The primary goal of this study was to generate a near-real time (NRT)
aerosol optical depth (AOD) product capable of providing a comprehensive
understanding of the aerosol spatial distribution over the Pacific Ocean, in
order to better monitor and track the trans-Pacific transport of aerosols.
Therefore, we developed a NRT product that takes advantage of observations
from both low-earth orbiting and geostationary satellites. In particular, we
utilize AOD products from the Moderate Resolution Imaging Spectroradiometer
(MODIS) and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared
Imaging Radiometer Suite (VIIRS) satellites. Then, we combine these AOD
products with our own retrieval algorithms developed for the NOAA
Geostationary Operational Environmental Satellite (GOES-15) and Japan
Meteorological Agency (JMA) Multi-functional Transport Satellite (MTSAT-2)
to generate a NRT daily AOD composite product. We present examples of the
daily AOD composite product for a case study of trans-Pacific transport of
Asian pollution and dust aerosols in mid-March 2014. Overall, the new
product successfully tracks this aerosol plume during its trans-Pacific
transport to the west coast of North America as the frequent geostationary
observations lead to a greater coverage of cloud-free AOD retrievals
equatorward of about 35
Although stricter emission control standards in the United States have led to a reduction in the domestic emissions of particulates since the 1980s, degraded air quality conditions over the western United States have occurred due to foreign dust and pollution aerosols (Yu et al., 2012). For instance, the expanding cities and rapid industrialization of East Asia are major source regions of pollution emissions while biomass burning across Southeast Asia releases smoke into the atmosphere (e.g., Streets et al., 2003). In addition, frequent dust storms originate from the Taklamakan and Gobi deserts in the late winter and early spring, with a daily average dust emission of 1.58 million tons in April (Zhao et al., 2006). These smoke and dust aerosols combine with the other pollutants in a trans-Pacific transport that occurs frequently during late winter and early spring when the East Asian winter monsoon is near its peak strength (Gong et al., 2006). This East Asian winter monsoon brings cold, dry air outbreaks, leading to strong surface winds that can efficiently pick up dust from the deserts. Once lofted in the atmosphere, the dust, smoke, and pollution aerosols are quickly transported to the western Pacific by a persistent offshore wind flow from the Asian continent (Talbot et al., 1997). Then, the aerosols are carried by strong mid- to upper-level westerly winds across the Pacific to the western United States where they can be transported from the free troposphere towards the ground. These transported aerosols can reduce the air quality across the United States which can increase the risk of lung cancer and cardiopulmonary mortalities (e.g., Pope et al., 2002). Consequently, limiting domestic emissions in the United States alone does not assure that the aerosol effects on human health will be reduced, as the total mass of aerosols entering the United States from overseas is similar to that emitted domestically (Yu et al., 2012). Not only can aerosols degrade the air quality, but they can also have direct and indirect radiative effects in the atmosphere through interaction with solar radiation and clouds which can have significant impacts on the climate and weather (e.g., Khain et al., 2005; Ault et al., 2011; Naeger et al., 2013a). Therefore, it is important that we continuously monitor aerosols at a global scale, and determine their concentration and spatial variability, especially during the late winter and early spring period when the trans-Pacific transport of Asian aerosols occurs rather frequently.
