In the present study, we report the first comparison between the aerosol
optical depth (AOD) and Ångström exponent (AE) of the Moderate Resolution
Imaging Spectroradiometer (MODIS) instruments on the Terra (AODt) and Aqua
(AODa) satellites and those measured using a sun photometer (AODSP) at
Camagüey, Cuba, for the period 2008 to 2014. The comparison of Terra and
Aqua data includes AOD derived with both deep blue (DB) and dark target (DT)
algorithms from MODIS Collection 6. Combined Terra and Aqua (AODta) data were
also considered. Assuming an interval of ±30 min around the
overpass time and an area of 25 km around the sun photometer site, two
coincidence criteria were considered: individual pairs of observations and
both spatial and temporal mean values, which we call collocated daily means.
The usual statistics (root mean square error, RMSE; mean
absolute error, MAE; median bias, BIAS), together with linear regression
analysis, are used for this comparison. Results show very similar values for
both coincidence criteria: the DT algorithm generally displays better statistics
and higher homogeneity than the DB algorithm in the behaviour of AODt, AODa,
AODta compared to AODSP. For collocated daily means, (a) RMSEs of 0.060 and 0.062 were obtained for Terra and Aqua with the DT
algorithm and 0.084 and 0.065 for the DB algorithm, (b) MAE follows the same
patterns, (c) BIAS for both Terra and Aqua presents positive and negative
values but its absolute values are lower for the DT algorithm; (d) combined
AODta data also give lower values of these three statistical indicators for
the DT algorithm; (e) both algorithms present good correlations for comparing
AODt, AODa, AODta vs. AODSP, with a slight overestimation of satellite data
compared to AODSP, (f). The DT algorithm yields better figures with slopes of
0.96 (Terra), 0.96 (Aqua) and 0.96 (Terra + Aqua) compared to the DB
algorithm (1.07, 0.90, 0.99), which displays greater variability. Multi-annual
monthly means of AODta establish a first climatology that is more comparable
to that given by the sun photometer and their statistical evaluation reveals
better agreement with AODSP for the DT algorithm. Results of the AE
comparison showed similar results to those reported in the literature
concerning the two algorithms' capacity for retrieval. A comparison between
broadband aerosol optical depth (BAOD), derived from broadband pyrheliometer
observations at the Camagüey site and three other meteorological stations
in Cuba, and AOD observations from MODIS on board Terra and Aqua show a poor
correlation with slopes below 0.4 for both algorithms. Aqua (Terra) showed
RMSE values of 0.073 (0.080) and 0.088 (0.087) for the DB and DT algorithms. As
expected, RMSE values are higher than those from the MODIS–sun photometer
comparison, but within the same order of magnitude. Results from the BAOD
derived from solar radiation measurements demonstrate its reliability in
describing climatological AOD series estimates.
Introduction
Atmospheric aerosols play an important role in weather and climate (IPPC,
2013). Depending on the physical, chemical and optical properties of
atmospheric aerosols together with their origin and spatial and temporal
distribution, they can affect the Earth's radiative budget as well as
dynamic, biogeochemical and chemical processes (Knippertz and Stuut, 2014;
Seinfeld and Pandis, 2016). All of these processes play a key role at a
global and regional scales due to the high spatio-temporal variability of
aerosol properties. Aerosols can also affect the biosphere and, in
particular, humans in several ways: for example, the Saharan dust transported
to America across the Atlantic supplies nutrients to the Amazon forest (Swap
et al., 1992; Yu et al., 2015). Moreover, in the Caribbean, in addition to
aerosols of local origin, dust makes the amount of aerosol exceed air quality
standards associated with human health (Prospero and Lamb, 2003; Prospero et
al., 2014). The great variability of Saharan dust transported to the
Caribbean basin has been documented using long-term observations in Barbados
(Prospero and Lamb, 2003; Prospero and Mayol-Bracero, 2013) and more recently
in Miami, Guadeloupe and Cayenne (Prospero et al., 2014). The Caribbean
region is thus of great importance for aerosol studies due to its low aerosol
background, which helps aerosol transport studies (Kaufman et al., 2005;
Denjean et al., 2016; Velasco-Merion et al., 2017). One difficulty, however, is that
it is an area where land and water are mixed in individual pixels when remote
satellite aerosol studies are carried out.
In order to improve calculations of aerosol climatology for Cuban land
areas, which remains ongoing, we compared aerosol ground-based observations
and available satellite data, as a first step towards assessing this
climatology. This involves a comparison between all the available
Camagüey sun photometer aerosol optical depth (AOD) data and the broadband aerosol
optical depth (BAOD)
provided by solar radiation measurements with the series of AOD (550 nm)
from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments
on board the Terra (2001 to 2015) and Aqua (2002 to 2015) satellites.
Selected observations were those spatially and temporally collocated between
satellite instruments and ground-based sites. In addition to the aerosol
load given by the AOD, we also evaluated the Ångström exponent (AE)
as a parameter that provides information about particle size for MODIS and sun
photometer data.
One of the challenges we faced was the low amount of potential coincident
AOD and AE from MODIS and the sun photometer. The same is true for AOD from
MODIS and broadband-pyrheliometer-derived BAOD, in both cases due to
existing gaps in the ground-based time series and also because this area is
strongly affected by clouds (mainly partial cloud cover). In order to
maximise the number of satellite and surface measurement pairs, we used
primary AOD and AE L2 products without any averaging as well as combined AOD
and AE from Terra and Aqua MODIS sensors as a whole data set. We also used
deep blue (DB) and dark target (DT) algorithms to evaluate the reliability
of satellite AOD and AE retrievals in selecting the most appropriate data set
to derive the climatology of both AOD and AE aerosol parameters in Cuba.
The earliest attempt to measure aerosol optical properties at ground level
in Cuba recorded in a scientific publication dates back to 1988 (Martinez,
1988), where the Linke turbidity factor and the Ångströmß
turbidity coefficient were derived from solar direct normal irradiance (DNI)
measurements. Twenty years later, a cooperation agreement between scientific
institutions in Spain and Cuba enabled a Cimel CE318 sun photometer to be
installed at Camagüey (Cuba) and for it to be included in the AErosol
RObotic NEtwork (AERONET, Holben et al., 1998). Several aerosol studies have
been conducted using the AOD and AE from
Camagüey's sun photometer observations (see Antuña-Marrero et al.,
2017; http://www.goac.cu/uva/, last access: 9 April 2018).
