GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-10-1107-2017Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0Di TomasoEnzaenza.ditomaso@bsc.esSchutgensNick A. J.https://orcid.org/0000-0001-9805-6384JorbaOriolhttps://orcid.org/0000-0001-5872-0244Pérez García-PandoCarloshttps://orcid.org/0000-0002-4456-0697Earth Sciences Department, Barcelona Supercomputing Center, SpainAtmospheric, Oceanic and Planetary Physics, University of Oxford,
UKNASA Goddard Institute for Space Studies, New York, USADepartment of Applied Physics and Applied Math, Columbia
University, New York, USAnow at: Faculty of Life & Earth Sciences, Vrije Universiteit, Amsterdam, the
Netherlandsnow at: Earth Sciences Department, Barcelona Supercomputing Center,
SpainEnza Di Tomaso (enza.ditomaso@bsc.es)10March2017103110711293August201621September201610February201717February2017This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/10/1107/2017/gmd-10-1107-2017.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/10/1107/2017/gmd-10-1107-2017.pdf
A data assimilation capability has been built for
the NMMB-MONARCH chemical weather prediction system, with a focus on mineral
dust, a prominent type of aerosol. An ensemble-based Kalman filter technique
(namely the local ensemble transform Kalman filter – LETKF) has been
utilized to optimally combine model background and satellite retrievals. Our
implementation of the ensemble is based on known uncertainties in the
physical parametrizations of the dust emission scheme. Experiments showed
that MODIS AOD retrievals using the Dark Target algorithm can help
NMMB-MONARCH to better characterize atmospheric dust. This is particularly
true for the analysis of the dust outflow in the Sahel region and over the
African Atlantic coast. The assimilation of MODIS AOD retrievals based on the
Deep Blue algorithm has a further positive impact in the analysis downwind
from the strongest dust sources of the Sahara and in the Arabian Peninsula.
An analysis-initialized forecast performs better (lower forecast error and
higher correlation with observations) than a standard forecast, with the
exception of underestimating dust in the long-range Atlantic transport and
degradation of the temporal evolution of dust in some regions after day 1.
Particularly relevant is the improved forecast over the Sahara throughout the
forecast range thanks to the assimilation of Deep Blue retrievals over areas
not easily covered by other observational datasets.
The present study on mineral dust is a first step towards data assimilation
with a complete aerosol prediction system that includes multiple aerosol
species.
Introduction
Among the different aerosol species, mineral dust is one of the main
components of the atmospheric aerosol loading and is of great interest for a
variety of reasons. Mineral dust plays an important role in the earth's
energy balance and has a relevant impact on economical activities, on the
ecosystem, on health, as well as on weather and climate . The
strong dust storms occurring near emission sources constitute a big hazard to
air traffic and road transport as they can reduce the visibility down to few
metres. However, dust does not affect
only local economies: because of its transport over thousands of kilometres,
it has an impact from local to global scales. Dust deposition provides
nutrients to continental and marine ecosystems. African dust for example has
a role in fertilization of the Amazon rainforest , while dust
deposition over oceans has implications for sea biogeochemistry as the iron
contained in the dust particles is a nutrient for phytoplankton, whose
photosynthetic activity in turn affects the carbon cycle . Dust
has health implications both close to and far from sources. For example,
studies have shown the usefulness of dust aerosol climatologies in predicting
part of the year-to-year variability of the seasonal incidence of meningitis
in Niger , while particulate matter measurements taken in areas
far from sources show that Saharan dust outbreaks have adverse effects of
cardiovascular and respiratory conditions .
Mineral particles perturb the earth-atmosphere's radiation budget through
their interaction with the short-wave radiation, through scattering and
absorption, and long-wave radiation, through absorption and re-emission. Due
to this redistribution of the energy, dust aerosols can have a strong impact
on atmospheric processes at short (weather) and long (climate) term periods,
while they can affect atmospheric circulations at large spatial scales (e.g.
Asian monsoons; ). Furthermore, this can generate feedback
processes since changes in weather and climate can in turn lead to changes in
the dust cycle.
Different types of ground-based (e.g., ) and space-borne
(e.g., ) observations have been utilized to describe
the variability of atmospheric dust. However, due to either insufficient
spatial representativeness or accuracy, the spatio-temporal features of dust
aerosols are not fully captured by the current observing system. Neither do
models accurately describe atmospheric and surface dust concentrations
. High uncertainties are also in our knowledge of the optical
and micro-physical properties of dust, and in our representation of its
vertical structure. The latter has implications for the radiation's budget
and transport. On the other hand, an accurate quantification of dust's
spatial and temporal distribution is key in correctly characterizing the
effect that it has on the earth's energy balance, as well as in improving the
skill in forecasting its concentrations in the atmosphere as well as in
forecasting the weather .
Regional and global centres, predicting the most important aerosol species or
dust only, participate in different model inter-comparison initiatives like
the Aerosol Comparisons between Observations and Models (AeroCom;
) project, the International Cooperative for Aerosol
Prediction (ICAP; ) initiative, and the WMO Sand and Dust
Storm Warning Advisory and Assessment System (SDS-WAS; ).
Multi-model ensemble spreads give an indication of large uncertainties in the
modelling schemes and confirm the need for a better characterization of
aerosols. Relatively recently because of these large uncertainties, the
atmospheric composition community has begun to make use of data assimilation
(DA) for better characterization and prediction of atmospheric constituents
such as aerosols and trace gases . Though their dynamic is
mainly driven by forcings such as emissions, recent studies showed that the
usage of observations through data assimilation has improved significantly
the accuracy of short-term forecast and the global monitoring of both
aerosols and trace gases . Since the first experiments
in the early 2000s, the assimilation of aerosol observations is now
operational in some of the main aerosol forecasting centres .
have highlighted in particular the importance of a combined
assimilation of satellite products for aerosol forecast.
The Earth Sciences Department of the Barcelona Supercomputing Center (ES-BSC)
is implementing a gas-aerosol module able to predict atmospheric composition
at different spatial and temporal scales within the NMMB (Non-hydrostatic
Multi-scale Model on the B grid; ) state-of-art
meteorological model. This modelling system is known as the Multiscale Online
Nonhydrostatic AtmospheRe CHemistry mode (NMMB-MONARCH). We report here on
the extension of NMMB-MONARCH with a data assimilation functionality using
satellite aerosol optical depth. NMMB-MONARCH version 1.0, as in where
the model was previously named NMMB/BSC-CTM, considers dust only,
but other aerosols are being implemented . The focus of this
work on mineral dust is justified by the operational services provided by
NMMB-MONARCH. This model provides an operational dust forecast for the
Barcelona Dust Forecast Centre under an initiative of the World
Meteorological Organization. It participates in the multi-model dust ensemble
of the aforementioned ICAP initiative, providing daily global dust forecasts
of up to 120 h. It also provides daily regional forecasts of up to 60 h to
the WMO SDS-WAS system. Before this work, the system did not have an aerosol
data assimilation capability and dust was produced uniquely from model
estimated surface emission fluxes. The present study on mineral dust is a
first step towards data assimilation with a complete aerosol prediction
system that includes multiple aerosol species (not only dust but also sea
salt, sulfate, and organics).
Previous studies of assimilation of dust aerosol only have been conducted for
the Chinese Unified Atmospheric Chemistry Environment – Dust (CUACE/Dust)
forecast system . These studies have used variational
data assimilation techniques (3D-Var) which require, in their most practical
implementation, pre-calculated and constant in time model error structures.
Alternatively, ensemble-based techniques use flow-dependent model error
amplitudes and structures which evolve during forecast and, in theory, should
be able to capture better instabilities in the background flow
. Dust AOD is currently assimilated at the UK Met Office
with a hybrid variational data assimilation technique (hybrid 4D-Var).
In this work we present the coupling of NMMB-MONARCH with an ensemble-based
technique known as local ensemble transform Kalman filter (LETKF;
). The LETKF scheme has been shown to be particularly
suitable for the assimilation of aerosol information since it has been
observed by , , and that aerosol
fields have limited spatial correlations. Long-range transport of dust could
be an exception to this. Since detailed studies of spatial correlation length
scales for dust long-range transport are still missing in the literature, in
this work we assume that what has been derived (limited spatial correlations)
in general for aerosols is valid for dust. The utility of ensemble-based
techniques for global aerosol simulations has been shown in previous studies
(; and more recently ). The
main novelty in our study is the creation of the ensemble, our implementation
is based on known uncertainties in the physical parametrizations of the
sophisticated dust emission scheme used by the NMMB-MONARCH model, as well as
in the use of observations particularly relevant for dust applications, like
MODIS Deep Blue.
The NMMB-MONARCH chemical weather prediction system is described in more
detail in Sect. , with emphasis on its dust module. A
description of the data assimilation scheme and of the assimilated
observations follows respectively in Sects. and .
We report then in Sect. about the characteristics of the
simulations that we have run, in Sect. about the evaluation
methodology that we have followed, and in Sect. about the
evaluation results of our simulation experiments. The final section concludes
the paper with a summary of this development, the main results achieved, and
future perspectives.
The NMMB-MONARCH model and its mineral dust component
The ES-BSC is implementing a new gas-aerosol module within the NMMB
meteorological model from the Unites States National Centers for
Environmental Prediction (NCEP). The new modelling system is known as
NMMB-MONARCH where it was previously named
NMMB/BSC-CTM, and is developed in collaboration
with NCEP and other research institutions. The chemistry (aerosols included)
and meteorology are fully online integrated. NMMB-MONARCH is able to work
with a wide range of spatial scales thanks to its unified non-hydrostatic
dynamical core, keeping consistent parametrizations at different spatial and
temporal scales. Furthermore, the dynamical core and the coupled modules are
computationally highly efficient, satisfying current and projected
operational requirements. The rest of this section will briefly describe some
characteristics of the dust component of NMMB-MONARCH, with particular focus
on the emission scheme.