A major issue when attempting to monitor aerosols across the Pacific is the large amount of cloud cover that often resides over this region. Mace et al. (2009) used merged data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat satellite to show that cloud cover exceeded 90 % throughout the North Pacific between July 2006 and June 2007. Aerosol optical depth (AOD) retrieval algorithms generally disregard AOD in cloudy regions via cloud screening techniques due to the difficulty in separating the visible reflectance of aerosols and clouds that lead to biases in the retrievals. However, even the more robust cloud screening techniques, such as those used for the Moderate Resolution Imaging Spectroradiometer (MODIS) (Frey et al., 2008), are unable to detect all clouds due to the highly diverse surface and atmospheric conditions that exist across the Earth (Ackerman et al., 2008). Further complicating matters include the adjacency effect where reflected light from clouds can brighten nearby cloud-free pixels (e.g., Wen et al., 2001). Zhang et al. (2005) found that cloud contamination and adjacency effects lead to an overestimation of 10–20 % in the monthly averaged MODIS AOD over oceans. The MODIS Collection 6.0 AOD product has been refined to reduce the cloud contamination issues in Collection 5.1 (Levy et al., 2013). Nonetheless, cloud cover continues to hinder our ability to monitor the trans-Pacific transport of aerosols when using observations from passive (low-earth orbiting, LEO) satellites alone. There has been recent progress in retrieving AOD for absorbing aerosols above clouds from MODIS (Jethva et al., 2013; Meyer et al., 2015) and the Ozone Monitoring Instrument (OMI) (Torres et al., 2012), which could prove very beneficial for tracking the trans-Pacific transport of aerosols. These retrieval techniques take advantage of the highly reflective nature of clouds along with the absorption characteristics of aerosols in the near-UV to visible wavelengths to retrieve AOD when aerosols are lofted above clouds. However, these retrieval techniques rely on a larger number of assumptions than cloud-free aerosol retrievals, which often leads to significant uncertainties of greater than 50 % (Jethva et al., 2013; Torres et al., 2012). The CALIPSO satellite carries the active Cloud-Aerosol Lidar and Orthogonal Polarization (CALIOP) instrument that is more equipped for detecting aerosol plumes above cloud layers (Winker et al., 2010). However, the CALIOP is of limited value for monitoring the spatial variability of aerosols due to its extremely narrow field of view; therefore, it will only be used to help validate our product.
US air quality agencies (e.g., Environmental Protection Agency, EPA) rely on near-real time (NRT) aerosol products to help provide better forecasts while incorporating the NRT data into their analysis (Al-Saadi et al., 2005). By providing the public with more accurate air quality forecasts, individuals can appropriately adjust their outdoor activities to avoid exposure to poor air quality conditions which can have harmful health impacts. Furthermore, the aerosol indirect effects have recently been incorporated into the Rapid Refresh (RAP) operational model forecast system at the NOAA National Centers for Environmental Prediction (Benjamin et al., 2016). Currently, aerosol fields initialized in the RAP model are based on climatology. Thus, NRT aerosol products can be used to help initialize these operational forecast models with a realistic depiction of the aerosol conditions.
In this study, we merge observations from LEO and geostationary (GEO) satellites in order to develop a NRT 6-hourly and daily AOD composite product centered over the Pacific Ocean. The new product will demonstrate the benefits of merging LEO and GEO satellite observations for tracking aerosol plumes in the atmosphere and has the potential to be useful for data assimilation and aerosol forecasting. For instance, the National Aeronautics and Space Administration (NASA) Goddard Earth Observing System version 5 (GEOS-5) model (Rienecker et al., 2008; Molod et al., 2012) only assimilates aerosol observations from MODIS, but having more frequent satellite observations such as from this study may help model simulation and forecasting of aerosol fields. In the following sections, we discuss the data products used in this study (Sect. 2), present the methodology for generating the NRT AOD composite product (Sect. 3), show results from a case study of trans-Pacific transport of Asian aerosols (Sect. 4), discuss uncertainties (Sect. 5) and validation (Sect. 6), and conclude with a summary and discussion (Sect. 6).
Table 1 provides a detailed summary of the different satellites and aerosol retrievals used in generating our NRT 6-hourly and daily AOD composite product.
Summary of satellites instruments used in producing the NRT AOD product
We utilize the MODIS instrument on board the LEO Aqua and Terra satellites,
which has 36 spectral bands with center wavelengths between 0.41 and 14.5
The 36 spectral bands of MODIS are also utilized to develop a robust cloud
mask that prevents cloud contamination from impacting the AOD retrievals. For
instance, thin cirrus clouds are difficult to detect and can cause biases in
AOD when located above aerosols (Huang et al., 2011); however, the 1.38
In addition to MODIS, our NRT product incorporates AOD retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (NPP) satellite (Jackson et al., 2013). The VIIRS instrument with its 22 spectral bands was designed to continue the decade-long success of retrieving AOD from MODIS; therefore, the VIIRS AOD retrieval algorithm has similar strengths to MODIS. For instance, the VIIRS aerosol retrieval uses the same nine ocean aerosol models as discussed for MODIS. The only minor difference between these ocean aerosol models is the very minimal difference in band wavelengths. Although both VIIRS and MODIS use five different aerosol models over land, they use different values of mean radius, standard deviation of volume distribution, and refractive indices to represent each model. Additionally, the dust aerosol models over land for these LEO retrievals differ in that the VIIRS dust model is based on Mie-scattering calculations that assume spherical particles while MODIS accounts for non-spherical particles. As a result, we expect VIIRS AOD to encounter higher uncertainties than MODIS when retrieving AOD for dust plumes over land. In addition, there are some significant differences between the VIIRS and MODIS cloud masks and internal screening tests (Jackson et al., 2013). Nevertheless, Liu et al. (2014) conducted an extensive validation of VIIRS AOD against the Maritime Aerosol Network (MAN) where they found that 71 % of VIIRS retrievals were within the expected uncertainty range of MODIS retrievals over ocean.