Broadband pyrheliometric DNI observations allow the BAOD to be determined, which complements sun photometer
aerosol observations at Camagüey, and provides aerosol information at
three other locations in Cuba. The main purpose of determining BAOD is to
offer information concerning aerosol variability over the island, also
making it possible to extend aerosol records back in time. The first BAOD
calculations used for DNI measurement were conducted at Camagüey under
clear-sky conditions for the period 1985–2007 using Gueymard's (1998)
improved parameterisations (Fonte and Antuña, 2011). García et al. (2015) used this kind of DNI observation for a longer period (1981–2013) and
compared this BAOD to sun photometer AOD data. They used observations under
the clear line of sight between the broadband pyrheliometer and a region of
5∘ around the sun as well as improved climatological values of
the integrated water vapour.
This comparative analysis does not aim to be a validation study of the MODIS
sensor since many works during the long history of the MODIS sensor on the
Terra and Aqua platforms have sought to improve its features (these include
Kaufman et al., 1997a, b; Tanré et al., 1997; Remer et al., 2002, 2005,
2006; Hsu, et al., 2004, 2006, 2013; Levy et al., 2007, 2009, 2010, 2013,
2015; Sayer et al., 2013, 2014; https://darktarget.gsfc.nasa.gov/atbd/overview, last access: 9 April 2018). However, compared to
other areas of the world, no studies have been reported in the Caribbean
region and in Cuba in particular (Papadimas et al., 2009; Mishchenko et
al., 2010; Kahn et al., 2011; Bennouna et al., 2011, 2013; Witte et al.,
2011; Gkikas et al., 2013, 2016; Levy et al., 2015).
As mentioned, our aim is to establish reliable aerosol climatology in Cuba
based on satellite and ground-based instruments. By making a detailed
comparison of similarities and differences between available data sets, the
present work seeks to make a contribution to this aim.
The article is structured as follows. Section 2 begins with the description
of the data sets, followed by the explanation of the coincidence criteria
between the AOD and AE MODIS L2 products and the same two variables from the
sun photometer and broadband pyrheliometer BAOD. This section ends with the
explanation of the statistical indices used. Section 3 is composed of
various sections designed to explain and discuss the large volume of results
that emerge from the comparison by taking two different retrieval AOD
aerosol algorithms for both the Terra and Aqua platforms, with the sun
photometer and BAOD. Section 4 contains the conclusions.
Materials and methodsMODIS satellite instruments
The twin MODIS instruments on board the Terra and Aqua satellites have
accumulated over 15 years of observations of several atmospheric parameters,
including AOD at several wavelengths and the AE parameter. These are the two most
common parameters for describing atmospheric aerosol optical properties.
Based on the assumptions about the properties of the Earth's surface and the
aerosol type expected over these surfaces, the MODIS Atmosphere team
developed three algorithms for processing MODIS observations (Levy et al.,
2013). Regions which appear visually dark from space, referred to as
dark target (DT), include the algorithm assumptions for vegetated land
surfaces (Kaufman et al., 1997a, b) and for remote ocean regions (Tanré
et al., 1997). The third algorithm, called the deep blue (DB) algorithm,
includes assumptions for surfaces which are visually bright from space
and uses near-UV wavelengths (DB band near 410 nm). Under these conditions,
the DB band provides a better signal than the visible wavelengths, improving
the information content for aerosol retrievals (Hsu et al., 2004, 2006) due
to lower surface albedo at this short wavelength. Levy et al. (2013) provide
a detailed explanation of basic MODIS retrieval concepts and improvements to
the DT algorithm in Collection 6 for aerosol products. In addition, Hsu et al. (2013) give a detailed explanation of the DB algorithm improvements in
Collection 6.
Following Levy et al. (2013), we summarise the MODIS calculus chain. MODIS
Level 0 (L0) is the basic data file containing raw observations from the
sensors. Observations grouped in 5 min swath scans (called granules)
are Level 1A (L1A), which after calibration become Level 1B (L1B). L1B data
feed the MODIS geophysical retrieval algorithms, generating the primary
geophysical observations, which include AOD and AE, designated Level 2 (L2).
This is followed by Level 3 (L3), consisting of daily and monthly statistics
of geophysical products, in 1∘× 1∘
latitude–longitude grid boxes. L2 aerosol products are stored in
the MOD04 (Terra) and MYD04 (Aqua) files.
Aerosol products from the MODIS Collection 6 data set used in the
present study.
ProductDescriptionDeep_Blue_Aerosol_Optical_Depth_550_Deep blue AOT at 0.55 micron for land with higherLand_Best_Estimatequality data (quality flag = 2, 3)Deep_Blue_Angstrom_Exponent_LandDeep blue Ångström exponent for land with allquality data (quality flag = 1, 2, 3)Optical_Depth_Land_And_OceanAOT at 0.55 micron for both ocean (average)(quality flag = 1, 2, 3) and land (corrected)(quality flag = 3)
We selected AOD at 550 nm from MODIS (both on Terra and Aqua satellites)
Collection 6, L2 data level derived using the two algorithms, DB for land
with the highest data quality (quality flag = 2, 3) and DT for land,
corrected (quality flag = 3). In addition, we selected the AE retrieved
over land from the DB algorithm using the corresponding pairs of AOD values
(412/470 or 470/650 nm) with the highest quality (quality flag = 2, 3),
since the DT algorithm only retrieves the AE over the ocean (Table B1 in
Levy et al., 2013). Table 1 lists the aerosol products used in the present
study.
At a global scale, it has been established that by using the DT algorithm over
land, MODIS-retrieved aerosol size parameters evidence poor quantitative
capacity, particularly AE (e.g. Levy et al., 2010; Mielonen et al., 2011).
However, for the DB algorithm, AE capacity increases for moderate or high
aerosol loadings, AOD > 0.3 (Sayer et al., 2013). We therefore
decided to conduct the comparison between the AE from the MODIS DB algorithm
and the AE from the Camagüey sun photometer to estimate its uncertainty.
