5.1), (ii) the mass distribution over model vertical layers
(see Sect. 2.5.2), (iii) the temporal disaggregation (see Sect. 2.5.3), and
(iv) the speciation mapping depending on the selected gas-phase and aerosol
chemical mechanisms (see Sect. 2.5.4). The emission calculation can combine
inventories that cover different geographic domains and/or emission sectors.
To prevent spatial overlapping between inventories a masking functionality is
included during the regridding phase. The user can define country-specific
masks that restrict the applicability of the original inventory to a given
region and country-specific scaling factors. Once the emissions have been
processed, HERMESv3_GR writes the output file following the requirements
and conventions of the atmospheric chemistry model selected by the user in
the general configuration file (see Sect. 2.5.5).
Schematic representation of the general structure of
HERMESv3_GR.
For each grid cell x and vertical layer l on the destination domain and
requested output species e, HERMESv3_GR computes the output hourly
emissions following Eq. (1).
Eoutx,le=∑i=1I∑s=1S∑x‾=1X‾{Ein(x‾)⋅RF(x‾)⋅VF(x‾,l)1⋅TF⋅SF}e‾,s,i,
where Ein(x‾)e‾,s,i is the input
emission flux (kg m-2 s-1) of the species e‾ and
pollutant sector s reported by inventory i on the source grid cell
x‾. RF(x‾)e‾,s,i is the
remapping weight value from source grid cell x‾ to the
destination grid cell x associated with species e‾ and pollutant
sector s of inventory i. VF(x‾,l)e‾,s,i
is the vertical weight factor for layer l and source grid cell
x‾ assigned to species e‾ and pollutant sector s of
inventory i (0 to 1). TFe‾,s,i is the temporal
weight factor t assigned to species e‾ and pollutant sector s
of inventory i. SFe‾,s,i is the speciation factor
assigned to species e‾ and pollutant sector s of inventory i.
The final Eout(x,l)e is the hourly emission for output species
e in destination grid cell x, layer l and is the sum of (i) all
X‾ source grid cells x‾ that contribute to
destination grid cell x, (ii) all S employed pollutant sources s and
(iii) all I used emission inventories i. The units of the output
emissions will vary according to the atmospheric chemistry model selected by
the user.
RF(x‾)e‾,s,i and
TFe‾,s,i are computed following Eqs. (2) and (3),
respectively.
2RF(x‾)e‾,s,i=W(x‾)i⋅MKx‾⋅SC(x‾)e‾,s,i,3TFe‾,s,i=Mm⋅Dd⋅H(h)e‾,s,i,
where W(x‾)i is the regridding weight value that describes
how the source grid cell x‾ contributes to the destination grid
cell x (0 to 1). MK(x‾)e‾,s,i is the
masking factor assigned to species e‾ and pollutant sector s of
inventory i on the source grid cell x‾ (1 or 0).
SC(x‾)e‾,s,i is the scaling factor assigned
to species e‾ and pollutant sector s of inventory i on the
source grid cell x‾. M(m)e‾,s,i is the monthly
factor for month m assigned to species e‾ and pollutant sector
s of inventory i (0 to 12). Dde‾,s,i is
the daily factor for day d assigned to species e‾ and pollutant
sector s of inventory i (0 to 28, 29, 30 or 31 depending on the total
number of days for month m). H(h)e‾,s,i is the hourly factor
for hour h assigned to species e‾ and pollutant sector s of
inventory i (0 to 24).
Emission data library and pre-processing
Table 1 lists all the global and regional inventories currently considered in
the HERMESv3_GR emission data library. On demand, new emission datasets
can be added. At a global scale, the inventories proposed for anthropogenic
emissions include the Air Pollutants and Greenhouse Gases Emission Database
for Global Atmospheric Research (EDGAR v4.3.2_AP, Crippa et al., 2018;
EDGARv4.3.2_VOC, Huang et al., 2017), the Community Emissions Data System
(CEDS; Hoesly et al., 2018) and the datasets derived from the Task Force
Hemispheric Transport of Air Pollution community (HTAPv2.2; Janssens-Maenhout
et al., 2015) and the Evaluating the Climate and Air Quality Impacts of
Short-Lived Pollutants project (ECLIPSEv5.a; Klimont et al., 2017). Also at a
global scale, biomass burning emissions are provided by the Global Fire
Assimilation System (GFASv1.2; Kaiser et al., 2012), whereas open burning of
domestic waste and volcanic degassing emissions can be estimated using the
inventories reported by Wiedinmyer et al. (2014) and Carn et al. (2017),
respectively. Two European regional anthropogenic emission inventories are
also considered, namely TNO-MACC_III (Kuenen et al., 2014) and the EMEP
(Mareckova et al., 2017). The emission data library compiles gaseous
(NOx; CO; non-methane volatile organic compounds, NMVOC;
SOx; NH3) and particulate (PM10;
PM2.5; black carbon, BC; organic carbon, OC) air pollutant
emissions. Depending on the inventory, NMVOC emissions are reported as a
single category (e.g. ECLIPSEv5.a), by individual species (e.g. GFASv1.2) or
following the 25 species groups as proposed within the Global Emission
Inventory Activity (GEIA) (Olivier et al., 1996) (e.g. EDGARv4.3.2_VOC).
Most of the inventories are reported at the monthly level and include time
series with multiple base years (past, present and future).
Summary of the input emission inventories currently available in the
HERMESv3_GR library.
a Based on the NMVOC breakdown ratios generated for the RETRO project (Schultz et al., 2007).
b Based on the NMVOC breakdown ratios generated for the AQMEII modelling exercise (Pouliot et al., 2015)
For each inventory, a specific pre-processing function has been developed to
rewrite the original datasets on a common format. All the gridded emission
inventory input files used by HERMESv3_GR (i) are in the Network Common
Data Form (NetCDF) format
(http://www.unidata.ucar.edu/software/netcdf/, last access:
May 2019), (ii) adhere to the Climate and
Forecast (CF1.6) Metadata Conventions, (iii) include information on the cell
centroids, boundary coordinates and cell areas of the working domain (needed
for the conservative remapping; see Sect. 2.5.1), (iv) report emissions in
the same units (kg m-2 s-1), (v) follow a unique pollutant naming
convention (e.g. “nox_no2” for NOx emissions expressed
as NO2 and “nox_no” for NOx emissions
expressed as NO), and (vi) follow a unique file data storage convention
(Sect. 2.4). Exceptionally, point source emission inventories (e.g. volcanic
degassing emissions) are stored in CSV files that include information on the
name of each source (e.g. name of the volcanoes), geographic coordinates,
altitude of injection of the emissions (in metres) and total amount of annual
emissions (kg s-1). For this type of inventory, no pre-processing
function is needed and the user can directly provide the data in the required
format.
