Poulidis et al.
Progress in Earth and Planetary Science(2024) 11:37
https://doi.org/10.1186/s40645-024-00642-x

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Chasing parts in quadrillion: applicationsof dynamical downscaling in atmosphericpollutant transport modelling during field
campaigns
Alexandros Panagiotis Poulidis1* , Sarah‑Lena Seemann1, Hans Schlager2 and Mihalis Vrekoussis1,3,4Abstract
Atmospheric transport and dispersion models (ATDMs) are widely used to study and forecast pollution events. In
the frame of the “Effect of Megacities on the transport and transformation of pollutants on the regional to global
scales” (EMeRGe) project, ATDM forecasts were carried out to identify potential airborne sampling areas of perfluoro‑
carbons (PFCs) emanating from controlled PFC releases. The forecasts involved short-distance transport over smallscale topographic maxima (Manilla; Philippines), short-distance transport over large-scale topographic maxima(Taipei, Taiwan) and long-distance transport over mixed topography (Nanjing, China, sampled over Taiwan). In situ
aircraft measurements of PFC mixing ratios down to a few parts per quadrillion (ppqv) provide us with a uniquedataset to explore the added benefits of dynamical downscaling. Transport simulations were repeated using
FLEXPART driven by ERA5 and IFS meteorological data and FLEXPART-WRF with dynamically downscaled IFS data
down to 1.1 km and four PBL parametrisations. Of the three cases studied, dynamical downscaling led to significant
differences for the Manilla and Taipei releases that can be interpreted through changes in the modelled orographic
flow regimes. The choice of PBL scheme also significantly impacted accuracy, but there was no systematically betterperforming option, highlighting the benefits of ensemble forecasting. Results show how convergence and diver‑gence between ensemble members can be utilised to help decision-making during field campaigns. This study high‑lights the role that dynamical downscaling can play as an important component in campaign planning when dealing
with observations over orographically complex areas.
Keywords Atmospheric pollutant transport, Orographic flow, Ensemble, WRF, FLEXPART​*Correspondence:
Alexandros Panagiotis Poulidis
alepou@uni-bremen.de1
Institute of Environmental Physics (IUP), University of Bremen,28359 Bremen, Germany
2
Institute of Atmospheric Physics, German Aerospace Center (DLR),
82234 Oberpfaffenhofen, Germany
3
Center of Marine Environmental Sciences (MARUM), Universityof Bremen, 28359 Bremen, Germany
4
Climate and Atmosphere Research Center (CARE‑C), The Cyprus
Institute, 2121 Nicosia, Cyprus

1 Introduction
Events leading to the release of significant amounts
of atmospheric pollutants are ubiquitous around theglobe. Releases from natural activities represent a relatively consistent forcing in the global emission budget
and include forest wild fires that can, among others,
emit carbonaceous aerosols, carbon monoxide (CO)and dioxide (CO2 ), methane (CH4 ), and non-methane organic compounds (NMOC) (Van der Werf et al.
2010; Akagi et al. 2011; Urbanski 2014; Alvarado et al.
2020; Daskalakis et al. 2022; Lin et al. 2023a) and volcanic eruptions that can release volcanic ash and gases;© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

primarily CO2 , sulphur dioxide (SO2 ), and brominemonoxide (BrO) (Bobrowski et al. 2007; von Glasow
et al. 2009). Short-lived, high-impact releases fromanthropogenic activities tend to be less consistentand generally occur due to accidents at power plants,
pipelines or chemical storage facilities that can lead toatmospheric releases of pollutants, such as SO2 , nitrogen oxides (NOx ), CO2 and CH4 (Rashad and Hammad 2000; Chatzimouratidis and Pilavachi 2007; Jiaet al. 2022), aerosols (e.g. ammonium nitrate; NH4
NO3 ; Ur Rehman et al. 2021) and radioactive material
such as caesium (134,137Cs), xenon (133 X) and iodine(129,131I) (Stohl et al. 2012; Le Petit et al. 2014; Achim
et al. 2014). In this study, the term “pollutants” will be
used as a catch-all term for the various scenarios justdescribed; however, the main focus will be on aerosols
and long-lived or chemically inert species.
In all cases, the transport of released pollutants canbe influenced by a variety of factors, including specific weather patterns (Fero et al. 2009; Beig et al. 2021),
topography (Cécé et al. 2016; Mathieu et al. 2018;
Poulidis et al. 2018; Quan et al. 2020), chemical transformations (Yadav et al. 2016; Daskalakis et al. 2022) andthe physical properties of the pollutants (Bagheri et al.
2015). Before deposition or dilution to background levels, the released material can travel large distances andhave adverse effects on air quality and human health(Carvalho et al. 2011; Schmidt et al. 2015; Santoso et al.
2020; Stewart et al. 2022; Milford et al. 2023) and the climate (Stocks et al. 1998; Robock 2000; Gillett et al. 2004;
Randerson et al. 2006; Carvalho et al. 2011; Urbanski
2014; Bethke et al. 2017; Aubry et al. 2022; Marshall et al.
2022), creating complex international hazards. As such,
understanding atmospheric pollutant transport is criticalfor developing effective air quality management strategies and mitigation measures (Gulia et al. 2015; Miranda
et al. 2015; Qu et al. 2023).
Atmospheric transport and dispersion models(ATDMs) are a critical component of pollution hazard
mitigation, used to predict the transport and depositionof pollutants in the atmosphere (Eliassen 1980; Rentai
et al. 2011; Folch 2012; Leelőssy et al. 2014). Such models can be used to cover hypothetical scenarios (pre-event
hazard assessment) and as emergency forecast tools. In
the case of emergencies, by accurately predicting the
movement of pollutants, it is possible to implement targeted mitigation measures to reduce the exposure of people and ecosystems to harmful pollutants, for example,
by issuing warnings and evacuating potentially affectedareas (Kukkonen et al. 2012; Webster et al. 2012; Beckett
et al. 2020). Resulting products of hazard assessment (e.g.
hazard maps) are used to inform stakeholders and local
authorities (Jenkins et al. 2015).

Page 2 of 28

Global meteorological datasets (either forecast data orreanalysis products) are commonly used to drive ATDMs
as they are easily accessible and available from organisation such as the National Center for AtmosphericResearch (NCAR) in the United States or the European
Centre for Medium-Range Weather Forecasts (ECMWF)
in Europe (e.g. Arnold et al. 2015; Macedonio et al. 2016).
The use of such datasets can provide valuable information on large-scale weather patterns and, as such, is especially relevant when it comes to transport over synoptic
scales (Takemura et al. 2011), but, at their current resolutions, is not able to accurately represent small-scale or
quickly evolving variations in weather conditions thatcan significantly impact pollutant transport and dispersion (Costa et al. 2013). These limitations can result ininaccurate predictions of pollutant concentrations, leading to poor decision-making for air quality management,
as seen, for example, after the Eyjafjallajökull eruption in
2010, which, partially due to the inability of ATDMs tocorrectly represent the dispersion and diffusion of fineash in the atmosphere (Beckett et al. 2020), led to an
overestimation of risk that cost a total of $2 billion to theaviation industry (Budd et al. 2011; Schmitt and Kuenz
2015).
One way to improve the accuracy of ATDMs is by using
downscaling techniques to generate high-resolution data
based on global data. In the case of dynamical downscaling, a regional model is used to provide high-resolution
meteorological data with improved accuracy, due to on
an increase in directly resolved physical phenomena, as
well as the improved representation of the local topography and land use. The overall impact of these factors
is an improved representation of the Planetary Boundary Layer (PBL) and local circulations (De Meij et al.
2019; Takemi and Ito 2020). This is particularly important in areas of complex topography, where near-surface
meteorological flow is driven by the complex interplay
between atmospheric stability and wind characteristics
at the PBL and localised circulations enforced by the
topography and thermal-gradient flows, such as the sea/land breeze and mountain/valley breeze circulations(Lu and Turco 1994). Local topographical flow phenomena are commonly known as “orographic effects”
and include mountain waves (i.e. oscillations after theatmosphere impinges on a mountain; Smith 1989), strong
downslope winds in the lee of mountains (Durran 1990)
and flow stagnation points in the windward side (Smith
1989; Smolarkiewicz and Rotunno 1989). These orographic effects depend both on the characteristics of themountain (Ólafsson and Bougeault 1996), as well as theincoming flow (Smolarkiewicz and Rotunno 1989). The
expected orographic flow regime (i.e. flow around or overthe mountain) is commonly characterised by the Froude

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

number ( Fr = UN −1 H −1, where U is the incoming windin m s−1, N is the Brunt-Väisälä frequency in s−1, and H is
the mountain height in m). The combination of modified
PBL characteristics and orographic effects can significantly impact areas of high pollution concentration andis, thus, especially important when dealing with transport
over densely populated urban areas (Cécé et al. 2016).
Despite potential advantages, dynamical downscaling is not without its limitations. To begin with, regionalmodels can be computationally expensive and requiresignificant expertise (Xu et al. 2019). Additionally, theaccuracy of the results depends on the quality of the
input data and the choice of model configuration options(Jankov et al. 2007; Etherton and Santos 2008; Mulena
et al. 2016; Poulidis and Iguchi 2021). Due to these sensitivities, standardising model configuration is difficultand evaluation needs to be carried out in a case-by-casebasis. To account for these inherent uncertainties in themodels, an ensemble approach is usually necessitated,
i.e. the statistical analysis of multiple model simulations
within a well-defined parameter space (Deppe et al. 2013;
Angevine et al. 2014; Kioutsioukis et al. 2016).
Here, the benefits of dynamical downscaling werestudied through the lens of atmospheric/chemical field
campaign management, specifically looking at tracer gas
release experiments. Tracer gas release experiments are
an important complementary method to acquire model
evaluation data (Ren et al. 2015) and involve the releaseof a chemically inert tracer gas, e.g. perfluorocarbons(PFC; Ren et al. 2015) or Krypton-85 (85Kr; Connan et al.
2013) into the atmosphere. Releases can take place undercontrolled conditions, allowing for a choice over meteorological and topographic complexity. After the release,pollutants can be tracked and measured directly, forexample through aircraft observation missions (Ren et al.
2015) or in situ measurements (Connan et al. 2013).
Dedicated dynamical downscaling during gas tracer
release field campaigns can be used for improved weather
forecasting, allowing for a better understanding of local
meteorological and chemical processes, ultimately leading to a better representation of the tracer plume. Thiscan be especially relevant when considering that target
mixing ratios can be down to units of parts per quadrillion with respect to volume (ppqv; Ren et al. 2015).
Considering the cost of such field campaigns and the
difficulty of repetition, accurate knowledge of the target
region is of utmost importance. Despite this, as air tracer
release campaigns require careful planning that spans
multiple organisations, such as local universities, civil
protection and aviation authorities, it can be difficult toallocate resources for dynamical downscaling. As such,use of atmospheric transport modelling has historically
been carried out over global meteorological datasets

Page 3 of 28

instead (Stohl et al. 1998), a choice that has persisted
until recent years (Connan et al. 2013; Freitag et al. 2014;
Ren et al. 2015; Andrés Hernández et al. 2022).
Considering the inherent difficulties but potential benefits of operational dynamical downscaling during a field
campaign, it is clear that further research into the potential advantages is necessary. The latter are examined
here, using tracer gas releases during the “Effect of Megacities on the transport and transformation of pollutantson the regional to global scales” (EMeRGe) campaign(Andrés Hernández et al. 2022) as a test case and looking into the potential differences in target areas or measurement forecasted by the ATDM during the campaign.
The rest of the paper is organised as follows. Section 2
contains detailed information on the EMeRGe campaign
PFC releases and a full description of the modelling configuration used. Section 3 includes a case-by-case presentation of model results against observations, an analysisof the overall performance of the models employed, andan analysis of the predicted areas of high PFC mixing
ratios. Section 4 is used to discuss the role of orographic
effects on the cases studied here and the potential benefits of dynamical downscaling during field campaigns
and emergencies, while Sect. 5 provides a summary of
the main conclusions.

2 Methodology
2.1 The EMeRGe campaign—regional setting
and observations

The release of PFC tracer gas was a key component during the EMeRGe campaign; an international effort tostudy the impact of major population centres (MPCs) on
the atmosphere in Europe and Asia (Andrés Hernández
et al. 2022; Förster et al. 2023; Lin et al. 2023a), with theprimary objective to improve understanding of MPC outflow plume photochemical and heterogeneous processing
along transport pathways. Measurements were carriedout using the Deutsches Zentrum für Luft- und Raumfahrt (German Aerospace Center; DLR) High Altitudeand LOng range research aircraft (HALO, http://www.
halo-spp.de/, last access: 16 June 2023) with a wide range
of instruments. The measurements were conducted inJuly 2017 in Europe and in March and April 2018 in Asia.
During the EMeRGe campaign, PFC gases were released
to establish Lagrangian connections between source anddownstream measurements aboard HALO and thus help
identify individual MPC sources of other chemical emissions. HALO flight paths were partially decided based on
Lagrangian transport model-based dispersal forecasts
driven by ECMWF’s Integrated Forecasting System (IFS;
Roberts et al. 2018).
PFC tracer gas mixing ratios were measured during
the EMeRGe campaign using the perfluorocarbon tracer