The enhanced DB algorithm methodology for deriving AE in Collection 6 is the
same as in Collection 5. It uses the Ångström power law and AOD
values at 412, 470 and 650 nm. Under non-vegetated surfaces, AE is derived
using the AOD from pair 412/470 nm. For vegetated surfaces, AE is derived
from the 470/650 nm pair. In the case of a surface with mixed vegetated and
non-vegetated areas, AE is derived using the AOD at the three wavelengths
mentioned (Hsu et al., 2013).
Camagüey AERONET sun photometer
The Camagüey sun photometer was installed thanks to an agreement between
the University of Valladolid (UVA), Spain, and the Meteorological Institute
of Cuba (INSMET) for joint aerosol research. It contributes to the NASA AErosol
RObotic NEtwork (AERONET; Antuña et al., 2012). Annual replacement of
the instrument for a calibrated instrument, sent from Valladolid to Camagüey,
encountered numerous transportation and customs delays, causing gaps in the
observation series. However, the collected series of observations does
represent a valuable data set of aerosol columnar optical properties in the
Caribbean, enabling GOAC–INSMET (Atmospheric Optics Group of Camagüey – Institute of Meteorology) and GOA-UVA (Atmospheric Optics Group of Valladolid University) to conduct preliminary aerosol
research (Antuña-Marrero et al., 2017).
The AERONET Cimel sun photometers have been conducting aerosol observations
at nine spectral narrow band filters for over two decades, producing
spectral AOD and column effective particle properties (Holben et al., 1998).
In general, Cimel sun photometer nominal wavelengths are 340, 380, 440, 500,
675, 870, 935, 1020 and 1640 nm. In some cases, the 1640 nm is replaced by a
1240 nm. Its processing algorithm, based on the Beer–Lambert–Bouguer law,
allows spectral optical depth values at an uncertainty level of approximately 0.01 to
0.02 to be determined (Holben et al., 1998; Eck et al., 1999). Because of
this low level of uncertainty, AERONET AOD observations commonly serve as
reference values (ground truth) to validate AOD measured by other remote
sensing sensors (Zhao et al., 2002). AERONET AE are derived for five
different wavelength intervals: 340–440, 380–500, 440–675, 440–870
and 500–870 nm. In the present study, the selected AE is the one in the
440–675 nm range (AESP).
We used Camagüey sun photometer Level 2.0 data as processed by AERONET,
i.e. cloud screened and quality assured (Smirnov et al., 2000), covering
the period from 7 October 2008 to 1 August 2014. This consisted of 29 940
single AOD (340 to 1640 nm) and AESP observations. By applying the
Ångström power law, we converted single sun photometer AOD
observations at 500 nm wavelength to AOD at 550 nm, (AODSP) using
the AESP from the same measurement:
AODSP=AOD500λ550λ500-AESP.
Information about Cuban actinometric stations
operating under the Solar Radiation Diagnostic Service (SRDS). Available
number of BAOD observations are included in column 6 and the period is covered in
the last column.
CodeStation nameLatitudeLongitudeHeight (m)No. observationsPeriod78355Camagüey (CMW)21.42-77.85122 m24952001–201578330Jovellanos (JVN)22.80-81.1423 m11822010–201578342Topes de Collantes (TPC)21.92-80.02766 m13582011–201578321Santa Fé (LFE)21.73-82.7732 m17562011–2015Solar direct irradiance measurements and derived BAOD
Four actinometric stations belonging to the Solar Radiation Diagnostic Service
in Cuba provided the DNI observations used to derive the BAOD
(Antuña et al., 2008, 2011). Table 2 lists the WMO code of the four
stations, the geographical location and the number of observations available
for the periods at each station. Figure 1 shows the geographical location of
the four stations. The stations are equipped with Yanishevsky manual
broadband solar radiation instruments supplied between the 1970s and 1980s
by the Hydrometeorological Service of the Soviet Union. The Yanishevski
broadband pyrheliometer is the M-3 model, a thermo-battery system with a
5∘ field of view connected to an analogic galvanometer,
GSA-1MA or GSA-1MB model (GGO, 1957).
Map of Cuba with the stations where the sun photometer and the
four broadband pyrheliometer observations are conducted.
Calibrations of all the actinometric instruments are conducted
periodically by comparison with a master broadband pyrheliometer and a
master pyranometer. Trained observers perform hourly manual observations
from sunset to sunrise, following the standard methodologies and quality
control procedures established for this set of instruments (GGO, 1957). Once
the manual measurement is conducted and recorded in a notebook designed for
that purpose, all the measurement information is digitised using Actino version
2.0 software (Estevan, 2010; Antuña et al., 2008) of the Diagnostic
Service of the Broadband Aerosol & Clouds Optical Depth for Cuba
(http://www.goac.cu/seoc/, last access: 9 April 2018), a public service provided by GOAC. The software
includes robust quality control of input data, its processing and output
quality control (Antuña et al., 2011). Because of the ageing of the
Soviet-era instruments, the magnitude of the error associated with the
broadband pyrheliometers currently operating in Cuba is estimated to be
around 10 %.
Based on the model parameterisation of solar broadband irradiances, the
integrated aerosol optical depth δa, BAOD, can be obtained
using Eq. (2), in which direct normal solar irradiance (DNI) is measured
and the remaining variables are determined independently (Gueymard, 1998).
δa=1malnE0nDNI-mRδc-mwδw-mntδnt
The individual atmospheric processes considered are Rayleigh scattering,
absorption by ozone (O3), stratospheric and tropospheric nitrogen
dioxide (NO2), uniformly mixed gases, water vapour, and extinction
(mostly scattering) by aerosols. The variables in Eq. (2) are optical
air mass of aerosols ma, Rayleigh scattering, uniformed
mixed gases, O3 absorption and stratospheric NO2mR, water vapour mw and tropospheric NO2mnt and the corresponding broadband optical
depths δ. The method makes a series of assumptions, i.e. using Bouguer's
law, in the strict sense that it is only valid for monochromatic radiation
and is applied to define broadband transmittance. For a detailed description
of the derivation of Eq. (2) and the parameterisation of the variables,
see Gueymard (1998), Fonte and Antuña (2011) and García et al. (2015)
for the method's application to our data.
In order to avoid cloud contamination in BAOD retrieval, we used only DNI
observations with the cloud-free condition in the line of sight to the sun.
In other words there is a clear line of sight between the broadband
pyrheliometer and a region of 5∘ around the sun (GOAC, 2010).
