AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-53-2016Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networksGarcíaR. D.rgarciac@aemet.eshttps://orcid.org/0000-0002-9451-1631GarcíaO. E.CuevasE.https://orcid.org/0000-0003-1843-8302CachorroV. E.BarretoA.Guirado-FuentesC.https://orcid.org/0000-0002-4578-8204KouremetiN.BustosJ. J.Romero-CamposP. M.de FrutosA. M.Izaña Atmospheric Research Center (IARC), Agencia Estatal de Meteorología (AEMET), Santa Cruz de Tenerife, SpainAtmospheric Optics Group, Valladolid University, Valladolid, SpainCimel Electronique, Paris, FrancePhysikalish-Meteorologisches Observatorium, Davos, World Radiation Center, Davos, SwitzerlandR. D. García (rgarciac@aemet.es)15January201691536223July20153September201518December201521December2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/53/2016/amt-9-53-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/53/2016/amt-9-53-2016.pdf
This paper presents the reconstruction of a 73-year time series of the
aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain
Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands,
Spain). For this purpose, we have combined AOD estimates from artificial
neural networks (ANNs) from 1941 to 2001 and AOD measurements directly
obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The
analysis is limited to summer months (July–August–September), when the
largest aerosol load is observed at IZO (Saharan mineral dust particles). The
ANN AOD time series has been comprehensively validated against coincident AOD
measurements performed with a solar spectrometer Mark-I (1984–2009) and
AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO,
obtaining a rather good agreement on a daily basis: Pearson coefficient, R,
of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD
estimates. In addition, we have analysed the long-term consistency between
ANN AOD time series and long-term meteorological records identifying Saharan
mineral dust events at IZO (synoptical observations and local wind records).
Both analyses provide consistent results, with correlations > 85 %.
Therefore, we can conclude that the reconstructed AOD time series captures well
the AOD variations and dust-laden Saharan air mass outbreaks on short-term
and long-term timescales and, thus, it is suitable to be used in climate
analysis.
Introduction
Solar radiation reaching the Earth's surface (SSR) plays a key role in our
climate and environment. In the last decades, numerous analyses have
demonstrated that SSR records have not been constant over time, but have
undergone climatologically significant decadal variations (e.g.
). From the 1930s
to the early 1950s the few data available suggest an increase of the SSR in
the first part of the 20th century, known as early brightening. This period
is followed by a widespread period of reduced solar radiation from the 1950s
to the end of the 1990s. This effect, extensively reported by the literature
at a global scale, is known as dimming, with a general decline between 4 and
6 % decade-1 considering worldwide distributed stations. Recently, a gradual
increase of the SSR has been documented, known as brightening, with trends
between +1 and +11 % decade-1 from the 1980s onwards
.
The causes of these phenomena are not fully understood currently, but it has
been pointed out that changes in the transmissivity of the Earth's atmosphere
play a significant role. These changes might be due to changes on global
cloud cover and atmospheric aerosol concentrations. found that
the changes are observed under all cloud-cover conditions, thus probably the
most important cause is the aerosol effects . In this
context, the study of the spatial and temporal variability of atmospheric
aerosols at sites in background conditions can offer crucial insights to
account for their key role on the observed SSR trends. For this purpose,
reliable long-term series of aerosol content and properties are fundamental.
However, these long-term series are only available typically since the middle
of the 1970s, due to the poor data quality and changes in measurements
methodology before this date. There are few studies treating aerosol
long-term series in the literature. The longest available series are those of
normal direct irradiance measured at various stations in Russia, Ukraine and
Estonia covering together a 102-year period (1906–2007) with which the
atmospheric transparency has been estimated .
estimated aerosol optical depth (AOD) combining broadband direct and diffuse
irradiance measurements performed at Tsukuba, Japan, from 1975 to 2008.
and studied long-term series of AOD from sun photometry at Mauna Loa since 1976, and derived AOD from
solar irradiance measurements at Izaña Atmospheric Observatory (IZO)
since 1976. All of these studies are based on solar spectrometry, but a
different approach is needed to obtain longer AOD time series.