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

system (PERTRAS) system, developed by the DLR asa companion to Lagrangian aircraft experiments, firstused in the “Stratospheric ozone: Halogen Impacts in a
Varying Atmosphere” (SHIVA) campaign in November
2011 (Ren et al. 2015). PETRAS includes a tracer release
unit, an adsorption tube sampler, and an analysing system (TD-GC-NICIMS) and is highly portable, allowing
its use in different platforms (ground station, ship, andaircraft). The tracer release rate ranges between 0.1 and
500 ml min−1 (2% uncertainty). It can release up to a totalof 30 kg of three different PFCs. The system’s laboratory detection limit was measured at 1.2 ppqv. The tube
samples were analysed in the DLR laboratory using a gas
chromatic technique as described in detail by Ren et al.
(2014).
Data from the three release experiments during the
EMeRGe-Asia campaign (March–April 2018) were analysed here (Table 1). PFCs were released at three locations: Manilla, Philippines (afternoon release, building
terrace), Taipei, Taiwan (afternoon release, building terrace), and Nanjing, China (morning release, building
terrace). The release experiments and associated simulations will be referred to based on the name of the releaselocation for the remainder of this study. In total, twotypes of PFCs were released, perfluoromethylcyclohexane (PMCH; Nanjing) and perfluorodimethylcyclohexane (PDMCH; Manilla, Taipei). Releases were carried out
over 1–2 h, releasing 5–10 kg of PFC at rates between
47 and 370 ml min−1 (assuming a density of 1.8 g ml),depending on the location.
Two short-scale (Manilla, Taipei) and one long-scale(Nanjing) transport experiments were carried out. For
the Manilla experiment, observations were carried out∼20 h after the release (20.03.2018, between 0234 and0338 UTC). The location sampled was over sea, 1.8◦
–2.0◦ to the west of the release and at an average altitudeof 1402 m asl (Fig. 3a). Similarly, for the Taipei release,observations were carried out ∼22 h after the release(28.03.2018, between 0553 to 0815 UTC), covering distances 2.0◦–3.4◦ to the south-west of the release location, along the eastern shoreline of the Taiwan island,at an average altitude of 746 m asl. Finally, for the Nanjing experiment, observations were carried out overthe same location as the Taipei release (i.e. along thePage 4 of 28

eastern shoreline of the Taiwan island), ∼48 h after the
release (07.04.2018, 0110 to 0209 UTC), at a distance of
7.6◦–9.5◦ from the release point at an average height of
1106 m asl.
Importantly, the areas surrounding both measurement sites have complex topographies (Fig. 1). In thecase of Manilla (Luzon island, central Philippines), themost relevant topographic features to the release site are
the Zambales mountain range, the Natib and Mariveles
mountains to the west and the Southern Sierra Madre
range to the east (Fig 1a). In all cases, individual peaks
can reach over 1500 m, but the width of the mountain
ranges is small (20–30 km). Of significance is also the
Mindoro mountain range to the south-west of the release
point (maximum height 2586 m). In the case of the Taipei and Nanjing campaigns, the measurements were carried out over the west–south-west coast of Taiwan. The
island is composed of two main mountain ranges: theCentral Mountain Range (CMR), which is the dominant
topographic feature on the island and is aligned fromthe south to the north-east with a maximum height of3825 m, and the Xueshan Range, which is located fromthe centre of the island to the northern coast with a maximum height 3886 m. Two adjacent secondary ranges areconnected to the CMR, the Yushan Range (centre-south;
maximum height 3952 m) and the Alishan Range (centre;
maximum height 2663 m).
In addition to the PFC observations, hourly averaged
values of wind speed and wind direction data from 420
stations surface meteorological stations of the Taiwan
Central Weather Bureau (CWB) Automatic Rainfall andMeteorological Telemetry System (ARMTS; e.g. Jianet al. 2022) were used to conduct an evaluation of the relevant modelled meteorological data. The location of thestations is shown in Fig. 1. The ARMTS is a dense observation network covering Taiwan with an average nearestneighbour horizontal distance of 7.1 km. The network
coverage is better over low altitudes, with 70 stations ataltitudes over 500 m and 37 stations over 1 km, creating a
possible model evaluation bias.
2.2 Modelling approach

To examine differences in the simulated transport and, byextension, the target areas identified for PFC sampling,

Table 1 PFC release details during the EMeRGe campaign
Name

Date

Time (UTC)Longitude

LatitudeHrel (m)MmolMref (kg)

Hobs (m asl)rB (ppqv)

Manilla19.03.2018

05:05–06.11

121.0630

14.5840210 asl

400

5.6

1154–1579 (1402)

12.0

Taipei27.03.2018

06:00–07.15

121.5389

25.0148

20 agl350

10.5

605–2448 (746)8.0

Nanjing

05.04.2018

22:30–00.30

118.9496

32.1198

38 asl400

8.0

887–4400 (1106)

11.5

Hrel is the height of the release point, Mmol is the molecular mass of the released PFC (350 for PMCH and 400 for PDMCH), Mrel is the mass released, Hobs is the heightsof the HALO observations (average in brackets) and rB is the background mixing ratio. The sampling area is shown in Fig. 3

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 5 of 28

Fig. 1 Topographic height (shown shaded) of: a central Philippines, b Taiwan. In (b) topographic height is overlaid with locations of the Taiwan
Central Weather Bureau surface weather stations (round markers). Relevant mountains and mountain ranges and their corresponding names areshown with different colours. Note that the area shown as the Central Mountain Range (CMR) also includes the Yushan and Alishan mountain
ranges

ATDM simulations were carried out for each release
experiment in two ways: 1. directly over global data and2. over dynamically downscaled data. In total, six ATDM
simulations were carried out per release, two over global
data and four over dynamically downscaled data using
different PBL parametrisation schemes. The impact ofdownscaling was initially examined through an error

evaluation by comparing simulated against observed values from the EMeRGe campaign. Furthermore, the evolution of the simulated PFC mixing ratio maxima wascross-examined between the two ATDM methodologies,
to highlight the possible differences in suggested areas forobservations during the EMeRGe campaign. The overall
methodology is presented as a flow chart in Fig. 2, while

Fig. 2 A flowchart describing the methodology employed. In total six meteorological datasets were used as input for ATDMs, two global
datasets (ERA5, IFS) and four datasets produced using the WRF model based on IFS data and different PBL parametrisation schemes. WRF
results for the Taipei and Nanjing releases were evaluated against surface observations and results from all ATDM simulations were evaluatedagainst aircraft PFC observations and were then post-processed to identify the evolution of areas of high pollutant mixing ratios

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

details on the simulations are provided in the following
sections. In all cases, ATDM simulations were carriedout at a grid spacing near the native of the meteorologicaldata.
2.2.1 Global data simulations

ATDM simulations over global data were carried out
using version 10.2 of the FLEXible PARTicle (FLEXPART) model (Pisso et al. 2019). FLEXPART is a Lagrangian transport model that is used to solve the equation of
motion (also accounting for turbulent fluctuation) for alarge number of “particle parcels” (Lagrangian approach;e.g. Stohl et al., 1998) over a predefined meteorologicalfield. Turbulence within the PBL is evaluated using the
Hanna parametrisation scheme (Hanna 1982). FLEXPART is one of the most commonly used Lagrangian
ATDMs, tested in a variety of settings, such as pollutant
plume transport (Halse et al. 2013), volcanic ash transport (Dacre et al. 2016), and nuclear accidents (Yasunari
et al. 2011)
Here, two forward-time simulations were carried out
per release point, using ECMWF’s hourly ERA5 (Hersbach et al. 2020) and IFS (Roberts et al. 2018) datasets.
Although the focus of the study is nominally on forecasting, ERA5 data are used here instead of the NCAR Global
Forecasting System (GFS; Saha et al. 2014) as they share
resolution (with a grid spacing of 0.25 deg or ∼30 km),but allow for an additional cross-dataset examination,i.e. high-resolution forecast data against lower resolution reanalysis data, while at the same time providing a
representation of the accuracy expected at that resolution. In all simulations, 106 trajectories were used for thesimulation. PFCs were included as passive tracers (i.e.
no dry or wet deposition or chemical transformations)with the appropriate molecular mass for each release

Page 6 of 28(Table 1). Model output was set at 10 min as the averagevalues of 1-min trajectory states. Gridded mixing ratio is
calculated internally in the model based on given output
grids surrounding the areas of aircraft measurements.
A horizontal grid spacing of 0.25◦ (0.09◦ ) was chosen
for the ERA5 (IFS) simulations, while in the vertical all
simulations follow the grid height profile of the ECMWF
137 model levels (e.g. see Hersbach et al. 2020) up to aheight of 6 km asl, then switch to one level per 1 km up to
10 km, topped with a final level until 20 km. In all cases,
output data are used between the surface and 6 km asl.
2.2.2 Dynamical downscaling simulations

The Weather Research and Forecasting (WRF) numerical
weather prediction model (version 4.2; Skamarock et al.
2019) was used to dynamically downscale IFS data forthe three trace release locations used. For all simulations
one-way nesting was used starting from an outer domain
grid spacing (x ) of 10 km, progressively focusing down
to a x = 1.1 km over the study areas (Fig. 3). In the
Nanjing release simulations as the emission source is too
far away to be included in the domain encompassing themeasurements, an additional high-resolution domain(x = 1.1 km) was used to improve the model representation around the release area. In the vertical 59 level
were used for all simulations, with 24 levels in the bottom
2 km to have a allow for the appropriate in-model representation of the wind field within the PBL. The first 12 hof all simulations is used to account for spin-up time. The
model top was set at 50 hPa, while a Rayleigh damping
layer was imposed at the top 5 km to reduce the errors
from spurious gravity waves being reflected on the topof the domain (Klemp et al. 2008). Model output interval was set at 3 h for the outermost domain, 30 min for
D2, and 10 min for domains D3 and D4. In the case of

Fig. 3 Maps of the extended areas around the measurement campaign, overlaid with the extents of the WRF modelling domains (D1–4)
and the release (star marker) and HALO observation locations (solid lines) for: a the Manilla (orange), b the Taipei (green) and Nanjing (blue)
experiments. Note that domain D4 shown in (b) is only used in the Nanjing WRF simulations

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

the Nanjing release the output interval of D2 was also set
at 10 min so that data from domains D2–4 can be used
for ATDM simulations. The model domain placement isshown in Fig. 3 and summarised in Table 2.
Within the WRF model, the Global 30 Arc-Second
Elevation digital elevation model (∼900 m resolution;
Gesch et al. 1999) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Land-data processing
dataset (Justice et al. 2002) were used as the sources forthe topography and land use classification respectively.
Figure 4 highlights the changes in the representation of
topography and land use at different scales in two areas
studied here, i.e. around Manilla, Philippines and theisland of Taiwan. The three different scales are shown fora grid spacing (x ) of 1.1 km (the resolution used here
and the nominal limit of the majority of mesoscale model
PBL schemes), 10 km (representing the ECMWF’s Integrated Forecasting System; IFS) and 31 km (representing
the ECMWF’s European Reanalysis 5; ERA5). The lossof fidelity is evident as grid spacing increases, in termsof both land use (classification details lost) and topography (significant smoothing of the topographic maxima),
which significantly impact the representation of the PBL
in the model. The smoothing of topography at lower resolutions particularly affects the area around Manilla as
most mountains are relatively tall (> 1 km) but narrow(20–30 km), meaning that they are not wide enough to beproperly represented in the model.
A PBL parametrisation scheme is used in the model
to predict tendencies of the prognostic variables due tounresolved turbulent motions. PBL schemes can largely
be divided in two groups, depending on the closure
schemes employed: first order (1.0 non-local) and oneand-a-half or higher order (1.5, 2, 3 local), also known
as turbulence kinetic energy (TKE) closure. Non-local

Page 7 of 28

schemes do not require additional prognostic equations
to express the effects of turbulence on mean variables,
while TKE closure schemes require one additional prognostic equation for TKE.
The two categories differ in two main ways. One isrelated to the extension of the region that is able to affectthe PBL variables at one point: in local closure schemes,
only the vertical layers symmetrically adjacent to a specific point can directly affect the variables at that point,
while non-local schemes also consider variables indeeper (potentially multiple) layers. Furthermore, thePBL height calculation is different between the two categories: in local schemes the height is determined as thelevel at which the TKE profile decreases to a threshold,while in non-local schemes it is calculated as one level
above height at which the bulk Richardson number (Rib)exceeds a threshold. The general expectation is that useof a suitable higher-order turbulence closure would leadto increased accuracy, but is tied with higher computational costs as it involves more prognostic equations that
often require shorter integration time steps.
The WRF model’s representation of near-surface meteorological flow has a known and extensively studied sensitivity to the PBL scheme (e.g. Borge et al. 2008; Shin andHong 2011; Gómez-Navarro et al. 2015; Banks et al. 2016;
Avolio et al. 2017; Gunwani and Mohan 2017; Onwukwe
and Jackson 2020). Despite the large number of studies conducted to evaluate WRF model configurations,
results are commonly inconclusive (Shin and Hong 2011;
Borge et al. 2008; Gunwani and Mohan 2017; Onwukwe
and Jackson 2020), hinting at the systematic uncertainties tied with mesoscale models (Hanna and Yang 2001;
Rife et al. 2004). As the releases studied here occurred
within the PBL, two non-local and two local closure
PBL schemes were used to create a “mini-ensemble”. The

Table 2 WRF model domain configuration description per release location, including the number of grid points in both horizontal
directions (nx , ny ), horizontal grid spacing (x = y ) and time step (t)ReleaseSimulation period (UTC)

Manilla18.03.2018 2100 – 20.03.2018 0600

Taipei

Nanjing

26.03.2018 1800 – 28.03.2018 1200

05.04.2018 0000 – 07.04.2018 0900

Domainnxnyx (km)t (s)

Outputinterval
(min)

D1

211

211

10

60

180

D2

361

361

3.3

20

30

D3

370

316

1.1

0.4

10

D1

211

211

10

60

180

D2

382

451

3.3

20

30

D3

391

451

1.1

2

10

D1

211

211

10

60

180

D2

382

451

3.3

6

10

D3

391

451

1.1

0.24

10

D4

391

451

1.1

0.24

10

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 8 of 28

Fig. 4 Examples of the model representation of land-use (main a–f) and topography (top and right sub-panels a–f) for the areas around Manilla,
Philippines a, c, e and Taipei, Taiwan b, d, f, depending on the horizontal grid spacing (x ): a, b 1.1 km, c, d 10 km, and e, f 31 km. Topographyheight (in km) is shown as the projection across latitudes (top sub-panels) and longitudes (right sub-panels), with different shading indicating
an increase in latitude and longitude, respectively. Land-use classes shown are aggregated (shown in the colourbar) based on the original MODIS
data and are shown for illustrative purposes. The coastline is plotted in black in all main panels for comparison

physical approaches taken for each PBL scheme are presented in Table 3.
Yonsei University (YSU; Hong et al. 2006) and Asymmetric Convection Model 2 (ACM2; Pleim 2007) schemes

are 1-order. YSU uses an explicit entrainment layer anda parabolic K-profile in an unstable mixed layer. ACM2
represents non-local upward mixing and local downward
mixing and has an eddy-diffusion component in stable

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 9 of 28

Table 3 Characteristics of the PBL schemes using in the study

2018; Abdi-Oskouei et al. 2020) and biogenics (Zhaoet al. 2016). However, as in the cases studied here, only
specified point emissions were introduced in the model,
reducing the expected impact of the land-surface model.
As such, only the PBL scheme was varied in the simulations presented here.
The FLEXPART-WRF model (version 3.1) was used tocarry out ATDM simulations based on the dynamicallydownscaled meteorological data. FLEXPART-WRF is a
specialised version of the FLEXPART model, adaptedfrom version 9.02 to carry out Lagrangian calculations
directly over WRF model output (Brioude et al. 2013).
As with the FLEXPART model, FLEXPART-WRF has
been used in various ATDM applications from regionaltransport of pollutants (Madala et al. 2016) down tolarge-eddy simulations (Cécé et al. 2016). Mirroring thesimulations over global data, a total of 106 particle trajectories were used per release. However, particle splitting(i.e. splitting of a single trajectory into two each representing half of the pollutant mass; Stohl et al. 2005), wasused every 3 h to account for the increase in the simulated wind field complexity, leading to a total numberof particles of 2.5×108 for the Manilla case, 5.1×108 for
the Taipei case and 4.1×109 for the Nanjing case. FLEXPART-WRF simulations were carried out for each model
domain, following the grid spacing of the original WRF
data. In the vertical, grid heights follow the vertical profile of WRF levels up to 6 km asl, then switch to one levelper 1 km up to 10 km, topped with a final level until
20 km. To account for turbulence, the Hanna scheme
was used instead of turbulence estimates from the WRF
model, as the latter has been shown to lead to insufficient
mixing within the PBL (Brioude et al. 2013).