Furthermore, to avoid errors associated with high-elevation zenith angles,
causing larger air masses, DNI observations performed at 06:00 and 18:00
local time (LT) were not used in the present study.
The main errors of the method for determining BAOD are associated with
instrumental errors and the error when estimating the precipitable water
(PW) component (Gueymard, 2013). In the first case, in order to ensure the
quality of the solar radiation data set from the four actinometric stations
used in this study, including DNI, they are regularly subject to a two-step
quality control procedure (Estevan et al., 2012). The first step applies the standard
procedures designed for Yanishevski-type actinometric instruments from the
former Soviet Hydrometeorological Service (Kirilov et al., 1957). Data that
pass this quality procedure are then evaluated following the standards set
by the Baseline Solar Radiation Network, BSRN (Ohmura, 1998; Long and Shi,
2006, 2008; Estevan et al., 2012).
The size of the field of view of the broadband pyrheliometers is another
potential source of error since, in certain cases, circumsolar radiation
causes more radiation to be measured than expected. In such cases, the
effect is an underestimation of BAOD. Nevertheless, this effect is low in
general, except in specific conditions such as large air masses or in the
presence of high aerosol loads or large-particle aerosols (Gueymard,
1998).
Monthly mean PW values at the four actinometric stations were used as
input to derive monthly mean δw values (Gueymard, 1998). For
Camagüey, we calculated the monthly mean PW values from the sun
photometer PW observations from 2008 to 2014 (García et al., 2015). For
each of the three other stations, we calculated the monthly mean PW values
using the vertical integrated water vapour (kg m-2) from spatially
coincident ERA-Interim reanalysis between 1979 and 2013 (Barja et al.,
2015). Taking into account all the above-mentioned errors, the total
uncertainty of the method used to determine BAOD is of the order of
10-2 (Gueymard, 1998).
Coincidence criteria for MODIS and sun photometer observations
Obtaining sufficient AOD satellite observations over land for climatological
studies in insular areas poses a challenge when compared to the amount of
data usually available over continental regions such as the USA, Europe or
China. The reason tends to be the small size of the islands. In the case of
Cuba, its particular narrow latitudinal and elongated longitudinal extension
combined with its irregular coasts renders the MODIS L3 product unsuitable
for climatological studies. As can be seen in Fig. 1, most of the
1∘ by 1∘ grid cells consist of both land and
sea areas, resulting from the merging AOD measured over the two surfaces.
The red grid cell in Fig. 1 is an example of the limitations of MODIS L3
products in representing land areas in the case of Cuba. In response to this,
we plan to use the MODIS L2 product to produce aerosol climatology for Cuba
rather than L3, which is commonly used for this type of study. In this
regard, it is vital to validate the single observations from MODIS L2 with
the single sun photometer observations. We designed and applied a method to
maximise the available pairs of MODIS L2 and sun photometer AOD and AE
observations that are coincident in space and time while avoiding duplicating the use of
any of them. Additionally, in an effort to increase the amount of data, we
tested the differences between Terra and Aqua L2 MODIS AOD and AE
observations in order to determine the possible combination of both Terra
and Aqua in a single data set.
Hereinafter, AODt, AODa, AODta and AODSP will denote
spatio-temporal AOD from collocated MODIS (Terra, Aqua and Terra + Aqua)
and AERONET sun photometer data respectively. Unless otherwise indicated,
“AOD” refers to AOD at 550 nm wavelength. Similarly, AE from Terra, Aqua
and Terra + Aqua derived using only the DB algorithm will be denoted by
AEt, AEa and AEta.
Given the challenges arising from the small amount of potential coincident
spatial and temporal AODt and AODa with AODSP and BAOD, as
explained above, we used MODIS L2 data to maximise the number of available
MODIS observations for comparison. Hereinafter, we call these observations
“single observation values”, using the same denomination for the
instantaneous sun photometer observations on each day and for hourly
broadband pyrheliometer observations. Another way to increase the amount of
data was to combine AODt and AODa (AODta) for comparison with
AODSP and BAOD. In these cases, different observations of AODSP
and BAOD match AODt and AODa because the time difference
established for coincidence (±30 min) is lower than the difference
between the Terra and Aqua daily overpass times.
Number of available non-negative AODa, AODt, AEa and
AEt data. They are spatially coincident with the Camagüey sun photometer in a
radius of 25 km for each retrieval algorithm, DB and DT for the whole period
2001–2015 as well as the period 2008–2014, when sun photometer data,
AODSP and AESP, are available.
Spatial coincidence criteria were guaranteed by selecting all the AODt
and AODa measured inside the 25 km radius around the sun photometer
site for the whole data period from each satellite sensor. Table 3 shows the
amount of spatial coincident information for non-negative AODt and
AODa values. It shows the amount of data available for the whole
period 2001 to 2015, when broadband pyrheliometer observations at
Camagüey are available, and 2008 to 2014, the period of available sun
photometer observations. There are at least twice as many available
observations from Terra as from Aqua for the two periods. The greater number
of available data from Terra compared to Aqua is associated with the different
overpass times of the two satellites over Cuba. Figure 2 shows that Terra
overpasses occur from mid-morning to late morning before convective activity
begins, while the Aqua overpasses take place in the early afternoon when
convection has already begun, causing a higher number of observations to be
discarded in AOD retrievals due to cloud presence.
Frequencies of the time of day (local time) overpass of Terra
and Aqua (blue and red respectively). Camagüey's sun photometer site is in a
radius of 25 km for the period 2001 to 2015. In green are the time frequencies
for the Camagüey's sun photometer observations in the period 2008 to
2014. In addition, the time frequencies for the direct radiation
observations are used to calculate the BAOD. The bar width is 10 min for
Terra, Aqua and the sun photometer and 1 h for the BAOD.
Collocated “Single observation” values and “daily mean”
values
All Aqua and Terra overpass times in a radius of 25 km around Camagüey
for the periods 2001 to 2015 (Terra) and 2002 to 2015 (Aqua) are shown in
Fig. 2. Overpass times, defined by the maximum and minimum values of all
the 25 km spatially coincident MODIS observations, are 10:12–11:49 (LT)
for Terra and 12:47–14:20 (LT) for Aqua. In addition, Fig. 2 shows the
diurnal frequency of sun photometer observations from 2008 to 2014 and the
diurnal frequency of the BAOD observations for Camagüey for the period
1981 to 2015. Note that the BAOD histogram shows only hourly frequency
values, since that is the time interval between the manual pyrheliometric
observations.