The dust emission scheme implemented in NMMB-MONARCH follows the empirical
relationship of and according to which the
vertical dust flux is proportional to the horizontal sand flux. The
horizontal to vertical flux ratio reflects the availability of dust in four
soil populations (clay, silt, fine/medium sand, and coarse sand)
. The horizontal sand flux is modelled as the flux of the
saltating particles H simulated according to and is
proportional to the third power of the wind friction velocity. A soil
moisture-dependent threshold on the friction velocity determines the velocity
above which the soil particles begin to move in horizontal saltation flux.
This threshold is dynamically estimated according to soil characteristics.
Soil moisture effects are included following through the soil
moisture correction parameter included in the expression for the threshold on
the friction velocity. A sectional approach is used for the transport of dust
particles, i.e. the dust size distribution is decomposed into small size
bins. More exactly, dust is modelled using eight dust size bins varying from
0.1 to 10 microns, and, within each transport bin, dust is assumed to have a
time-invariant lognormal distribution . The total vertical flux
mass is distributed among the dust transport bins according to a specific
dust distribution at sources. NMMB-MONARCH uses a distribution over sources
derived from which assumes that the vertical dust flux is size
distributed according to three lognormal background source modes. More
explicitly, the dust vertical mass flux Fb (kg s-1 m-2) in a
given transport bin b at each grid cell is given by
Fb=CS(1-V)αH∑i=03miMi,bb=1,…,8,
where S is a source erodibility factor defined on bare ground surfaces,
representing the probability of having accumulated sediments in the given
grid cell that are potential dust sources; (1-V) is the grid's fraction of
bare soil; α (m-1) is the horizontal to vertical flux ratio calculated for four soil
population classes (clay, silt, fine/medium sand, and coarse sand); H
(kg s-1 m-1) is the horizontal sand flux; Mi,k is the mass
fraction of background source mode i carried in a transport bin k
calculated following , and weighted by a specific background
source mode coefficient mi; and C is a global tuning factor empirically
set to 0.768, which is meant to compensate for the uncertainty associated
with the different components of Fk. More details about the above
formulation of dust emission can be found in .
The mineral dust module has been extensively evaluated in studies at global
and regional scales , showing that its
evaluation scores lie in the upper range of the AEROCOM model evaluation
performance scores. However, these evaluation efforts confirmed, similarly to
other modelling systems, different sources of uncertainty in the NMMB-MONARCH
dust modelling.
The data assimilation scheme
We have coupled NMMB-MONARCH with the LETKF scheme
with four-dimensional extension as
described in , in order to estimate optimal initial conditions
for our dust model. The overall scheme implements an iterative approach
consisting in a forecast step and state estimation step. The state estimation
step combines information from mineral dust observations and a prior first
guess, or background from our model. A short-term forecast is used as
background information. The background incorporates information from past
observations; therefore, the analysis is estimated using both current and
past observations. LETKF is a development of the ensemble-based transform
Kalman filter (ETKF; ) and of the local ensemble Kalman
filter (LEKF; ), and is particularly suited to
high-performance computing applications. A very attractive feature of an
ensemble-based technique is the use of a flow-dependent background error
covariance, which is derived from the ensemble of model states at the
assimilation time, and evolves during forecast. At any given time in fact the
state estimate is represented by an ensemble of system states and its
uncertainty is derived from the ensemble. LETKF has the advantageous feature
that it applies localization, i.e. it performs the analysis locally (at each
grid point only observations within a certain distance are assimilated),
allowing the global analysis to explore a much higher-dimensional space than
the subspace spanned by the ensemble (whose dimensionality is limited by the
number of ensemble members). Localization also reduces the effect of spurious
long-range covariances, effectively eliminating them after a given distance.
This is particularly suitable for the assimilation of aerosol information
since, as mentioned in the introduction, it has been observed that aerosol
fields have limited spatial correlations (∼ 100 km).
have already shown the positive impact of
assimilating aerosol ground station observations using a LETKF assimilation
system for the SPRINTARS model, while used it to assimilate
CALIOP vertical profiles in the MASINGAR model and used it to
ingest MODIS observations in the NICAM-SPRINTARS model.
Here we discuss the basic concepts behind the LETKF algorithm; a more
detailed description of the scheme can be found in . Consider a
state vector x of the dynamic variables of a system (for our
application these are dust mass mixing ratios). The mean analysis increment
at a grid point is estimated as a linear combination of the background
ensemble perturbations Xb:
x‾a=x‾b+Xbw,
where we use the superscripts “a” and “b” to denote respectively the
analysis and background state vector, and where the ith column of the
matrix Xb is
xb(i)-x‾b, {i=1,2,…,k} with
k ensemble members, i.e. the difference between the ith ensemble forecast
xb(i) and the ensemble forecast mean
x‾b. w is termed the “weight” matrix
specifying what linear combination of the background ensemble perturbations
is added to the background mean to obtain the analysis ensemble. The
“weight” matrix is given by
w=[YbR-1Yb+(k-1)I]-1YbR-1(yo-y‾b),
where Yb is the background ensemble perturbation matrix in
observation space (or background observation ensemble perturbation matrix),
R is the observation error covariance matrix which we assume
diagonal, I is the identity matrix, yo is the
vector of observations, and y‾b is the mean
background observation ensemble. The background observation ensemble is
obtained by applying the observation operator h(⋅) to the ensemble
forecast members xb(i), i.e. yb(i)=h(xb(i)).
LETKF uses R-localization, i.e. the localization is performed in the
observation error covariance matrix, making the influence of an observation
on the analysis decay gradually toward zero as the distance from the analysis
location increases. To achieve this, the observation error is divided by a
distance-dependent function that decays to zero with increasing distance:
e-dist2l2, where “dist” is the distance in the grid
space between an observation and the model grid in which the analysis is
calculated, and l is the horizontal localization factor.
Ensemble perturbations
We run the data assimilation scheme under an imperfect model scenario
assumption: each ensemble member is run with a different perturbation of
uncertain model parameters in the dust emission scheme. In dust modelling,
the emission source term is a particularly large contributor to model error
. In the case of NMMB-MONARCH one of the components of the
uncertainty in the emission term has been identified for example in the
vertical flux distribution at sources .
The model ensemble is created by perturbing the vertical flux of dust in each
of the eight dust bins. As described in Sect. , NMMB-MONARCH
follows a sectional approach for dust, i.e. the size distribution is
decomposed into small size bins that from bin 1 to bin 8 go from 0.1 to
10 µm with division intervals at 0.18, 0.3, 0.6, 1, 1.8, 3, and
6 µm. This is equivalent to perturbing the total vertical flux as
well as its size distribution at sources. The perturbations are extracted
imposing some physical constraint: correlated noise is used across the bins
so that noise correlation decreases with increased difference of the
normalized cubic radius among the bins; the noise is applied multiplicatively
and has mean 1 and standard deviation of 30 % of the unperturbed value in
each bin; and the final distribution is unimodal.
Figure shows how the vertical flux is perturbed in our
ensemble simulations. Additionally, we have perturbed the threshold friction
velocity for dust emission by extracting a multiplicative random factor from
a normal distribution with mean 1 and spread 0.4. This considers the
uncertainty of the model with respect to both surface winds and soil
humidity. At low resolution, model surface winds are typically underestimated
over dust sources. Also, the model uses the scheme of to
calculate the increase in the threshold friction velocity with soil humidity,
which is typically overestimated in arid regions . The spin-up
period for the ensemble ensures that perturbations applied at the sources
propagate everywhere in the globe. For this reason at this first stage of
development of our ensemble system we did not consider a combined meteorology
and source perturbation necessary. The structure of our source perturbations,
for both types of perturbations, is temporally and spatially constant.
Distribution of the mass vertical flux at sources across the eight
dust transport bins for the different ensemble members in different colours,
where the bin sizes from bin 1 to bin 8 go from 0.1 to 10 µm
with division intervals at 0.18, 0.3, 0.6, 1, 1.8, 3, and 6 µm.
The distribution derived from , and used in the
standard forecast, is the dashed red line, with horizontal bars indicating
the standard deviation of the noise used to create the perturbations. The
mean of the ensemble perturbations is the dash-dotted line.
Observation operator
Our state vector is the dust mass mixing ratios. Therefore an observation
operator is needed to map the ensemble mean state vector into the observation
space. The simulated AOD at wavelength λ is calculated at a given
observation location according to the following linear operator:
AODλ=∑b=1834ρbrbMbQλb,
where ρb (kg m-3) is the particle mass density,
rb (m) is the effective radius, Mb (kg m-2) is the
dust column mass loading calculated from each dust bin mixing ratio, and
Qλb is the extinction efficiency factor which is calculated for
using the Mie scattering theory assuming dust spherical, non-soluble
particles, and, within a bin, a lognormal distribution for dust with a
geometric radius of 0.2986 µm and a standard deviation of
2.0.
When using in the state vector the total mass mixing ratio, as we will
explain in Sect. , an ensemble averaged extinction efficiency
is calculated as in as an average of the extinction
efficiency of the individual bins weighted by the bin mixing ratios.
Hereafter, when we will use the term AOD without specifying the wavelength,
we imply that we refer to aerosol optical depth at a wavelength of 550 nm,
which is the most commonly reported value in the literature.
Observational dataMODIS Dark Target and OMI
We consider for assimilation the MODIS Level 3 AOD product produced by the US
NRL and the University of North Dakota (hereafter called NRL MODIS). The NRL
MODIS product is produced for the purpose of assimilation into aerosol
transport models , post-processing the Level 2
MODIS Dark Target product from the so-called Collection 5
, and is available over both land and ocean.