All the pre-processing functions used to transform the original inventories
are included in the HERMESv3_GR code repository. It is important to note
that the original gridded emission inventories are not stored inside the
HERMESv3_GR database and that users need to download them from the
corresponding data provider's platform (e.g. EDGAR inventories are obtained
from http://edgar.jrc.ec.europa.eu/, last access: May 2019). This decision is based on the fact that
(i) some of the emission inventories that HERMESv3_GR can process cannot
be passed on to third parties without the data provider's consent and (ii) we
believe it is good practice that users access the original files through the
official source of information, so that the data providers can monitor the
usage of their datasets. With the aim of helping the users, the
HERMESv3_GR wiki contains a section that provides information on each
emission inventory, including the official downloading website or contact person
(see Sect. 5). This information is also included in a README section inside
each pre-processing function.
HERMESv3_GR only includes anthropogenic, biomass burning and volcano
emission inventories. Natural emissions such as biogenic NMVOCs, mineral dust
aerosols, ocean DMS (dimethyl sulfide) or lightning, and soil NO, which have functional
dependencies on meteorological variables, are assumed to be calculated online
during the execution of the corresponding atmospheric chemistry model (e.g. the NMMB-MONARCH dust module; Pérez et al., 2011) or using specific emission
models (e.g. MEGANv2.1; Guenther et al., 2012).
General configuration file
The general configuration options (e.g. start and end date, output file name,
working domain description) can be passed to HERMESv3_GR via a
configuration file, arguments or a combination of both. The arguments passed
by command line take priority over the ones that appear in the configuration
file.
The general configuration file is divided into four different sections (see
example in Appendix A).
General: this section defines the main paths of the
processing system (i.e. input, output, data), the name of the output emission
file and time step configuration parameters, including start and end dates,
temporal resolution (i.e. monthly, daily, hourly), and number and frequency of
time steps (e.g. 24 time steps every 3 h).
Domain selection: this section defines the working grid where
emissions will be calculated (e.g. spatial extension, horizontal and vertical
description). Currently, HERMESv3_GR can calculate emissions on grids with
the following map projections: regular lat–long for global domains and rotated
lat–long and Lambert conformal conic for regional domains. Other coordinate
systems and combinations (e.g. regular lat–long for regional domains) could be
added upon request. In this section of the configuration file, the user also
selects the format of the output emission file. Currently, HERMESv3_GR is
able to write NetCDF emission output files following the CMAQ, WRF-Chem or
NMMB-MONARCH conventions and can easily be extended to other projections and
atmospheric chemistry model conventions.
Emission inventory configuration: this section defines the path
to the file describing the configuration of the emission inventories (see
Sect. 2.4).
Profiles selection: this section defines the profile files that
will be applied to perform the vertical distribution, temporal disaggregation
and speciation treatment of the original emission inventories (see
Sect. 2.5.1 to 2.5.4).
Emission inventory configuration file
The emission inventory configuration file allows the user to select the base
emission inventories, pollutant sectors and species to combine and overlay
for their simulations and to choose the corresponding temporal, vertical
and speciation profiles and optional scaling and masking factors that will
be applied to the original emissions for their adaptation to the CTM
requirements.
Each line of the emission inventory configuration file belongs to a specific
emission inventory, pollutant sector and pollutant species group, for which
the user can define
country-specific scaling factors that multiply the original emissions;
country-specific masks that restrict the applicability of the original
inventory to a given region;
a vertical profile to distribute the original emissions across the vertical
layers of the working domain;
a monthly, daily and hourly profile to temporally disaggregate the original
emissions;
a speciation profile to map the original pollutants species to a specific
gas-phase and aerosol chemical mechanism.
Figure 2 shows five examples of emission inventory configuration files and
the resulting emission outputs calculated by HERMESv3_GR. The first column
(“ei”) indicates the name of the emission inventory, followed by the name
of the pollutant sector (“sector”), the reference year of the emission
inventory (“ref_year”), the requested pollutant species to be computed
(“pollutants”) and a field that indicates if this sector is activated or
not (“active”, 0 or 1). HERMESv3_GR combines all this information in
order to select the corresponding file from the emission data library. In the
first example (Fig. 2a), we selected the 2010 HTAPv2.2 OC transport emissions, while in the second one (Fig. 2b) this inventory is
combined with OC biomass burning emissions from GFASv1.2. The resulting
output shows an increase in emissions in those areas typically affected by
forest fires (e.g. central Africa).
Examples of organic carbon global emission outputs regridded onto a
0.5∘×0.7∘ global regular lat–long domain obtained
with HERMESv3_GR using five different versions of the emission inventory
configuration file: HTAP road transport (a), HTAP road
transport + GFAS (b), HTAP road transport with scaling factors
over China (5) and India (0.5) (c), HTAP road transport masking out
China and India (d), and ECLIPSE road transport (China and
India) + HTAP road transport (rest of countries) (e). The
corresponding emission inventory configuration files used in each example are
shown at the top.
The following two columns of the configuration file are optional parameters
that can be used to define country-specific scaling factors that multiply the
original emissions (“factor_mask”) and country-specific masks that
restrict the applicability of the original emissions to the defined region
(“regrid_mask”). Country-specific scaling factors are defined combining
the ISO 3166-1 alpha-3 country code of the targeted country
(https://unstats.un.org/unsd/tradekb/knowledgebase/country-code,
last access: May 2019) with a
numerical factor. Scaling factors for more than one country need to be
separated by a comma. Our third example (Fig. 2c) shows the original 2010
HTAPv2.2 OC transport emissions scaled by a factor of 5 in China and 0.5 in
India (CHN 5, IND 0.5). On the other hand, country-specific masks are defined
using the ISO 3166-1 alpha-3 country code preceded by either a “+” sign,
which restricts the applicability of the inventory only to the targeted
country, or a “-” sign, which restricts the applicability of the
inventory to all the countries except the targeted one. The masks defined by
the user can include more than one country. In the fourth example (Fig. 2d),
the HTAPv2.2 OC transport emissions are restricted to all countries except
China and India (- CHN,IND), while in the fifth example (Fig. 2e) the OC
transport emissions from ECLIPSEv5a are only applied to China and India
(+ CHN,IND).