PBL scheme

Closure order

PBL height
methodThresholdYSU

1.0 non-local

Rib (from surface)

0.0 (neutral), 0.25
(stable)ACM2

1.0 non-local

Rib (above neutral
buoyancy level)0.25 (all)BouLac1.5 local

TKE threshold

5.0×10−3 m 2 s−2

MYNN3

2.0 local

TKE threshold

1×10−6 m 2 s−2conditions, in addition to the explicit non-local transport
in unstable conditions. Bougeault–Lacarrere (BouLac;
Bougeault and Lacarrere 1989) and the Mellor-Yamada
Nakanishi Niino Level 3 (MYNN3; Nakanishi and Niino
2006) schemes are 1.5 and 2-order, respectively. BouLac
was developed for the meso–beta-scale and uses a prognostic equation for TKE, with the second-order moments
parameterised based on an eddy coefficient approximation. The turbulence length scale is designed to capture
turbulence induced by gravity waves over complex terrain and is based on the mean TKE of the layer and buoyancy. Finally, the MYNN3 scheme expresses stability andmixing length based on the results of large-eddy simulations rather than observations to minimise underestimation of TKE and convective layer growth associated withother Mellor-Yamada-type schemes.
Other than the PBL parametrisation, a full physics suite
was used, consistent for all simulations: the Goddard
microphysics’ scheme (Tao et al. 1989), the New Goddard radiation scheme (Chou and Suarez 1999; Chou et al.
2001), the Revised MM5 surface layer scheme (GómezNavarro et al. 2012), the Unified Noah Land Surface
Model (Tewari et al. 2004), and the Kain-Fritsch cumulous scheme (Kain 2004), which was only used for theoutermost domain. During the simulation design, thedecision was made to focus solely on the PBL schemes,
as they were expected to have the largest impact on thesimulation results, due to the specific conditions of thethree scenarios examined. The WRF model is also known
to have sensitivities to the microphysical and land-surface parametrisation schemes (Borge et al. 2008; Baróet al. 2015; Rizza et al. 2018; Abdi-Oskouei et al. 2020).
When it comes to pollutant transport, the microphysics’
scheme can be expected to have an impact when there
is significant precipitation (Borge et al. 2008); however,for the cases studied there was little precipitation in theareas of interest between the release and observationsof the PFCs. The sensitivity to land-surface schemes iscommonly studied using online chemistry models suchas WRF-chem (Grell et al. 2005), due to its combined
impact on the near-surface flow characteristics (Misenis
and Zhang 2010) and the emissions of dust (Rizza et al.

2.2.3 Evaluation metrics

During the EMeRGe campaign, the PERTRAS system
carried out continuous measurements, with output
every 100 sec. In order to compare model results against
the measurements, values in the model were sampled
every 5 sec to account for the aeroplane’s movement.
All error analysis was carried out based on the averagemodelled PFC mixing ratio. Two commonly used error
metrics ( E = P − O, where O is the observed value and
P is the √prediction) are the Root Mean Square Error(RMSE= < E 2 > , where the angular bracket signifies
averaging over a chosen dimension) and the Mean Bias
Error (MBE=< E >). Despite its ubiquitous use in theliterature, RMSE has well-documented asymmetries:
for a variable with values in the non-negative real numbers, the error due to overestimation tends to infinity,
while the error for underestimation tends to the average observation. For linearly changing quantities without a systematic bias, this is often neglected; however,Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

use of the conventional error-based metrics can skew
model result interpretation when it comes to quantities that change exponentially. Use of the logarithmic
error (LE=log10 (PO−1 )), which represents the error
as orders of magnitude, has been proposed as a workaround this asymmetry for relevant applications (Tofallis 2015; Morley et al. 2018). Here, logarithmic errorbased metrics(Root Mean Square Logarthmic Error,√
RMSLE= < LE 2 > ; Mean Bias Logarithmic Error,
MBLE=< LE >) were used for the mixing ratio-based
errors to provide an unbiased evaluation of the transportmodelling; however, all values of PFC mixing ratio (rPFC )less than 1 ppqv, were replaced by 0.1 to account for thedevice’s detection limit. Finally, Kendall’s rank correlation (τ ) was used for the mixing ratio instead of the morecommonly used Pearson’s correlation coefficient (p) as it
is invariant under monotone transformations and thus
more appropriate for exponentially changing variables(Kursa 2022). Conventional error metrics (RMSE, MBE)
were used for the meteorological data analysis to provide
values comparable to the literature.

3 Results
3.1 Meteorological flow and PFC mixing ratios
during the observation period
3.1.1 Manilla release

In order to have a visual representation of the PFC
transport and the meteorological flow field, thePage 10 of 28

simulated and observed PFC mixing ratio (as a maximum across all measurement heights) is shown in
Fig. 5, overlaid with the average wind field during the
HALO measurements (shown as streamlines). A moredetailed comparison of the modelled against observed
values follows in Fig. 6. Across all simulations, thebroad characteristics of the flow are similar: easterly
winds during the release and subsequent transport
led to westwards PFC dispersal, following the climatologically expected conditions (Matsumoto et al. 2020).
Significant deviations, however, occur over and to thewest of the Zambales mountain range. In both global
datasets, there is a relatively smooth transition froman anticyclonic circulation to the north of the domain
to easterlies in the middle and southern parts of thedomain, with the centre point situated to the west of
the Zambales range (Fig. 5a, b). This transition leads tonortherlies over the Luzon island, parallel to the range.
The impact of the topography at all domain points issignificantly different in the WRF simulations. However, the specific characteristics of the resolved flow
depend on the PBL scheme (Fig. 5c–f ). In the northernparts there is no consistent anticyclonic flow west of
the mountain range in all simulations due to the complex representation of the lee flow over the mountain
range that shows a number of gap flows as individual
peaks are resolved in the model. This behaviour leads toa southward shift in the dispersal direction. West of theFig. 5 Maximum simulated PFC mixing ratio between 1.0 and 1.5 km asl (shaded) overlaid with observations (round markers) and the averagewind field between the same altitudes and time period for simulations based on: a ERA5, b IFS, c WRF[YSU] (i.e. WRF using the YSU PBL scheme),
d WRF[ACM2], e WRF[BouLac], and f WRF[MYNN3]. Note that results are shown only for the innermost WRF domain. The release location is shownwith a star marker

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 11 of 28

Fig. 6 Comparison of observed and simulated values of PFC mixing ratio following the Manilla release: a temporal evolution of PFC mixing ratio(rPFC ), b box-and-whiskers plot of rPFC values, and c vertical rPFC profiles. Note that the background is subtracted from the observed values (Table 1)

release point, blocking and flow around the individual
peaks of the Zambales range and the Mindoro mountains are visible. For all PBL schemes used, the combination of flow around the mountains causes a channel
flow east of Manilla bay, visible in Fig. 5c–f as an intensification of the wind field.
The varied representation of the atmospheric flow
leads to significant differences in the simulated PFC
transport. In the simplest case, FLEXPART simulations
based on ERA5 data show westwards advection closely
following the average wind field (Fig. 5a). The PFC plumeis elongated laterally with a single area of high PFC values stretching between 14.2◦ N and 15.2◦ N. Only a smallpart of the plume has been separated, located to thesouth-east of the main plume. Similar to the ERA5 case,
the FLEXPART-IFS simulation also results in the plumesituated to the west of the release point; however, there isalso part of the plume lingering over the island of Luzon(Fig. 5b). This is caused by blocking of the surface flow,
since at the IFS data resolution the Zambales range (westof the release point) is, at least, partially resolved. Unlike
the ERA5-based simulations, the main plume shows amore complex structure with three local maxima, due
to the impact of the Zambales range. Still, flow is largely
towards the west of the release point, extending between
14 and 15.5◦ N. Simulated flow for both datasets showsthat the HALO observations were carried out over theplume’s location, but model results show significant
overestimation.
In all WRF-based simulations the plume shows acomplex structure with multiple maxima reflecting theresolved atmospheric flow (Fig. 5c–f ). The most significant change between the FLEXPART and FLEXPARTWRF simulations is the southwards component of theflow in the lee of the Zambales range, combined withoverall higher wind speeds in the WRF data. This leads
to similarities in the overall flow for all WRF-based simulations: westerly to south-westerly dispersal, with the
maximum concentration of the plume near the lower left
corner of the domain. Significant deviations from thisare only seen for the WRF[MYNN3] simulation (Fig. 5f ),where the southwards shift west of the release pointis less significant, leading to most of the plume having
already exited the domain by the time of the observations(i.e. the time period shown in Fig. 5). The resolution of
the topography over the Zambales range leads to blocking west of the release point, causing part of the PFC
plume to remain over Luzon, mirroring the IFS simulation results.
The overall southwards shift in the plume transport
points towards the fact that HALO observations werecarried out over areas of low PFC mixing rations. This

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

is also reflected in the observed mixing ratio values(Fig. 6). During the first hour of sampling, elevated values (rPFC > 10 ppqv) were only noted in two instances(Fig. 6a). Both simulations based on global data overestimated rPFC ; for the ERA5-driven simulation this is true
for the duration of the measurement period, while for
the IFS-driven simulation, this is mainly true for the first
20 min of the observations. On the other hand, rPFC estimates from the WRF-based simulations remain within
the range of the observed values; however, all simulations
underestimate mixing ratios at the start of the observation period.
A comparison of the simulated against observed rPFC
range is shown in Fig. 6b. Aside from a few outliers,
observations ranged between 1–10 ppqv for the duration of the measurement campaign. ERA5-based FLEXPART simulations systematically overestimate valuesby O(1), while IFS ranges from a O(1) overestimation
to O(2) underestimation, with the median still being an
overestimation. FLEXPART-WRF rPFC values are within
the expected range for all PBL schemes except MYNN3,
which led to systematic underestimation due to highwind speeds. Of the four PBL schemes, ACM2 provided
the best match, followed by the BouLac scheme. The
vertical PFC profiles in all cases show that the traces gas
is not mixed well within the PBL, with elevated tracer

Page 12 of 28

values near the PBL height for all cases outside of the
MYNN3 simulation, where the less-mixed head of theplume has already passed by the sampled area. Overall,the calculated RMSLE values ranged between 1.21–1.59(MBLE 0.2 to 1.3, i.e. overall overestimation) for the
FLEXPART simulations and 1.05–1.2 (MBLE −0.91–−0.47; underestimation) for the FLEXPART-WRF simulations. All FLEXPART-WRF simulations ranked better
than the FLEXPART simulations, with the best-matching
simulation being FLEXPART-WRF[BouLac].
3.1.2 Taipei release

The Taipei experiment allows for the examination of a different transport setting, as the dominant elements in the
topography were the main ridges on the island that are
overlaid with a number of smaller peaks that can reacha height of ∼4000 m asl. Even at a lower resolution, themain ridge structure of the island remains; however, individual peaks are smoothed, and the height of the island
decreases (Fig. 4d–f ). Despite the presence of the mainridge structure at low resolutions, the overall smoothingof the orography had a significant impact on the resolved
flow around the island in the different models (Fig. 7).
In all cases, incoming wind direction is easterly–northeasterly (i.e. within the climatological expectations for
March; Cheng 2001), meaning that the incoming flow

Fig. 7 As Fig. 5, but for the Taipei release and heights between 0.5 and 1.0 km

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

meets the island of Taiwan at an angle. Results from allmodels show flow splitting on the eastern (windward)
side and vortices on the western (lee) side; however,there are distinct differences between the global datasets(Fig. 7a, b) that show strong southerly winds to the westof Taiwan and a small lee vortex area over the southern
tip of the island and the WRF simulations (Fig. 7c–f ) thatfeature a more extended lee area south–south-west of theisland. The elongated vortices on the lee side better agreewith the theoretically expected flow, given an incidence
angle of −30◦ as described in Wells et al. (2008).
The different representation of flow on the releaseside and to the west side of Taiwan leads to very different representations of PFC transport in the models. In
the FLEXPART simulations (Fig. 7a, b) the PFC plume
was transported as a nearly homogeneous unit towards
the south-western coast of Taiwan. In the FLEXPARTWRF simulations, there were two significant flow splitting points into the western side of Taiwan independentlyof the PBL used (Fig. 7c–f ); one immediately west of therelease point (approximately 120.5◦ W, 25◦ N) and one on
the south-western coast similar to the global data simulations. These additional points of flow-splitting lead to a
divided PFC plume, with the main part of the PFC plumetransported directly west of the release site and a secondary plume transported to the south of Taiwan, across the
HALO observations’ area. Of the four PBL schemes used,

Fig. 8 As Fig. 6, but for the Taipei release

Page 13 of 28

three lead to a qualitatively similar flow (Fig. 7c–e). The
exception is the MYNN3-based simulation, where thewestwards-headed part of the plume has already left thecomputational domain by the time of the observations(Fig. 7f ). A significant part of the tracer was transported
to the eastern side of Taiwan.
PFC mixing ratio measurements were carried out in
two periods on the same day, from 0550–0610UTC and
0800–0815UTC, i.e. 24–26 h after the release. Elevatedmixing ratio values over a background of 8 ppqv wereobserved in both periods, up to 27 ppqv in the first and4.7 ppqv in the second (Fig. 8a). As with the Manilla case,
ERA5-driven FLEXPART simulations overestimate concentrations during the initial measurement period, while
IFS-driven simulations show a realistic representationof the upper range, but underestimate the lower range(Fig. 8a, b). FLEXPART-WRF simulations, on the other
hand, tend to underestimate the observations (Fig. 8b)
systematically. However, the performance depends on
the PBL scheme. The YSU PBL scheme produces the best
match, which captures quartiles and median values, but
not the full upper range, while the ACM2 scheme underestimates the PFC mixing ratio by up to O(2). In all models the PFC vertical profile is not well mixed and tends tohave multiple maxima with altitude, reflecting the complexity of the flow around the island (Fig. 8c). The FLEXPART simulations are associated with RMSLEs between