The MODIS Level 2 product is an average of the 1 km by 1 km retrievals
(at nominal resolution) generated by the Dark Target algorithm applied to
top-of-atmosphere reflectances observed by the MODIS sensor onboard NASA
polar-orbiting satellites Terra and Aqua. The NRL MODIS Level 3 product is
filtered and corrected in order to eliminate outliers and gross systematic
biases, spatially aggregated into a 1∘ by 1∘ mesh in order to
avoid the assimilation of sub-grid features, and an error is estimated for
each observation. The product is generated every 6 h at 00:00, 06:00, 12:00,
and 18:00 UTC and is based on MODIS Level 2 observations in a 6 h
interval around those times. The retrieval errors estimated by NRL/University
of North Dakota were used for the observation errors. They include the
instrumental error variance and the spatial representation error variance.
Following the approach in , we assume uncorrelated observation
errors. These observations pertain to the total atmospheric aerosol column,
not just to dust AOD. The selection of observations in dust-dominated
conditions is performed using retrievals of the Ångström exponent
(AE) from the original MODIS Level 3 product , for
information about the size of the particles, and using retrievals of the
Aerosol Absorbing Index (AAI) from the Ozone Monitoring Instrument (OMI)
sensor , for information about the absorption characteristics
of the particles. Ångström exponent (AE) values are based on
quality-assurance-weighted 470 and 660 nm optical depths over land, and 550
and 865 nm optical depths over sea. Observations are selected when daily
MODIS Aqua or Terra products contain a value for AE < 0.75 and daily OMI
products contain a value for AAI > 1.5. Figure shows an
example of the NRL MODIS Level 3 product for a day of May 2007 after the
filter for dust-dominated conditions is applied.
Aerosol optical depth (top) and its associated observation error
(bottom) for 10 May 2007 for the NRL MODIS Level 3 product after the
application of a filter for dust-dominated conditions.
MODIS Deep Blue
The MODIS Dark Target product does not provide information over very bright
reflective surfaces, including deserts, as the retrieval algorithm assumes
low surface albedo. We consider the assimilation of the MODIS Deep Blue Level
3 daily AOD product from Collection 6 whose algorithm retrieves AOD also
over bright arid land surfaces, such as deserts. The Collection 6 product
covers all cloud-free and snow-free surfaces, and can be potentially very
useful for mineral dust applications as it is able to provide an
observational constraint close to dust sources. The Deep Blue algorithm uses
top-of-atmosphere reflectances at 412 and 470 nm. In the presence of
heavy dust load the reflectance at 650 nm is also used. The algorithm
exploits the fact that, over most surfaces, a darker surface and stronger
aerosol signal is seen in the blue wavelength range than at longer
wavelengths. The quality of the MODIS Deep Blue AOD product is improved in
Collection 6 compared to Collection 5, as the work of ,
based on Level 2 retrievals, showed. Similar findings, for the northern
African and Middle East deserts, were reported by , who used
Level 3 retrievals over the period 2002–2014.
We have applied to this product the same filter for dust-dominated conditions
described in Sect. . In addition we have masked out Level 3
retrievals obtained with less than 30 Level 2 retrievals, since
showed that the agreement between MODIS-AERONET is improved
when the sub-pixel spatial representativeness is increased. The MODIS Deep
Blue observations are not corrected for possible systematic biases; however,
we are aware that for future applications we should address any possible bias
in the product. It is important to notice that the Level 3 product is an
aggregation of Level 2 retrievals that is produced using the
highest-quality retrievals (i.e. retrievals with quality-assurance flag value
3). Furthermore, we have applied a quality control to all the assimilated
observations based on normalized first-guess departures. As a proxy for the
normalization factor, we have used the standard deviation of first-guess
departures.
Aerosol optical depth (top) and its associated observation error
(bottom) for 10 May 2007 for the MODIS Deep Blue Collection 6 Level 3
product after the application of a filter for dust-dominated conditions.
Characteristics of the simulation runs.
ExperimentEnsembleDust initial conditionsSpin-upDust initial conditionsnameconfigurationat 00:00 UTC on day 1periodat 00:00 UTC after day 1ControlNoCold start1 monthFC + 24from previous day runENS-free-runYesWarm start11 daysFC + 24from Controlof the individual membersfrom previous day runDA-NRLYesWarm startNoneAnalysis at 00:00 UTCfrom ENS-Free-runof the individual membersfrom previous day DA cycleDA-NRL-DBYesWarm startNoneAnalysis at 00:00 UTCfrom ENS-Free-runof the individual membersfrom previous day DA cycleAN-initializedNoWarm startNoneEnsemble mean analysisfrom Controlfrom DA-NRL-DB
A study by shows that the highest-quality data have an
absolute uncertainty of approximately
(0.086 + 0.56 AOD550) / AMF, where AMF is the geometric air
mass factor with a typical AMF value of 2.8. We have used this quantification
of the uncertainty for the Level 3 product. Furthermore, we have defined
the representation component of the error as the standard deviation of the
values used in the aggregated product. Although a more accurate treatment for
the representation error could be envisaged following for example the
approach of , we deem small the impact that our approximation
has on the analysis. Figure shows an example of the MODIS
Deep Blue Collection 6 Level 3 product for a day of May 2007 after the
filter for dust-dominated conditions is applied.
The number of MODIS Deep Blue and Dark Target observations used over the
experimental period is shown in Fig. .
AERONET
For validation purposes we have used observations from the ground-based
stations of the global Aerosol Robotic Network (AERONET; ) of
direct-sun photometers. These observations have not been assimilated in our
test simulations. In particular, we have used their retrievals of
column-integrated aerosol optical depth from direct-sun photometric
measurements. The retrievals are obtained observing the extinction of direct
solar radiation due to the presence of aerosols in the atmosphere. For this
reason AERONET retrievals are not available under cloudy sky conditions and
during night-time. These observations suffer from a relatively sparse spatial
coverage but are very valuable for validation purposes as their uncertainty
in these retrievals is estimated to be between 0.01 and 0.02. Several studies have in fact used the AERONET data for
validation purposes, or for the correction of biases in satellite
measurements . We considered cloud-screened and
quality-assured (Level 2.0) direct-sun AOD retrievals between 440 and
870 nm. AERONET AOD at 550 nm was obtained using the Ångström
law.
Number of NRL MODIS and MODIS Deep Blue Level 3 observations
assimilated between May and August 2007.
Numerical simulation set-up
We have run a set of different experiments (listed in Table ):
a control experiment to produce a 5-day forecast (hereafter called the
Control experiment) with the same operational configuration (but at a coarser
resolution) and version that provides daily global forecast to the
aforementioned ICAP multi-model ensemble, and which is initialized for dust
from the previous day 24 h forecast (FC + 24). Assimilation experiments
were run with NRL MODIS AOD (hereafter called the DA-NRL experiment) and with
NRL MODIS AOD and MODIS Deep Blue AOD (hereafter called the DA-NRL-DB
experiment) with a pre-processing to the observations as described in
Sect. . Additionally, we have also run free ensemble simulations
without assimilating any observation (hereafter called ENS-free-run). We have
also run a 5-day forecast experiment initialized from the analysis produced
by the DA-NRL-DB experiment (hereafter called the AN-initialized experiment)
in order to evaluate the impact of the analysis on the forecast. The Control
experiment was run for 5 months in the spring/summer period of 2007 (from
1 April to 31 August 2007) starting from a cold start for dust and with a
spin-up period of 1 month (April 2007) which is excluded from the analysis of
the results. Also, the ensemble is spun up before data assimilation is
applied.
We use a 24 h assimilation window and observations are considered for
assimilation at four time slots within the window, at 00:00, 06:00, 12:00,
and 18:00 UTC. The system uses as first guess a 1-day forecast with output
every 6 h. Simulated observation and background departures are calculated
at each time slot. The time slots are exactly the ones corresponding to the
times in which NRL MODIS AOD observations are available. We are using the
LETKF implementation with a four-dimensional extension as described in
. The state vector comprises the mixing ratio at all the time
slots considered and so does the observation AOD vector. Background
observation means y‾j and perturbation matrices
Yj are formed at the various time slots j when the observations
are available. They are then vertically concatenated to form a combined
background observation mean y‾ and perturbation matrix
Y. y‾ and Y are used for the
calculation of a weight matrix w using the standard LETKF, i.e. we
calculate a single w based on all innovations throughout the day.
This same w is then applied to the state vector at different times
throughout the assimilation window.
We have tuned different aspects of the data assimilation system, including
testing the number of ensemble members, different perturbations of the
ensemble, and a different state vector for the control variables. Using 24
ensemble members did not produce a significant impact on the dust analysis
compared to the use of 12 ensemble members. This could be explained by our
setting of a localization factor which makes the influence of an observation
on the analysis decay gradually toward zero as the distance from the analysis
location increases. We have set the horizontal localization factor to the
value 1 in all the data assimilation experiments. This means that after two
grid points the localization function is very close to zero. The value chosen
is in the range of the ones used in previous studies such as
and . Covariance localization in fact effectively eliminates
background spatial correlations after a certain distance, and might have
solved possible sampling errors introduced by the low dimensionality of the
12-member ensemble compared to the 24-member ensemble. We also apply
vertical localization following the approach of localizing the
error covariance vertically for radiance assimilation. The observation error
is divided by the square of the model AOD normalized sensitivity function.
We have tested the usage of different perturbations of the dust emission
scheme: a perturbation of the mass vertical flux per dust bin, or a
perturbation of both the mass vertical flux and the threshold on the wind
friction velocity. As we show in the next section, the latter configuration
was deemed better as it spans a larger space of possible system states.