The column “frequency” defines the temporal resolution of the inventory (i.e.
annual, monthly, daily). The column “path” defines the root path of the
emission files of each inventory. For all inventories, the root path consists
of the common “〈data_path〉” defined in the general
configuration file followed by the name of the institution providing the
inventory, the name of the inventory and the temporal frequency. As shown in
the first example, the root path of the HTAPv2.2 emission files is
“〈data_path〉/jrc/htapv22/monthly_mean”.
The alphanumeric codes specified in columns “p_vertical”, “p_month”
“p_day” “p_hour” and “p_speciation” refer to the vertical,
monthly, daily, hourly and speciation profile IDs assigned to process the
original emissions. All the codes are cross-referenced with text files where
the vertical, temporal and speciation numerical factors are defined. As shown
in the first example, the “p_hour” field allows the user to define
specific diurnal profiles for weekdays, Saturdays and Sundays, which may be
of relevance for certain pollutant sectors such as road transport (e.g. Mues
et al., 2014). For the GFASv1.2 biomass burning emissions (second example),
the “p_vertical” field is not filled with a vertical profile ID but with
two parameters that define (i) the maximum altitude of the fire plume
injection height (“method”) and (ii) how the emissions are distributed
across the layers below this maximum height (“approach”) (see Sect. 2.5.2).
Finally, the “comment” column is an optional field in which the user can
add an observation.
Emission core module
The following sections describe the main functionalities of HERMESv3_GR,
namely the spatial, vertical, temporal and speciation processing of the
original emissions and the writing of the output file.
Spatial regridding
This function regrids the selected inventories from their original source
grid to the user-defined destination grid. The regridding process consists of
two steps. The first step uses the Earth System Modeling Framework (ESMF)
regrid weight generation application (Hill et al., 2004) to calculate a
regridding weight matrix that describes how points in the source grid
contribute to points in the destination grid. The regridding method is
first-order conservative, which means that it preserves the integral of the
source field across the regridding. The weight for a particular source cell
i and destination cell j (Wi,j) is based on the ratio of the source
cell area overlapped with the corresponding destination cell area (Eq. 4):
Wi,j=fi,j⋅ASiADj,
where fi,j is the fraction of the source cell i contributing to
destination cell j and ASi and ADj are the
areas of the source and destination cells.
The second step is the multiplication of the emissions on the source grid by
the regridding weight matrix and, if previously defined by the user in the
emission inventory configuration file, the corresponding scaling and/or
masking factors to produce emissions on the destination grid.
Country-specific scaling and masking factors are generated with a gridded
country mask created during the initialization process. A current limitation
of the masking method is that it does not consider that country border cells
may include emissions of more than one country (i.e. it is assumed that all
emissions belong to the country that contains the largest fraction of the
cell). This limitation is mainly driven by the fact that most of the original
inventories do not provide the information on the emitting country (i.e
EDGAR, HTAP, ECLIPSE and CEDS report total emissions per grid cell but do not
specify which fraction corresponds to which country). Future improvements
will include the use of this information when given by the original inventory
(i.e. EMEP and TNO_MACC-iii).
In the case of point source inventories (e.g. volcano degassing emissions)
that are not reported on a regular grid but on specific lat–long locations,
the remapping is performed using a nearest-destination-to-source approach.
(When multiple source points are mapped into the same grid cell, the
destination is the sum of the source emission values.) For point source
emissions, neither scaling nor masking options are available, as the user
can directly modify and/or erase individual point sources in the
corresponding inventory input file.
The regridding process allows the user to remap the original emissions to
global or regional grids with flexible spatial resolutions and several map
projections, including regular lat–long, rotated lat–long, Lambert conformal
conic and Mercator. Other map projections (e.g. polar stereographic) can
potentially be added to the processing system in future releases. Figure 3
shows an example of the 0.1∘×0.1∘ HTAPv2.2 BC transport emissions regridded onto (a) a 1∘×1.4∘ global
regular lat–long domain, (b) a 0.1∘×0.1∘ regional rotated
lat–long domain, (c) a 50 km×50 km regional Mercator grid and (d) a
4 km×4 km regional Lambert conformal conic grid.
Examples of the HTAPv2.2 black carbon transport emissions regridded
onto a 1∘×1.4∘ global regular lat–long
domain (a), 0.1∘×0.1∘ rotated lat–long
domain (b), 50 km×50 km Mercator grid (c) and
4 km×4 km Lambert conformal conic grid (d). All maps are
displayed in an equirectangular projection.
In its current version, HERMESv3_GR does not use any type of spatial proxy
(e.g. land use, population data) during the remapping process. The main
reason for this is that most of the inventories currently available in the
emission data library have a spatial resolution that is higher and suitable
enough for global and regional air quality modelling (i.e. 0.1∘×0.1∘). However, for those inventories with low spatial resolution
(e.g. ECLIPSEv5a, 0.5∘×0.5∘) the application of sector-specific spatial proxies may be of importance when performing the remapping
onto finer working domains. Future work will focus on improving this
limitation by rebalancing the interpolation weights derived from ESMF with
weight factors based on spatial proxies.
Vertical distribution
Once the emissions are allocated to the horizontal grid, the next step is to
distribute them across the vertical layers of the destination domain. For
this task, two input files are required: (i) a CSV file containing a
description of the domain's vertical layers (i.e. approximate heights above
the ground of the top of each vertical layer, in metres) and (ii) a CSV file
containing a description of the vertical profile ID previously assigned by
the user in the emission inventory configuration file (i.e. fraction of
emissions assigned to each vertical layer, between 0 and 1). Using this
information, HERMESv3_GR interpolates the original emissions to the
modelling domain layers.
Note that HERMESv3_GR is currently designed as an off-line model and
cannot use or take into account the variability of the vertical layer depth
used by atmospheric chemistry models based on sigma vertical coordinates.
Consequently, the system cannot distribute the emissions to the exact sigma
levels of the models (which vary slightly in time and space) but to a set of
fixed vertical levels that are close to them. This assumption is in line with
previous modelling works (e.g. Mailler et al., 2013). Moreover, the impact of
this limitation can be assumed to be minor when compared to the large
uncertainty and variability associated with the emission vertical profiles
available in the literature (e.g. Bieser et al., 2011).
Figure 4 shows a graphical example of how the vertical distribution is
performed. In the example, the destination modelling domain is defined as
six layers (including the surface layer) with top heights of 75, 140, 190, 500 and 1200 m above ground level
(m a.g.l.). On the other hand, the proposed vertical profile ID (V001)
indicates that 0 % of the total emissions should be assigned between 0
and 100 m a.g.l., 10 % between 100 and 200 m a.g.l., and the remaining
90 % between 200 and 1000 m a.g.l. Note that the number and description
of the vertical layers used to define the vertical profiles do not have to
match the ones of the destination domain. HERMESv3_GR internally
interpolates homogenously the original weight fractions to the modelling
domain's layers taking into account the thickness of each layer.