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

0.94 and 1.06 (MBLEs 0.02 for the ERA5-driven simulation and −0.57 for the IFS-driven simulation), while the
FLEXPART-WRF simulations with RMSLEs between
0.99 and 1.48 (MBLEs −1.4–−0.7, i.e. systematic underestimation). Due to significant underestimation in theobserved mixing ratios by FLEXPART-WRF, the bestperforming model configuration was FLEXPART[ERA5],
followed by FLEXPART-WRF[YSU]. One point toacknowledge is that the Taipei case features the lowest
amount of HALO data—approximately 35 min, leading
to only 9 data points, potentially introducing a bias in theanalysis.
3.1.3 Nanjing release

The Nanjing release is a case of long-range transportmodelling. Compared to the Taipei release, a smallchange in the wind direction (north to north-easterly)
incidence angle of the incoming flow (from −30 to ∼ 0◦)
significantly simplifies the flow around the island, leading
to flow splitting along the small axis of the island and theminimum affected area (Fig. 9). In all simulation results
the resolved flow is similar and adheres to the theoretical expectations for the flow regime (Cheng 2001). Mostof the orographically based impact occurs either overthe island (where there are areas of reverse flow) or thesouthern side (i.e. lee side); however, the PFC plume isFig. 9 As Fig. 5, but for the Nanjing release

Page 14 of 28

separated from the generated vortices. The most important difference between the global and the dynamically downscaled data is that the overall wind velocity
is increased in the latter, leading to an approximately 1 ◦southern shift in the centre of the PFC plume (i.e. comparing Fig. 9a, b to c–f ).
The characteristics of the simulated PFC tracer mixing
ratios are very similar across all model setups, reflecting
the similarities in the overall meteorological flow andtransport (Fig. 10). Observed PFC mixing ratios ranged
between 0 to 7 ppqv over the background, with one
exception at 20 ppqv. Most model setups result in a slightoverestimation of the average; however, the observed
range is largely reproduced for all models (Fig. 10a, b).
Notably, no model was able to reproduce the short-lived
maximum in the observed values. Unlike the shortrange transport simulations of the Manilla and Taipei
releases, results for the Nanjing release were qualitatively
similar for all setups, with the RMSLE ranging between
0.65 and 0.68 (MBLEs − 0.27– 0.32) for FLEXPART and
0.64–0.74 (MBLEs −0.34–0.33) for the FLEXPARTWRF simulations, with the best-performing configuration being FLEXPART-WRF[YSU]. The lowest RMSEs
were associated with the FLEXPART[IFS] and FLEXPART-WRF[YSU] simulations respectively. As expected
for a long-range transport simulation, results from allPoulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 15 of 28

Fig. 10 As Fig. 6, but for the Nanjing releasesimulations showed a well-mixed profile within the PBL(Fig. 10c).
3.2 Overall model performance evaluation

The overall comparison between all model simulations
carried out, including all 3 WRF resolutions is shownin Fig. 11. As was highlighted during the individual caseanalysis (i.e. Sects. 3.1.1–3.1.3), comparing FLEXPART
to FLEXPART-WRF simulations did not show a systematic reduction of errors, although individual FLEXPARTWRF configurations did provide the best results in two
out of the three release experiments (Fig. 11a). The RMSE
of rPFC was included to emphasise the possible bias in acase such as the one studied here (Fig. 11b): compared to
the RMSLE, all RMSE values for FLEXPART-WRF simulations tend to be artificially low compared to FLEXPART
simulations due to the respective systematic underestimation and overestimation of rPFC (Fig. 11c).
Between the two FLEXPART configurations, despite
being based on forecast data, the IFS dataset consistently led to higher simulation accuracy for all metrics
except for the Taipei case. When comparing the FLEXPART against the FLEXPART-WRF simulations, there
was no systematic pattern of change between the different WRF resolutions. In the case of the Manilla release,
RMSLE and the Kendall correlation coefficient slightly
improve, but MBLE worsens with increased resolution(i.e. overall improved error and correlation, but consistent underestimation). In the Taipei release case, there issignificant improvement between FLEXPART[IFS] and
FLEXPART-WRF at a 10-km grid spacing for all metrics
and most PBL schemes, but accuracy overall decreases
with increased resolution. The Nanjing case, on the other
hand, shows a relatively consistent increase in accuracy
with increasing resolution.
When comparing the four PBL schemes used for the
FLEXPART-WRF simulations, the BouLac scheme performed better than the other schemes at lower resolutions, which can be expected as it is a meso-beta scaleparametrisation (Bougeault and Lacarrere 1989). Overall,
the YSU scheme performed most consistently across all
resolutions (in agreement with past studies on WRF PBL
sensitivities; Banks et al. 2016; Avolio et al. 2017), while
the MYNN3 scheme ranked the lowest. However, acrossall schemes, the differences in performance start relatively large at low resolution and converge towards similar values as resolution increases.
Up to this point the evaluation focused exclusively on
the observed PFC mixing ratios. An important question is whether the performance results for the transportmodelling correlate with an evaluation against meteorological data from surface stations. To answer this, observations from the CWB meteorological station network
were compared against near-surface simulation data forPoulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 16 of 28

Fig. 11 Error analysis of all simulations: a Root Mean Square Logarithmic Error (RMSLE), b Root Mean Square Error (RMSE), c Mean Bias
Logarithmic Error (MBLE), and d Kendall’s coefficient (τ ). In all cases, different colours denote different release experiments, while in the caseof the FLEXPART-WRF simulations, individual PBL schemes are shown separately

the Taipei and Nanjing releases, focusing exclusively on
the FLEXPART-WRF simulations and the performanceof the different PBL schemes. As an example of the timeseries examined, average observed and modelled windspeed and wind direction values from 32 stations within
Taipei are shown in Fig. 12, as the northernmost point of
the island was important both in the Taipei and Nanjing
releases (release and splitting point respectively).
In both days, the overall evolution of wind speed andwind direction is similar, with relatively weak E to NE
winds, with slightly increased wind speeds observed
during the Nanjing release experiment. In both days
and irrespective of the PBL scheme, the model is able tobroadly replicate the evolution of wind speed (albeit withan average overestimation) and show a very good reproduction of the observed wind direction. All PBL schemes
lead to very similar behaviour in the resolved flow characteristics; only the YSU and BouLac schemes show alarge error in wind direction roughly 18 h after the tracer
release, but this is tied to very small wind speed values(Fig. 12c). The overall error diagnostics for the two simulations are shown in Table 4.
The distribution of wind speed and wind direction
errors over Taiwan shows systematic differences between
the two quantities (Fig. 13). Wind speed (first and thirdcolumns in Fig. 13) tends to be associated with higher
RMSE values in the northernmost and southernmost
edges of the island and mainly over low heights. In

contrast, wind direction (second and fourth columns in
Fig. 13) tends to be associated with increased RMSE values at the stations with higher elevation. The overall distribution of the errors over the island is similar between
the Taipei (first and second columns) and the Nanjing(third and fourth columns) release days; however, windspeed during the Nanjing experiment is associated withoverall higher RMSE values. As with the results from thestation subset (Fig. 12), there are only minor differences
between the errors for the different PBL schemes.
In order to examine the relation between the wind
quantities’ errors against the observed wind speed, theRMSE and MBE of wind speed and wind direction forthe merged dataset of the Taipei and Nanjing simulations
are plotted against each other, shaded using the averageobserved wind speed (Fig. 14). Both for the wind speed
and wind direction, 67% of the data are contained close
to the RMSE and MBE values of the simulations (shownwith the black contour in Fig. 14); however, in the caseof wind speed the distribution is heavily skewed towards
negative MBE values (model overestimation), while forwind direction it is symmetric across the x-axis.
The two wind-related quantities also show a different
behaviour when it comes to the higher errors. In the caseof wind speed, high RMSE values are associated witheither systematic overestimation (positive MBE; mainly
occurring with low average wind speed values) or systematic underestimation (negative MBE; only high windPoulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 17 of 28

Fig. 12 Average values of: a, c wind speed and b, d wind direction, across 42 stations shown against time since the tracer release for the Taipei (a,b) and Nanjing (c, d) release experiments

Table 4 Error diagnostics for wind speed (WS; in m s−1) andwind direction (WD; in degrees) based on the model evaluation
against surface stations for the Taipei and Nanjing simulations
Simulation Quantity YSU
Taipei
Nanjing

ACM2

BouLacMYNN3

WS

1.95 (1.20) 2.06 (1.32) 1.90 (0.90)

1.58 (0.67)

WD

48.9 (5.2)

49.6 ( 4.4)

47.5 (5.4)

WS

2.64 (1.60) 2.52 (1.42) 2.45 (1.16)

2.42 (1.15)

WD

51.2
(− 2.1)47.2 (3.7)
51.4(− 1.8)55.2 (− 4.1) 52.3 (− 3.2)

In each case RMSE values are accompanied by MBE values in brackets

speed values). On the other hand, wind direction MBE
remain close to the zero MBE line even for higher RMSE
values. Unlike wind speed, high wind direction RMSE
values are associated primarily with low wind speed values. As with Figs. 12, 13 the different PBL schemes do notlead to any systematic differences in the results.
In order to draw out broad conclusions for the impactof the PBL parametrisation, each option used was ranked
based on the RMSE or RMSLE or wind speed, winddirection and tracer concentration. Results were ranked
for each simulation and the overall ranking based on the
average across all simulations is shown in Table 5. Out of

all PBL options, YSU was the best performing in termsof both the wind field as well as the tracer mixing ratio,
followed by the ACM2 scheme—both first-order nonlocal closure schemes. The BouLac scheme performed
well based on the tracer transport, but is the worst performer for the meteorological evaluation, while MYNN3
performs best for the wind speed, but fidelity decreased
for the wind direction and tracer transport. Still, as seen
with the actual RMSE values (i.e. Table 4; Fig. 11) the differences are very small and often come down to decimal
places.
3.3 Impact of downscaling on PFC mixing ratio maxima

Despite potential benefits in the representation of themeteorological flow (e.g. Takemi and Ito 2020), there wasno systematic improvement in the representation of themixing ratio for the simulations presented. Although at
least one FLEXPART-WRF configuration performed better than (or in the Taipei release on-par with) the FLEXPART model, no PBL scheme led to a systematically
better simulation. This outcome is also to be expected as
the sensitivity to the PBL scheme is one of the most wellknown and studied aspects of the model (e.g. GarcíaDíez et al. 2013; Banks et al. 2016; Avolio et al. 2017;
Gunwani and Mohan 2017). Still, irrespective of the PBL

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 18 of 28

Fig. 13 Surface station RMSE of wind speed (first and third columns) and wind direction (second and fourth columns) for the Taipei (firstand second columns) and Nanjing (third and fourth columns) simulations. Rows show results for the different PBL schemes used: a–d YSU, e–hACM2, i–l BouLac, and m–p MYNN3

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 19 of 28

Fig. 14 Surface station MBE against RMSE for wind speed (first column) and wind direction (second column), based on the combined datasetof the Taipei and Nanjing simulations. Rows show results for the different PBL schemes used: a, b YSU, c, d ACM2, e, f BouLac, and g, h MYNN3. In all
panels the limits where RMSE= |MBE| are shown using dashed lines

scheme, results from the WRF model were qualitatively
consistent in the simulated meteorological flow features.
Considering the low PFC values observed, based on the
IFS-driven forecast simulations, it is also important toanalyse the results in terms of predicted “observation
target area”, i.e. the area according to the models wherehigh PFC values were expected (and be extension themost appropriate area to sample). To do this, FLEXPARTPoulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Table 5 Model performance (for the 1.1 km domain) dependingon the PBL scheme
PBL scheme

Closure order

Wind
speed
rankingWind
direction
ranking

Tracerranking

YSU

1.0 non-local

1*

2

1*

ACM2

1.0 non-local

1*

1

3

BouLac1.5 local

4

4

1*

MYNN3

2.0 local

1*

3

4

Ranks (out of 4) are presented based on the results from all cases. Ranks
marked with an asterisk (*) denote the same average point count. Note thatthe meteorological evaluation was only carried out for the Taipei and Nanjing
simulations

and FLEXPART-WRF were analysed as two mini-ensembles. For each ensemble member, areas with PFC mixing
ratios over 3 and 10 ppqv were identified as “target areas”
that were then averaged over the ensemble to allow fora probabilistic approach. Areas with at least a 50% probability of measurements over each limit (i.e. agreementof two or more different PBL simulations in the case of
FLEXPART-WRF) are shown in Figs. 15, 16, shaded
depending on the occurrence timing of the maxima inhours before the end of the observation period.

Page 20 of 28

When the flight track is compared against the target area based on the FLEXPART simulations thereis good agreement for both low and high mixing ratios(Figs. 15a–c, 16a–c, respectively). This is to be expected
since the actual forecasts that aided in the decisionmaking during the EMeRGe campaign were carried out
over IFS data. Even though the HYSPLIT model (Steinet al. 2015) was used during the campaign, a good match
between the locations can be expected despite FLEXPART being used here as the two models have beenshown to perform similarly (Hegarty et al. 2013). A comparison between the FLEXPART and FLEXPART-WRF
results, however, effectively summarises the differences
discussed in Sects. 3.1.1–3.1.3. For low PFC mixing
ratios, there is overlap between the two models (i.e. comparing individual columns of Fig. 15). This reflects boththe actual measurements, as well as the simulated values that, for most model configurations, tended to beover 3 ppqv. As expected, the largest difference between
FLEXPART and FLEXPART-WRF simulations is the Taipei release (Fig. 15b, e), in which case (according to the
FLEXPART-WRF simulations) the observations mainly
took place over an area of low model confidence (i.e. outside of the highlighted area in Fig. 15e), or 3–6 h too late.
Notably, the area directly west of the release point is also

Fig. 15 Simulated areas with ≥ 50% probability of exceedance of rPFC = 3 ppqv, shaded based on the time before the end of the EMeRGe
campaign observation period for the releases from: a, d Manilla, b, e Taipei, and c, f Nanjing. First row a–c show results from the FLEXPART
simulations, while d–f show results from the FLEXPART-WRF simulations. Overlaid are the observation locations (black lines), while the red contour
indicates the area for the duration of the measurements

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Page 21 of 28

Fig. 16 As Fig. 15, but for rPFC = 10 ppqv

marked as a high-confidence area while completely missing in the FLEXPART simulation results.
Results change drastically when focusing on higher
expected mixing ratios. Even for a target mixing ratio of
10 ppqv there are significant differences across all cases(Fig. 16). For the Manilla release (Fig. 16a, d), FLEXPART simulations identify an extensive area, comparable to the results for 3 ppqv (Fig. 15a). On the other
hand, when compared to FLEXPART-WRF simulations,the actual observations track is over the past location of
the plume and to the north-east of the identified highconfidence area, i.e. according to the FLEXPART-WRF
simulations the observations would have been better carried out at a different location or at an earlier time. The
Taipei release shows the most significant disagreement
between the two sets (Fig. 16b, e), as FLEXPART showsa large area of high confidence to the south of Taipei. In
the case of the FLEXPART-WRF set, for the time of theobservations, there is no identified high-confidence area
to the south of the release point. That does not mean that
FLEXPART-WRF does not predict high rPFC values oversouthern Taiwan, but rather that all ensemble members
simulate non-overlapping areas of high rPFC , highlighting
the difficulty in the accurate prognosis over that region
for the day. Agreement between the different ensemble
members is still visible to the west of the release point,
or 6–9 h before the observation period ends. Finally, for
the Nanjing release (Fig. 16c, f ), despite the similarities in

the identified target areas, there is a notable southwards
shift in the location, which is in better agreement withthe location of the observed maxima (Fig. 9).