We have tested two different options for the state vector: a control variable
consisting of the mixing ratio of eight individual dust bins or the total
dust mixing ratio defined as the sum of the eight dust bins at each grid
point and for all the vertical levels. In the latter case the mixing ratios
for the individual dust bin after data assimilation are determined from the
background, i.e. from their relative fractions before assimilation. The
observation operator is calculated using the original mixing ratio following
the approach for the observation operator in . The tests that
we have performed show that representing individually the bins in the state
vector does not have any significant impact on the analysis, while it
increases the computational cost of the assimilation compared to using the
total mixing ratio. Moreover, the use of a bulk approach is common in systems
assimilating total AOD values as the observations are not able to fully
constrain the individual bin profiles. We should note that this choice of
state vector makes still meaningful the creation of the ensemble perturbing
the vertical flux for the individual bins, as this allows us to express in
the background the uncertainty in the size distribution at sources, and to
span a larger space of possible system states.
In the next section we show the results of assimilating NRL MODIS NRL and
MODIS Deep Blue observations using 12 ensemble members obtained by perturbing
the mass vertical flux per bin at sources together with the threshold on the
wind friction velocity, as described in Sect. , and using
the total dust mixing ratio as the analysis variable in the state vector. All
simulations were run on a global domain with 40 hybrid pressure-σ
layers, 5 hPa top pressure, and a horizontal resolution of 2.8∘ by
2∘. The NCEP final analysis at 1∘ by 1∘ at
00:00 UTC was used to initialize the meteorology at every forecast run.
Methodology for the evaluation of the simulations
The evaluation of the simulations is done in three stages: (a) an internal
check of the data assimilation system; (b) evaluation of the analysis using
as reference independent observations; (c) evaluation of a 5-day forecast
with and without analysis initialization using as reference independent
observations.
Regional domains and respective groups of AERONET stations used for
validation purposes.
Regional domain (short name)AERONET stationsLong Atlantic transport (LongAtl)La_Parguera, White_Sands_HELSTF, Univ_of_HoustonShort Atlantic transport (ShortAtl)Capo_Verde, Dakar, La_LagunaSub-Sahel (SubSahel)IER_Cinzana, Banizoumbou, Ilorin, AgoufouSahara (Sahara)Tamanrasset_INMExtended Mediterranean (ExtMediter)Saada, FORTH_CRETE, Lecce_University, Rome_Tor_VergataVillefranche, Avignon, Evora, Barcelona, GranadaMiddle East (MiddleEast)SEDE_BOKER, Solar Village, HamimCentral Asia (CenAsia)–East Asia (EastAsia)–
Map of AERONET stations and of the different regional domains used
for validation purposes. The regional domains are indicated with different
colours: Long Atlantic transport (LongAtl) in blue, Short Atlantic transport
(ShortAtl) in red, Sub-Sahel (SubSahel) in orange, Sahara (Sahara) in green,
Extended Mediterranean (ExtMediter) in yellow, Middle East (MiddleEast) in
pink, Central Asia (CenAsia) in granada, and East Asia (EastAsia) in cyan.
The consistency of the data assimilation system is checked through
considerations of statistics of the ensemble, of simulation departures from
assimilated observations, and of the temporal mean of assimilation
increments. The ensemble mean and the coefficient of variation for the
ensemble are calculated with and without data assimilation. The coefficient
of variation is defined as the ratio of the standard deviation of the
ensemble to the ensemble mean. Additionally, statistics for first-guess (FG)
and analysis (AN) departures are calculated, where departures are defined as
the difference between assimilated observations and simulations (first-guess
or analysis), while mean increments are defined as the temporal mean of
differences between analysis and first guess at the different time slots of
the assimilation window.
Dust optical depth averaged for the month of May 2007 for the
Control (top left), ENS-free-run (top right), DA-NRL (centre left), and
DA-NRL-DB (centre right) experiments, and dust optical depth difference
between the DA-NRL (bottom left), DA-NRL-DB (bottom right), and ENS-free-run
experiment.
The evaluation of analysis and forecast with respect to independent
observations are performed in terms of statistics of model field errors ei
from observations, where ei=mi-oi, with index i indicating an instance
of observation oi and where mi is the model field in observation space,
bi-linearly interpolated at the observation location. We consider the root
mean square error (RMSE), the mean error (BIAS), the standard deviation of
the error (SD), the fraction gross error (FRGE), and the correlation
coefficient (CORR) of the model AOD compared to either quality-assured
(Level 2.0) AERONET or satellite retrievals. The FRGE =2n∑i=0noi-mioi+mi is added to the most
widely used set of statistics for the error as it behaves symmetrically with
respect to underestimation and overestimation without emphasizing the
outliers, and is normalized to the sum of observation and simulation values.
The SD of the error, though it can be derived from the other statistics, is
also reported so as to make more explicit the changes in the bias-free mean
square error and aid the interpretation of the evaluation results. The above
set of evaluation statistics are calculated for measurements from individual
ground-based stations, groups of stations, regional domains observed by
satellite sensors, and globally.
For AERONET AOD measurements dust-dominated conditions are identified using
the approach of as follows: AOD is classified as “Dust” AOD
when the associated AE < 0.75; we set “Dust” AOD to 0 when the
associated AE > 1.3; we identify a mixed aerosol type when the associated
0.75 < AE < 1.3. The latter values are excluded from the validation.
We use the AERONET AOD value closest to the model time step and within a
±30 min interval. For satellite AOD retrievals we use the set of
satellite observations quality-controlled and filtered for dust-dominated
conditions used in the assimilation step. We use these satellite observations
to validate uniquely the forecast range following the assimilation window. We
show the forecast evaluation statistics corresponding to measurements and
simulations at 12:00 UTC only, so that they refer to an approximately equal
number of pairs of observations and model simulated values at each forecast
lead time that we are considering. Hence a smaller number of AERONET
observations (at 12:00 UTC only) are used to verify the forecast compared to
the ones used in the evaluation of the analysis.
We have identified eight regions of interest for the validation purposes in
our study period, namely Long Atlantic transport (LongAtl), Short Atlantic
transport (ShortAtl), Sub-Sahel (SubSahel), Sahara (Sahara), Extended
Mediterranean (ExtMediter), Middle East (MiddleEast), Central Asia (CenAsia),
and East Asia (EastAsia). These names do not necessary correspond to the
conventional names of exact geographical locations but are meant to identify
regional domains in a convenient way according to dust intrusions and to
group observational stations. Most of the regional domains contain
ground-based stations with a minimum number of observations during the study
period (stations with fewer than 30 “Dust” observations are discarded),
with the exception of Central and East Asia. The ground-based stations are
listed in Table and shown in the map in
Fig. together with regional domains used for the validation
of the experiments against either ground-based or satellite observations.
Coefficient of variation for the month of May 2007 for the
ENS-free-run (top), DA-NRL (centre), and DA-NRL-DB (bottom) experiments, when
the ensemble is created perturbing the emitted mass vertical flux for each
dust bin and the threshold on the friction velocity generating dust
horizontal flux.
Coefficient of variation for the month of May 2007 for the
ENS-free-run (left) and DA-NRL-DB (right) experiments, when the ensemble is
created perturbing the emitted mass vertical flux for each dust bin.
Evaluation resultsEnsemble, departure, and increment statistics
We compare here the dust fields in the Control, ENS-free-run, DA-NRL, and
DA-NRL-DB experiments in terms of mean values and, when applicable, ensemble
spread. Figure shows the dust AOD values averaged over a
month of the study period for the four above experiments, and the difference
in AOD between the data assimilation experiments and the ENS-free-run. By
visual inspection it can be noticed that the ensemble mean of the
ENS-free-run experiment compares well with the Control experiment, which
suggests that the ensemble perturbations are altering only to a small extent
the model mean state as defined by a standard run. The analysis clearly shows
conspicuous changes in the dust field compared to the Control experiment or
the ENS-free-run. Figure shows the coefficient of
variation for AOD in the
Statistics of departures of first guess and analysis from
assimilated observations, calculated for May to August 2007.
ENS-free-run and the data assimilation experiments.
Data assimilation clearly lowers the values of the coefficient of variation
in the regions where observations are present, with values lower for the
DA-NRL-DB than for the DA-NRL experiment, which indicates a reduction of the
ensemble spread due to the assimilated observations. The high values of the
coefficient of variation in the Southern Hemisphere, with or without data
assimilation, are due to the perturbation of the dust sources present in the
southern part of the globe. These values are not negligible due to
differences among the ensemble members normalized to small dust AOD values.
The ensemble of Fig. (and Fig. ) is
created by perturbing the emitted mass vertical flux for each dust bin and
the threshold on the friction velocity generating dust horizontal flux.
Creating the ensemble without perturbing the threshold on the friction
velocity produces a reduced spread. See Fig. for this second
configuration of the ensemble with a coefficient of variation for the
ENS-free-run in the left panel and for the experiment with data assimilation
in the right panel. Perturbing the threshold on the friction velocity has an
impact on the spread also outside source regions because, as explained
earlier, the spin-up period for the ensemble ensures that perturbations
applied at the sources propagate everywhere. Furthermore, this ensemble
configuration better represents model uncertainty since the ratio of the
prior total spread (the square root of the sum of the ensemble background
variance and the observation error variance) to the prior RMSE (of the
ensemble background against NRL MODIS and MODIS Deep Blue observations) is
closer to 1 compared to when no perturbation is applied to the threshold on
the friction velocity. It should be noted, however, that this chosen ensemble
configuration under-represents uncertainty since it has a prior total spread
smaller than the RMSE (ratio equal to 0.82). Other better perturbations
should be tested for a future implementation since an underrepresentation of
the background uncertainty might translate into giving a lower weight to the
observations with respect to the background.