For each day, we compared the corresponding time of each single sun
photometer measurement with the time of each single AODt and AODa
observation located in a radius of 25 km around the sun photometer site (an
area of almost 2000 km2) and in the time window of ±30 min
between both types of observations. The former selection process includes,
for each satellite, the AODt and AODa values derived both with the
DB and DT processing algorithms separately, producing four independent bulk
data sets, two for Aqua and two for Terra. We then identified four different
cases of matching data per day in the bulk coincident data sets. The first
consisted of days with only one AODSP value and one AODt
(AODa) coincident value, and the second consisted of only one AODSP value
coincident with multiple AODt (AODa) values each day. In the third
case, only one AODt (AODa) value coincided with multiple
AODSP values. Finally, the fourth case consisted of multiple AODSP
values coincident with multiple AODt (AODa) values.
Coincident cases were then selected for comparison on a case by case basis. In the
first instance, we selected all cases. In the second case, because of the
MODIS instruments spatio-temporal sampling geometry, time differences between
MODIS and sun photometer observations are of the order of 1 min. As a
result, only the criterion of the minimum distance between the positions of
the AODt (AODa) and the sun photometer was applied to determine
the pair of coincident values, therefore not allowing any repeated
AODSP and AODt (AODa) values to be selected. Since it
consists of only one AODt (AODa) measurement and multiple
AODSP observations, in the third case the distance is the same; hence
the selection criteria was the minimum of the time differences between
AODSP and AODt (AODa) observations. The fourth case, the
most complicated, allowed both criteria to be applied. No differences in the amount of coincident data were
found when testing whether the order in which the two criteria were applied
had any impact.
Another approach, the most commonly used for comparison (Bennouna et al.,
2011; Sayer et al., 2014), involves averaging all the AODSP values in the interval of ±30 min and comparing them to the MODIS instrument
overpass time (note that AODt and AODa averages are really the
daily values of MODIS) located in a radius of 25 km around the sun
photometer. At least two single AODSP and two single AODt
(AODa) observations were required to calculate the spatio-temporal
average. We applied a similar approach to calculate collocated daily means of
AESP, AEt and AEa. The procedures described above generated a
series of collocated daily means of AODSP vs. AODt (AODa)
and AESP vs. AEt (AEa). Hence, by combining the former
generated series of AOD (AE) for Terra and Aqua, we produced the coincident
(Terra + Aqua) data set. The term “collocated daily mean AOD” will be used hereinafter, although it
should be stressed that this approach reduces the number of observations
generated by virtually a third.
After explaining the coincidence criteria adopted here, it is well known
that this type of comparison shows major differences depending on the
spatial and/or temporal resolution taken for the MODIS sensor in relation to
the ground-based instruments used (Santese et al., 2007; Levy et al., 2009;
Bennouna et al., 2011, 2013). The justification for using a single-observation data set and a collocated daily means data set separately
to analyse this comparison is based on (a) the characteristics of the
surface area under study, with nearby areas of water and land; (b) the
difference concerning how cloud cover affects data during the overpass time
of the Terra and Aqua platforms; (c) the possibility of including the
largest amount of data; and (d) the fact that only single observations can be
compared in the case of BAOD pyrheliometer measurements.
Statistics
The statistics used in the present study are those commonly used (e.g.
Sayer et al., 2014). These are the root mean square error (RMSE), mean
absolute error (MAE), median bias (BIAS), the Pearson linear correlation
coefficient (R), the number of coincident MODIS and sun photometer cases
(cases) and the fraction (f) of the MODIS/AERONET AOD retrievals in
agreement within the expected uncertainty. Expected uncertainty, defined as
a 1 standard deviation confidence interval, entails the sum of the absolute
and relative AOD errors. Usually referred to as expected error (EE), it
was applied in accordance with Eq. (3) (Sayer et al., 2014).
EEDT=±(0.05+0.15AOD)
The aim is to compare the performance of the DB and DT algorithms directly
(Sayer et al., 2014). All of these statistical indicators were evaluated for
the whole set of collocated AODt, AODa, AODta with AODSP,
and BAOD; AEt, AEa, AEta with AESP; as
well as time frequencies (Fig. 2) and histograms of these quantities. We
also evaluated these statistics on a monthly scale for the AOD values.
Results and discussion
This section is divided into four subsections. In the first subsection, we
analyse in detail the main results from comparing the AOD satellite MODIS
sensors and the sun photometer data given by the statistical indicators and
linear correlations as a result of taking two different criteria, which were two
different retrieval AOD aerosol algorithms for the Terra and Aqua
platforms. Section 3.2 analyses the same type of results but from the
perspective of monthly values, since they represent the climatology of AOD
and the associated uncertainties. Section 3.3 shows AE behaviour and Sect. 3.4 analyses the comparison of satellite MODIS data in relation to broadband
aerosol optical depth from solar radiation.
Comparison of AOD retrievals from sun photometer and MODIS satellite instruments
As explained, we selected MODIS AODt (AODa) and sun photometer
AODSP data based on two different criteria for their comparison.
Results are shown in Tables 4 and 5, corresponding to collocated daily means
and single observations respectively. The values of all the statistics of
these two tables are extraordinarily similar, with analogous behaviour for
the different algorithm and platforms. In truth, no substantial differences
are found. It must be noted that Table 4 for collocated daily means contains
a third less data than Table 5 based on single observations. In contrast,
however, the latter data have a higher associated error than daily mean
data. This result cannot be foreseen a priori but clearly demonstrates that
either criterion may be taken, since the result is basically the same.
Statistics from comparing collocated daily means of
AODt and AODa with AODSP and the combined
AODta.
AODSP vs. AODtAODSP vs. AODaAODSP vs. AODtaDBDTDBDTDBDTRMSE0.0840.0600.0650.0620.0780.061MAE0.0620.0450.0460.0470.0560.046BIAS-0.053-0.001-0.0330.006-0.0460.002R0.7300.7290.7850.7790.7410.753f0.6560.8030.7630.7950.6940.800Cases311335169254480589
Collocated daily mean density scatter plots of the coincident
AOD observations from the sun photometer and Terra and Aqua MODIS
instruments for DB and DT algorithms: (a) to (c) show AODSP vs. AODt, AODa and AODta
respectively for the DB algorithm; (d) to (f) show the same for the DT algorithm. The data density is represented by the colour scale,
showing the number of data points located in a particular area of the plot.