Schematic representation of the emission vertical distribution
process implemented within HERMESv3_GR. Left side shows an example of a
vertical profile description (“V001”), which allocates 10 % of
emissions between 100 and 200 m a.g.l. and the remaining 90 % between
200 and 1000 m a.g.l. Right side shows an example of the vertical
description of the domain. Original vertical weights are interpolated to the
model vertical layers according to their thickness.
The user is able to define and assign any vertical profile to any emission
inventory, pollutant sector or pollutant species. Some suggested vertical
profiles for the energy and manufacturing industry (Bieser et al., 2011) and
the air traffic sectors (Olsen et al., 2013) are included in the
HERMESv3_GR database.
For the GFASv1.2 biomass burning inventory, the vertical emission
distribution is not performed with a fixed vertical profile but using two
parameters that define (i) the maximum altitude of the fire plume injection
height (“method”) and (ii) how the emissions are distributed across the
layers below this maximum height (“approach”). The fire plume injection
height is directly provided by GFASv1.2 following two different methods. The
first method (“sofiev”) is based on a semi-empirical parameterization
detailed in Sofiev et al. (2013). The second method (“prm”) consist of a
plume rise model described by Paugam et al. (2015). Regarding the approach,
users can also choose between two options. The first one (“uniform”),
consist of distributing uniformly all the emissions across the layers below
the maximum injection height. The second one (“50_top”) indicates that
50 % of all emissions are allocated to the vertical layer that intersects
with the maximum injection height, and the other 50 % are distributed
uniformly across the layers below the maximum injection height. The two
approaches are derived from the work by Veira et al. (2015), in which a sensitivity analysis is performed to see the impact of the vertical distribution
of forest fire emissions on black carbon concentrations. Although uniform
vertical distributions are used in most modelling studies, some work has
also shown that fires with high injection heights might emit a large
fraction of the emissions into the upper part of the plumes (e.g. Luderer et
al., 2006).
Similarly, in the case of point source emission inventories (e.g. volcano
degassing), the vertical distribution is not defined using a fixed vertical
profile but with the injection height field included in the input inventory
file, which can be adjusted individually for each point source. Emissions
are distributed homogenously across all the layers below the defined
injection height.
Temporal distribution
This process temporally distributes the emissions from their original
resolution (e.g. annual) to the one defined by the user (monthly, daily or
hourly). The emissions are multiplied by the user-defined monthly, weekly
and hourly weight factors, which are specified in separated CSV files with
the corresponding profile ID (i.e. “MXXX”, “DXXX” and “HXXX” for
monthly, weekly and hourly profiles, “XXX” being a three-digit numeric
code that starts at “001”). Alternatively, users can also provide the
temporal profiles using gridded files, which contain specific weight factors
for each grid cell.
As in the case of the vertical profiles, the user is left free to define and
assign any temporal profile to each pollutant sector and species. The
HERMESv3_GR database includes by default the monthly, daily and hourly
temporal profiles reported by LOTOS-EUROS (Denier van der Gon et al., 2011),
which are partially based on the GENEMIS project (Friedrich and Reiss, 2004)
and Hodzic et al. (2012).
HERMESv3_GR estimates emissions in coordinated universal time (UTC).
However, all the user-defined hourly temporal profiles need to be introduced
in local standard time (LST). For each cell of the destination grid and time
step, HERMESv3_GR converts the UTC simulation date to the corresponding
LST and assigns to it the adequate local temporal factor. This conversion is
done using as a basis a time zone grid created during the initialization
process. Having the time zone information on each cell allows HERMESv3_GR
to take into account daylight saving time (DST) changes, which do not
necessarily occur on the same date every year and in every country.
Figure 5 shows an example of the 6-hourly evolution (00:00, 06:00, 12:00 and
18:00 UTC) of the ECLIPSEv5a NOx transport emissions for a
24 h simulation performed on a 0.5∘×0.7∘ global grid for the
23 February 2015. It is observed how the diurnal variation of emissions in
different cities is in line with their local time. For instance, at
00:00 UTC time (first time step of the simulation), emissions in China are
at their morning peak (08:00 LST), whereas in Barcelona they are at their minimum
(01:00 LST) and in New York close to their afternoon peak (19:00 LST).
Global hourly NOx transport emissions
(mol s-1 m-2) derived from ECLIPSEv5a at 00:00 (a),
06:00 (b), 12:00 (c) and 18:00 (d) UTC and the
diurnal evolution estimated in the grid cells where different global cities
are located (e).
The application of gridded profiles can be of importance for those emission
sectors whose temporal variation is not uniform across space due to local
influences such as climatology conditions (e.g. the effect of temperature on
residential combustion emissions) or socio-demographic patterns (e.g. the
effect of farming practices on agricultural emissions). Figure 6 compares the
monthly agricultural soil NH3 emissions (March and June 2010)
reported by EDGARv4.3.2 in east Asia when using its default temporal profile
(Fig. 6a and c) and when combined with updated gridded temporal weights that
consider the effect of meteorology and crop calendars (Fig. 6b and d). These
gridded profiles were derived from the monthly inventories reported by Zhang
et al. (2018) for China and Paulot et al. (2014) for rest of the world, the seasonality of which is based on the temporal parameterizations reported by Skjøth
et al. (2011).
Monthly NH3 agricultural soil emissions (mol s-1 m-2)
estimated with HERMESv3_GR in east Asia (0.5∘×0.7∘) for March and June using the default temporal profiles
reported by EDGARv4.3.2 (a, c) and a gridded temporal profile
derived from the works of Paulot et al. (2014) and Zhang et
al. (2018) (b, d), and monthly weight factors obtained in China and
India for each case (e, f).
Results show large differences between the two results, especially in China
and India, the main emitter countries for this sector. According to Fig. 6e,
in China the default profile allocates most of the emissions in March,
whereas the updated temporal profile gives more weight to the months of June
and July. Similarly, the default profile presents a flat distribution over
India, whereas the improved profile indicates a peak during the months of May
and June (Fig. 6f). In both cases, the updated monthly distribution is more
in line with the seasonality of the NH3 volume mixing ratio derived
from the NASA's Atmospheric Infrared Sounder (AIRS) instrument (Warner et
al., 2017). The possibility offered by HERMESv3_GR to use gridded temporal
profiles derived from meteorological parameterizations can be extended to
other sources such as the residential combustion sector, for which the
application of the heating degree day approach has been proved to be
effective (e.g. Mues et al., 2014).