Binned scatter plots of the counts of the logarithm of assimilated
observations and first guess (left plot) and analysis (right plot) for the
DA-NRL experiment (top row) and DA-NRL-DB experiment (central and bottom
rows), calculated for May to August 2007. A logarithmic scale is used for the
counts.
Mean dust AOD analysis increments for May to August 2007 at 12:00 UTC
for the DA-NRL experiment (left) and for the DA-NRL-DB experiment (right).
Time series of AOD values for May and August 2007 in La Parguera
(top left), Dakar (top right), Ilorin (centre left), Tamanrasset INM (centre
right), Lecce University (bottom left), and Hamim (bottom right) for the
Control (blue), DA-NRL (green), and DA-NRL-DB (red) experiments, for MODIS
AOD (NRL and DB; magenta circles), and for AERONET AOD (black triangles) in
dust-dominated conditions. Analysis values are used for the data assimilation
experiments.
Maps of validation statistics: BIAS, RMSE, CORR, and FRGE for the
Control (left), DA-NRL (centre), and DA-NRL-DB (right) experiments calculated
against AERONET AOD for a selection of stations providing observations during
the study period (May to August 2007). Maps of the observation counts used
for validation are shown in the bottom row.
We evaluate in the rest of this section the assimilation experiments in terms
of statistics of the departures of the analysis and first guess from the
assimilated satellite observations. Figure shows for May
to August 2007 first-guess dust AOD (in the left panels) and analysis dust
AOD (in the right panels) versus observations for the DA-NRL and DA-NRL-DB
experiments. The departure statistics with respect to the two sets of
observations that we have assimilated are in Table . In both
experiments a smaller scatter and a higher correlation coefficient for the
analysis indicate that the assimilation improves the agreement with
observations and hence a positive sanity check of the data assimilation
system. The asymmetric behaviour of all the analysis scatter plots suggests
that the system is more successful in correcting too high AOD values than
correcting too low AOD values, which could be due to the fact that usually we
have larger observation errors and a smaller ensemble spread for low AOD
values. The BIAS is significantly smaller than the RMSE and the RMSE improves
in the analysis over the forecast. The issue of a higher BIAS in the analysis
departures compared to the first-guess departures has been identified in
other assimilation systems (see , Sect. 4) and might be
attributed to the fact that AOD is a positive definite variable, as this
provides a deviation from the Gaussianity condition in the prior which is
assumed in the analysis step. A solution to this problem worth investigating
in the future would consist in applying a transformation of the state
variables into new variables which present Gaussian features, a procedure
known as Gaussian anamorphosis .
Figure shows global maps of mean dust AOD analysis
increments, i.e. the monthly averaged difference between analysis and
short-term forecast respectively in the case in which only NRL MODIS AOD
observations are assimilated and in the case in which also MODIS Deep Blue
AOD observations are assimilated. Both experiments show non-zero systematic
increments which are to be interpreted as systematic corrections that these
sets of observations are making, in particular removing mass close to sources
and, to a lesser extent, adding mass in the outflow. The spatial distribution
of the increments highlights the role that MODIS Deep Blue observations play
in particular over the Sahara dust sources. There are some regions where the
two data assimilation experiments show opposite increments. This could be due
to unresolved conflicting biases between the two types of MODIS retrievals.
Validation of the analysis
We perform in this section a validation of the dust fields simulated either
with or without data assimilation through a comparison with observations from
ground-based stations that have not been assimilated for May to August 2007.
We calculate the statistics for individual stations and for groups of
stations. Figure shows the time series of dust AOD
values for May to August 2007 for the Control experiment (blue), for the
analysis of the DA-NRL (green) and of the DA-NRL-DB (red) experiment, and for
AERONET observations in dust-dominated conditions (black) at six locations
within the different regional domains of Fig. , which are in
the proximity of dust sources (Tamanrasset in Algeria), affected by
short-range dust transport (Dakar in Senegal, Ilorin in Nigeria, and Hamim in
the United Arab Emirates), or affected by long-range dust transport in Europe
(Lecce in Italy), and across the Atlantic (La Parguera in Puerto Rico). For
reference, the MODIS AOD observations from the assimilated dataset (NRL and
Deep Blue), which are at the closest distance and within a 2∘
radius from the location of the AERONET station, are also included in the
time series (magenta circles). Note, however, that these latter observations
are not an independent reference for validation of the analyses, nor are they
entirely representative of the observational constraint used to calculate the
analysis in the given station location. The time series show an
overestimation in the Control experiment of the optical depth near the
sources, and to a smaller extent in the transport, which clearly suggests
that the model tends to overestimate dust emissions. The current calibration
for model version 1.0 has the shortcoming of accurately capturing long-range
transport at the expense of an overestimation over the sources. This
overestimation is reduced with data assimilation. By a first eyeball
inspection, the AOD simulation variance is reduced by data assimilation and
is more in accordance with the AOD observation variance.
BIAS, RMSE, CORR, FRGE, and SD for the Control experiment, for the
experiment assimilating MODIS NRL observations (DA-NRL), and for the
experiment assimilating MODIS NRL and MODIS Deep Blue observations
(DA-NRL-DB) calculated against AERONET observations for all the stations in
Fig. . The dust mean AOD for the observations used for
validation during the experiment period is also reported.
Maps in Fig. show results of validation statistics
calculated for the full study period at each AERONET station for the three
experiments performed. These maps allow us to appreciate the strongest
features of the three simulations at individual AERONET stations and how
those stations are representative of the regional domains that we have
identified. The Control experiment shows that the strongest BIAS and highest
RMSE are in the sub-Sahel region. The BIAS indicates that the model
systematically overpredicts AOD in that region. The highest FRGEs are in the
long transport over the Atlantic or Europe, as expected in areas of low AOD
values. The correlation between model and observation values is in general
lower near source areas than in outflow regions. This could be due to the too
coarse model resolution not able to follow as well as the observations the
dynamic of the dust field near source areas. The assimilation of MODIS NRL
observations decreases some of the strongest biases, in particular in the
dust outflow regions in the Sahel and over the African Atlantic coast, which
is reflected in a reduced FRGE and RMSE, and is associated with improved
correlation. The assimilation of the MODIS Deep Blue observations
additionally to the NRL MODIS observations is of further benefit: it reduces
the BIAS and RMSE downwind from the strongest dust sources of the Sahara. It
is also relevant to notice that the additional assimilation of MODIS Deep
Blue observations improves the correlation over the above areas and in the
Arabian Peninsula.
BIAS, RMSE, CORR, FRGE, and SD for the Control experiment, the
DA-NRL experiments, and the DA-NRL-DB experiment calculated against AERONET
observations for groups of stations within the regional domains in
Fig. . The dust mean AOD for the observations used for
validation during the experiment period is also reported.
BIAS, RMSE, CORR, and FRGE for the forecast at 12, 36, 60,
84, and 108 h of the Control (blue) and AN-initialized (red)
experiments, i.e. the experiment initialized with the DA-NRL-DB analysis,
calculated against AERONET observations (left) and against global satellite
retrievals, both NRL MODIS and MODIS Deep Blue, (right) filtered for
dust-dominated conditions. The AERONET stations are the ones in
Fig. . The dust mean AOD for the observations used to validate
the 12 h forecast during the experiment period is also reported.
As the right panel of Fig. but for
the different regional domains of Fig. .
The chart plots for the validation statistics calculated for all the AERONET
stations considered (hereafter called global statistics) and for stations
grouped according to regional domains of interest are respectively in
Figs. and . Global
statistics show that assimilation produces in general a better representation
of dust concentrations in the atmosphere, and that the assimilation of Deep
Blue retrievals has a positive impact over the assimilation of Dark Target
retrievals only.
When considering the regional domains, the assimilation of NRL MODIS AOD has
a positive impact on the quality of the analysis everywhere, with the only
exception of a slight increase in RMSE in the Middle East region. This
positive impact is more pronounced in the short Atlantic transport and in the
sub-Sahel region. The additional assimilation of MODIS Deep Blue AOD has a
considerable positive impact in the Sahara, sub-Sahel, and Middle East
regions, and is neutral or slightly detrimental in the rest of the transport,
in particular in the long-range Atlantic transport. The correlations for the
global domain and for all the regional domains are highly statistically
significant with the exception of the Sahara region (in the Control and
DA-NRL experiments only), where the number of observations is smaller than
other regional domains.
It should be noted, however, when interpreting the above statistics that the
validation against AERONET observations may introduce significant errors when
comparing a global model grid box against a point observation .
Validation of the forecast
We have validated the forecast up to 5 days ahead initialized at 00:00 UTC
from either the Control experiment or an
analysis (from DA-NRL-DB). We have calculated for May to August 2007 the
errors for the forecast at 12, 36, 60, 84, and 108 h (hereafter indicated as
FC + 12, FC + 36, FC + 60, FC + 84, and FC + 108) with
respect to either AERONET observations or satellite observations. As
mentioned when describing our evaluation methodology, we use as a reference
the set of satellite observations from the Dark Target and Deep Blue
algorithm ingested in the assimilation step, i.e quality-controlled and
filtered for dust-dominated conditions. They are used only to validate the
forecast range following the assimilation window. As expected, all the
validation statistics worsen with increased forecast step in both experiments
(see Fig. for global statistics). The impact
of initializing the model with a dust analysis is positive on the first day.
The analysis produces a better forecast in terms of BIAS and RMSE (and also
SD of the error) up to FC + 108, and a better correlation on the first
day. The correlation is slightly lower from FC + 36 onwards. The
conclusions drawn by validating against AERONET or satellite observations are
equivalent. Results calculated for regional domains
(Fig. ) show that the Control experiment tends to
overestimate AOD everywhere with the exception of Central and East Asia. This
suggests an overestimation in particular of the Sahara emissions which is
consistent with the bias found in the analysis and which is maintained during
the forecast. The correlations for the global domain and for all the regional
domains, at all forecast lead times, are highly statistically significant.