Linear regression is given by the black discontinuous line and the
corresponding equation. The number of data points appears in the right
bottom.
Taking Table 5 together with Fig. 3 of collocated daily mean values, we
then analyse the different behaviours of the two algorithms for the Terra and
Aqua platforms. AODt (AODa) from the satellite are compared with
the sun photometer, AODSP. Figure 3 shows the density plots of the
collocated daily mean AOD values from the sun photometer vs. those of
MODIS instruments for Terra, Aqua and Terra+Aqua for DB (top plots) and DT
(bottom plots) algorithms. The least squares linear fit lines and equations
are also shown in the figure, while the correlation coefficients (R values)
are in Table 5. In general, the plots show that low-loading aerosols
predominate and that scatter increases for higher aerosol loadings, with a
slight overestimation of AODt (AODa) satellite data compared to
AODSP. In all cases, the slopes are between 1 and 0.9 and the
intercepts are of the order of 10-2 (with lower values for the DT
algorithm), showing very good values of these parameters for Terra and Aqua
for both the DT and DB algorithms.
Statistics from comparing collocated single observation
of AODt and AODa with AODSP and combined AODta.
AODSP vs. AODtAODSP vs. AODaAODSP vs. AODtaDBDTDBDTDBDTRMSE0.0810.0610.0630.0640.0760.062MAE0.0590.0460.0440.0500.0540.047BIAS-0.0480.007-0.0270.017-0.0420.010R0.7160.7010.8170.7940.7440.742f0.6640.7730.7730.7840.6990.777Cases88090041950012991400
Monthly means and statistics (RMSE, MAE… ) resulting from the comparison between AODSP and AODta for both
DB and DT algorithms: (a) monthly means of the AODSP and AODta for
both DB and DT algorithms, (b) RMSE for the comparison between AODSP and
AODta for both DB and DT algorithms, (c) the same for MAE, (d) BIAS, (e)R and (f)f.
The blue discontinuous line at f= 68 % represents
1 standard deviation confidence interval for the EE indicator.
Frequency distribution of the Ångström exponent (AE) values from
both MODIS instruments Terra and Aqua and the sun photometer are coincident in
±30 min and 25 km radius around Camagüey.
Figure 3 shows that the DT algorithm generally displays better behaviour than
the DB algorithm. The DT algorithm evidences more unified behaviour, as can be
seen for the slope values (0.96 for both Aqua and Terra), while DB changes,
giving a value above 1 (1.069) for Terra and below 1 for Aqua (0.901).
However, these differences are not very relevant since both algorithms give
almost identical R values, and the difference appears for the platforms
with higher values for Aqua than for Terra (∼ 0.78 and
∼ 0.73 respectively). A compensation effect can be observed
when data are combined, since in this case the slope of the DB algorithm is
closer to 1 than the DT algorithm, although the intercept is higher (closer
to 0 for the DT algorithm). For combined data, the two algorithms show more
similar behaviour than for separate Aqua or Terra results. Table 5 shows that
the magnitudes of the RMSE, MAE, BIAS and f statistics are lower for the DT
than for the DB algorithm (see the higher values of DB for Terra, column 1,
and the more similar values in the other columns). As mentioned, the values
of these four parameters show that the DT algorithm presents more unified
behaviour for both platforms than the DB, which has similar values for Aqua
but changes significantly for Terra.
Although the statistical numbers in the comparison depend on the area under
study, comparisons between areas are always possible. A recent validation of
MODIS Collection 6 AODa (Aqua), derived using the DB algorithm with
AODSP from six AERONET stations in Central and South America (CSA) and
seven in eastern North America (ENA), was reported by Sayer et al. (2013).
The number of pairs of collocated MODIS and AERONET daily averaged
observations for CSA (ENA) was 3032 (4155). Sun photometer data were
averaged within the 30 min MODIS overpass time and MODIS data were
averaged in the 25 km radius around the sun photometer site, which makes the
comparison appropriate. We selected the BIAS and R statistics in Table 1,
which were defined as in the present study (Sayer et al., 2013).
Statistics from comparing AEt,
AEa and AEta with AESP for single
observations and daily mean values.
Single observations Single observations Collocated daily means Collocated daily means (Except AE 1.5 & 1.8) (Except AE 1.5 & 1.8) AEtAEaAEtaAEtAEaAEtaAEtAEaAEtaAEtAEaAEtaRMSE0.6370.6920.6580.5750.6090.5870.6370.6590.6450.5480.5780.561MAE0.4940.5530.5160.4460.4960.4640.4900.5120.4980.4310.4660.445BIAS-0.327-0.337-0.331-0.129-0.101-0.119-0.398-0.384-0.393-0.189-0.139-0.167R-0.187-0.426-0.272-0.191-0.444-0.269-0.259-0.414-0.308-0.124-0.400-0.236Cases615374989353189542311169480172120292
Single observations density scatter plots of the coincident BAOD
observations from the broadband pyrheliometer and Terra and Aqua MODIS
instruments for DB and DT algorithms: panels (a) to (c) show BAOD vs. AODt and AODa
and AODta for the DB algorithm; (d) to (f) show the same for the DT algorithm. The data density is represented by the colour scale,
showing the number of data points located in a particular area of the plot.
Linear regression line is shown by the black discontinuous line and the
corresponding equation. The number of data points appear in the right
bottom.
We compare those statistics with the ones given in Tables 4 and 5,
calculated for Camagüey. The BIAS for the CSA (ENA) stations is -0.016
(0.0094), although those of Camagüey for both single observations and
collocated daily means are -0.027 and -0.033, thus showing higher values
for Camagüey, similar signs for CSA and the opposite signs for ENA. R
values for Camagüey for single observations and collocated daily means
are 0.82 and 0.79 respectively, which are lower by around 10 % (5 %) than the
R
values of 0.96 (0.86) for the CSA (ENA). However, it should be noted that
the number of cases used for the statistics at Camagüey was 419 for
single observations and 169 for collocated daily means, representing 6 %
and 14 % of the 3032 cases used in the cited study. In addition, none of
the stations in the CSA (ENA) regions were located in the Caribbean, but
were further south and north (Sayer et al., 2014). Despite the significant difference in the
number of cases used in both studies and the location of the six stations,
the results show reasonable agreement.