Although VIIRS follows the same orbit track as MODIS Aqua, it is capable of providing additional information on the spatial distribution of AOD due to a wider swath width and higher spatial resolution at swath edge than MODIS (Hillger et al., 2013). VIIRS has a swath width of 3000 vs. 2330 km for MODIS and a spatial resolution at swath edge of approximately 1.5 vs. 5 km for MODIS. We process AOD data from the VIIRS aerosol Environmental Data Record via the NOAA Comprehensive Large Array-data Stewardship System (CLASS) subscription service. However, the VIIRS AOD product via NOAA CLASS has approximately a 7 h latency (i.e., time between the satellite observation and availability of the data product) compared to the 90 min latency of MODIS AOD via NASA LANCE data system. Consequently, VIIRS retrievals are only incorporated into our daily 24 h AOD composite product and not into our 6-hourly product.
Even though the advanced MODIS and VIIRS instruments provide high-quality AOD retrievals, they fly on board LEO satellites that observe the same location only once per daytime period. Thus, cloud cover can lead to large gaps in the coverage of AOD when only analyzing data from instruments on board LEO satellites, especially over the generally cloudy Pacific Ocean. To mitigate this issue and more effectively track the trans-Pacific transport of aerosols, we incorporate the high temporal resolution measurements from the NOAA Geostationary Operational Environmental Satellite (GOES-15) and Japan Meteorological Agency (JMA) Multi-functional Transport Satellite (MTSAT-2) geostationary platforms into the NRT AOD composite product. This study uses the northern hemispheric scan modes with a 30 min temporal resolution for both GOES-15 and MTSAT-2. The high temporal resolution of these GEO sensors can lead to a higher frequency of cloud-free observations than that provided by the LEO sensors, which can help increase the spatial coverage of AOD.
The major disadvantage of these imagers on board the GOES-15 and MTSAT-2 is that they only have five spectral bands, consisting of one visible and four infrared bands (Table 2), compared to the 22 and 36 bands of MODIS and VIIRS. Consequently, these GEO sensors are very limited in their capability to distinguish between various aerosol types in the atmosphere; therefore, we are unable to replicate the complex aerosol retrievals of MODIS and VIIRS. Instead, we develop simplified aerosol retrievals based on only two aerosol models (continental and desert) as discussed in Sect. 3.3. Similar to the VIIRS retrieval, the desert aerosol model used in these GEO retrievals assumes spherical particles. The surface reflectance retrievals (Sect. 3.2) and cloud masks (Sect. 3.4) developed for these GEO sensors are also less robust than the LEO sensors due to the lower availability of spectral bands. As a result, AOD retrievals from these GEO sensors will typically have higher uncertainties than those from LEO sensors as shown by the validation study of Paciorek et al. (2008) where the GOES AOD uncertainty range over dark land surfaces was 18–34 % with lower values expected over water. Similar uncertainties were associated with a MTSAT AOD retrieval algorithm when validated against ground-based instrumentation at the AErosol RObotic NETwork (AERONET) stations (Kim et al., 2008). Note that easily accessible archives containing AOD data are not available for the MTSAT-2 satellite; therefore, we develop our own retrieval algorithms for the GEO sensors in order to ensure a consistency between their algorithms. Nevertheless, the GOES AOD retrieval algorithm developed for this study has similarities to the GOES Aerosol/Smoke Product (GASP) discussed in Prados et al. (2007). The latency of our GOES and MTSAT AOD retrievals is typically less than 30 min.
Center wavelength and spatial resolution of spectral bands on board GOES-15 and MTSAT-2. MTSAT-2 is denoted in parentheses. The GOES-15 imager carries bands 1–4 and 6 while the MTSAT-2 imager carries bands 1–5.