Initializing the 00:00 UTC forecast with the DA-NRL-DB dust analysis reduces
the overestimation compared to satellite retrievals on the first day of the
forecast consistently with the improvement observed in the analysis in the
previous section. However, this produces an underestimation of AOD in the
long-range Atlantic transport during all the forecast lead times, which,
because of the relatively small AOD values in that area, is reflected in
particular in the FGRE. Although there is an overestimation of AOD, there is
a better agreement of the temporal evolution in that region. The
underestimation of AOD in the Atlantic transport might be due to too strong a
deposition which affects in particular the long-range transport, and in the
standard run is compensated for by an overestimation over the sources. As
said earlier, a shortcoming of the current model calibration is to capture
well the long-range transport at the expense of an overestimation over the
sources, which data assimilation reduces. To identify the exact cause of it
will require, however, further investigation together with a better
adjustment of the current model parameters. With the exception of this
underestimation of AOD across the Atlantic, all the error statistics and
correlation coefficients are improved in the first day of the forecast in all
the regional domains. The error of the analysis-initialized forecast is lower
also in the rest of the forecast range (up to 5 days), though, after day 1,
the correlation with satellite observations in some regions (SubSahel and
ShortAtl) is lower for the analysis-initialized forecast than for a standard
forecast. It is particularly relevant to notice that the dust forecast over
the Sahara is improved for all the statistics and throughout the forecast
range.
Conclusions
We have developed a data assimilation system for the NMMB-MONARCH model
version 1.0, which considers dust only, while other aerosols are being
implemented. We have coupled NMMB-MONARCH with an ensemble-based data
assimilation technique known as LETKF. For this purpose we have created a
forecast ensemble based on known uncertainties in the physical
parametrizations of the mineral dust emission scheme. We have processed
satellite aerosol optical depth retrievals for assimilation with a dust
filter. Due to the presence of other aerosols in the selection of
dust-dominated conditions, uncertainties might have been introduced into our
assimilation process. It should be noted however that the identification of
dust-dominated conditions is performed in this study as a proof of concept to
demonstrate the potential of using data assimilation in NMMB-MONARCH, and
will not be strictly necessary in a future model upgrade including all the
major aerosol species. Still, efforts towards aerosol speciation could
continue to be pursued when assimilating information about total aerosol
optical properties. In this respect, operational centres currently rely
merely on model background to distribute assimilation increments among the
different aerosol species.
Assimilation experiments showed that aerosol optical depth retrieved with the
Dark Target algorithm can help NMMB-MONARCH to better characterize
atmospheric dust. This is particularly true for the analysis of the dust
outflow in the Sahel region and over the African Atlantic coast. The
additional assimilation of Deep Blue retrievals has a further positive impact
in the analysis downwind from the strongest dust sources of the Sahara and in
the Arabian Peninsula.
An analysis-initialized forecast performs better (lower forecast error and
higher correlation) than a standard forecast everywhere on the first day of
the forecast. The only exception to this is an underestimation of the
forecast of AOD in the long-range Atlantic transport. The error of the
analysis-initialized forecast is lower also in the rest of the forecast range
(up to 5 days), though, after day 1, in sub-Sahel and short Atlantic
transport the temporal evolution of dust is less in agreement with
independent observations, compared to a standard forecast. Particularly
relevant is the improved forecast over the Sahara throughout the forecast
range thanks to the assimilation of Deep Blue retrievals over areas not
easily covered by other observational datasets. To the best of our knowledge,
this is the first study quantifying the benefit of assimilating MODIS Deep
Blue from Collection 6 specifically for mineral dust simulations. This
product is currently operationally assimilated by the UK Met Office, who
consider only Deep Blue observations over desert, and by the European Centre
for Medium-Range Weather Forecasts.
In our future implementation of the forecast ensemble, we plan to exploit
spatial patterns of variation in model parameter uncertainty, for example
source-dependent uncertainties, as well as uncertainties in the deposition
term. A better representation of uncertainties in dust emission flux
inherently will help the representation of uncertainties in other parts of
the dust cycle. A recent study by shows that, for their
system, combined meteorology and aerosol source ensembles are necessary to
produce sufficient spread in outflow regions. Notwithstanding that their
conclusion might be system-dependent, we will take into account their results
in our future studies.
Copies of the code and data are readily available upon request
from the corresponding authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was funded by the SEV-2011-00067 grant of the Severo Ochoa Program
awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish
Ministry of Economy and Competitiveness, and the ACTRIS Research
Infrastructure Project of the European Union's Horizon 2020 research and
innovation programme under grant agreement no. 654169. The authors thank all
the Principal Investigators and their staff for establishing and maintaining
the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3
product, and the MODIS and OMI mission scientists and associated NASA
personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully
acknowledge the computer resources at MareNostrum and the technical support
provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They
also thank Francesco Benincasa for his technical support.
Carlos Pérez García-Pando acknowledges
long-term support from the AXA Research Fund, as well as the support received
through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish
Ministry of Economy and Competitiveness. Comments from two anonymous
reviewers are gratefully acknowledged. Edited
by: A. Lauer Reviewed by: two anonymous referees
ReferencesAmezcua, J. and van Leeuwen, P. J.: Gaussian anamorphosis in the analysis
step of the EnKF: a joint state-variable/observation approach, Tellus A, 66,
23493, 10.3402/tellusa.v66.23493, 2014.
Anderson, T. L., Charlson, R. J., Winker, D. M., Ogren, J. A., and
Holmèn, K.: Mesoscale Variations of Tropospheric Aerosols, J. Atmos.
Sci., 60, 119–136, 2003.Badia, A., Jorba, O., Voulgarakis, A., Dabdub, D., Pérez García-Pando, C., Hilboll, A.,
Gonçalves, M., and Janjic, Z.: Description and evaluation of the Multiscale
Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH) version 1.0:
gas-phase chemistry at global scale, Geosci. Model Dev., 10, 609–638, 10.5194/gmd-10-609-2017, 2017.Basart, S., Pérez, C., Cuevas, E., Baldasano, J. M., and Gobbi, G. P.:
Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean
Basin and Middle East from direct-sun AERONET observations, Atmos. Chem. Phys., 9, 8265–8282,
10.5194/acp-9-8265-2009, 2009.Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J.,
Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S.,
Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis
and forecast in the European Centre for Medium-Range Weather Forecasts
Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114,
D13205, 10.1029/2008JD011115, 2009.Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive sampling with
the ensemble transform Kalman filter, Part I: Theoretical aspects, Mon.
Weather Rev., 129, 420–436, 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2, 2001.Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G.
R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L.,
Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale,
C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry
models: current status and future prospects for coupled chemistry meteorology
models, Atmos. Chem. Phys., 15, 5325-5358, 10.5194/acp-15-5325-2015,
2015.Chaboureau, J.-P., Richard, E., Pinty, J.-P., Flamant, C., Di Girolamo, P.,
Kiemle, C., Behrendt, A., Chepfer, H., Chiriaco, M., and Wulfmeyer, V.:
Long-range transport of Saharan dust and its radiative impact on
precipitation forecast: a case study during the Convective and
Orographically-induced Precipitation Study (COPS), Q. J. Roy. Meteor. Soc.,
137, 236–251, 10.1002/qj.719, 2011.Dai, T., Schutgens, N. A. J., Goto, D., Shi, G., and Nakajima, T.:
Improvement of aerosol optical properties modeling over Eastern Asia with
MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol
transport model, Environ. Pollut., 195, 319–329, 10.1016/j.envpol.2014.06.021, 2014.D'Almeida, D. A.: On the variability of desert aerosol radiative
characteristics, J. Geophys. Res., 92, 3017–3026, 10.1029/JD092iD03p03017, 1987.
Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N.
T., Slutsker, I., and Kinne, S.: Wavelength dependence of the optical depth
of biomass burning, urban, and desert dust aerosols, J. Geophys. Res., 104,
31333–31349, 1999.
Elbern, H. and Schmidt, H.: Ozone episode analysis by four-dimensional
variational chemistry data assimilation, J. Geophys. Res., 106, 3569–3590,
2001.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J. Geophys.
Res., 99, 10143–10162, 1994.Fécan, F., Marticorena, B., and Bergametti, G.: Parametrization of the
increase of the aeolian erosion threshold wind friction velocity due to soil
moisture for arid and semi-arid areas, Ann. Geophys., 17, 149–157,
10.1007/s00585-999-0149-7, 1999.
Gama C., Basart, S., Baldasano, J. M., Pio, C., Borrego, C., and Tchepel, O.:
Assessing the size distribution of dust emissions in the NMMB/BSC-Dust model,
8th International Workshop on Sand/Duststorms and Associated Dustfall,
Lisbon, Portugal, 2–3 May 2016, 50, 2016.