Monthly means values and statistics
Given the close similarity in the results from single observations and
collocated daily means data, it seems reasonable to evaluate monthly mean
values based on only one of them, i.e. for the collocated daily means data.
Figure 4 shows the monthly means (based on the mean of each month for every
year of the measured period) and the statistics resulting from the comparison
between AODSP and AODta for both the DB and DT algorithms. Tables S1 and S2
(see Supplement) also illustrate this comparison,
although they add separate information for Terra and Aqua (see Supplement). In Fig. 4a, the multi-annual monthly means from the combined
AODta and AODSP for both the MODIS DB and DT algorithm are shown,
providing an initial overview of aerosol AOD climatology in Camagüey. It can
also be seen that the DT algorithm gives the best match with monthly mean
AODSP.
Number of coincident cases of AODt, AODa, AODta
with BAOD both for the DB and DT algorithms.
StationBAOD vs. AODtBAOD vs. AODaBAOD vs. AODtaDBDTDBDTDBDTCamagüey1661716679232250Topes de Collantes1121384976161214Jovellanos6565353410099La Fe3466468580151All combined377440196274573714
The monthly RMSE and MAE plots in Fig. 4b and c generally show
increases, with an increase in the AODta for the DT algorithm and also
for the DB algorithm. The exception is the minimum in April for the DT
algorithm (this means greater differences between satellite and
sun photometer in summer than in winter). These results are consistent with
the fact that the AOD uncertainty depends on the AOD itself (see Eq. 3) and
greater AOD variability in summer. The AODta peaks for the DT algorithm
in March in both RMSE and MAE are also present in the results for AODt
and AODa separately, and the number of cases available for the
statistics is among the highest of all the months seen in Tables S1 and S2. In Table S2, for the DT algorithm, we can see
that the number of cases of AODta from March to April drops by 55 %.
However, something similar happens for the DB algorithm in Table S1, with
the number of AODta cases falling from March to April by 61 %.
Sampling cannot therefore be seen as the cause of the RMSE and MAE peaks for
the DT algorithm. We plan to revisit this feature in future studies. In
summer, the RMSE and MAE show their maximum values associated with the maximum
values of the AOD resulting from Saharan dust reaching Cuba from across the
Atlantic. The BIAS is negative in summer for both Terra and Aqua AOD,
showing that AODt and AODa observations have higher magnitudes
than AODSP.
Tabulated results of the comparison between AODt, AODa and
AODta with AODSP on a monthly scale also show better results for
the DB (see Table S1) than for the DT (Table S2) algorithm. Here, we only
discuss the results of the joint AODta data set using both the DT and DB
algorithms. In Fig. 4d, the BIAS for the DT algorithm is positive from
December to May, a period of the year with predominantly lower AODta and
AODSP values. During this period, AODta underestimates AODSP.
BIAS then becomes negative from June to November, which is when Saharan dust
reaches the Caribbean basins. At the same time, the BIAS of the AODta
derived with the DB algorithm is negative for the whole year, with higher
absolute values than those from the DT algorithm.
Statistics from comparing the single observations of BAOD
at the four actinometric stations coincident in space and time with the
single observation (L2) of AODt, AODa and
AODta. In bold are the values of best agreement.
Camagüey, La Fe, Topes de Collantes & Jovellanos BAOD vs. AODtBAOD vs. AODaBAOD vs. AODtaDBDTDBDTDBDTRMSE0.0800.0870.0730.0880.0780.088MAE0.0550.0630.0480.0660.0520.064BIAS0.0010.0270.0140.0490.0050.035R0.4550.3250.5010.4170.4680.355Cases373436191268564704
The correlation coefficient, R, in Fig. 4e is the statistic which shows
almost the same agreement for the DB and DT algorithms. However, the DT shows
a higher number of R values bearing higher magnitudes. R magnitudes remain
over 0.5 almost the whole year round, except in December and January when
lower AOD values occur.
Figure 4f shows the fraction of the AODta (f) in agreement with
AODSP within the expected uncertainty, showing higher values over
80 % from November to January, in general for both algorithms. This is
the period of the year with the lowest monthly mean values of both
AODta and AODSP. During the rest of the year, including the period
of the Saharan dust arrivals, it shows its lowest values between 60 and
75 % for the DT algorithm, while values for DB below 50 % occur in four
of the months between June and October. The discontinuous blue line at
f= 68 % denotes a 1 standard deviation confidence interval, which is selected to
describe EE. The f values above that value mean the algorithm works better
than expected. All the statistics demonstrate that the DT algorithm performs
better than the DB for the region of study. However, the lowest R values for
those months with the highest f values would seem to be contradictory. At
present, we have no explanation for this.
Comparison of Ångström exponent by sun photometer and MODIS satellite instruments
Figure 5 shows the frequency distribution of the coincident AESP with
both AEt and AEa using the DB algorithm, as explained. As can be
seen in the literature, the Ångström exponent varies between 0 and
2. Our Ångström exponent data obtained from the AERONET sun
photometer measurements are within this range, with a wide and smooth
frequency distribution of values and with a not well-defined maximum in the
range 1.2–1.6. Neither AEt nor AEa present any real
distribution shape because there are practically no values below 1.
Most are around AE = 1.5, followed by a second maximum at AE= 1.8. The
first, 1.5, is a regional default value for AEt and AEa (Hsu et
al., 2013; Sayer et al., 2013) assumed by the DB algorithm in the case of
low AOD values (AODt or AODa < 0.2). The second is
associated with the fact that the AEt and AEa values allowed by
the aerosol optical models in Collection 6 are constrained between 0 and 1.8
to avoid unrealistic values (Sayer et al., 2013).