Schematic flow diagram of the steps involved in generating the NRT AOD composite product. The numbers 1–4 on the right-hand side of the schematic highlight the four major steps involved in the GEO AOD retrieval algorithms.
In this study, we use ground-based instrumentation at AERONET stations to
assist with developing accurate AOD retrieval algorithms for the GOES and
MTSAT satellites. AERONET stations are located throughout the globe, but we
focus on 10 of those set across eastern Asia, since aerosols usually
propagate over this region prior to their trans-Pacific transport. The
sun-sky radiometer instruments at the AERONET stations provide very accurate
measurements of aerosol optical properties from the ultraviolet to the
near-infrared (Holben et al., 1998). We utilize AOD and single scattering
albedo (SSA) data at 500 and 675 nm, then calculate the Ångström
exponent by logarithmic interpolation between these wavelengths to derive
AOD at 550 nm. Reported uncertainties for the Level 2.0 cloud-screened and
quality-assured AOD and SSA retrievals are approximately
We also utilize AOD retrievals from the Multi-angle Imaging
SpectroRadiometer (MISR) on board the Terra satellite to compare against the NRT AOD composite product.
The MISR instrument observes the Earth in four spectral bands (0.446, 0.557,
0.671, 0.866
The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) carries the active CALIOP instrument that emits pulses of light at 532 and 1064 nm to produce vertical profiles of the atmosphere (Winker et al., 2003). CALIOP has the unique ability to measure aerosols above clouds; therefore, we use the Level 1B 532 attenuated backscatter profiles to help confirm areas of high AOD among clouds in the AOD composite product. In addition, CALIOP measures the altitude where aerosols are located in the atmosphere, which we use as an input into the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) to forecast the transport path of Asian aerosols. Note that AERONET, MISR, and CALIOP data are only used to help validate our AOD composite product. In other words, AOD retrievals from these instruments are completely independent of our product.
The schematic flow diagram in Fig. 1 shows the steps involved in generating the NRT AOD composite product. In this section, we discuss each of these steps, but place much of the focus on the four major steps involved in the MTSAT and GOES AOD retrieval algorithms (right side of Fig. 1).
In order to effectively describe the MTSAT and GOES retrieval algorithms, we
present an example of how AOD is retrieved for a MTSAT-2 image on 18 March
2014 at 05:00 UTC when a polluted dust plume was being transported over the
Yellow Sea and Sea of Japan. For the MTSAT-2 imager visible data, we must
convert the nominal reflectance (
For the second step, we retrieve the surface reflectance (
All panels pertain to 18 March 2014 at 05:00 UTC.
The third step consists of retrieving the GOES and MTSAT AOD via additional
LUTs, which are created similarly to LUT
MTSAT cloud clearing algorithm with the various techniques and thresholds in the left column; the center column shows the conditions when the techniques are used, and the technique no. relating to cloud/dust detection algorithm results in Fig. 2c is in the right column. The “time” subscript indicates a temporal technique.
For the fourth and final step of the MTSAT and GOES retrieval algorithms, we
use cloud and dust detection procedures to disregard contaminated AOD
retrievals and identify any pixels influenced by dust. Developing accurate
cloud detection algorithms for these GEO satellites is not a trivial step
due to their lower spatial resolution and limited number of spectral bands.
In an effort to obtain as much information as possible from four spectral bands
(6.7
Figure 2c shows the overall results of the cloud/dust procedure for
Since possible dust regions can be mislabeled as cloud by our procedure due
to their similar spectral characteristics, we use four dust techniques to
locate pixels mislabeled as cloud and relabel them as cloud-free. The
special techniques were developed based on the fact that dust regions can
have strong positive 3.8–10.8
While retrieving AOD from the GEO imager data, we also process the Aqua and
Terra MODIS AOD via the NASA LANCE data system and VIIRS AOD via the NOAA
CLASS service (left side of Fig. 1). To help prevent poor-quality MODIS AOD
from being introduced into our AOD composite maps, we use the MODIS cloud
fraction parameter and quality assurance flags to ignore retrievals
associated with marginal confidence and cloud cover > 70 %. We
disregard poor-quality VIIRS AOD by using the quality flags to ignore
retrievals where at least one pixel among the 8
In order to generate the NRT AOD composites, we use all the valid GEO and
LEO AOD data that were processed over a 24 h period beginning 12:00 UTC
each day. At the end of the 24 h AOD composite period, AOD data from the GEO
and LEO sensors are regridded and averaged onto a common domain with
0.5
The top three panels display the daily AOD composites with central time at
00:00 UTC on 18 March 2014.