Speciation mapping
This process converts the pollutants provided in the original emission
inventories to the species needed by the atmospheric chemistry model of
interest and its corresponding gas-phase and aerosol chemical mechanism. The
conversion is performed using a speciation CSV file, in which the user
defines mapping expressions between the source inventory pollutants and
destination chemical species. Each mapping expression defines the
pollutant-to-species relationships and factors for converting the input
emissions pollutant to the desired model species.
These conversion factors are mass-based (i.e. gram of chemical
species per gram of source pollutant) for all source
inventory pollutants except for NMVOC, which requires a specific approach
(see paragraph below). The factors proposed for NOx assume a
split of 0.9 for NO and 0.1 for NO2 for all sectors (Houyoux et
al., 2000) except for road transport and biomass burning, for which specific
factors are derived from the works by Burling et al. (2010) and Rappenglueck
et al. (2013). In the case of PM2.5, the factors are derived from
multiple sources of information including the particular matter SPECIATE
(Simon et al., 2010) and SPECIEUROPE (Pernigotti et al., 2016) databases and
the works by Visschedijk et al. (2007) and Reff et al. (2009).
Source-specific organic matter (OM) to OC fractions are derived from Klimont
et al. (2017). For pollutants that have only one way of being speciated (e.g.
mapping the CO pollutant to the CO species) a default factor of 1 is proposed
for all sources and inventories. During the chemical speciation process,
HERMESv3_GR also performs a conversion from mass to moles for the
gas-phase species using a molecular weight CSV file included in the input
database of the system. Note that for NOx two molecular
weights are proposed since some inventories report emissions as NO
(“nox_no”, 30 g mol-1) and some as NO2 (“nox_no2”,
46 g mol-1).
For NMVOC emissions reported as individual chemical compounds (e.g.
C2H4O in GFASv1.2) or following the GEIA 25 NMVOC groups (e.g.
voc15 in EDGARv4.3.2_VOC), the proposed conversion factors are mole-based
(i.e. mol of chemical species per mol of source
pollutant) and are
derived from the mechanism-dependent mapping tables developed by
Carter (2015). In this case, the conversion from mass to moles of original
emissions is performed beforehand and also using the information on the
molecular weight CSV file.
Finally, for NMVOC emissions reported as a single category (i.e. as a sum of
n individual chemical compounds) (e.g. EMEP), the conversion factors
proposed in HERMESv3_GR for each inventory i, pollutant sector s and
chemical species e‾ (SFe‾,s,i) were
estimated as follows (Eq. 5):
SFe‾,s,i=∑j=1nXj,sMWj⋅Cj,e‾,
where Xj,s is the mass fraction of chemical compound j to total NMVOC
emissions for source s, MWj is the molecular weight of
chemical compound j and Cj,i is the mole-based conversion factor of
chemical compound j to destination chemical species e‾.
Xj,s values are obtained from the NMVOC SPECIATE database, while
MWj and Cj,i were obtained from Carter (2015). The unit of resulting proposed
conversion factors is mol of chemical species per gram of source
pollutant.
Example of speciation profiles included in HERMESv3_GR for
mapping the GFASv1.2 emissions to CB05 and AERO5 and the CEDS road transport
emissions to RADM2 and MADE/SOGARM chemical mechanisms. Any species not mentioned in this table are defined in Table A2 in the Appendix.
a GFASv1.2 NOx emissions are reported as NO. b CEDS NOx emissions are reported as NO2.
Each line of the speciation CSV file corresponds to a specific profile, which
is cross-referenced with the profile ID previously defined in the emission
inventory configuration file (i.e “EXXX”, “XXX” being a three-digit
numeric code that starts at “001”). The columns of the file refer to the
names of the destinations species, which need to match the atmospheric
chemistry model registry names of the emission variables. The HERMESv3_GR
database currently includes speciation profiles for the Carbon Bond 05 (CB05,
CB05e51) (Whitten et al., 2010) and the Regional Acid Deposition Model second-generation (RADM2) (Stockwell et al., 1990) gas-phase mechanisms, as well as
the fifth- and sixth-generation aerosol modules (AERO5, AERO6) (Roselle et
al., 2008; Appel et al., 2017) and the Modal Aerosol Dynamics Model for
Europe with the Secondary Organic Aerosol Model (MADE-SORGAM) aerosol
mechanisms (Ackermann et al., 1998; Schell et al., 2001). As in the case of the
temporal and vertical weight factors, the user can create their own speciation
profiles using other sources of information.
As an illustration, Table 2 shows two examples of proposed speciation profiles
and corresponding mapping expressions included in the HERMESv3_GR
database. The first one maps the original GFASv1.2 emission species to the
CB05 gas-phase and AERO5 aerosol chemical mechanisms. As shown, original
NOx (which is expressed as NO) is mapped to the CB05
species nitrogen monoxide (NO), nitrogen dioxide (NO2) and nitrous
acid (HONO) using mass-based conversion factors of 0.72 (“nox_no*0.72”),
0.18 (“nox_no*0.18”) and 0.1 (“nox_no*0.1”) (Burling et al., 2010).
The terminal olefin bond (OLE) CB05 species is composed of the following
GFASv1.2 NMVOCs: C8H16, C5H10, C3H6,
C4H8, C6H12 and 50 % of other high alkanes
(“c8h16+c5h10+c3h6+c4h8+c6h12+0.5*high alkanes”). On the other hand,
the difference between total primary PM2.5 and carbonaceous species
(OC and BC) is mapped to the other fine aerosols (PMFINE) AERO5 species
(“pm2.5-oc-bc”). In the second example, the CEDS road transport emissions
are mapped to the RADM2 gas-phase mechanism and the MADE-SORGAM aerosol
module. NOx emissions (which are originally reported as
NO2) are mapped to NO and NO2 using mass-based conversion
factors of 0.84 (“nox_no2*0.84”) and 0.16 (“nox_no2*0.16”)
(Rappenglueck et al., 2013). The toluene (TOL) RADM2 species is estimated
to be the sum of the voc14 (toluene) and 29.3 % of the voc13 (benzene) GEIA
groups (“0.293*voc13+voc14”) (Carter, 2015). Total BC emissions are
assumed to be 20 % in nucleation mode (ECI, “bc*0.2”) and 80 % in
accumulation mode (ECJ, “bc*0.8”) (Tuccella et al., 2012). As shown in
these examples, the mapping expressions can combine different types of
mathematical expressions (i.e. addition, subtraction, multiplication).
Writing module
The calculated emissions are written in NetCDF4 uncompressed files following
the conventions of the selected atmospheric chemistry model. During this
process, the following actions take place: (i) conversion of units and
(ii) inclusion of mandatory global attributes.
