Remote Sensing,
Journal Year:
2024,
Volume and Issue:
16(13), P. 2487 - 2487
Published: July 7, 2024
Long-term
(1982–2019)
satellite
climate
data
records
(CDRs)
of
aerosols
and
clouds,
reanalysis
meteorological
fields,
machine
learning
techniques
are
used
to
study
the
aerosol
effect
on
deep
convective
clouds
(DCCs)
over
global
oceans
from
a
climatological
perspective.
Our
analyses
focused
three
latitude
belts
where
DCCs
appear
more
frequently
in
climatology:
northern
middle
(NML),
tropical
(TRL),
southern
(SML).
It
was
found
that
marine
may
be
detected
only
NML
long-term
averaged
cloud
observations.
Specifically,
particle
size
is
susceptible
compared
other
micro-physical
variables
(e.g.,
optical
depth).
The
signature
can
easily
obscured
by
covariances
for
macro-physical
variables,
such
as
cover
top
temperature
(CTT).
From
analysis,
we
primary
(i.e.,
without
feedbacks
covariances)
partially
explain
invigoration
CTT
need
included
accurately
capture
invigoration.
our
singular
value
decomposition
(SVD)
effects
leading
principal
components
(PCs)
about
one
third
variance
satellite-observed
significant
positive
or
negative
trends
observed
lead
PC1
variables.
component
an
effective
mode
detecting
DCCs.
results
valuable
evaluation
improvement
aerosol-cloud
interactions
simulations
models.
npj Climate and Atmospheric Science,
Journal Year:
2023,
Volume and Issue:
6(1)
Published: Dec. 20, 2023
Abstract
Traditional
statistical
methods
(TSM)
and
machine
learning
(ML)
have
been
widely
used
to
separate
the
effects
of
emissions
meteorology
on
air
pollutant
concentrations,
while
their
performance
compared
chemistry
transport
model
has
less
fully
investigated.
Using
Community
Multiscale
Air
Quality
Model
(CMAQ)
as
a
reference,
series
experiments
was
conducted
comprehensively
investigate
TSM
(e.g.,
multiple
linear
regression
Kolmogorov–Zurbenko
filter)
ML
random
forest
extreme
gradient
boosting)
approaches
in
quantifying
trends
fine
particulate
matter
(PM
2.5
)
during
2013−2017.
evaluation
metrics
suggested
that
can
explain
variations
PM
with
highest
from
ML.
The
showed
insignificant
differences
(
p
>
0.05)
for
both
emission-related
$${{\rm{PM}}}_{2.5}^{{\rm{EMI}}}$$
PM2.5EMI
meteorology-related
components
between
TSM,
ML,
CMAQ
modeling
results.
estimated
least
difference
CMAQ.
Considering
medium
computing
resources
low
biases,
method
is
recommended
weather
normalization
.
Sensitivity
analysis
further
optimized
hyperparameters
exclusion
temporal
variables
produce
reasonable
results
Geophysical Research Letters,
Journal Year:
2024,
Volume and Issue:
51(3)
Published: Feb. 3, 2024
Abstract
Aerosol
measurements
during
the
DOE
ARM
Layered
Atlantic
Smoke
Interactions
with
Clouds
(LASIC)
campaign
were
used
to
quantify
differences
between
clean
and
smoky
cloud
condensation
nuclei
(CCN)
budgets.
Accumulation‐mode
particles
accounted
for
∼70%
of
CCN
at
supersaturations
<0.3%
in
conditions.
Aitken‐mode
contributed
<20%
sea‐spray‐mode
<10%
<0.3%,
but
>0.3%
Aitken
contributions
increased
30%–40%
CCN.
For
conditions,
Hoppel
minimum
diameter
was
correlated
accumulation‐mode
number
concentration,
indicating
aerosol‐correlated
activation
controlling
lower
cutoff
which
serve
as
increase
correlation
is
masked
by
lower‐hygroscopicity
smoke.
These
results
provide
first
multi‐month
situ
quantitative
constraints
on
role
aerosol
size
distributions
tropical
boundary
layer.
Journal of Geophysical Research Atmospheres,
Journal Year:
2023,
Volume and Issue:
128(15)
Published: July 15, 2023
Abstract
Reducing
ambient
black
carbon
(BC)
relies
on
the
targeted
control
of
anthropogenic
emissions.
Measuring
emission
changes
in
source‐specific
BC
aerosol
is
essential
to
assess
effectiveness
regulatory
policies
but
difficult
due
presence
meteorology
and
multiple
co‐existing
Herein,
we
propose
a
data‐driven
approach,
combining
dispersion‐normalized
factor
analysis
(DN‐PMF)
with
machine
learning
weather
adjustment
(deweathering)
technique,
decompose
into
source
emissions
meteorological
drivers.
Six
refined
sources
were
extracted
from
aethalometer
multi‐wavelength
concurrent
observational
datasets.
In
addition
widely
reported
dominant
sources,
such
as
vehicular
(VE)
coal/biomass
burning
(BB),
discernible
port
shipping
identified
potential
impacts
coastal
air
quality.
The
showed
abrupt
response
interventions
(e.g.,
holidays)
after
separating
weather‐related
confounders.
Significant
reductions
deweathered
coal
BB,
VE,
local
dust
verified
policies,
clean
winter‐heating
support
for
Clean
Air
Actions.
As
revealed
by
post‐hoc
model
explanation
evolution
boundary
layer
was
predominant
driver
exerting
opposite
impact
respect
distant
regional‐wide
that
is,
npj Climate and Atmospheric Science,
Journal Year:
2024,
Volume and Issue:
7(1)
Published: July 13, 2024
Abstract
Natural
aerosols
are
an
important,
yet
understudied,
part
of
the
Arctic
climate
system.
marine
biogenic
aerosol
components
(e.g.,
methanesulfonic
acid,
MSA)
becoming
increasingly
important
due
to
changing
environmental
conditions.
In
this
study,
we
combine
in
situ
observations
with
atmospheric
transport
modeling
and
meteorological
reanalysis
data
a
data-driven
framework
aim
(1)
identify
seasonal
cycles
source
regions
MSA,
(2)
elucidate
relationships
between
MSA
variables,
(3)
project
response
based
on
trends
extrapolated
from
variables
determine
which
contributing
these
projected
changes.
We
have
identified
main
areas
be
Atlantic
Pacific
sectors
Arctic.
Using
gradient-boosted
trees,
were
able
explain
84%
variance
find
that
most
for
indirectly
related
either
gas-
or
aqueous-phase
oxidation
dimethyl
sulfide
(DMS):
shortwave
longwave
downwelling
radiation,
temperature,
low
cloud
cover.
undergo
shift,
non-monotonic
decreases
April/May
increases
June-September,
over
next
50
years.
Different
different
months
driving
changes,
highlighting
complexity
influences
natural
component.
Although
oceanic
(sea
surface
DMS
emissions,
sea
ice)
precipitation
remains
seen,
here
show
will
likely
shift
solely
changes
variables.