One of the most powerful tools used in science in the last decades are the
artificial neural networks (ANNs). The ANNs have been employed in diverse
applications and fields such as robotics, pattern recognition, forecasting,
medicine, power systems, etc. In atmospheric science the use of ANNs is quite
recent, for example, ANNs have been successfully used for estimating solar
radiation values
or cloud properties
. However, their use for AOD estimations is quite
recent and limited to short periods. For example, estimated
AOD values from all-sky images at Granada (Spain) between 2005 and 2006,
finding uncertainties of 0.019 and 0.014 for AOD at 440 and 670 nm,
respectively, by comparing with AERONET (AErosol RObotic NETwork;
http://aeronet.gsfc.nasa.gov) AOD observations. Also,
used ANNs to obtained AOD from global, diffuse and
direct normal irradiance in Granada between 2006 and 2008. They found
uncertainties of ∼13 % with respect to AERONET AOD values.
(a) MODIS/Terra image showing a strong Saharan dust outbreak over
the study area (the Canary Islands) on 25 June 2012; (b) vertical
cross section of Tenerife with a scheme of the vertical atmospheric
stratification (marine boundary layer (MBL), inversion layer, and free
troposphere) and the main atmospheric flows affecting IZO (NW clean
subtropical subsident air masses, low-level NE trade winds, and E–SE Saharan
dust intrusions). This figure has been adapted from
.
In this context, the goal of this paper is to estimate the long-term AOD time
series of Saharan mineral dust events at IZO and to document its quality and
long-term consistency by a comprehensive validation study. This has been done
by using ANN techniques and, as input parameters, in situ meteorological
observations performed at IZO between 1941 and 2001. The estimated ANN AOD
time series has been completed with AOD observations from sun photometry since
2003. Given the strategic location of IZO, very close to the Saharan desert,
the reconstructed ANN AOD time series provide interesting clues on the
intensity and the interannual and interdecadal variability of Saharan dust
outbreaks over the North Atlantic. This might have important implications for
climate analysis. To address this study, this paper has been divided as follows. Section describes the main characteristics of the
site where the ANN AOD estimates have been obtained, while Sect.
presents the architecture, training process and input parameters used to
select the optimal ANN configuration, as well as an error analysis of ANN AOD
estimations. Section shows the validation of ANN AOD
estimates with coincident AOD measurements, whereas the comparison between
long-term ANN AOD and meteorological records is addressed in
Sect. . Finally, a summary and the main conclusions are given in
Sect. .
Description of site and aerosol conditions
Izaña Atmospheric Observatory (http://izana.aemet.es) is a
high-mountain observatory located in Tenerife (Canary Islands) at
28.3∘ N, 16.5∘ W, 2373 ma.s.l., and situated
approximately 300 km west from the African coast (Fig. a). IZO is
managed by the Izaña Atmospheric Research Center (IARC) which forms part
of the Meteorological State Agency of Spain (AEMET).
The observatory is located above a strong subtropical temperature inversion
layer, which acts as a natural barrier for local pollution
(Fig. b). In addition, IZO is affected by a quasi-permanent
subsidence regime typical of subtropical latitudes, therefore the air
surrounding the observatory is representative of the background free
troposphere (especially at night-time). The combination of these two features
makes IZO excellent for in situ and remote sensing atmospheric measurements
and those features highlight the historical importance of the site. The first
meteorological observations date from 1916 . In 1984 IZO
became a World Meteorological Organization (WMO) Background Atmospheric
Pollution Monitoring Network (BAPMon), and afterwards (1989), a Global
Atmosphere Watch (GAW) station. IZO has been part of NDACC (Network for the
Detection of Atmospheric Composition Change) since 2001, and has actively
contributed to international aerosols and radiation networks such as GAW/PFR
(Precision Filter Radiometer Network) since 2001, AERONET (Aerosol Robotic
Network) since 2004 and BSRN (Baseline Surface Radiation Network) since 2009.
In 2014, IZO was appointed by WMO as a CIMO (Commission for Instruments and
Methods of Observation) Testbed for Aerosols and Water Vapour Remote Sensing
Instruments .
The typical background free troposphere conditions at IZO are only
significantly modified in summer, mainly in July and August, when the most
intense and relatively frequent Saharan air mass outbreaks in the subtropical
free troposphere reach the observatory .
During these months, Saharan
dust long-range transport over the Atlantic that can reach the Caribbean is
driven by incursions of the so-called Saharan air layer (SAL) over the North
Atlantic and references therein.