The emission core module of HERMESv3_GR is parallelized using a domain
decomposition strategy. This approach is considered to be the most effective
since emissions are computed independently for each destination grid cell and
no communication between cells is needed during the calculation process (see
Eq. 1). Moreover, applying domain decomposition also allows
decreasing the memory consumption per computational node.
Figure 7 shows a schematic representation of the domain decomposition
strategy applied in HERMESv3_GR. During the spatial regridding, the
destination working domain is divided into vertical sections, maintaining
each column as indivisible. The number of divisions is equal to the number of
processors to be used (P_0, P_1, …), which is defined by
the user. The emission regridding process is performed independently in each
processor and for each vertical section. The maximum number of cores to be
used is equal to half of the number of columns of the destination domain.
This limitation is defined by the ESMF software, which needs, at least, two
complete columns to perform the spatial regridding. The 2-D regridded
emissions are kept in memory until the writing operation. During this task,
the vertical (v0, v1, …) and temporal (t0, t1, …)
weight factors previously estimated in the vertical and temporal distribution
functions are applied to each emission subdomain in order to transform the 2-D
arrays (longitude, latitude) into 4-D arrays (time, vertical layer, longitude,
latitude). This strategy allows reducing the time during which the memory
consumption is higher. Finally, each worker process simultaneously writes its
result to a common NetCDF4 file, which ensures the gathering of the different
subsets of the working domain into a single output. Alternatively, the user
can select the option of executing the writing function in serial mode (i.e.
using only one processor).
Schematic representation of the parallelization of the emission core
module of HERMESv3_GR (a) and computational times obtained for each functionality
(regrid, vertical, temporal, speciation, and parallel writing or serial writing)
for the scalability test performed (b). The destination working
domain is divided into vertical sections, according to the number of
processors to be used (P_0, P_1, …). Vertical (v0, v1, …)
and temporal (t0, t1, …) weight factors are applied to each section in
order to transform the 2-D arrays (longitude, latitude) into 4-D arrays
(time, vertical layer, longitude, latitude).
A scalability test was performed using the supercomputer MareNostrum 4, which
is hosted by the BSC, in order to determine the capability of HERMESv3_GR to
scale up the emission calculation process. MareNostrum 4 is a supercomputer
based on Intel Xeon Platinum processors at 2.1 GHz from the Skylake
generation. It is a Lenovo system composed of SD530 compute racks, an Intel
Omni-Path high-performance network interconnect and running SuSE Linux
Enterprise Server as an operating system. It consists of 48 racks housing 3456
nodes, each one equipped with 48 cores and 96 Gb of memory (2 Gb per core)
(https://www.bsc.es/marenostrum/marenostrum, last access:
May 2019). HERMESv3_GR was executed
using a number of cores from 1 to 510, doubling the number in each successive
test until using all cores of a node (i.e. 1, 2, 4, 8, …, 48) and then
adding 48 (a whole node) until 510 (i.e. 96, 144, …, 510). Two
separate sets of tests were performed: one using the parallel writing
function and another using the serial approach.
All the tests were performed using a rotated lat–long destination grid of
0.1∘×0.1∘ with 701 rows, 1021 columns and 48 vertical layers
covering North Africa, Europe and the Middle East (Fig. 3b). Hourly CB05 and
AERO5 speciated emissions were estimated for 24 time steps using as input all
the available emission pollutants and sectors of the TNO_MACC_III
(Europe) and HTAPv2.2 (remaining countries) inventories.
As shown in the stacked area chart of Fig. 7, the increased number of cores
used in the simulations speeds up the computations. The total execution time
decreases from 4842.6 s (1 core) to 1247 s (510 cores), the lowest value
being observed when using 32 cores (800.7 s). The most time-demanding
function changes according to the number of cores used. For 1 to 8 cores,
most of the computational work is done during the spatial regridding (between
54 % and 34 %) and the temporal distribution (between 39 % and
25 %), whereas for the other cases (16 to 510 cores), the writing process
increasingly becomes the main time consumer (up to 84 % of the total time
when using 510 cores). These results clearly indicate that the parallel
writing function does not scale properly. The reason behind this behaviour
comes from the fact that the NetCDF4 Python library writes the results in
row-major order (C style), while during the spatial regridding ESMF divides
the domain into vertical sections (column-major order; FORTRAN style). For each
vertical division, NetCDF4 Python has to call the writing function as many
times as the number of rows that conform to the domain. Subsequently, an
increase in cores (i.e. an increase in vertical divisions) directly increases
the execution time of the parallel writing process. The performance of the
system when applying the serial writing approach (black line with markers)
varies as a function of the processors used. For a low number of cores (i.e.
1 to 48), parallel writing is faster than serial writing. Nevertheless,
when using 96 processors or more, serial writing becomes faster since its
execution time remains almost constant, in contrast to what is experienced
with the parallel approach. This fact allows reducing the total execution
time by a factor of up to 1.5 when using 510 cores. The potential
disadvantage of using serial writing is that for large emission
experiments (i.e. large domains) the user may run into memory problems since
all the data need to be treated by a single processor. In the present test,
we solved this issue by using all the memory resources of a compute node
without sharing them with other users (i.e. 96 Gb). Considering the
advantages and disadvantages of each method, both the serial and parallel
writing approaches are enabled in HERMESv3_GR.
The low performance of the parallel writing function will be addressed in
future versions of HERMESv3_GR. For this, two strategies will be tested,
including (i) the integration of an I/O (input–output) server that allows writing completed
rows in row-major order and (ii) the use of other libraries specific for
parallel writing (e.g. pnetcdf). Despite this shortcoming, the current
parallelization strategy allows HERMESv3_GR execution time to be minimized
to less than 15 min per run (32 cores), which can be considered
acceptable in an operational environment.
Implementations
HERMESv3_GR has been successfully tested in different atmospheric
chemistry models. The system is currently implemented within the
NMMB-MONARCH, which contributes to the multi-model ensemble forecasts of the
International Cooperative for Aerosol Prediction (ICAP)
(http://icap.atmos.und.edu/, last access:
May 2019). HERMESv3_GR has also been
coupled with the CMAQ in the framework of the AIRE-CDMX air quality
forecasting system for Mexico City
(http://www.aire.cdmx.gob.mx/pronostico-aire/, last access:
May 2019). In the first case,
HERMESv3_GR is used to provide global primary aerosol emissions to NMMB-MONARCH, whereas in the AIRE-CDMX it is used to process the
biomass burning emissions reported by GFASv1.2. Besides the two
aforementioned implementations, HERMESv3_GR has been also used to perform
simulations with the CALIOPE air quality forecasting system, which is based
on CMAQ (http://www.bsc.es/caliope/en/forecasts?language=en, last
access: May 2019) and in several tests
using the WRF-Chem model.