All panels show CALIOP 532 nm attenuated backscatter profiles on
18 March 2014. CALIPSO transects are indicated in Fig. 4c (black lines),
which are increasing in time from east to west with
In our current methodology, we do not apply techniques to account for the differences in observation times or spatial resolutions between the GEO and LEO sensors, which may lead to undesirable jumps in AOD when merging the different sensors on a common grid. However, according to the case in Sect. 4, our simplified approach does not lead to frequent AOD jumps in our daily composite product.
The top three panels in Fig. 3 display the daily AOD composites for the case
study on 18 March 2014. The central time of the AOD composite product is
00:00 UTC, since we used satellite information between 12:00 UTC on 17 March
and 12:00 UTC on 18 March to generate the product. Figure 3a is an example of
the daily AOD composite with only GEO (i.e., MTSAT/GOES) retrievals, while
Fig. 3b is an example with only LEO (i.e., MODIS/VIIRS) retrievals. All
these satellite retrievals are combined onto the same map to generate the
final version of the daily AOD composite product, which shows a more
complete picture of the spatial distribution of aerosols during this 24 h
period (Fig. 3c). A massive aerosol plume extends from eastern China to
almost the central Pacific region as moderate AOD of approximately 0.6 is
identified around 38
Although the spatial patterns of AOD compare fairly well between Fig. 3a and b,
some important differences exist between the maps. First, the LEO sensors
are capable of providing AOD retrievals among broken clouds in the
midlatitudes, while the GEO sensors generally provide a minimal number of
AOD retrievals in these regions, which is tied to the much finer spatial
resolution of the LEO sensors (< 1 km at nadir for the LEO sensors
and > 4 km at nadir for the GEO sensors). This is highlighted by
the 18 March case where the LEO sensors provide numerous AOD retrievals
among the area of broken clouds around 38
The CALIPSO made several transects directly over the aerosol plumes across
the western Pacific and eastern Asia on 18 March. We analyze the CALIPSO
transects indicated in Fig. 3c (black lines) from east to west. Figure 4a
shows the 532 nm attenuated backscatter profiles from about 03:20 UTC on 18
March where moderate backscatter values are measured from an aerosol plume
at approximately 3 km in height (box 1). The CALIOP Vertical Feature Mask
(VFM) and aerosol subtype browse images confirmed this region of moderate
backscatter as aerosol consisting of dust and polluted dust. This aerosol
layer is likely interacting with the high, thick clouds to the north in Fig. 4a.
The daily AOD composite product (Fig. 3c) reveals moderate to high AOD
in the vicinity of this aerosol layer. Several noteworthy aerosol plumes are
measured by CALIOP during the transect at about 05:00 UTC (Fig. 4b). First,
moderate backscatter values are associated with an aerosol layer from about
18–24
We investigate the transport pathways for three of the aerosol plumes
identified in the CALIOP 532 nm attenuated backscatter profiles (boxes 1,
2, and 5) through the online HYSPLIT tool from NOAA Air Resources
(
NOAA HYSPLIT model forward trajectory results for the 4-day time period beginning 18 March 2014 at 05:00 UTC. We initialize the model from locations 1, 2, and 5 along the CALIPSO transects in Fig. 5. CALIOP aerosol height information at these locations is also used to initialize the model.
To confirm whether this polluted dust plume reached the western coast of North America, we generated the daily AOD composite with a central time of 00:00 UTC on 23 March shown in Fig. 6c. The daily AOD composite depicts moderate AOD of 0.4 to 0.5 over the extreme eastern Pacific, which suggests that remnants of the plume in the western Pacific experienced trans-Pacific transport. However, the majority of the HYSPLIT ensemble runs predict the aerosol plume to be further east than shown in the AOD composite with a handful of runs showing the plume over western North America by 22 March at 05:00 UTC. These differences in aerosol plume location between the ensemble runs and the AOD composite are likely attributed to the fact that the online HYSPLIT tool does not account for the sedimentation and deposition of aerosol particles. This case study shows the utility of the AOD composites for model validation and the potential benefit of using the composites for model assimilation.
