Satellite instruments provide a vantage point for studying aerosol
loading consistently over different regions of the world. However, the
typical lifetime of a single satellite platform is on the ...order of 5–15 years; thus, for climate studies, the use of multiple satellite sensors
should be considered. Discrepancies exist between aerosol optical depth
(AOD) products due to differences in their information content, spatial and
temporal sampling, calibration, cloud masking, and algorithmic assumptions.
Users of satellite-based AOD time-series are confronted with the challenge
of choosing an appropriate dataset for the intended application. In this
study, 16 monthly AOD products obtained from different satellite sensors and
with different algorithms were inter-compared and evaluated against Aerosol
Robotic Network (AERONET) monthly AOD. Global and regional analyses
indicate that products tend to agree qualitatively on the annual, seasonal
and monthly timescales but may be offset in magnitude. Several approaches
were then investigated to merge the AOD records from different satellites
and create an optimised AOD dataset. With few exceptions, all merging
approaches lead to similar results, indicating the robustness and stability
of the merged AOD products. We introduce a gridded monthly AOD merged
product for the period 1995–2017. We show that the quality of the merged
product is as least as good as that of individual products. Optimal
agreement of the AOD merged product with AERONET further demonstrates the
advantage of merging multiple products. This merged dataset provides a
long-term perspective on AOD changes over different regions of the world,
and users are encouraged to use this dataset.
Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in ...satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R 2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R 2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.
Three decades of rapid economic development is causing severe and widespread PM2.5 (particulate matter ≤ 2.5 μm) pollution in China. However, research on the health impacts of PM2.5 exposure has been ...hindered by limited historical PM2.5 concentration data.
We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1° resolution using the most recent satellite data and evaluated model performance with available ground observations.
We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model-predicted PM2.5 concentrations from 2004 to early 2014 using ground observations.
The overall model cross-validation R(2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM2.5 concentrations with little bias at the monthly (R(2) = 0.73, regression slope = 0.91) and seasonal (R(2) = 0.79, regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 μg/m(3) between 2004 and 2007 and a decrease of 0.46 μg/m(3) between 2008 and 2013.
Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.
Celotno besedilo
Dostopno za:
CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Proven by multiple theoretical and practical studies,
multi-angular spectral polarimetry is ideal for comprehensive retrieval of
properties of aerosols. Furthermore, a large number of advanced space
...polarimeters have been launched recently or planned to be deployed in the
coming few years (Dubovik et al.,
2019). Nevertheless, at present, practical utilization of aerosol products
from polarimetry is rather limited, due to the relatively small number of
polarimetric compared to photometric observations, as well as challenges in
making full use of the extensive information content available in these
complex observations. Indeed, while in recent years several new algorithms
have been developed to provide enhanced aerosol retrievals from satellite
polarimetry, the practical value of available aerosol products from
polarimeters yet remains to be proven. In this regard, this paper presents
the analysis of aerosol products obtained by the Generalized Retrieval of
Atmosphere and Surface Properties (GRASP) algorithm from POLDER/PARASOL
observations. After about a decade of development, GRASP has been adapted
for operational processing of polarimetric satellite observations and
several aerosol products from POLDER/PARASOL observations have been
released. These updated PARASOL/GRASP products are publicly available (e.g.,
http://www.icare.univ-lille.fr, last access: 16 October 2018, http://www.grasp-open.com/products/, last access: 28 March 2020); the dataset used in the current study is
registered under https://doi.org/10.5281/zenodo.3887265 (Chen
et al., 2020). The objective of this study is to comprehensively evaluate the GRASP aerosol
products obtained from POLDER/PARASOL observations. First, the validation of
the entire 2005–2013 archive was conducted by comparing to ground-based
Aerosol Robotic Network (AERONET) data. The subjects of the validation are
spectral aerosol optical depth (AOD), aerosol absorption optical depth
(AAOD) and single-scattering albedo (SSA) at six wavelengths, as well as
Ångström exponent (AE), fine-mode AOD (AODF) and coarse-mode AOD
(AODC) interpolated to the reference wavelength 550 nm. Second, an
inter-comparison of PARASOL/GRASP products with the PARASOL/Operational,
MODIS Dark Target (DT), Deep Blue (DB) and Multi-Angle Implementation of
Atmospheric Correction (MAIAC) aerosol products for the year 2008 was
performed. Over land both satellite data validations and inter-comparisons
were conducted separately for different surface types, discriminated by bins
of normalized difference vegetation index (NDVI): < 0.2, 0.2 ≤
and < 0.4, 0.4 ≤ and < 0.6, and ≥ 0.6. Three
PARASOL/GRASP products were analyzed: GRASP/HP (“High Precision”),
Optimized and Models. These different products are consistent but were
obtained using different assumptions in aerosol modeling with different
accuracies of atmospheric radiative transfer (RT) calculations.