Gkikas, A., Basart, S., Korras-Carraca, M., Papadimas, C., Hatzianastassiou,
N., Sayer, A., Hsu, C., and Baldasano, J. M.: Intercomparison of MODIS-Aqua
C051 and C006 Level 3 Deep Blue AOD and Ångström exponent retrievals
over the Sahara desert and the Arabian Peninsula during the period
2002–2014, Geophys. Res. Abstracts, 17, EGU2015-13537, 2015.Gkikas, A., Basart, S., Hatzianastassiou, N., Marinou, E., Amiridis, V.,
Kazadzis, S., Pey, J., Querol, X., Jorba, O., Gassó, S., and Baldasano,
J. M.: Mediterranean intense desert dust outbreaks and their vertical
structure based on remote sensing data, Atmos. Chem. Phys., 16, 8609–8642,
10.5194/acp-16-8609-2016, 2016.Grini, A., Thulet, P., and Gomes, L.: Dusty weather forecasts using the
MesoNH mesoscale atmospheric model, J. Geophys. Res., 111, 2156–2202,
10.1029/2005JD007007, 2006.Haustein, K., Pérez, C., Baldasano, J. M., Jorba, O., Basart, S., Miller,
R. L., Janjic, Z., Black, T., Nickovic, S., Todd, M. C., Washington, R.,
Müller, D., Tesche, M., Weinzierl, B., Esselborn, M., and Schladitz, A.:
Atmospheric dust modeling from meso to global scales with the online
NMMB/BSC-Dust model – Part 2: Experimental campaigns in Northern Africa,
Atmos. Chem. Phys., 12, 2933–2958, 10.5194/acp-12-2933-2012, 2012.Haustein, K., Washington, R., King, J., Wiggs, G., Thomas, D. S. G., Eckardt,
F. D., Bryant, R. G., and Menut, L.: Testing the performance of
state-of-the-art dust emission schemes using DO4Models field data, Geosci.
Model Dev., 8, 341–362, 10.5194/gmd-8-341-2015, 2015.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET-A federated instrument network and
data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16,
1998.Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne,
S., Bauer, S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R.,
Fillmore, D., Ghan, S., Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol,
M. C., Landing, W., Liu, X., Mahowald, N., Miller, R., Morcrette, J.-J.,
Myhre, G., Penner, J., Perlwitz, J., Stier, P., Takemura, T., and Zender, C.
S.: Global dust model intercomparison in AeroCom phase I, Atmos. Chem. Phys.,
11, 7781–7816, 10.5194/acp-11-7781-2011, 2011.Huneeus, N., Basart, S., Fiedler, S., Morcrette, J.-J., Benedetti, A.,
Mulcahy, J., Terradellas, E., Pérez García-Pando, C., Pejanovic, G.,
Nickovic, S., Arsenovic, P., Schulz, M., Cuevas, E., Baldasano, J. M., Pey,
J., Remy, S., and Cvetkovic, B.: Forecasting the northern African dust
outbreak towards Europe in April 2011: a model intercomparison, Atmos. Chem.
Phys., 16, 4967–4986, 10.5194/acp-16-4967-2016, 2016.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica
D, 230, 112–126, 2007.Hubanks, P. A., King, M. D., Platnick, S. A., and Pincus, R. A.: MODIS
Atmosphere L3 Gridded Product Algorithm Theoretical Basis Document, MODIS
Algorithm Theoretical Basis Document No. ATBD-MOD-30 for Level-3 Global
Gridded Atmosphere Products (08 D3, 08E3, 08M3), available at:
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod30.pdf (last access:
6 October 2015), 2008.Hyer, E. J., Reid, J. S., and Zhang, J.: An over-land aerosol optical depth
data set for data assimilation by filtering, correction, and aggregation of
MODIS Collection 5 optical depth retrievals, Atmos. Meas. Tech., 4, 379–408,
10.5194/amt-4-379-2011, 2011.
Janjic, Z. I. and Gall, R.: Scientific documentation of the NCEP
nonhydrostatic multiscale model on the B grid (NMMB) – Part 1 Dynamics,
Technical Report, NCAR/TN-489+STR, 2012.Jickels, T. D., An, Z. S., Andersen, K. K., Baker, A. R., Bergametti, G.,
Brooks, N., Cao, J. J., Boyd, P. W., Duce, R. A., Hunter, K. A., Kawahata,
H., Kubilay, N., LaRoche, J., Liss, P. S., Mahowald, N., Prospero, J. M.,
Ridgwell, A. J., Tegen, I., and Torres, R.: Global Iron Connections Between
Desert Dust, Ocean Biogeochemistry, and Climate, Science, 308, 67–71,
10.1126/science.1105959, 2005.Jorba, O. Dabdub, D., Blaszczak-Boxe, C., Pérez García-Pando, C.,
Janjic, Z., Baldasano, J. M., Spada, M., Badia, A., and Gonçalves, M.:
Potential significance of photoexcited NO2 on global air quality with the
NMMB/BSC chemical transport model, J. Geophys. Res., 117, D13301,
10.1029/2012JD017730, 2012.
Kalnay, E., Li, H., Miyoshi, T., Yang, S.-C., and Ballabrera-Poy, J.: 4D-Var
or ensemble Kalman filter?, Tellus A, 59, 758–773, 2007.Kaufman, Y. J., Koren, I., Remer, L. A., Tanre, D., Ginoux, P., and Fan, S.:
Dust transport and deposition observed from the Terra-Moderate Resolution
Imaging Spectroradiometer spacecraft over the Atlantic Ocean, J. Geophys.
Res., 110, D10S12, 10.1029/2003JD004436, 2005.Kim, D., Chin, M., Yu, H., Eck, T. F., Sinyuk, A., Smirnov, A., and Holben,
B. N.: Dust optical properties over North Africa and Arabian Peninsula
derived from the AERONET dataset, Atmos. Chem. Phys., 11, 10733–10741,
10.5194/acp-11-10733-2011, 2011.
Knippertz, P. and Stuut, J.-B. W. (Eds.): Mineral Dust: A Key Player in the
Earth System, Springer Science, Dordrecht, the Netherlands, 509 pp., 2014.
Lau, K. M., Kim, M. K., and Kim, K. M.: Asian summer monsoon anomalies
induced by direct forcing: the role of the Tibetan plateau, Clim. Dynam., 26,
855–864, 2006.Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties
and application to Moderate Resolution Imaging Spectroradiometer aerosol
retrieval over land, J. Geophys. Res.-Atmos., 112, D13210,
10.1029/2006JD007815, 2007a.Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.:
Second-generation operational algorithm: Retrieval of aerosol properties over
land from inversion of Moderate Resolution Imaging Spectroradiometer spectral
reflectance, J. Geophys. Res.-Atmos., 112, D13211, 10.1029/2006JD007811,
2007b.Luo, T., Wang, Z., Zhang, D., Liu, X., Wang, Y., and Yuan, R.: Global dust
distribution from improved thin dust layer detection using A-train satellite
lidar observations, Geophys. Res.Lett., 42, 620–628,
10.1002/2014GL062111, 2015.Mallone, S., Stafoggia, M., Faustini, A., Gobbi, G. P., Marconi, A., and
Forastiere, F.: Saharan dust and associations between particulate matter and
daily mortality in Rome, Italy, Environ. Health Persp., 119, 1409–1414,
10.12989/ehp.1003026, 2011.Marticorena, B. and Bergametti, G.: Modeling the atmospheric dust cycle: 1.
design of a soil-derived dust emission scheme, J. Geophys. Res., 100,
16415–16430, 10.1029/95JD00690, 1995.Marticorena, B., Bergametti, G., Aumont, B., Callot, Y., N'Doumé, C., and
Legrand, M.: Modeling the atmospheric dust cycle, 2. simulation of Saharan
dust sources, J. Geophys. Res., 102, 4387–4404, 10.1029/96JD02964,
1997.Miyoshi, T. and Yamane, S.: Local ensemble transform Kalman filtering with an
AGCM at a T159/L48 resolution, Mon. Weather Rev., 135, 3841–3861, 10.1175/2007MWR1873.1, 2007.
Morman, S. A. and Plumlee, G. S.: The role of airborne mineral dusts in human
disease, Aeolian Res., 9, 203–212, 2013.Niu, T., Gong, S. L., Zhu, G. F., Liu, H. L., Hu, X. Q., Zhou, C. H., and
Wang, Y. Q.: Data assimilation of dust aerosol observations for the
CUACE/dust forecasting system, Atmos. Chem. Phys., 8, 3473–3482,
10.5194/acp-8-3473-2008, 2008.Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza,
M., Kalnay, E., Patil, D. J., and Yorke, J. A.: A local ensemble Kalman
filter for atmospheric data assimilation, Tellus A, 56, 415–428,
10.1111/j.1600-0870.2004.00076.x, 2004.Pandolfi, M., Tobias, A., Alastuey, A., Sunyer, J., Schwartz, J., Lorente,
J., Pey, J., and Querol, X.: Effect of atmospheric mixing layer depth
variations on urban air quality and daily mortality during Saharan dust
outbreaks, Sci. Total Environ., 494–495, 283–289, 10.1016/j.scitotenv.2014.07.004, 2014.Pérez, C., Haustein, K., Janjic, Z., Jorba, O., Huneeus, N., Baldasano,
J. M., Black, T., Basart, S., Nickovic, S., Miller, R. L., Perlwitz, J. P.,
Schulz, M., and Thomson, M.: Atmospheric dust modeling from meso to global
scales with the online NMMB/BSC-Dust model – Part 1: Model description,
annual simulations and evaluation, Atmos. Chem. Phys., 11, 13001–13027,
10.5194/acp-11-13001-2011, 2011.Pérez García-Pando, C., Nickovic, S., Pejanovic, G., Baldasano, J.
M., and Özsoy, E.: Interactive dust-radiation modeling: a step to improve
weather forecasts, J. Geophys. Res., 111, D16206, 10.1029/2005JD006717, 2006.Pérez García-Pando, C., Stanton, M. C., Diggle, P. J., Trzaska, S.,
Miller, R. L., Perlwitz, J. P., Baldasano, J. M., Cuevas, E., Ceccato, P.,
Yaka, P., and Thomson, M. C.: Soil Dust Aerosols and Wind as Predictors of
Seasonal Meningitis Incidence in Niger, Environ. Health Persp., 112,
679–686, 10.1289/ehp.1306640, 2014.Pey, J., Querol, X., Alastuey, A., Forastiere, F., and Stafoggia, M.: African
dust outbreaks over the Mediterranean Basin during 2001–2011: PM10
concentrations, phenomenology and trends, and its relation with synoptic and
mesoscale meteorology, Atmos. Chem. Phys., 13, 1395–1410,
10.5194/acp-13-1395-2013, 2013.Remer, L. A., Kleidman, R. G., Levy, R. C., Kaufman, Y. J., Tanré, D.,
Mattoo, S., Martins, J. V., Ichoku, C., Koren, I., Yu, H., and Holben, B. N.:
Global aerosol climatology from the MODIS satellite sensors, J. Geophys.