The overall results in Fig. 6 are similar to those shown for the 18 March
case. Again, the GEO retrievals in Fig. 6a are generally in close agreement
to the LEO retrievals in Fig. 6b. Thus, the AOD composite product in Fig. 6c
shows a robust spatial distribution of aerosols without encountering
anomalous jumps in AOD between adjacent grid boxes. Many of the same
features are revealed on the GEO and LEO AOD maps, such as the dense aerosol
plume with AOD > 1.0 over eastern Asia. However, the GEO AOD
retrievals reveal areas of moderately thick aerosols over northern China
(
Panels show the daily AOD composites with central time at 00:00 UTC
on 23 March 2014.
The AOD coverage for the individual LEO and GEO retrievals and for the daily AOD composite product (COMP). We present the coverage statistics for 6 days, including the 18 and 23 March case studies analyzed in this paper. The number of valid AOD retrievals across our AOD composite domain along with the percent coverage based on the total number of available grid boxes are shown.
To quantitatively show the gain in spatial coverage due to the inclusion of
the GEO sensors in our daily AOD composite maps, we calculate the number of
valid AOD retrievals along with the percent coverage of those retrievals
based on the total number of available grid boxes (70 400 grid boxes) across
our composite domain. These statistics are calculated for the individual GEO
and LEO AOD maps along with our final AOD composite maps that merge the GEO
and LEO retrievals. We show statistics for a 6-day period, which includes
the 18 and 23 March case studies presented in this paper (Table 4). Overall,
the LEO sensors provide more spatial coverage compared to the GEO sensors
(67 vs. 60 %) during this 6-day period. As shown in this paper,
the higher percentage for the LEO sensors is partly due to the fact that
they have better coverage throughout the northern regions of the AOD
composite domain. The LEO sensors also have better coverage over the central
Pacific and over the far eastern and western portions of the composite
domain, which is due to the limited geographical coverage of the GEO
sensors. The GEO retrieval algorithms developed in this study further limit
their geographical coverage by restricting
Location of GFS profiles used to estimate the uncertainty associated with US standard profiles in the 6SV code4.
Observed
To understand the uncertainty with using temperature, water vapor, and ozone
information from US standard profiles in the AOD retrieval procedure, we
conducted a sensitivity analysis where atmospheric profiles from
0.5
A significant source of uncertainty in our study is associated with the
The most significant assumption in creating the LUTs in our GEO AOD
retrieval algorithms is the selection of the aerosol model as the simulated
Average difference between observed
We found a couple of dust plumes nearby their source region in the Taklamakan
desert on 25 and 29 April 2014. The Taklamakan AERONET site retrieved SSA
values from 0.93 to 0.95 for these pure dust plumes, which are comparable to
that found for dust during the Asian Pacific Regional Aerosol
Characterization Experiment (ACE-Asia) from 30 March to 3 May 2001.
Therefore, to better represent the dust optical properties, we also
incorporated the desert aerosol model into our AOD retrieval algorithm,
which uses a more appropriate SSA at 500 nm of about 0.95 that increases
with larger wavelengths. Overall, for the instances when our AOD retrieval
algorithm correctly identifies polluted and pure dust events, Table 6
suggests that RMSEs will be minimal (
When generating our AOD composite product, uncertainties may arise from
averaging the individual GEO and LEO AOD retrievals onto a common grid
(Sect. 3.6) due to differences in the AOD algorithms, spatial resolution,
observation times, and viewing geometry. We expect minimal uncertainties
when averaging the GEO AOD retrievals onto a common grid, since the GOES and
MTSAT AOD algorithms and spatial resolutions are very similar. Additionally, there
will only be a small number of instances when both GOES and MTSAT AOD
retrievals fall within the same grid box due to the limited overlap between
their geographical coverage. Conversely, when averaging the MODIS and VIIRS
AOD, significant uncertainties may arise due to the differences between
their AOD algorithms as discussed in Sect. 2.2. Figure 9a–b shows an example
of the MODIS Level 2 AOD retrievals and VIIRS AOD retrievals for the 18
March 2014 case study presented in Sect. 4.1. These LEO sensors show a
very similar spatial distribution of AOD throughout the domain where both
retrieve AOD > 1 for the polluted dust plume extending northeast
from eastern China (
We validate our daily AOD composites using Level 1.5 AERONET 550 nm AOD from
15 different sites across East Asia and five different sites across the western