4 Discussion
A comprehensive performance evaluation of the WRF,
FLEXPART and FLEXPART-WRF model has been presented for three different transport settings and using
four PBL parametrisations (in the case of WRF and
FLEXPART-WRF). Despite the fact that the PBL option
had an overall significant impact on the resulting tracer
mixing ratio, there was no systematically better-performing option across all simulations and tested quantities.
This is an expected outcome and reflects the establishedliterature (e.g. Borge et al. 2008; Onwukwe and Jackson2020). Still, even though differences among the domainwide performance were small the two first-order PBL
schemes (YSU and ACM2) performed marginally better than the other options. This also broadly reflects theconsensus of similar studies (e.g. Banks et al. 2016; Avolio
et al. 2017; Gunwani and Mohan 2017; He et al. 2022).
The central idea behind the evaluation carried out
was to quantify the potential impact of downscaling on
campaign management. In this context, even if the meteorological representation of flow is better at higher resolution, this would not add a significant benefit in termsof campaign management if the predicted area of concentration maxima remained the same. In general, using

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

global data and the FLEXPART model led to relatively
simple dispersal pathways leading to a concentrated
plume with a systematic overestimation of observations, while using regional model data and FLEXPARTWRF led to complex dispersal pathways, separation of
the plume into multiple smaller units, and was generally
associated with systematic underestimation of observations. The analysis, based on all PFC measurements forthe different cases, showed that errors associated with
the FLEXPART-WRF simulations either stayed within
the same order of magnitude as the FLEXPART simulations or marginally improved. However, in two out of thethree cases studied (namely the short-range transport
cases), the dynamical downscaling simulations revealed
a considerably different representation of the flow andidentified areas for observation. These differences inthe simulations can be broadly explained based on orographic effects (Smith 1989).
4.1 General impact of topography

The topography surrounding Manilla plays a significant role in the meteorological flow over the islands as
the various topographic maxima can lead to a combination of flow splitting and gap flows west–south-westof Manilla. The resulting complex flow is known to bea deciding factor in rainfall over the Philippines islands(Pullen et al. 2015; Hilario et al. 2021) and atmosphericpollutant outflow from Manilla (Bagtasa and Yuan 2020).
In the Manilla simulations, most of the topography westof the release point is practically removed at lower resolutions. Due to the complex topography identifying an
undisturbed “incoming” flow is difficult, but considering
average wind speed values of 3 m s−1 places the expected
flow over the Zambales mountain range from “flow over”( Fr ∼ 1; ERA5 simulation), to a “flow around” regime( Fr ∼ 0.2; ERA5 simulation). Changes seen in the pollutant transport pattern (and subsequently the predicted
target area) between the two models, can be expected bythis change in the simulated flow regime.
The topography of Taiwan presents another modelling challenge. The horizontal extent and height of the
topography force the island in a “flow around” regime
and exerts significant control over meteorological conditions, even impacting typhoon tracks (Yeh and Elsberry
1993; Lin et al. 2002). The actual wind flow around theisland is a result of the interplay between the synoptically enforced dominant wind and the local sea breeze
and orographic effects (Huang and Chang 2018; Wu
et al. 2019; Cheng et al. 2022). On top of this, air quality
over Taiwan, as well as the biochemical cycle of the surrounding area, is also known to be impacted by atmospheric flow acceleration over the Taiwan Strait that canoccur (e.g. as in the Nanjing case here) due to channelling

Page 22 of 28

between the mountains in south-east China and Taiwan(Lee and Hills 2003; Lin et al. 2012). Naturally, the orographic flow regime also controls pollution outflow fromthe island, with air quality critically impacted depending, amongst other parameters, on the incidence angle
between the oncoming flow and the main island ridges(Cheng 2001; Hsu et al. 2023).
In the simulations here, results depended critically on
the incidence angle and transport length. As shown in
Fig. 4, the main orographic feature (CMR) in Taiwan isrepresented relatively well at lower resolutions. In the
Taipei release case, this means that the expected flow
regime remains similar across the different models (withFr ranging from ∼0.5 for the ERA5 data to ∼0.33 for IFS
and ∼0.25 for the WRF simulations). In all cases, thisplaces the expected flow within the “flow around” regime
for an elongated mountain ridge (Ólafsson and Bougeault1996). As such, the differences seen in the way PFC transport was represented across the different model resolutions mainly come down to the representation of thenorthernmost mountain range (Xueshan), as smoothing
on the edges of the island eliminates the north-western flow splitting point, which, critically, is located justwest of the release point. To add to this complexity, theresolved flow critically depends on the incidence angleof the incoming wind (Wells et al. 2008). In contrast to
the Taipei release, in the Nanjing case, the flow, impacted
by channelling over the Taiwan strait (Lee and Hills
2003; Lin et al. 2012), was aligned with the long axis of
the mountain range, meaning that changes in the flow
regimes were minimal. On top of this, the PFC plume
was already well-mixed and of significant spatial extend(similar to the size of Taiwan), meaning that even moderate change in the simulated representation of the plume
had a less significant impact on the forecasted target area.
Results broadly conform to theoretical expectations
when analysing the areas from the scope of orographic
effects. The Manilla release represents a case where
topography is relevant but not resolved. Intuitively,
dynamical downscaling can be expected to be important. On the other hand, the Nanjing release represents
a case where the detailed resolution of topography can be
expected to be largely irrelevant as most of the transport
occurs over water. The Taipei release represents the mostinteresting and challenging case presented; even though
it could be considered an in-between case of the previously discussed two extremes. Critically examining themodel’s limitations in this case is especially importantdue to the model’s inability to accurately reproduce theobserved mixing ratios at higher resolution (i.e. ensemble-average error increasing with increasing resolution).
Considering the limited amount of observations after the
Taipei release, it is impossible to generalise the overall

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

accuracy of the FLEXPART-WRF simulations, especiallyas all simulations included a splitting of the plume, withthe main part heading westwards, towards an unsampledarea.
Despite conclusions based on observations and simulations presented here, it is important to acknowledge thatthe study is still based on three cases; as such, generalisation of results is difficult. First, the transport evaluation
was carried out over the limited data available, leading
to a potential bias, especially due to the inhomogeneityof the PFC mixing ratio profiles and low mixing ratios
encountered in most experiments. Considering theimportant benefits that can be expected from downscaling, as well as the inherent difficulties in model evaluation, a more extensive and focused study on the subject
should be carried out over an area with a dense data
network to allow for a more robust analysis. It is also
important to point out that all areas studied here involve
complex topography; it stands to reason that over simple topography (for example, as represented here with
the Nanjing case), the benefits of dynamical downscaling
would be reduced.
Another important point is the fact that what wasstudied here are isolated cases of inert pollutant transport from isolated sources that are more prone to shortlived or isolated biases (e.g. Poulidis and Iguchi 2021).
Although important, as they can be considered as representative of natural or anthropogenic disasters, results
here do not apply for continuous emissions over a longtime, or emissions from multiple sources in an extendedarea, as local biases can be expected to cancel out. Finally,
the analysis focused on inert pollutants. As such, care isneeded to extrapolate results when considering chemically active species as chemical transformations can be
expected to introduce non-linear effects whose appropriate inclusion in the modelling can exert a first-order control over the transport results (Lin et al. 2023a, b).
4.2 Applications in campaign flight decision‑making

A critical question is whether dynamical downscaling
could have been used to change decision-making during the EMeRGe campaign. The use of model ensembles
has already been suggested as a way to adjust observation
campaign targets in order to tackle model uncertainty ininverse modelling (Dunbar et al. 2022); however, here,
we focused on the forward problem, in a similar way toDacre and Harvey (2018), i.e. by utilising divergence inthe ensemble members. When comparing the FLEXPART and FLEXPART-WRF predicted “target areas” for10 ppqv observations (i.e. Fig. 16) there are two points tofocus: (i) the existence of a high-confidence target area
(west of the release point) missing from the FLEXPART
simulations and (ii) divergent results over the south

Page 23 of 28

of Taiwan with increased model resolution. Following
point (i) would mean that reconsideration of the area tobe observed would be advisable (similar to the Manillacase, although due to a different reason). Point (ii) could
help identify a high risk of low PFC mixing ratio measurements over the south of Taiwan comes at a higher riskof a low mixing ratio. In this case, the suggested action
would either be a reconsideration of the measurementtiming on the same day, or, if possible, delay for a daywith easier-to-forecast meteorological conditions (e.g. as
in the Nanjing release).
Naturally, the study here is a theoretical reanalysis exercise; due to the complexity of the field campaign organisation involving different partners and civil agencies, it
might be impossible to amend campaign targets. Still,
combined results from the different cases, showcase thestrengths of an ensemble approach that can be facilitatedthrough the use of regional modelling, due to the abundance of physical parametrisation options, e.g. Deppeet al. (2013). Furthermore, unlike pollutant releases outside of the PBL (e.g. Poulidis and Iguchi 2021), results
for the near-surface releases studied here highlight thatdifferent PBL schemes at the same resolution are different enough to allow for an ensemble approach. Considering the cost and difficulties of repeating field campaigns,
the ability to carry out probabilistic forecasting can bea deciding factor that can help identify days that should
be avoided due to particularly difficult-to-forecast conditions; or, at least, can provide additional data and help
have well-defined expectations in order to take a calculated risk.
Although the focus of the study has been on field campaign management, the findings can be applied to any
scenario that leads to a pollutant release from an isolated
source, such as a power plant accident or volcanic eruption. When forecasting is employed in such cases, misidentifying high pollutant concentration areas can haveimportant ramifications (Dacre and Harvey 2018). Furthermore, direct sampling, which can be vital in understanding the specific hazard, can be associated with ahigh risk, while the window of opportunity for sampling
can be small. Given these extreme conditions, additional
input that can be provided, as shown in the current study,
could make the difference between a successful and failed
risk management case (Budd et al. 2011; Schmitt andKuenz 2015).

5 Conclusions
Dynamical downscaling is a commonly used technique
to obtain high-resolution atmospheric data over a specified region of interest, which can be analysed directlyor used to increase the accuracy of secondary models,such as ATDMs. Key applications of the latter include

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

hazard management and field campaign organisation.
However, dynamical downscaling requires resources,
computational time, and expertise, potentially leading
to increased complexity in the organisation of measurement efforts. The potential benefits of using dynamical
downscaling were examined here, taking advantage of
the EMeRGe campaign perfluorocarbons (PFC) release
experiments, carried out at three locations in Asia in
March–April 2018: Manilla (Philippines; 19.03.2018),
Taipei (Taiwan; 27.03.2018), and Nanjing (China;
05.04.2018). Aside from an evaluation of model accuracy,differences in the location of simulated PFC mixing ratio
maxima were identified in order to assess what would
have been the ideal location for the sampling carried out
during the campaign.
Overall, based on the limited observations available, theuse of dynamically downscaled data to drive the ATDM
did not lead to a consistent increase in model accuracy;
the results varied significantly depending on the locationand model configuration options. Furthermore, it wasseen that a model configuration that performs well basedon meteorological evaluation is not necessarily tied to agood performance when it comes to secondary modelling applications (here the tracer transport). However, thesimulations highlighted fundamental differences between
the global-based and dynamical-downscale-based simulations, stemming from the different representations of
the study areas in the models. In the simple cases of theManilla and Nanjing releases, increased resolution led
to similar PFC transport patterns, but with significant
spatial shifts. In the complex case of the Taipei release,dynamical downscaling revealed fundamentally different
PFC transport patterns.
On top of simulation accuracy, which is the typicalbenefit commonly associated with and expected fromdynamical downscaling, results were analysed in the context of ensemble modelling. For isolated emission sources
within the boundary layer, simulations here showed thatdifferent WRF PBL schemes led to a sufficient spread in
the ATDM results. Although, no PBL scheme led to systematically improved accuracy, we demonstrated how theinherent difficulties in representing local flows over complex topography, leading to divergence within ensemble
members can be exploited and utilised as a tool to aid indecision-making.
Abbreviations
ACM2	Asymmetric convection model 2
ARMTS	Automatic rainfall and meteorological telemetry system
ATDM	Atmospheric transport and dispersion model
BouLac	Bougeault–Lacarrere
CMR	Central Mountain Range
CWB	Central Weather Bureau
DLR	German Aerospace Center
ECMWF	European Centre for Medium-Range Weather Forecasts
EMeRGe	
Effect of Megacities on the Transport and Transformation ofPage 24 of 28

Pollutants on the Regional to Global Scales
ERA5	European Reanalysis 5
HALO	High Altitude and Long Range Research Aircraft
IFS	Integrated Forecasting System
FLEXPART​	Flexible particle (model)
LE	Logarithmic error
MAPE	Mean Absolute Percentage Error
MBLE	Mean Bias Logarithmic Error
MODIS	Moderate resolution imaging spectroradiometer
MPC	Major Population Centre
MYNN3	Mellor–Yamada Nakanishi Niino Level 3
NCAR​	National Science Foundation
NMOC	Non-methane organic compound
PBL	Planetary boundary layer
PDMCH	Perfluorodimethylcyclohexane
PETRAS	Perfluorocarbon tracer system
PFC	Perfluorocarbons
PMCH	Perfluoromethylcyclohexane
RMSE	Root Mean Square Error
RMSLE	Root Mean Square Logarithmic Error
SHIVA	Stratospheric Ozone: Halogen Impacts in a Varying Atmosphere
WRF	Weather Research and Forecasting (model)
YSU	Yonsei University
Acknowledgements
The authors would like to thank J. P. Burrows and A. Hernández (IUP Bremen)
for coordinating the EMeRGe project. We would also like to acknowledge theteam supporting the tracer experiments during EMeRGe-Asia, in particular M.
Lichtenstern, R. Baumann (DLR), C. K. Chou (Academia Sinica, Taipei, Taiwan), V.
A. Velazco, R. Macatangay (University of the Philippines, Manila), and T. Wang,
M. Xie (University of Nanjing, China). The results contain modified Copernicus
Climate Change Service information 2024. Neither the European Comission
nor ECMWF is responsible for any use that may be made of the Copernicus
information or data it contains. All simulations were carried out at the AETHER
cluster of the LAMOS group, University of Bremen.
Author contributions
A.P.P., S.-L.S., H.S., and M.V. contributed to conceptualisation, methodology,
and writing—review and editing; A.P.P. and S.-L.S. contributed to software;
A.P.P. and H.S. performed validation and data curation; A.P.P. performed formal
analysis, investigation, visualisation, and writing—original draft preparation;
H.S. and M.V. contributed to resources; M.V. performed supervision; H.S. and
M.V. contributed to project administration; H.S. and M.V. were involved infunding acquisition. All authors have read and agreed to the published versionof the manuscript.
Funding
Open Access funding enabled and organized by Projekt DEAL. Alexandros
Poulidis was partially funded by Germany’s Excellence Strategy (University
Allowance, EXC 2077, University of Bremen). The HALO deployment during
EMeRGe was funded by a consortium comprising the German Research Foun‑dation (DFG) Priority Program HALO-SPP 1294, the Institute of AtmosphericPhysics of DLR, the Max Plank Gesellschaft (MPG) and the Helmholtz-Gemein‑schaft. The PFC tracer release and analysis was funded by the DFG project
MEPOLL (SCHL 1857/4-1) within the Priority Program HALO-SPP 1294. The HPC
cluster Aether at the University of Bremen was financed by DFG within thescope of the Excellence Initiative.
Availability of data and materials
The datasets used and/or analysed during the current study are available fromthe corresponding author on reasonable request. Near-surface meteorological
data were obtained from the Central Weather Bureau archive.