Conclusions
This paper presents HERMESv3_GR, a stand-alone multi-scale emission
processing system that estimates gas and aerosol emissions for use in
atmospheric chemistry models. HERMESv3_GR is designed to combine and
process existing inventories for the generation of emission input files for
global and regional air quality modelling. During the execution, emissions
from different inventories, sources and species are combined and regridded to
the destination domain and are vertically and temporally disaggregated,
speciated and converted to the required format of the atmospheric chemistry
model of interest. HERMESv3_GR is driven by configuration files that
provide a flexible and transparent platform for the design and implementation
of intercomparison and sensitivity modelling experiments.
HERMESv3_GR represents an effort to homogenize the currently available
information on emission inventories and to process them in a transparent
and flexible way to produce emission outputs that can be used directly by
multiple atmospheric chemistry models.
There are several features that makes HERMESv3_GR an unique emission
processing system, including the following.
User-defined grid and choice between different map projections: emissions
can be computed on any global or regional domain with a regular lat–long,
rotated lat–long, Mercator or Lambert conformal conic projection.
Choice between different emission inventories: the emission data library of
HERMESv3_GR includes current state-of-the-art global and regional
inventories that cover different sources (anthropogenic, biomass burning,
volcanoes), pollutants (ozone precursor gases, acidifying gases and primary
particulates) and base years (past, present and future). Moreover,
country-specific scaling and masking factors defined by the user can be
applied to the base inventories in order to combine and adjust them.
Choice between different vertical, temporal and speciation profiles:
HERMESv3_GR includes a dataset of profiles reported by the literature, but
it also allows the user to add its own weighting factors for any pollutant
sector and species. Additionally, the processing system is able to combine
base inventories with gridded temporal profiles, which can be of importance
for those source sectors whose temporal variation is not uniform across space
(e.g. residential combustion emissions driven by temperature).
Choice between different atmospheric chemistry models: the generated emission
files can be used as input for the CMAQ, WRF-CHEM and NMMB-MONARCH chemical
transport models.
Choice between different chemical mechanisms: base pollutants can be mapped
to several gas-phase and aerosol chemical mechanism, including CB05, CB05e51,
RADM2, AERO5, AERO6 and MADE-SORGAM. All these mechanisms are widely used in
the air quality modelling community.
Parallel implementation: the emission core module of HERMESv3_GR is
parallelized using a domain decomposition strategy, which allows decreasing
the execution time and memory consumption of the model. This feature can be
of importance when using the processing system in operational air quality
forecasting systems, for which the simulations need to be completed within
the required time constraints.
Several emission outputs obtained with HERMESv3_GR are provided in this
paper to illustrate its potential. The software is implemented within
NMMB-MONARCH and CMAQ in the framework of the ICAP multi-model ensemble and
the AIRE-CDMX air quality forecasting system for Mexico City, respectively.
It is worth noting that despite providing a flexible and simplified framework
for the processing of emissions, users should have a clear knowledge of the
original inventories when using HERMESv3_GR. Combining parts from
different inventories could lead to substantial errors (e.g. double counting)
because the definition of what is included or excluded in certain sectors
and/or inventories can differ significantly (e.g. agricultural waste burning
emissions are sometimes included under the agriculture source sector and
sometimes excluded). It is therefore recommended that users carefully check
the original descriptions of each inventory before using them. With the aim
of facilitating this task, the HERMESv3_GR wiki (see Sect. 5) includes a
section with a general description of each inventory and links to the
official references.
Future work will consider the expansion of the emission data library to
include regional inventories of regions such as Asia or America, emission
datasets that are currently being developed in the framework of the
Copernicus Atmosphere Monitoring Service (CAMS), as well as datasets that
report emissions of greenhouse gases, so that HERMESv3_GR can also serve
as input for climate modelling. Other efforts will focus on the
implementation of a functionality to handle the remapping of emissions to
unstructured destination grids (e.g. octahedral grid), which are starting to
be widely used in global models due to their computational efficiency and
effective resolution, as well as on the inclusion of sector-dependent spatial
proxies during the remapping process and the improvement of the scalability
of the writing function.
Code availability
The HERMESv3_GR code package, pre-processing functions
to homogenize the emission inventories (listed in Table 1), sample
configuration and ancillary input files (vertical, temporal and speciation
profiles), and test case data are available at the following gitlab
repository: https://earth.bsc.es/gitlab/es/hermesv3_gr (last access:
May 2019).
A wiki of the processing system with further instructions is also included in
the gitlab repository, as well as the links and references for downloading
and citing the original gridded emission inventories that HERMESv3_GR can
process. The required libraries need to be installed by the user in the
computer infrastructure on which the processing system is planned to be run.
HERMESv3_GR general configuration file
(hermes.conf).