In order to discriminate these two atmospheric conditions at IZO (clean free
troposphere and presence of the SAL) we have combined AOD and
Ångström exponent (α) information. While AOD provides the
overall solar extinction effect of aerosols, α characterizes the AOD
spectral variation, which is related to the aerosol median size
. High α values indicate fine particle predominance,
while low α values are related to coarse particles .
Figure illustrates an example of AOD–α
distributions, showing the daily AOD time series at 500 nm labelled with the
corresponding daily α values since AERONET records are available at
IZO (2004 onwards). Values of AOD ≤ 0.10 and α≥ 0.75 (zone I,
63 % of days), correspond to background conditions, while values of
AOD ≥ 0.20 and ≤ 0.50 (zone II, 9 % of days), are associated with
Saharan dust episodes. Finally, the zone III, characterized by
0.10 < AOD < 0.20 and 0.50 <α<0.75 (28 % of days), describes the
periods of transition between these two patterns. As observed, the Saharan
dust events at IZO are mainly detected in summer months (July, August and
September) with a median AOD value of 0.13 ± 0.02 and α of
0.47 ± 0.03, in contrast with the clean conditions observed in other
months (median AOD and α of 0.05 ± 0.01 and 1.17 ± 0.02,
respectively, for zone I and median AOD and α of 0.07 ± 0.02 and
0.67 ± 0.03, respectively, for zone III). Therefore, in this work, we have
limited the ANN AOD estimation to summer months in order to assess the
long-term variability of Saharan outbreaks over the subtropical Eastern North
Atlantic. Note that, hereafter, we use AOD medians instead of means because
the AOD values dramatically change by orders of magnitude from background
conditions to dusty conditions. The errors are given as ±1 SEM (standard
error of the median).
Daily AOD at 500 nm time series from AERONET between 2004 and 2013
at IZO. The colour scale indicates the daily Ånsgtröm exponent (α)
values. The dashed lines distinguish the different AOD–α zones: (I)
AOD ≤ 0.10 and α≥ 0.75; (II) AOD ≥ 0.20 and
α≤ 0.50, and (III) 0.10 < AOD < 0.20 and
0.50 <α<0.75.
Artificial neural networks (ANNs)
ANN is a statistical data modelling tool, inspired by the human brain, capable
of simulating highly nonlinear and complex relationships between inputs and
outputs by a learning process, the so-called training process. This tool
mainly consists of three layers of neurons: the input layer groups the input
data in the input vector p and connects them with the hidden layer. In this
layer the input vector is transformed into a net input vector, a′, by using
adaptive weights, Wh, biases, bh, and a transfer function,
TFh, such as a′= TFh(n), where n= (Whp+bh). Then, the
hidden layer is connected with the output layer, in which the outputs
obtained in the previous step, a′, are transformed into the net input for the
output layer, n′= (Wouta′+bout). Finally, the output transfer
function, TFout, is applied to n′ to obtain the final output of the ANN,
aand references therein. The weights and biases used both in
the hidden (Wh and bh) and in the output layer (Wout and
bout) were previously computed in the training process.
In this work, the ANNs have been implemented by using the Matlab Neural
Network Toolbox with the architecture shown in Fig. 3: the
input parameters of the input layer are different meteorological observations
taken at IZO (Sect. details the selection of these inputs), and
the hidden layer is made up of 30 neurons with a transfer function defined by
the hyperbolic tangent function:
φ=tanh(n)=e2n-1e2n+1,
where n is the corresponding net input. The hyperbolic tangent is
one of the most used transfer function in ANN, since it successfully combines
a fast learning rate with reliable results . Finally,
the output layer has one neuron with the linear transfer function, which is
often used in forecasting and approximation tasks .
Schematic representation of the artificial neural network
used in this study, where pi=pi (Ndi, VISi, FCSi,
RHi, Tempi) with i=1,…,N, with N being the total number of
observations and a= ANN AOD.
Training process
The learning or training procedure plays a key role in the ANN design and
setting. In this process a set of inputs with known outputs (targets) are
used to calculate the weights (Wh, Wout) and biases (bh,
bout) to be applied in the neural network, as explained in the previous
section.