Res.-Atmos., 113, D14S07, 10.1029/2007JD009661, 2008.Rubin, J. I., Reid, J. S., Hansen, J. A., Anderson, J. L., Collins, N., Hoar,
T. J., Hogan, T., Lynch, P., McLay, J., Reynolds, C. A., Sessions, W. R.,
Westphal, D. L., and Zhang, J.: Development of the Ensemble Navy Aerosol
Analysis Prediction System (ENAAPS) and its application of the Data
Assimilation Research Testbed (DART) in support of aerosol forecasting,
Atmos. Chem. Phys., 16, 3927–3951, 10.5194/acp-16-3927-2016, 2016.Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and
Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua's
e-Deep Blue, Dark Target, and “merged” data sets, and usage
recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, 10.1002/2014JD022453, 2014.Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Applying an
ensemble Kalman filter to the assimilation of AERONET observations in a
global aerosol transport model, Atmos. Chem. Phys., 10, 2561–2576,
10.5194/acp-10-2561-2010, 2010a.Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Sensitivity
tests for an ensemble Kalman filter for aerosol assimilation, Atmos. Chem.
Phys., 10, 6583–6600, 10.5194/acp-10-6583-2010, 2010b.
Schutgens, N., Nakata, M., and Nakajima, T.: Estimating Aerosol Emissions by
Assimilating Remote Sensing Observations into a Global Transport Model,
Remote Sens., 4, 3528–3543, 2012.Schutgens, N. A. J., Nakata, M., and Nakajima, T.: Validation and empirical
correction of MODIS AOT and AE over ocean, Atmos. Meas. Tech., 6, 2455–2475,
10.5194/amt-6-2455-2013, 2013.Schutgens, N. A. J., Gryspeerdt, E., Weigum, N., Tsyro, S., Goto, D., Schulz,
M., and Stier, P.: Will a perfect model agree with perfect observations? The
impact of spatial sampling, Atmos. Chem. Phys., 16, 6335–6353,
10.5194/acp-16-6335-2016, 2016.Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data
assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39–49,
10.5194/acp-10-39-2010, 2010.Sessions, W. R., Reid, J. S., Benedetti, A., Colarco, P. R., da Silva, A.,
Lu, S., Sekiyama, T., Tanaka, T. Y., Baldasano, J. M., Basart, S., Brooks, M.
E., Eck, T. F., Iredell, M., Hansen, J. A., Jorba, O. C., Juang, H.-M. H.,
Lynch, P., Morcrette, J.-J., Moorthi, S., Mulcahy, J., Pradhan, Y., Razinger,
M., Sampson, C. B., Wang, J., and Westphal, D. L.: Development towards a
global operational aerosol consensus: basic climatological characteristics of
the International Cooperative for Aerosol Prediction Multi-Model Ensemble
(ICAP-MME), Atmos. Chem. Phys., 15, 335–362, 10.5194/acp-15-335-2015,
2015.Shi, Y., Zhang, J., Reid, J. S., Holben, B., Hyer, E. J., and Curtis, C.: An
analysis of the collection 5 MODIS over-ocean aerosol optical depth product
for its implication in aerosol assimilation, Atmos. Chem. Phys., 11,
557–565, 10.5194/acp-11-557-2011, 2011.Shinozuka, Y. and Redemann, J.: Horizontal variability of aerosol optical
depth observed during the ARCTAS airborne experiment, Atmos. Chem. Phys., 11,
8489–8495, 10.5194/acp-11-8489-2011, 2011.Spada, M., Jorba, O., Pérez García-Pando, C., Janjic, Z., and
Baldasano, J. M.: Modeling and evaluation of the global sea-salt aerosol
distribution: sensitivity to size-resolved and sea-surface temperature
dependent emission schemes, Atmos. Chem. Phys., 13, 11735–11755,
10.5194/acp-13-11735-2013, 2013.
Spada, M., Jorba, O., Pérez García-Pando, C., Tsigaridis, K., Soares,
J., and Janjic, Z.: Global aerosols in the online multiscale NMMB-MONARCH version 1.0,
Geosci. Model Dev., in preparation, 2017.Tegen, I., Harrison, S. P., Kohfeld, K., Prentice, I. C., Coe, M., and
Heimann, M.: Impact of vegetation and preferential source areas on global
dust aerosol: results from a model study, J. Geophys. Res., 107, 4576,
10.1029/2001JD000963, 2002.
Terradellas, E., Nickovic, S., and Zhang, X.: Airborne dust: a hazard to
human health, environment and society, WMO Bulletin, 64, 42–46, 2015.Torres, O., Tanskanen, A., Veihelmann, B., Ahn, C., Braak, R., Bhartia, P.
K., Veefkind, P., and Levelt, P.: Aerosols and surface UV products from Ozone
Monitoring Instrument observations: An overview, J. Geophys. Res., 112,
10.1029/2007JD008809, 2007.Tsigaridis, K., Daskalakis, N., Kanakidou, M., Adams, P. J., Artaxo, P.,
Bahadur, R., Balkanski, Y., Bauer, S. E., Bellouin, N., Benedetti, A.,
Bergman, T., Berntsen, T. K., Beukes, J. P., Bian, H., Carslaw, K. S., Chin,
M., Curci, G., Diehl, T., Easter, R. C., Ghan, S. J., Gong, S. L., Hodzic,
A., Hoyle, C. R., Iversen, T., Jathar, S., Jimenez, J. L., Kaiser, J. W.,
Kirkevåg, A., Koch, D., Kokkola, H., Lee, Y. H., Lin, G., Liu, X., Luo,
G., Ma, X., Mann, G. W., Mihalopoulos, N., Morcrette, J.-J., Müller,
J.-F., Myhre, G., Myriokefalitakis, S., Ng, N. L., O'Donnell, D., Penner, J.
E., Pozzoli, L., Pringle, K. J., Russell, L. M., Schulz, M., Sciare, J.,
Seland, Ø., Shindell, D. T., Sillman, S., Skeie, R. B., Spracklen, D.,
Stavrakou, T., Steenrod, S. D., Takemura, T., Tiitta, P., Tilmes, S., Tost,
H., van Noije, T., van Zyl, P. G., von Salzen, K., Yu, F., Wang, Z., Wang,
Z., Zaveri, R. A., Zhang, H., Zhang, K., Zhang, Q., and Zhang, X.: The
AeroCom evaluation and intercomparison of organic aerosol in global models,
Atmos. Chem. Phys., 14, 10845–10895, 10.5194/acp-14-10845-2014, 2014.van Leeuwen, P. J.: Representation errors and retrievals in linear and
nonlinear data assimilation, Q. J. Roy. Meteorol. Soc., 141, 1612–1623,
10.1002/qj.2464, 2014.Wang, H. and Niu, T.: Sensitivity studies of aerosol data assimilation and
direct radiative feedbacks in modeling dust aerosols, Atmos. Environ., 64,
208–218, 10.1016/j.atmosenv.2012.09.066, 2013.
White, B. R.: Soil transport by winds on Mars, J. Geophys. Res., 84,
4643–4651, 10.1029/JB084iB09p04643, 1979.Yu, H., Chin, M., Yuan, T., Bian, H., Remer, L. A., Prospero, J. M., Omar,
A., Winker, D., Yang, Y., Zhang, Y., Zhang, Z., and Zhao, C.: The fertilizing
role of African dust in the Amazon rainforest:A first multiyear assessment
based on data from Cloud-Aerosol Lidar and Infrared Path finder Satellite
Observations, Geophys. Res. Lett., 42, 1984–1991, 10.1002/2015GL063040, 2015.Yumimoto, K. and Takemura, T.: Direct radiative effect of aerosols estimated
using ensemble–based data assimilation in a global aerosol climate model,
Geophys. Res. Lett., 38, L21802, 10.1029/2011GL049258, 2011.Yumimoto, K., Nagao, T. M., Kikuchi, M., Sekiyama, T. T., Murakami, H.,
Tanaka, T. Y., Ogi, A., Irie, H., Khatri, P., Okumura, H., Arai, K., Morino,
I., Uchino, O., and Maki, T.: Aerosol data assimilation using data from
Himawari-8, a next-generation geostationary meteorological satellite,
Geophys. Res. Lett., 43, 5886–5894, 10.1002/2016GL069298, 2016.Zender, C. S., Bian, H., and Newman, D.: Mineral Dust Entrainment and
Deposition (DEAD) model: Description and 1990s dust climatology, J. Geophys.
Res., 108, 4416, 10.1029/2002JD002775, 2003.Zhang, J. and Reid, J. S.: MODIS aerosol product analysis for data
assimilation: assessment of Level 2 aerosol optical thickness retrievals, J.
Geophys. Res.-Atmos., 111, D22207, 10.1029/2005JD006898, 2006.Zhang, J., Reid, J. S., Westphal, D. L., Baker, N. L., and Hyer, E. J.: A
system for operational aerosol optical depth data assimilation over global
oceans, J. Geophys. Res., 113, D10208, 10.1029/2007JD009065, 2008.
Zhang, J., Campbell, J. R., Hyer, E. J., Reid, J. S., Westphal, D. L., and
Johnson, R. S.: Evaluating the impact of multisensor data assimilation on a
global aerosol particle transport model, J. Geophys. Res.-Atmos., 119,
4674–4689, 2014.