Parameters and examplesDescription and commentsGenerallog_level = 3Defines the logging level, which is associated with the amount of information that will appear in the log file. The options are 1, 2 or 3 (recommended for debugging)〉input_dir = /gpfs/projects/HERMESv3/INDefines the general input directory of the model〉data_path = /gpfs/scratch/data/Defines the common directory path where all the homogenized emission inventories used by HERMESv3_GR are stored. The complete path to each specific emission inventory file is specified in the emission inventory configuration file〉output_dir = /gpfs/projects/HERMESv3/OUTDefines the directory where the output emission files will be stored〉output_name = HERMESv3_〈date〉.ncName of the output emission file. The string 〈date〉 is automatically replaced by the starting date of the simulation day. The complete path to the output file is the combination of output_dir and output_name.〉start_date = 2010/01/01 00:00:00Starting date of the simulation (in UTC). Date formats accepted by HERMESv3_GR include – YYYYMMDD: 20150101 – YYYYMMDDhh: 2015010100 – YYYYYMMDD.hh: 20150101.00 – YYYY/MM/DD: 2015/01/01 – YYYY/MM/DD_hh: 2015/01/01_00 – YYYY/MM/DD_hh:mm:ss: 2015/01/01_00:00:00 – YYYY/MM/DD hh:mm:ss: 2015/01/01 00:00:00 – YYYY-MM-DD_hh: 2015-01-01_00 – YYYY-MM-DD_hh:mm:ss: 2015-01-01_00:00:00 – YYYY-MM-DD hh:mm:ss: 2015-01-01 00:00:00.〉end_date = 2010/01/02 00:00:00Optional: ending date of the simulation (in UTC). If it is not set, then end_date = start_date.〉output_timestep_type = hourlyTemporal resolution of the output file. The options are – hourly – daily – monthly.〉output_timestep_num = 24Number of time steps to simulate〉output_timestep_freq = 1Frequency between time stepsDomainoutput_model = CMAQDefines the format of the output emission file as a function of the atmospheric chemistry model conventions. Current options are – MONARCH – CMAQ – WRF_CHEM.output_attributes=〈input_dir〉/data/cmaq_global_attributes.csvPath to the file that contains the global attributes that need to be included in the output NetCDF file according to the corresponding chemical transport modeldomain_type= lccDefines the grid projection on which the emission fields will be generated. Options are as follows. – Global: regular lat–long grid – Rotated: rotated lat–long grid – lcc: Lambert conformal conic grid – Mercator: Mercator grid.vertical_description =〈input_dir〉/data/profiles/vertical/ vert.csvPath to the file that contains the vertical description of the desired outputaux_files_path =〈input_dir〉/data/aux_files/〈domain_type〉_〈res〉Path to the directory where the necessary auxiliary files (e.g. time zones file) will be created if they do not exist. If they already exist, HERMESv3_GR will just read them.# if domain_type == global: inc_lat = 0.5 inc_long = 0.703125Parameters that define a global regular lat–long grid: – inc_lat: latitudinal grid resolution (degrees) – inc_long: longitudinal grid resolution (degrees).# if domain_type == rotated: centre_lat = 35 centre_long = 20 west_boundary =-51 south_boundary =-35 inc_rlat = 0.1 inc_rlon = 0.1Parameters that define a regional rotated lat–long grid: – centre_lat = central geographic latitude of the grid (non-rotated degrees) – centre_long = central geographic longitude of grid (non-rotated degrees, positive east) – west_boundary = grid's western boundary from centre point (rotated degrees) – south_boundary = grid's southern boundary from centre point (rotated degrees) – inc_rlat = latitudinal grid resolution (rotated degrees) – inc_rlon = longitudinal grid resolution (rotated degrees).
Continued.
Parameters and examplesDescription and commentsDomain# if domain_type == lcc: lat_1 = 37 lat_2 = 43 long_0 =-3 lat_0 = 40 nx = 278 ny = 298 inc_x = 1000 inc_y = 1000 x_0 = 253 151.59375 y_0 = 43 862.90625Parameters that define a regional Lambert conformal conic grid: lat_1 = standard parallel 1 (in degrees). lat_2 = standard parallel 2 (in degrees). long_0 = longitude of the central meridian (in degrees). lat_0 = latitude of the origin of the projection (in degrees). nx = number of grid columns. ny = number of grid rows. inc_x =x-coordinate cell dimension (in metres). inc_y =y-coordinate cell dimension (in metres). x_0 =x-coordinate origin of grid (in metres). y_0 =y-coordinate origin of grid (in metres).# if domain_type == Mercator: lat_ts =-2.84 long_0 =-79.16 nx = 99 ny = 81 inc_x = 1000 inc_y = 1000 x_0 =-49500.13899 y_0 =-355986.692Parameters that define a regional Lambert conformal conic grid: lat_ts = latitude of true scale (in degrees). long_0 = longitude of projection centre (in degrees). nx = number of grid columns. ny = number of grid rows. inc_x =x-coordinate cell dimension (in metres). inc_y =y-coordinate cell dimension (in metres). x_0 =x-coordinate origin of grid (in metres). y_0 =y-coordinate origin of grid (in metres).emission_inventory_configurationcross_table =〈input_dir〉/conf/EI_conf.csvDefines the path to the emission inventory configuration fileemission_inventory_profilesp_vertical =〈input_dir〉/data/profiles/vertical/vert_prof.csvDefines the path to the file that contains the vertical profiles.p_month =〈input_dir〉/data/profiles/temporal/month.csvDefines the path to the file that contains the monthly temporal profiles.p_day =〈input_dir〉/data/profiles/temporal/day.csvDefines the path to the file that contains the daily temporal profiles.p_hour =〈input_dir〉/data/profiles/temporal/hour.csvDefines the path to the file that contains the hourly temporal profiles.p_speciation =〈input_dir〉/data/profiles/speciation/spec_cb05aero5.csvDefines the path to the file that contains the speciation profiles.molecular_weights =〈input_dir〉/data/profiles/speciation/MW.csvDefines the path to the file that contains the molecular weights of the input pollutant species.world_info =〈input_dir〉/data/profiles/temporal/tz_iso3166.csvDefines the path to the file that contains the mapping between worldwide time zones and country ISO3 codes. This file is used to create the time zone grid for the temporal disaggregation of the emissions.
Description of the species included in the CB05, RADM2, AERO5 and MADE-SORGAM chemical mechanisms.
Chemical mechanismSpeciesDescriptionCB05ALD2AcetaldehydeALDXC3+ aldehydesBENZENEBenzeneCOCarbon monoxideETHEtheneETHAEthaneETOHEthanolFORMFormaldehyde and parts of molecules that rapidly form formaldehydeHONONitrous acidIOLEInternal olefin carbon bond (R-C=C-R)ISOPIsopreneMEOHMethanolNH3AmmoniaNONitric oxideNO2Nitrogen dioxideOLETerminal olefin bond (R-C=C)PARParaffin carbon bond (C-C)
MG conceived and coordinated the development of HERMESv3_GR,
prepared all the input databases (vertical, temporal and speciation profiles)
and selected the inventories to be included in the emission data library.
MP helped preparing the input databases and
perform software tests. CT developed the HERMESv3_GR code and ran the
experiments to test the performance of the parallel implementation. OJ helped
conceive HERMESv3_GR and its implementation within NMMB-MONARCH. CPGP helped conceiving HERMESv3_GR and supervised the work. MG
prepared the paper with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The research leading to these results has received funding from the
Ministerio de Economía y Competitividad (MINECO) as part of the PAISA
project CGL2016-75725-R and the NUTRIENT project CGL2017-88911-R. The authors
acknowledge PRACE for awarding access to Marenostrum4 based in Spain at the
Barcelona Supercomputing Center through the Tier-0 HHRNTCP and Tier-0 EEDMC
projects. Carlos Pérez García-Pando acknowledges long-term support
from the AXA Research Fund, as well as the support received through the
Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of
Economy and Competitiveness. The authors would also like to thank the two
anonymous referees for their thorough comments, which helped improve the
quality of the paper.
Review statement
This paper was edited by Augustin Colette and reviewed by two anonymous referees.
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