Declarations
Competing of interests
The authors declare no conflict of interest.

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Received: 22 December 2023 Accepted: 11 June 2024

References
Abdi-Oskouei M, Carmichael G, Christiansen M, Ferrada G, Roozitalab B, Sob‑hani N, Wade K, Czarnetzki A, Pierce R, Wagner T et al (2020) Sensitivityof meteorological skill to selection of WRF-Chem physical parameteriza‑
tions and impact on ozone prediction during the Lake Michigan Ozone
Study (LMOS). J Geophys Res Atmosph 125:2019–031971. https://​doi.​
org/​10.​1029/​2019J​D0319​71
Achim P, Monfort M, Le Petit G, Gross P, Douysset G, Taffary T, Blanchard X,
Moulin C (2014) Analysis of radionuclide releases from the Fukushima
Dai-ichi nuclear power plant accident part II. Pure Appl Geophys
171:645–667. https://​doi.​org/​10.​1007/​s00024-​012-​0578-1
Akagi S, Yokelson RJ, Wiedinmyer C, Alvarado M, Reid J, Karl T, Crounse J, Wenn‑berg P (2011) Emission factors for open and domestic biomass burningfor use in atmospheric models. Atmos Chem Phys 11:4039–4072.
https://​doi.​org/​10.​5194/​acp-​11-​4039-​2011
Alvarado L, Richter A, Vrekoussis M, Hilboll A, Kalisz Hedegaard AB, Schneising
O, Burrows JP (2020) Unexpected long-range transport of glyoxal andformaldehyde observed from the Copernicus Sentinel-5 Precur‑sor satellite during the 2018 Canadian wildfires. Atmos Chem Phys
20:2057–2072. https://​doi.​org/​10.​5194/​acp-​20-​2057-​2020
Andrés Hernández MD, Hilboll A, Ziereis H, Förster E, Krüger OO, Kaiser K,
Schneider J, Barnaba F, Vrekoussis M, Schmidt J et al (2022) Overview:on the transport and transformation of pollutants in the outflow ofmajor population centres-observational data from the EMeRGe Euro‑pean intensive operational period in summer 2017. Atmos Chem Phys
22:5877–5924. https://​doi.​org/​10.​5194/​acp-​22-​5877-​2022
Angevine WM, Brioude J, Mckeen S, Holloway JS (2014) Uncertainty in Lagran‑gian pollutant transport simulations due to meteorological uncertainty
from a mesoscale WRF ensemble. Geosci Model Dev 7:2817–2829.
https://​doi.​org/​10.​5194/​gmd-7-​2817-​2014
Arnold D, Maurer C, Wotawa G, Draxler R, Saito K, Seibert P (2015) Influenceof the meteorological input on the atmospheric transport modellingwith FLEXPART of radionuclides from the Fukushima Daiichi nuclear
accident. J Environ Radioact 139:212–225. https://​doi.​org/​10.​1016/j.​
jenvr​ad.​2014.​02.​013
Aubry TJ, Farquharson JI, Rowell CR, Watt SF, Pinel V, Beckett F, Fasullo J, Hop‑croft PO, Pyle DM, Schmidt A et al (2022) Impact of climate change on
volcanic processes: current understanding and future challenges. Bull
Volcanol 84:58. https://​doi.​org/​10.​1007/​s00445-​022-​01562-8
Avolio E, Federico S, Miglietta M, Feudo TL, Calidonna C, Sempreviva AM (2017)
Sensitivity analysis of WRF model PBL schemes in simulating boundarylayer variables in southern Italy: an experimental campaign. Atmos Res
192:58–71. https://​doi.​org/​10.​1016/j.​atmos​res.​2017.​04.​003
Bagheri G, Bonadonna C, Manzella I, Vonlanthen P (2015) On the characteriza‑tion of size and shape of irregular particles. Powder Technol 270:141–
153. https://​doi.​org/​10.​1016/j.​powtec.​2014.​10.​015
Bagtasa G, Yuan C-S (2020) Influence of local meteorology on the chemical
characteristics of fine particulates in Metropolitan Manila in the Philip‑
pines. Atmos Pollut Res 11:1359–1369. https://​doi.​org/​10.​1016/j.​apr.​
2020.​05.​013
Banks RF, Tiana-Alsina J, Baldasano JM, Rocadenbosch F, Papayannis A, Solo‑
mos S, Tzanis CG (2016) Sensitivity of boundary-layer variables to PBL
schemes in the WRF model based on surface meteorological observa‑
tions, lidar, and radiosondes during the HygrA-CD campaign. Atmos
Res 176:185–201. https://​doi.​org/​10.​1016/j.​atmos​res.​2016.​02.​024
Baró R, Jiménez-Guerrero P, Balzarini A, Curci G, Forkel R, Grell G, Hirtl M, Hon‑zak L, Langer M, Pérez JL et al (2015) Sensitivity analysis of the micro‑
physics scheme in WRF-Chem contributions to AQMEII phase 2. Atmos
Environ 115:620–629. https://​doi.​org/​10.​1016/j.​atmos​env.​2015.​01.​047
Beckett FM, Witham CS, Leadbetter SJ, Crocker R, Webster HN, Hort MC, JonesAR, Devenish BJ, Thomson DJ (2020) Atmospheric dispersion modellingat the London VAAC: a review of developments since the 2010 Eyjafjal‑
lajökull volcano ash cloud. Atmosphere 11:352. https://​doi.​org/​10.​3390/​
atmos​11040​352

Page 25 of 28

Beig G, Rathod A, Tikle S, Maji S, Sobhana S (2021) Association of retreat‑ing monsoon and extreme air pollution in a megacity. J Environ Sci
106:97–104. https://​doi.​org/​10.​5094/​APR.​2015.​033
Bethke I, Outten S, Otterå OH, Hawkins E, Wagner S, Sigl M, Thorne P (2017)
Potential volcanic impacts on future climate variability. Nat Clim Chang
7:799–805. https://​doi.​org/​10.​1038/​nclim​ate33​94
Bobrowski N, Von Glasow R, Aiuppa A, Inguaggiato S, Louban I, Ibrahim O, Platt
U (2007) Reactive halogen chemistry in volcanic plumes. J Geophys Res
Atmosph. https://​doi.​org/​10.​1029/​2006J​D0072​06
Borge R, Alexandrov V, Del Vas JJ, Lumbreras J, Rodríguez E (2008) A compre‑
hensive sensitivity analysis of the WRF model for air quality applications
over the Iberian Peninsula. Atmos Environ 42:8560–8574. https://​doi.​
org/​10.​1016/j.​atmos​env.​2008.​08.​032
Bougeault P, Lacarrere P (1989) Parameterization of orography-induced turbu‑
lence in a mesobeta-scale model. Mon Weather Rev 117:1872–1890.
https://​doi.​org/​10.​1175/​1520-​0493(1989)​117<​1872:​POOIT​I>2.​0.​CO;2
Brioude J, Arnold D, Stohl A, Cassiani M, Morton D, Seibert P, Angevine W, Evan
S, Dingwell A, Fast JD et al (2013) The Lagrangian particle dispersion
model FLEXPART-WRF version 3.1. Geosci Model Dev 6(6):1889–1904
Budd L, Griggs S, Howarth D, Ison S (2011) A fiasco of volcanic proportions?
Eyjafjallajökull and the closure of European airspace. Mobilities 6:31–40.
https://​doi.​org/​10.​1080/​17450​101.​2011.​532650
Carvalho A, Monteiro A, Flannigan M, Solman S, Miranda AI, Borrego C (2011)
Forest fires in a changing climate and their impacts on air quality.
Atmos Environ 45:5545–5553. https://​doi.​org/​10.​1016/j.​atmos​env.​2011.​
05.​010
Cécé R, Bernard D, Brioude J, Zahibo N (2016) Microscale anthropogenic
pollution modelling in a small tropical island during weak trade winds:
Lagrangian particle dispersion simulations using real nested LES mete‑orological fields. Atmos Environ 139:98–112. https://​doi.​org/​10.​1016/j.​
atmos​env.​2016.​05.​028
Chatzimouratidis AI, Pilavachi PA (2007) Objective and subjective evaluationof power plants and their non-radioactive emissions using the analytic
hierarchy process. Energy Policy 35:4027–4038. https://​doi.​org/​10.​1016/j.​enpol.​2007.​02.​003
Cheng W-L (2001) Spatio-temporal variations of sulphur dioxide patterns
with wind conditions in central Taiwan. Environ Monit Assess 66:77–98.
https://​doi.​org/​10.​1023/A:​10264​80231​637
Cheng K-S, Ho C-Y, Teng J-H (2022) Wind and sea breeze characteristics forthe offshore wind farms in the central coastal area of Taiwan. Energies
15:992. https://​doi.​org/​10.​3390/​en150​30992
Chou M-D, Suarez MJ (1999) A solar radiation parameterization for atmos‑
pheric studies. Technical report. NASA/104606
Chou M-D, Suarez MJ, Liang X-Z, Yan MM-H, Cote C (2001) A thermal infrared
radiation parameterization for atmospheric studies. Technical report .
NASA/104606
Connan O, Smith K, Organo C, Solier L, Maro D, Hébert D (2013) Comparisonof RIMPUFF, HYSPLIT, ADMS atmospheric dispersion model outputs,
using emergency response procedures, with 85Kr measurements
made in the vicinity of nuclear reprocessing plant. J Environ Radioact
124:266–277. https://​doi.​org/​10.​1016/j.​jenvr​ad.​2013.​06.​004
Costa A, Folch A, Macedonio G (2013) Density-driven transport in the umbrella
region of volcanic clouds: implications for tephra dispersion models.
Geophys Res Lett 40:4823–4827. https://​doi.​org/​10.​1002/​grl.​50942
Dacre HF, Harvey NJ (2018) Characterizing the atmospheric conditions leading
to large error growth in volcanic ash cloud forecasts. J Appl Meteorol
Climatol 57:1011–1019. https://​doi.​org/​10.​1175/​JAMC-D-​17-​0298.1
Dacre HF, Harvey NJ, Webley PW, Morton D (2016) How accurate are volcanic
ash simulations of the 2010 Eyjafjallajökull eruption? J Geophys Res
Atmosph 121:3534–3547. https://​doi.​org/​10.​1002/​2015J​D0242​65
Daskalakis N, Gallardo L, Kanakidou M, Nüß JR, Menares C, Rondanelli R,
Thompson AM, Vrekoussis M (2022) Impact of biomass burning andstratospheric intrusions in the remote South Pacific Ocean tropo‑
sphere. Atmos Chem Phys 22:4075–4099. https://​doi.​org/​10.​5194/​
acp-​22-​4075-​2022
De Meij A, Zittis G, Christoudias T (2019) On the uncertainties introducedby land cover data in high-resolution regional simulations. Meteorol
Atmos Phys 131:1213–1223. https://​doi.​org/​10.​1007/​s00703-​018-​0632-3
Deppe AJ, Gallus WA Jr, Takle ES (2013) A WRF ensemble for improved windspeed forecasts at turbine height. Weather Forecast 28:212–228.
https://​doi.​org/​10.​1175/​WAF-D-​11-​00112.1