Table 6 shows the results of the comparison of coincident AEt, AEa
and AEta with ESP. For both single observations and collocated
daily mean data the statistics were calculated for the two options: the
first including all values and the second excluding cases with AE = 1.5 and
1.8. The statistics in Table 6 present similar values
for those derived by single observation and for collocated daily mean
values. This was expected once we knew the results for AOD, although similar values
also appear for Terra and Aqua (no clear distinction appears between Terra
and Aqua). These statistics present very high values if compared with those
shown for AOD. The R correlation coefficient presents very low
values, which are below 0.5 (the poor correlation is observed in the scatter
plots similarly to those in Fig. 6, not shown here). Excluding AEt and
AEa values equal to 1.5 or 1.8 entails no substantial difference, only
lower BIAS values. Overall, the results of the comparison showed the low
quantitative skill of the AEt and AEa for this site. One factor
contributing to this result is that the AE from the MODIS DB algorithm
displays great uncertainty for low-AOD conditions, since AE is obtained as a
gradient between two small AOD numbers (Wagner and Silva, 2008).
Comparison of AOD between MODIS products and BAOD for the four Cuban actinometric stations
Two main facts limit the number of available BAOD values coincident in time
with AODt and AODa: the hourly time step between manual DNI
observations and the required condition of a clear line of sight between the
pyrheliometer and a region of 5∘ around the sun. Consequently,
only one BAOD measurement could coincide each day with AODt and
another with AODa given the time coincidence criteria. Table 7 lists
the number of coincident AODt, AODa, AODta observations in
space and time with BAOD for the DB and DT algorithms for each
of the actinometric stations. Since the number of coincident observations
at each station is low, we decided to combine all the pairs of AODt,
AODa and AODta coincident with BAOD in the four sites
in order to conduct the comparison. In addition, we did not
consider the very few cases with values of BAOD > 0.6, around 1 % of all cases, so as to avoid the possibility of inadvertent cloud
contamination.
Table 8 contains almost the same statistics used in previous comparison
satellite-sun photometer data (see Tables 4 and 5), both for the DB and
DT algorithms for the four actinometric stations together. The only
statistic not included in Table 8 is f, the fraction of the MODIS/AERONET
AOD retrievals in agreement within the expected uncertainty, because this
uncertainty still has to be established for BAOD. We highlighted the best-performing algorithm in bold for each of the statistics. The AODa
derived with the DB algorithm performs better than the other three
combinations of AODt, AODa, for DT and DB in accordance with all
four statistics, except for BIAS, where the best-performing algorithm is still the DB
algorithm, but for AODt. However, in general, and taking into account
the low amount of data and the fact that we have single observations, the
RMSE, MAE and BIAS for AODt, AODa, AODta derived with both DB
and DT algorithms remain in the same order of magnitude as in Tables 4
and 5, with the exception of the low values of the correlation coefficient
R. The BIAS shows almost similar behaviour except for its best-performing
value. This contrasting behaviour of algorithms and platforms with respect to
the earlier results of Tables 4–5 is clearly shown in Fig. 6, where the
scatter plots of the BAOD vs. AODt, AODa, and AODta are
depicted. What is clear is the poor correlation given by the very low values
of the slope with respect to value 1 and also the relatively high values
of the intercept in relation to 0. Hence it results in low values of the
R coefficient. BAOD shows a high uncertainty for low values of AOD (below
2; see this range over the x axis in the plots), which are those prevalent in
this area (l).
Conclusions
This study addresses the comparisons of different sources of AOD and AE from
a ground-based sun photometer (AERONET level 2.0 data), MODIS instruments
(Terra, Aqua, and Terra + Aqua) and retrievals from direct normal solar
irradiance observations in Cuba.
The comparison of spatial and temporal coincident single observations and
collocated daily means of AODt, AODa, AODta vs. AODSP
shows, in general, a better performance for the dark target (DT) than for
the deep blue (DB) algorithm for Camagüey. In particular we found (1) small differences between AODt and AODa, thus
justifying the combination of these observations in a single data set for
climatological studies; (2) both DT and DB algorithms are better than
expected (f around 80 %) between November and January, but in other months
f is of the order of 1 standard deviation (f= 68 %) for DT and
significantly lower for DB; (3) from linear correlation analysis, MODIS
slightly overestimates AOD compared to the sun photometers; and (4) data from
both MODIS instruments are well correlated with AERONET AOD with regression
slopes close to 1 and the DT algorithm outperforming the DB algorithm. In
addition, the comparison of multi-annual monthly means of AODta with AODSP
indicate better agreement with results from the DT algorithm (compared to
DB), consistent with the findings above.
The Ångström exponents AEt, AEa and AEta do not
show good agreement with the spatial and temporal coincident AESP
values when the default -1.5 and the constrained -1.8 values are or are
not considered. Those results corroborate the limited skills of the MODIS-derived AE, as indicated in previous studies.
In the comparison of BAOD vs. AODt, AODa, AODta, where only
individual observations can be compared, the statistics indicate larger
uncertainties but of the same order of magnitude as the statistics of
MODIS photometer. Although correlations are very poor, these results support
the potential for BAOD to be a reliable source of aerosol information for
climatological studies in areas that lack a sun photometer or any other
surface aerosol measurement.
Data availability
MODIS Aerosol Product MxD04 Version 6.0 DOI information and links for downloading can be accessed from the MODIS Data
Product at the NASA Goddard Space Flight Center (GSFC) from https://modis.gsfc.nasa.gov/data/dataprod/mod04.php (NASA, 2018a).
Sun photometer aerosol observations for Camagüey, Cuba, from the AErosol RObotic NEtwork (AERONET) from Goddard Space Flight Center
(GSFC) are available from http://aeronet.gsfc.nasa.gov (NASA, 2018b).
The supplement related to this article is available online at: https://doi.org/10.5194/amt-11-2279-2018-supplement.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
This work has been supported by the Cuban National Program “Meteorology and
sustainable development for Cuba” research grant P211LH007-20 and by the
joint agreement between the University of Valladolid, Spain, and the Cuban
Meteorological Institute for aerosol research. Juan Carlos Antuña-Marrero wishes to thank
Loraine Remer and Andrew Sayer for their contributions to understanding
MODIS algorithms. This research has received funding from the European
Union's Horizon 2020 Research and Innovation Program under grant agreement
no. 654109 (ACTRIS-2). We acknowledge the funding provided by MINECO
(CTM2015-66742-R) and by the Junta de Castilla y León (VA100U14).
Edited by: Marloes Gutenstein-Penning de Vries
Reviewed by: two anonymous referees
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