United States during the 6-day period (18–23 March 2014) of the
trans-Pacific transport event presented in this paper. To conduct a proper
validation, we calculated the average of all the available AERONET AOD
retrievals for each site during the 24 h period of the daily AOD
composites. Then, we use the nearest neighbor approach to find the closest
composite grid box to each AERONET site. Figure 10a shows a high correlation
(
The primary goal of this study was to generate a NRT daily AOD composite product that combines GEO and LEO satellite observations to assist with monitoring and tracking the trans-Pacific transport of aerosol plumes. In this paper, we present examples of the AOD composite product for a case study of trans-Pacific transport of Asian aerosols in mid-March 2014. Although the MODIS and VIIRS LEO satellites generally provide high-quality AOD retrievals in cloud-free scenes (excluding the polar region), they only observe the same area once during the daytime period, which increases the likelihood of cloud and sun glint contamination. This can cause gaps in daily AOD coverage as shown by the case studies presented in this paper. Thus, we take advantage of the high temporal resolution of the GOES-15 and MTSAT-2 GEO satellites by developing AOD retrieval algorithms based on the continental and desert aerosol models in the 6SV1.1 RTM. We also develop a unique cloud/dust detection algorithm utilizing spectral, spatial, and temporal techniques to disregard cloud-contaminated pixels and locate dust pixels for the desert aerosol model. Overall, when merging the GEO and LEO retrievals, we generated a daily AOD composite product that provided additional spatial coverage of AOD across our domain from Asia to North America. We showed that the coverage of aerosol plumes propagating from Asia to the western Pacific were better captured by our AOD composite product than the individual GEO and LEO products. Our AOD composite also showed increased spatial coverage of AOD across the eastern Pacific, which can assist with tracking the trans-Pacific transport of aerosols. Additionally, the incorporation of the 6SV desert model into our AOD retrieval algorithms helped depict areas of dust plumes over the Gobi and Taklamakan deserts that were not shown by the LEO products.
Although the AOD composite product showed an increase in spatial coverage of
AOD across our domain from Asia to North America, we noted several issues
pertaining to the GEO AOD retrievals. We showed that high biases in AOD can
appear in the tropical Pacific due to cloud artifacts impacting the GEO
retrievals, which were more apparent over the tropical western Pacific than
over the tropical eastern Pacific. Additionally, we found low biases in AOD
that were associated with cloud artifacts impacting the 28-day composite
technique and
We did not show examples of the 6-hourly AOD composite product, since the daily product is more pertinent for the focus of this paper where we track the trans-Pacific transport of aerosols. Nevertheless, it is important to note that the GEO AOD retrievals have a greater impact on this product, since LEO satellites have limited coverage across our domain during the 6 h time window. Thus, the 6-hourly product can have important implications for aerosol forecasting, as the shorter time window is more appropriate for the AOD assimilation process. Thus, the assimilation of both LEO and GEO satellite AOD retrievals can provide a more comprehensive coverage of AOD into chemistry models (e.g., Weather Research and Forecasting coupled with Chemistry (WRF-Chem)), which can help improve the representation of the simulated aerosol fields. This can ultimately improve air quality forecasts and the simulation of the aerosol–cloud–precipitation processes.
The future capability of GEO satellites for monitoring and tracking aerosol
plumes will be greatly enhanced with the upcoming launch of the
next-generation NOAA GOES-R and recently launched JMA Himawari-8 satellites.
These advanced GEO satellites perform full disk scans every 5 min, and
carry sensors consisting of 16 spectral bands with 0.5 km spatial resolution
at nadir for the 0.64
Aqua and Terra MODIS AOD products are available in near-real time at
We acknowledge the Land, Atmosphere Near real-time Capability for Earth observing
system (LANCE) for access to the MODIS Level 2 AOD products. LANCE is operated by the
NASA/GSFC/Earth Science Data and Information System (ESDIS) with funding
provided by NASA/HQ. We also thank the NOAA Comprehensive Large Array-data Stewardship
System (CLASS) subscription service for near-real-time delivery of AOD data
from the VIIRS Environmental Data Record, the Atmospheric Science Data Center at NASA
Langley Research Center for access to the MISR and CALIOP data products, and the
NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website
(