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Dunbar OR, Howland MF, Schneider T, Stuart AM (2022) Ensemble-based
experimental design for targeting data acquisition to inform climate
models. J Adv Model Earth Syst 14:2022–002997. https://​doi.​org/​10.​1029/​2022M​S0029​97
Durran DR (1990) In: Blumen W (ed) Mountain waves and downslope winds,
pp 59–81. American Meteorological Society, Boston, MA. https://​doi.​
org/​10.​1007/​978-1-​935704-​25-6_4
Eliassen A (1980) A review of long-range transport modeling. J Appl Meteorol
Climatol 19:231–240. https://​doi.​org/​10.​1175/​1520-​0450(1980)​019<​
0231:​AROLR​T>2.​0.​CO;2
Etherton B, Santos P (2008) Sensitivity of WRF forecasts for South Florida toinitial conditions. Weather Forecast 23:725–740. https://​doi.​org/​10.​
1175/​2007W​AF200​6115.1
Fero J, Carey SN, Merrill JT (2009) Simulating the dispersal of tephra from the1991 Pinatubo eruption: implications for the formation of widespread
ash layers. J Volcanol Geoth Res 186:120–131. https://​doi.​org/​10.​1016/j.​
jvolg​eores.​2009.​03.​011
Folch A (2012) A review of tephra transport and dispersal models: evolu‑tion, current status, and future perspectives. J Volcanol Geoth Res
235:96–115. https://​doi.​org/​10.​1016/j.​jvolg​eores.​2012.​05.​020
Förster E, Bönisch H, Neumaier M, Obersteiner F, Zahn A, Hilboll A, Kalisz
Hedegaard AB, Daskalakis N, Poulidis AP, Vrekoussis M et al (2023)
Chemical and dynamical identification of emission outflows during
the HALO campaign EMeRGe in Europe and Asia. Atmos Chem Phys
23:1893–1918. https://​doi.​org/​10.​5194/​acp-​23-​1893-​2023
Freitag S, Clarke A, Howell S, Kapustin V, Campos T, Brekhovskikh V, Zhou
J (2014) Combining airborne gas and aerosol measurements with
HYSPLIT: a visualization tool for simultaneous evaluation of air mass
history and back trajectory consistency. Atmosph Measure Tech 7:107.
https://​doi.​org/​10.​5194/​amt-7-​107-​2014
García-Díez M, Fernández J, Fita L, Yagüe C (2013) Seasonal dependence of
WRF model biases and sensitivity to PBL schemes over Europe. Q J R
Meteorol Soc 139(671):501–514. https://​doi.​org/​10.​1002/​qj.​1976
Gesch DB, Verdin KL, Greenlee SK (1999) New land surface digital elevation
model covers the Earth. EOS Trans Am Geophys Union 80:69–70.
https://​doi.​org/​10.​1029/​99EO0​0050
Gillett N, Weaver A, Zwiers F, Flannigan M (2004) Detecting the effect ofclimate change on Canadian forest fires. Geophys Res Lett. https://​doi.​
org/​10.​1029/​2004G​L0208​76
Glasow R, Bobrowski N, Kern C (2009) The effects of volcanic eruptions on
atmospheric chemistry. Chem Geol 263:131–142. https://​doi.​org/​10.​1016/j.​chemg​eo.​2008.​08.​020
Gómez-Navarro JJ, Raible CC, Dierer S (2015) Sensitivity of the WRF model
to PBL parametrisations and nesting techniques: evaluation of windstorms over complex terrain. Geosci Model Dev 8:3349–3363. https://​doi.​org/​10.​5194/​gmd-8-​3349-​2015
Grell GA, Peckham SE, Schmitz R, McKeen SA, Frost G, Skamarock WC, Eder B(2005) Fully coupled “online’’ chemistry within the wrf model. Atmos
Environ 39:6957–6975. https://​doi.​org/​10.​1016/j.​atmos​env.​2005.​04.​027
Gulia S, Nagendra SS, Khare M, Khanna I (2015) Urban air quality management:a review. Atmos Pollut Res 6:286–304. https://​doi.​org/​10.​5094/​APR.​
2015.​033
Gunwani P, Mohan M (2017) Sensitivity of WRF model estimates to various
PBL parameterizations in different climatic zones over India. Atmos Res
194:43–65
Halse AK, Eckhardt S, Schlabach M, Stohl A, Breivik K (2013) Forecasting longrange atmospheric transport episodes of polychlorinated biphenyls
using FLEXPART. Atmos Environ 71:335–339. https://​doi.​org/​10.​1016/j.​
atmos​env.​2013.​02.​022
Hanna SR (1982) In: Nieuwstadt FTM, Dop H (eds) applications in air pollution
modeling, pp 275–310. Springer, Dordrecht. https://​doi.​org/​10.​1007/​
978-​94-​010-​9112-1_7
Hanna SR, Yang R (2001) Evaluations of mesoscale models’ simulations of nearsurface winds, temperature gradients, and mixing depths. J Appl Mete‑orol Climatol 40:1095–1104. https://​doi.​org/​10.​1175/​1520-​0450(2001)​
040<​1095:​EOMMS​O>2.​0.​CO;2
He J, Chen D, Gu Y, Jia H, Zhong K, Kang Y (2022) Evaluation of planetary
boundary layer schemes in WRF model for simulating sea-land breezein Shanghai, China. Atmosph Res 278:106337. https://​doi.​org/​10.​1016/j.​
atmos​res.​2022.​106337

Page 26 of 28

Hegarty J, Draxler RR, Stein AF, Brioude J, Mountain M, Eluszkiewicz J, Nehrkorn
T, Ngan F, Andrews A (2013) Evaluation of Lagrangian particle disper‑sion models with measurements from controlled tracer releases.
J Appl Meteorol Climatol 52:2623–2637. https://​doi.​org/​10.​1175/​
JAMC-D-​13-​0125.1
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas
J, Peubey C, Radu R, Schepers D et al (2020) The ERA5 global reanalysis.
Q J R Meteorol Soc 146:1999–2049. https://​doi.​org/​10.​1002/​qj.​3803
Hilario MRA, Olaguera LM, Narisma GT, Matsumoto J (2021) Diurnal character‑istics of summer precipitation over Luzon Island, Philippines. Asia-Pac J
Atmos Sci 57:573–585. https://​doi.​org/​10.​1007/​s13143-​020-​00214-1
Hong S-Y, Noh Y, Dudhia J (2006) A new vertical diffusion package withan explicit treatment of entrainment processes. Mon Weather Rev
134(9):2318–2341. https://​doi.​org/​10.​1175/​MWR31​99.1
Hsu T-H, Chen W-T, Wu C-M, Hsieh M-K (2023) The observation-based index
to investigate the role of the lee vortex in enhancing air pollution overnorthwestern Taiwan. J Appl Meteorol Climatol 62:427–439. https://​doi.​
org/​10.​1175/​JAMC-D-​22-​0102.1
Huang W-R, Chang Y-H (2018) Characteristics and mechanisms of the diurnal
variation of winter precipitation in Taiwan. Int J Climatol 38:3058–3068.
https://​doi.​org/​10.​1002/​joc.​5482
Jankov I, Gallus WA Jr, Segal M, Koch SE (2007) Influence of initial conditions
on the WRF-ARW model QPF response to physical parameteriza‑tion changes. Weather Forecast 22:501–519. https://​doi.​org/​10.​1175/​
WAF998.1
Jenkins S, Barsotti S, Hincks T, Neri A, Phillips J, Sparks RSJ, Sheldrake T,
Vougioukalakis G (2015) Rapid emergency assessment of ash and gas
hazard for future eruptions at Santorini Volcano, Greece. J Appl Volcanol
4:1–22. https://​doi.​org/​10.​1186/​s13617-​015-​0033-y
Jia M, Li F, Zhang Y, Wu M, Li Y, Feng S, Wang H, Chen H, Ju W, Lin J et al (2022)
The Nord Stream pipeline gas leaks released approximately 220,000
tonnes of methane into the atmosphere. Environ Sci Ecotechnol
12:100210. https://​doi.​org/​10.​1016/j.​ese.​2022.​100210
Jian G-J, Teng J-H, Wang S-T, Cheng M-D, Cheng C-P, Chen J-H, Chu Y-J (2022)
An overview of the tropical cyclone database at the Central Weather
Bureau of Taiwan. Terrest Atmosph Oceanic Sci 33:26. https://​doi.​org/​
10.​1007/​s44195-​022-​00023-1
Jiménez PA, Dudhia J, González-Rouco JF, Navarro J, Montávez JP, GarcíaBustamante E (2012) A revised scheme for the WRF surface layer
formulation. Mon Weather Rev 140:898–918. https://​doi.​org/​10.​1175/​
MWR-D-​11-​00056.1
Justice C, Townshend J, Vermote E, Masuoka E, Wolfe R, Saleous N, Roy D,
Morisette J (2002) An overview of MODIS Land data processing andproduct status. Remote Sens Environ 83:3–15. https://​doi.​org/​10.​1016/​
S0034-​4257(02)​00084-6
Kain JS (2004) The Kain-Fritsch convective parameterization: an update. J Appl
Meteorol 43:170–181. https://​doi.​org/​10.​1175/​1520-​0450(2004)​043<​
0170:​TKCPA​U>2.​0.​CO;2
Kioutsioukis I, Meij A, Jakobs H, Katragkou E, Vinuesa J-F, Kazantzidis A (2016)
High resolution WRF ensemble forecasting for irrigation: multi-variableevaluation. Atmos Res 167:156–174. https://​doi.​org/​10.​1016/j.​atmos​res.​2015.​07.​015
Klemp J, Dudhia J, Hassiotis A (2008) An upper gravity-wave absorbing layer
for NWP applications. Mon Weather Rev 136:3987–4004. https://​doi.​
org/​10.​1175/​2008M​WR2596.1
Kukkonen J, Olsson T, Schultz DM, Baklanov A, Klein T, Miranda A, Monteiro A,
Hirtl M, Tarvainen V, Boy M et al (2012) A review of operational, regionalscale, chemical weather forecasting models in Europe. Atmos Chem
Phys 12:1–87. https://​doi.​org/​10.​5194/​acp-​12-1-​2012
Kursa MB (2022) Kendall transformation brings a robust categorical repre‑
sentation of ordinal data. Sci Rep 12:8341. https://​doi.​org/​10.​1038/​
s41598-​022-​12224-2
Le Petit G, Douysset G, Ducros G, Gross P, Achim P, Monfort M, Raymond P,
Pontillon Y, Jutier C, Blanchard X et al (2014) Analysis of radionuclide
releases from the Fukushima Dai-Ichi nuclear power plant accident
Part I. Pure Appl Geophys 171:629–644. https://​doi.​org/​10.​1007/​
s00024-​012-​0581-6
Lee Y, Hills P (2003) Cool season pollution episodes in Hong Kong, 1996–2002.
Atmos Environ 37:2927–2939. https://​doi.​org/​10.​1016/​S1352-​2310(03)​00296-6

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Leelőssy Á, Molnár F, Izsák F, Havasi Á, Lagzi I, Mészáros R (2014) Dispersion
modeling of air pollutants in the atmosphere: a review. Central Eur J
Geosci 6:257–278. https://​doi.​org/​10.​2478/​s13533-​012-​0188-6
Lin Y-L, Ensley DB, Chiao S, Huang C-Y (2002) Orographic influences on rainfall
and track deflection associated with the passage of a tropical cyclone.
Mon Weather Rev 130(12):2929–2950. https://​doi.​org/​10.​1175/​1520-​
0493(2002)​130<​2929:​OIORA​T>2.​0.​CO;2
Lin C-Y, Sheng Y-F, Chen W-N, Wang Z, Kuo C-H, Chen W-C, Yang T (2012) The
impact of channel effect on Asian dust transport dynamics: a case insoutheastern Asia. Atmos Chem Phys 12:271–285. https://​doi.​org/​10.​
5194/​acp-​12-​271-​2012
Lin C-Y, Chen W-M, Sheng Y-F, Chen W-N, Liu C-Y (2023) Exploration of thedownward transport mechanisms of biomass burning emissions
from Indochina at the low boundary layer in east asia. Atmos Environ
314:120117. https://​doi.​org/​10.​5194/​acp-​23-​2627-​2023
Lin C-Y, Chen W-C, Chien Y-Y, Chou CC, Liu C-Y, Ziereis H, Schlager H, Förster E,
Obersteiner F, Krüger OO et al (2023) Effects of transport on a biomassburning plume from Indochina during EMeRGe-Asia identified by
WRF-Chem. Atmos Chem Phys 23:2627–2647. https://​doi.​org/​10.​5194/​
acp-​23-​2627-​2023
Lu R, Turco RP (1994) Air pollutant transport in a coastal environment. Part I:
two-dimensional simulations of sea-breeze and mountain effects. J
Atmosph Sci 51:2285–2308. https://​doi.​org/​10.​1175/​1520-​0469(1994)​
051<​2285:​APTIA​C>2.​0.​CO;2
Macedonio G, Costa A, Scollo S, Neri A (2016) Effects of eruption source
parameter variation and meteorological dataset on tephra fallout haz‑ard assessment: example from Vesuvius (Italy). J Appl Volcanol 5:1–19.
https://​doi.​org/​10.​1186/​s13617-​016-​0045-2
Madala S, Prasad KH, Srinivas C, Satyanarayana A (2016) Air quality simulationof NOX over the tropical coastal city Chennai in southern India withFLEXPART-WRF. Atmos Environ 128:65–81. https://​doi.​org/​10.​1016/j.​
atmos​env.​2015.​12.​052
Marshall LR, Maters EC, Schmidt A, Timmreck C, Robock A, Toohey M (2022)
Volcanic effects on climate: recent advances and future avenues. Bull
Volcanol 84:54. https://​doi.​org/​10.​1007/​s00445-​022-​01559-3
Mathieu A, Kajino M, Korsakissok I, Périllat R, Quélo D, Quérel A, Saunier O,
Sekiyama TT, Igarashi Y, Didier D (2018) Fukushima Daiichi-derived
radionuclides in the atmosphere, transport and deposition in Japan: areview. Appl Geochem 91:122–139. https://​doi.​org/​10.​1016/j.​jenvr​ad.​2014.​02.​013
Matsumoto J, Olaguera LMP, Nguyen-Le D, Kubota H, Villafuerte MQ (2020)
Climatological seasonal changes of wind and rainfall in the Philippines.
Int J Climatol 40:4843–4857. https://​doi.​org/​10.​1002/​joc.​6492
Milford C, Torres C, Vilches J, Gossman A-K, Weis F, Suárez-Molina D, García
OE, Prats N, Barreto Á, García RD et al (2023) Impact of the 2021 La
Palma volcanic eruption on air quality: insights from a multidisciplinary
approach. Sci Total Environ 869:161652. https://​doi.​org/​10.​1016/j.​scito​
tenv.​2023.​161652
Miranda A, Silveira C, Ferreira J, Monteiro A, Lopes D, Relvas H, Borrego C, Roe‑beling P (2015) Current air quality plans in Europe designed to support
air quality management policies. Atmos Pollut Res 6:434–443. https://​doi.​org/​10.​5094/​APR.​2015.​048
Misenis C, Zhang Y (2010) An examination of sensitivity of WRF/Chem predic‑
tions to physical parameterizations, horizontal grid spacing, and nest‑
ing options. Atmos Res 97:315–334. https://​doi.​org/​10.​1016/j.​atmos​res.​2010.​04.​005
Morley SK, Brito TV, Welling DT (2018) Measures of model performance basedon the log accuracy ratio. Space Weather 16:69–88. https://​doi.​org/​10.​
1002/​2017S​W0016​69
Mulena GC, Allende DG, Puliafito SE, Lakkis SG, Cremades PG, Ulke AG (2016)
Examining the influence of meteorological simulations forced bydifferent initial and boundary conditions in volcanic ash dispersion
modelling. Atmos Res 176:29–42. https://​doi.​org/​10.​1016/j.​atmos​res.​2016.​02.​009
Nakanishi M, Niino H (2006) An improved Mellor–Yamada level-3 model: its
numerical stability and application to a regional prediction of advec‑
tion fog. Bound-Layer Meteorol 119:397–407. https://​doi.​org/​10.​1007/​
s10546-​005-​9030-8Ólafsson H, Bougeault P (1996) Nonlinear flow past an elliptic mountain ridge.
J Atmosph Sci 53:2465–2489. https://​doi.​org/​10.​1175/​1520-​0469(1996)​
053<​2465:​NFPAE​M>2.​0.​CO;2