Specifically, when using GRASP/HP or Optimized there is direct retrieval of
the aerosol size distribution and spectral complex index of refraction. When
using GRASP/Models, the aerosol is approximated by a mixture of several
prescribed aerosol components, each with their own fixed size distribution
and optical properties, and only the concentrations of those components are
retrieved. GRASP/HP employs the most accurate RT calculations, while
GRASP/Optimized and GRASP/Models are optimized to achieve the best trade-off
between accuracy and speed. In all these three options, the underlying
surface reflectance is retrieved simultaneously with the aerosol properties,
and the radiative transfer calculations are performed “online” during the
retrieval. All validation results obtained for the full archive of PARASOL/GRASP
products show solid quality of retrieved aerosol characteristics. The
GRASP/Models retrievals, however, provided the most solid AOD products, e.g.,
AOD (550 nm) is unbiased and has the highest correlation (R ∼ 0.92) and the highest fraction of retrievals (∼ 55.3 %)
satisfying the accuracy requirements of the Global Climate Observing System
(GCOS) when compared to AERONET observations. GRASP/HP and GRASP/Optimized
AOD products show a non-negligible positive bias (∼ 0.07) when
AOD is low (< 0.2). On the other hand, the detailed aerosol
microphysical characteristics (AE, AODF, AODC, SSA, etc.) provided by
GRASP/HP and GRASP/Optimized correlate generally better with AERONET than do
the results of GRASP/Models. Overall, GRASP/HP processing demonstrates the
high quality of microphysical characteristics retrieval versus AERONET.
Evidently, the GRASP/Models approach is more adapted for retrieval of total AOD,
while the detailed aerosol microphysical properties are limited when a
mixture of aerosol models with fixed optical properties are used. The results of a comparative analysis of PARASOL/GRASP and MODIS products
showed that, based on validation against AERONET, the PARASOL/GRASP AOD (550 nm) product is of similar and sometimes of higher quality compared to the
MODIS products. All AOD retrievals are more accurate and in good agreement
over ocean. Over land, especially over bright surfaces, the retrieval
quality degrades and the differences in total AOD products increase. The
detailed aerosol characteristics, such as AE, AODF and AODC from
PARASOL/GRASP, are generally more reliable, especially over land. The global
inter-comparisons of PARASOL/GRASP versus MODIS showed rather robust
agreement, though some patterns and tendencies were observed. Over ocean,
PARASOL/Models and MODIS/DT AOD agree well with the correlation coefficient
of 0.92. Over land, the correlation between PARASOL/Models and the different
MODIS products is lower, ranging from 0.76 to 0.85. There is no significant
global offset; though over bright surfaces MODIS products tend to show
higher values compared to PARASOL/Models when AOD is low and smaller values
for moderate and high AODs. Seasonal AOD means suggest that PARASOL/GRASP
products show more biomass burning aerosol loading in central Africa and
dust over the Taklamakan Desert, but less AOD in the northern Sahara. It is
noticeable also that the correlation for the data over AERONET sites are
somewhat higher, suggesting that the retrieval assumptions generally work
better over AERONET sites than over the rest of the globe. One of the
potential reasons may be that MODIS retrievals, in general, rely more on
AERONET climatology than GRASP retrievals. Overall, the analysis shows that the quality of AOD retrieval from
multi-angular polarimetric observations like POLDER is at least comparable
to that of single-viewing MODIS-like imagers. At the same time, the
multi-angular polarimetric observations provide more information on other
aerosol properties (e.g., spectral AODF, AODC, AE), as well as additional
parameters such as AAOD and SSA.
We estimated global fine particulate matter (PM2.5) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to ...global geophysically based satellite-derived PM2.5 estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998–2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM2.5 estimates were highly consistent (R 2 = 0.81) with out-of-sample cross-validated PM2.5 concentrations from monitors. The global population-weighted annual average PM2.5 concentrations were 3-fold higher than the 10 μg/m3 WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM2.5 data sources can yield valuable improvements to PM2.5 characterization on a global scale.
Many applications of geophysical data - whether from surface observations, satellite retrievals, or model simulations - rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal ...(e.g. daily and monthly) resolution than the highest available from the technique. Almost all of these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for normally distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to log-normal, for which a geometric mean and standard deviation would be more appropriate. This study presents a method of assessing whether a given sample of data is more consistent with an underlying normal or log-normal distribution, using the Shapiro-Wilk test, and tests AOD frequency distributions on spatial scales of 1.sup." and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse, and MISR and MODIS represent different satellite retrieval techniques and sampling. As a model simulation, G5NR is spatiotemporally complete. As timescales increase from days to months to seasons, data become increasingly more consistent with log-normal than normal distributions, and the differences between arithmetic- and geometric-mean AOD become larger, with geometric mean becoming systematically smaller. Assuming normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is small enough (<0.01) to be unimportant for many applications, especially on daily timescales. However, in continental outflow regions and near source regions over land, and on monthly or seasonal timescales, the difference is frequently larger than the Global Climate Observation System (GCOS) goal uncertainty in a climate data record (the larger of 0.03 or 10 %). This is important because it shows that the sensitivity to an averaging method can and often does introduce systematic effects larger than the total goal GCOS uncertainty. Using three well-studied AERONET sites, the magnitude of estimated AOD trends is shown to be sensitive to the choice of arithmetic vs. geometric means, although the signs are consistent. The main recommendations from the study are that (1) the distribution of a geophysical quantity should be analysed in order to assess how best to aggregate it, (2) ideally AOD aggregates such as satellite level 3 products (but also ground-based data and model simulations) should report a geometric-mean or median AOD rather than (or in addition to) arithmetic-mean AOD, and (3) as this is unlikely in the short term due to the computational burden involved, users can calculate geometric-mean monthly aggregates from widely available daily mean data as a stopgap, as daily aggregates are less sensitive to the choice of aggregation scheme than those for monthly or seasonal aggregates. Furthermore, distribution shapes can have implications for the validity of statistical metrics often used for comparison and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.
Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and ...uncertainties during the period 1998–2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5–3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010–2019 by 1.6–2.6 μg/m3/year, with decreases beginning 2–3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.
To better understand and characterize current uncertainties in the important observational constraint of climate models of aerosol optical depth (AOD), we evaluate and intercompare 14 satellite ...products, representing nine different retrieval algorithm families using observations from five different sensors on six different platforms. The satellite products (super-observations consisting of 1°×1° daily aggregated retrievals drawn from the years 2006, 2008 and 2010) are evaluated with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data. Results show that different products exhibit different regionally varying biases (both under- and overestimates) that may reach ±50 %, although a typical bias would be 15 %–25 % (depending on the product). In addition to these biases, the products exhibit random errors that can be 1.6 to 3 times as large. Most products show similar performance, although there are a few exceptions with either larger biases or larger random errors. The intercomparison of satellite products extends this analysis and provides spatial context to it. In particular, we show that aggregated satellite AOD agrees much better than the spatial coverage (often driven by cloud masks) within the 1°×1° grid cells. Up to ∼50 % of the difference between satellite AOD is attributed to cloud contamination. The diversity in AOD products shows clear spatial patterns and varies from 10 % (parts of the ocean) to 100 % (central Asia and Australia). More importantly, we show that the diversity may be used as an indication of AOD uncertainty, at least for the better performing products. This provides modellers with a global map of expected AOD uncertainty in satellite products, allows assessment of products away from AERONET sites, can provide guidance for future AERONET locations and offers suggestions for product improvements. We account for statistical and sampling noise in our analyses. Sampling noise, variations due to the evaluation of different subsets of the data, causes important changes in error metrics. The consequences of this noise term for product evaluation are discussed.
The interactions between aerosols, clouds, and precipitation remain among the largest sources of uncertainty in the Earth's energy budget. Biomass-burning aerosols are a key feature of the global ...aerosol system, with significant annually-repeating fires in several parts of the world, including Southeast Asia (SEA). SEA in particular provides a "natural laboratory" for these studies, as smoke travels from source regions downwind in which it is coupled to persistent stratocumulus decks. However, SEA has been under-exploited for these studies. This review summarizes previous related field campaigns in SEA, with a focus on the ongoing Seven South East Asian Studies (7-SEAS) and results from the most recent BASELInE deployment. Progress from remote sensing and modeling studies, along with the challenges faced for these studies, are also discussed. We suggest that improvements to our knowledge of these aerosol/cloud effects require the synergistic use of field measurements with remote sensing and modeling tools.
Geophysical data sets derived from satellite sensors, ground/airborne instrumentation, and computational models are often compared against each other. A common example is the validation of satellite ...aerosol optical depth (AOD) retrievals against measurements from Aerosol Robotic Network (AERONET) Sun photometers. Spatiotemporal mismatch between data set sampling means that uncaptured variation in the underlying geophysical field introduces apparent disagreement into such comparisons, known as representation or collocation matchup uncertainty. This study uses variogram analysis of AERONET data to estimate temporal mismatch uncertainties and decorrelation time scales for the global AERONET record. As well as total AOD, the fine‐ and coarse‐mode AODs, Ångström Exponent (AE), and fine‐mode fraction (FMF) of AOD are analyzed. Globally, a time difference of 30 min typically induces from 0.011–0.035 variation in AOD. For total, fine, and coarse AODs the typical time to decorrelation is around 2–10 days. For AE and FMF it is 3–33 days; that is, aerosol systems often persist significantly longer than individual events in them. Biomass burning regions tend to show the largest and fastest subdaily AOD variability and also longest times to decorrelation. Some sites show significant season‐to‐season variations in behavior. These results can be used to inform site‐specific time collocation thresholds for aerosol validation analyses and account for temporal variation when estimating data set uncertainty. They also have implications for comparisons between different satellite products or models, data aggregation, and time series analyses. Results are provided on a site‐by‐site basis to facilitate use by other researchers.
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