Page 27 of 28

Onwukwe C, Jackson PL (2020) Meteorological downscaling with wrf model,
version 4.0, and comparative evaluation of planetary boundary layer
schemes over a complex coastal airshed. J Appl Meteorol Climatol
59:1295–1319. https://​doi.​org/​10.​1175/​JAMC-D-​19-​0212.1
Pisso I, Sollum E, Grythe H, Kristiansen NI, Cassiani M, Eckhardt S, Arnold D,
Morton D, Thompson RL, Groot Zwaaftink CD et al (2019) The Lagran‑gian particle dispersion model FLEXPART version 10.4. Geosci Model
Dev 12:4955–4997. https://​doi.​org/​10.​5194/​gmd-6-​1889-​2013
Pleim JE (2007) A combined local and nonlocal closure model for the atmos‑
pheric boundary layer. Part I: model description and testing. J Appl
Meteorol Climatol 46:1383–1395. https://​doi.​org/​10.​1175/​JAM25​39.1
Poulidis AP, Iguchi M (2021) Model sensitivities in the case of high-resolution
Eulerian simulations of local tephra transport and deposition. Atmos
Res 247:105136. https://​doi.​org/​10.​1016/j.​atmos​res.​2020.​105136
Poulidis AP, Phillips JC, Renfrew IA, Barclay J, Hogg A, Jenkins SF, Robertson
R, Pyle DM (2018) Meteorological controls on local and regional
volcanic ash dispersal. Sci Rep 8:6873. https://​doi.​org/​10.​1038/​
s41598-​018-​24651-1
Pullen J, Gordon AL, Flatau M, Doyle JD, Villanoy C, Cabrera O (2015) Multiscale
influences on extreme winter rainfall in the Philippines. J Geophys Res
Atmosph 120:3292–3309. https://​doi.​org/​10.​1007/​s13143-​020-​00214-1
Qu K, Wang X, Cai X, Yan Y, Jin X, Vrekoussis M, Kanakidou M, Brasseur GP, ShenJ, Xiao T et al (2023) Rethinking the role of transport and photochemis‑
try in regional ozone pollution: insights from ozone concentration andmass budgets. Atmos Chem Phys 23:7653–7671. https://​doi.​org/​10.​
5194/​acp-​23-​7653-​2023
Quan J, Dou Y, Zhao X, Liu Q, Sun Z, Pan Y, Jia X, Cheng Z, Ma P, Su J et al(2020) Regional atmospheric pollutant transport mechanisms over the
North China Plain driven by topography and planetary boundary layer
processes. Atmos Environ 221:117098. https://​doi.​org/​10.​1016/j.​atmos​
env.​2019.​117098
Randerson JT, Liu H, Flanner MG, Chambers SD, Jin Y, Hess PG, Pfister G, Mack
M, Treseder K, Welp L et al (2006) The impact of boreal forest fire on
climate warming. Science 314:1130–1132. https://​doi.​org/​10.​1126/​
scien​ce.​11320​75
Rashad S, Hammad F (2000) Nuclear power and the environment: compara‑tive assessment of environmental and health impacts of electricitygenerating systems. Appl Energy 65:211–229. https://​doi.​org/​10.​1016/​
S0306-​2619(99)​00069-0
Ren Y, Schlager H, Martin D (2014) The application of TD/GC/NICI-MS with an­Al2O3-PLOT-S column for the determination of perfluoroalkylcycloal‑
kanes in the atmosphere. Chromatographia 77:309–316. https://​doi.​
org/​10.​1007/​s10337-​013-​2584-6
Ren Y, Baumann R, Schlager H (2015) An airborne perfluorocarbon tracer
system and its first application for a Lagrangian experiment. AtmosphMeasure Tech 8:69–80. https://​doi.​org/​10.​5194/​amt-8-​69-​2015
Rentai Y et al (2011) Atmospheric dispersion of radioactive material in radio‑
logical risk assessment and emergency response. Progress Nucl Sci
Technol 1:7–13. https://​doi.​org/​10.​15669/​PNST.1.7
Rife DL, Davis CA, Liu Y, Warner TT (2004) Predictability of low-level winds bymesoscale meteorological models. Mon Weather Rev 132:2553–2569.
https://​doi.​org/​10.​1175/​MWR28​01.1
Rizza U, Miglietta MM, Mangia C, Ielpo P, Morichetti M, Iachini C, Virgili S, Pas‑
serini G (2018) Sensitivity of WRF-Chem model to land surface schemes:
assessment in a severe dust outbreak episode in the Central Mediter‑
ranean (Apulia Region). Atmos Res 201:168–180. https://​doi.​org/​10.​1016/j.​atmos​res.​2017.​10.​022
Roberts CD, Senan R, Molteni F, Boussetta S, Mayer M, Keeley SP (2018) Climate
model configurations of the ECMWF Integrated Forecasting System(ECMWF-IFS cycle 43r1) for HighResMIP. Geosci Model Dev 11:3681–
3712. https://​doi.​org/​10.​5194/​gmd-​11-​3681-​2018
Robock A (2000) Volcanic eruptions and climate. Rev Geophys 38:191–219.
https://​doi.​org/​10.​1029/​1998R​G0000​54
Saha S, Moorthi S, Wu X, Wang J, Nadiga S, Tripp P, Behringer D, Hou Y-T,
Chuang H-y, Iredell M et al (2014) The NCEP climate forecast system ver‑sion 2. J Clim 27:2185–2208. https://​doi.​org/​10.​1175/​JCLI-D-​12-​00823.1
Santoso M, Lestiani DD, Kurniawati S, Damastuti E, Kusmartini I, Atmodjo DPD,
Sari DK, Hopke PK, Mukhtar R, Muhtarom T et al (2020) Assessmentof urban air quality in Indonesia. Aerosol Air Qual Res 20:2142–2158.
https://​doi.​org/​10.​4209/​aaqr.​2019.​09.​0451

Poulidis et al. Progress in Earth and Planetary Science(2024) 11:37

Schmidt A, Leadbetter S, Theys N, Carboni E, Witham CS, Stevenson JA, Birch
CE, Thordarson T, Turnock S, Barsotti S et al (2015) Satellite detection,
long-range transport, and air quality impacts of volcanic sulfur dioxidefrom the 2014–2015 flood lava eruption at Bárdharbunga (Iceland). J
Geophys Res Atmosph 120:9739–9757. https://​doi.​org/​10.​1002/​2015J​
D0236​38
Schmitt AR, Kuenz A (2015) A reanalysis of aviation effects from volcano erup‑tion of Eyjafjallajökull in 2010. In: 2015 IEEE/AIAA 34th digital avionics
systems conference (DASC), pp 1–31. https://​doi.​org/​10.​1109/​DASC.​2015.​73113​35 . IEEE
Shin HH, Hong S-Y (2011) Intercomparison of planetary boundary-layer
parametrizations in the WRF model for a single day from CASES99. Bound-Layer Meteorol 139:261–281. https://​doi.​org/​10.​1007/​
s10546-​010-​9583-z
Skamarock WC, Klemp JB, Dudhia J, Gill, DO, Liu Z, Berner J, Wang W, Powers
JG, Duda MG, Barker DM et al (2019) A description of the advanced
research WRF model version 4. Technical report. NCAR/TN-556+STR
Smith RB (1989) Hydrostatic airflow over mountains. Adv Geophys 31:1–41.
https://​doi.​org/​10.​1016/​S0065-​2687(08)​60052-7
Smith RB (1989) Mountain-induced stagnation points in hydrostatic flow. Tel‑lus A Dyn Meteorol Oceanograph 41:270–274. https://​doi.​org/​10.​3402/​
tellu​sa.​v41i3.​11839
Smolarkiewicz PK, Rotunno R (1989) Low Froude number flow past threedimensional obstacles. Part I: baroclinically generated lee vortices. J
Atmosph Sci 46:1154–1164. https://​doi.​org/​10.​1175/​1520-​0469(1989)​
046<​1154:​LFNFP​T>2.​0.​CO;2
Stein A, Draxler RR, Rolph GD, Stunder BJ, Cohen M, Ngan F (2015) NOAA’s
HYSPLIT atmospheric transport and dispersion modeling sys‑tem. Bull Am Meteor Soc 96:2059–2077. https://​doi.​org/​10.​1175/​
BAMS-D-​14-​00110.1
Stewart C, Damby DE, Horwell CJ, Elias T, Ilyinskaya E, Tomašek I, Longo BM,
Schmidt A, Carlsen HK, Mason E et al (2022) Volcanic air pollution andhuman health: recent advances and future directions. Bull Volcanol
84:11. https://​doi.​org/​10.​1007/​s00445-​021-​01513-9
Stocks BJ, Fosberg M, Lynham T, Mearns L, Wotton B, Yang Q, Jin J, Lawrence K,
Hartley G, Mason J et al (1998) Climate change and forest fire potential
in Russian and Canadian boreal forests. Clim Change 38:1–13. https://​doi.​org/​10.​1023/A:​10053​06001​055
Stohl A, Hittenberger M, Wotawa G (1998) Validation of the Lagrangian particle
dispersion model FLEXPART against large-scale tracer experiment data.
Atmos Environ 32:4245–4264. https://​doi.​org/​10.​1016/​S1352-​2310(98)​00184-8
Stohl A, Forster C, Frank A, Seibert P, Wotawa G (2005) The Lagrangianparticle dispersion model FLEXPART version 6.2. Atmosph Chem Phys
5(9):2461–2474
Stohl A, Seibert P, Wotawa G, Arnold D, Burkhart JF, Eckhardt S, Tapia C,
Vargas A, Yasunari TJ (2012) Xenon-133 and caesium-137 releases
into the atmosphere from the Fukushima Dai-ichi nuclear power
plant: determination of the source term, atmospheric dispersion, anddeposition. Atmos Chem Phys 12:2313–2343. https://​doi.​org/​10.​5194/​
acp-​12-​2313-​2012
Takemi T, Ito R (2020) Benefits of high-resolution downscaling experiments forassessing strong wind hazard at local scales in complex terrain: a casestudy of Typhoon Songda (2004). Prog Earth Planet Sci 7:1–16. https://​doi.​org/​10.​1186/​s40645-​019-​0317-7
Takemura T, Nakamura H, Takigawa M, Kondo H, Satomura T, Miyasaka T, Naka‑
jima T (2011) A numerical simulation of global transport of atmosphericparticles emitted from the Fukushima Daiichi Nuclear Power Plant. Sola
7:101–104. https://​doi.​org/​10.​2151/​sola.​2011-​026
Tao W-K, Simpson J, McCumber M (1989) An ice-water saturation adjust‑ment. Mon Weather Rev 117:231–235. https://​doi.​org/​10.​1175/​1520-​
0493(1989)​117<​0231:​AIWSA​>2.​0.​CO;2
Tewari M, Chen F, Wang W, Dudhia J, LeMone M, Mitchell K, Ek, M, Gayno G,
Wegiel J, Cuenca R, et al (2004) Implementation and verification of theunified NOAH land surface model in the WRF model. In: 20th confer‑ence on weather analysis and forecasting/16th conference on numeri‑cal weather prediction, vol 1115, pp 2165–2170
Tofallis C (2015) A better measure of relative prediction accuracy for model
selection and model estimation. J Oper Res Soc 66:1352–1362. https://​doi.​org/​10.​1057/​jors.​2014.​103

Page 28 of 28

Ur Rehman S, Ahmed R, Ma K, Xu S, Aslam MA, Bi H, Liu J, Wang J (2021)
Ammonium nitrate is a risk for environment: a case study of Beirut (Leb‑anon) chemical explosion and the effects on environment. Ecotoxicol
Environ Saf 210:111834. https://​doi.​org/​10.​1016/j.​ecoenv.​2020.​111834
Urbanski S (2014) Wildland fire emissions, carbon, and climate: emission fac‑
tors. For Ecol Manage 317:51–60. https://​doi.​org/​10.​1016/j.​foreco.​2013.​
05.​045
Webster H, Thomson D, Johnson B, Heard I, Turnbull K, Marenco F, Kristiansen
N, Dorsey J, Minikin A, Weinzierl B et al (2012) Operational prediction ofash concentrations in the distal volcanic cloud from the 2010 Eyjafjal‑
lajökull eruption. J Geophys Res Atmosph. https://​doi.​org/​10.​1029/​
2011J​D0167​90
Wells H, Vosper S, Ross A, Brown A, Webster S (2008) Wind direction effects on
orographic drag. Quart J R Meteorol Soc A J Atmosph Sci Appl Meteorol
Phys Oceanograph 134(632):689–701. https://​doi.​org/​10.​1002/​qj.​247
Werf GR, Randerson JT, Giglio L, Collatz G, Mu M, Kasibhatla PS, Morton DC,
DeFries R, Jin Yv, Leeuwen TT (2010) Global fire emissions and thecontribution of deforestation, savanna, forest, agricultural, and peat
fires (1997–2009). Atmos Chem Phys 10:11707–11735. https://​doi.​org/​
10.​5194/​acp-​10-​11707-​2010
Wu Y-c, Wang S-YS, Yu Y-C, Kung C-Y, Wang A-H, Los SA, Huang W-R (2019)
Climatology and change of extreme precipitation events in Taiwan
based on weather types. Int J Climatol 39:5351–5366. https://​doi.​org/​
10.​1002/​joc.​6159
Xu Z, Han Y, Yang Z (2019) Dynamical downscaling of regional climate: A
review of methods and limitations. Sci China Earth Sci 62:365–375.
https://​doi.​org/​10.​1007/​s11430-​018-​9261-5
Yadav R, Sahu L, Beig G, Jaaffrey S (2016) Role of long-range transport andlocal meteorology in seasonal variation of surface ozone and its precur‑sors at an urban site in India. Atmos Res 176:96–107. https://​doi.​org/​10.​1016/j.​atmos​res.​2016.​02.​018
Yasunari TJ, Stohl A, Hayano RS, Burkhart JF, Eckhardt S, Yasunari T (2011)
Cesium-137 deposition and contamination of Japanese soils due tothe Fukushima nuclear accident. Proc Natl Acad Sci 108:19530–19534.
https://​doi.​org/​10.​1073/​pnas.​11120​58108
Yeh T-C, Elsberry RL (1993) Interaction of typhoons with the Taiwan orography.
Part I: Upstream track deflections. Monthly Weather Rev 121(12):3193–
3212 https://​doi.​org/​10.​1175/​1520-​0493(1993)​121<​3193:​IOTWT​T>2.​0.​
CO;2
Zhao C, Huang M, Fast JD, Berg LK, Qian Y, Guenther A, Gu D, Shrivastava M,
Liu Y, Walters S et al (2016) Sensitivity of biogenic volatile organic com‑pounds to land surface parameterizations and vegetation distributions
in California. Geosci Model Dev 9:1959–1976. https://​doi.​org/​10.​5194/​
gmd-9-​1959-​2016

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in pub‑lished maps and institutional affiliations.