The first step on this process is to randomly divide the set of known inputs
and target values into three different subsets: training (70 % of the
data), validation (15 % of the data) and test (15 % of the data). The
weights (Wh, Wout) and biases (bh, bout) are computed for
each neuron. Then the validation subset is used to estimate the error by
comparing the obtained outputs with the targets of the validation subset. The
computation of weights and biases and the subsequent error estimation is
iteratively repeated until the error is lower than a required value or if the
assignation of new weights and biases does not decrease the error. In this
work the estimation of the error is supervised by the Levenberg–Marquardt
optimization algorithm, which has proved to be efficient and fast for small
and medium-sized networks, such as the architecture used here (Foresee and
Hagan, 1997; Hao and Wilamowski, 2011). The mentioned error is computed by
the mean square error (MSE) defined by the following equation:
MSE=1n∑i=1N(ti-ai)2,
where N is the dimension of the validation subset, ti the targets in the
validation subset and ai the ANN outputs obtained from the validation
subset inputs. Finally, the test subset, not used in the training process, is
used to check the quality of the obtained ANN by applying it to “clean”
inputs, that is, inputs and targets not used in the training process
.
Given that the division of known data in training, validation and test
subsets is random, we have repeated the training process 1000 times. Then,
the best ANN is selected as the one showing the highest Pearson correlation
coefficient (R), slope closest to 1 and lowest intercept with respect to
the known outputs .
The AOD measurements used to train the ANN were performed with one of the
most accurate and stable instruments dedicated for atmospheric aerosol
monitoring, a Precision Filter Radiometer (hereafter PFR AOD), developed at
the World Radiation Center Physikalisch-Meteorologisches Observatorium
(www.pmodwrc.ch). It was installed at IZO in the framework of a high-precision world network for AOD characterization and monitoring (GAW/PFR) in
June 2001, but continuous observations are only available since 2003. The PFR
measures direct solar radiation, with a field of view of 2.5∘, at
862, 500, 412 and 368 nm. The AOD is estimated at all these wavelengths with
an expected uncertainty of ±0.01 . In this study, we have
used Level 3.0 of Version 3.0 AOD at 500 nm.
Input parameters and ANN AOD estimates
The other critical step in the ANN design is the selection of an appropriate
set of input parameters, since they should be able to adequately capture the
actual variability of the target. A parameter describing the extinction of
the solar radiation similarly to AOD is the horizontal visibility (VIS). The
VIS is estimated by human observations (by observers) manually as the maximum
distance at which the naked eye of an observer can distinguish a
predetermined marker object (a building, a mountain, etc.) from the
background . Therefore, it is very sensitive to the extinction
of radiation by atmospheric aerosols – , and references
therein – but also by the presence of hydrometeors (snow, fog, rain, …) and
clouds. To minimize the latter impacts on the variability of the VIS, we only
work with cloud-free days filtered with an average sky cover of 0 oktas.
Furthermore, we have introduced the fraction of clear sky (FCS) defined as
the ratio between SD performed with a Campbell Stokes sunshine recorder
and the maximum daily sunshine duration SDmax to
account for the remaining variability introduced by the presence of clouds,
fog, etc. The introduction of FCS in the ANN training allows the ANN to
discriminate the patterns associated with possible residual cloud cover for
the days with oktas = 0. The cloud-free days for the study were selected by
considering a median number of oktas equal to zero, but this value was
calculated from only three observations per day. Thereby, some episodes with
cloud contamination are likely and, as pointed out by the referee, this
residual cloud contamination could give artificial ANN AOD values.
Times series of the ANN AOD monthly medians (July, August and
September) at 500 nm between 1941 and 2009 at IZO. Shadings show the range
of ±1 SEM (standard error of the monthly median).
To complete the characterization of the meteorological conditions, we have
considered the relative humidity (RH) and temperature records (T). The latter
inputs are only available every 3 h between 06:00 and 18:00 UTC, thus we
have calculated the daily medians. Finally, to account for the seasonal
variation of each parameter we have also introduced the day of year (Nd) as
input parameter. The time series at IZO are from 1916 up to now for T and RH,
from 1921 to present for FCS, and from 1941 to 2009 for VIS. Therefore, the
latter time series determines the period in which the ANN AOD time series can
be estimated with ANN techniques: 1941–2009. These data were taken from the
AEMET climatological database (http://www.aemet.es).
In order to study the relative importance of each input parameter and select
the best configuration, several combinations of the input parameters has been
trained, validated and tested in the period 2003–2009 (period with coincident
PFR AOD and input parameter measurements). The different combinations
considered were: (A) Nd and VIS; (B) Nd, VIS and FCS; (C) Nd, VIS, FCS and
RH; and (D) Nd, VIS, FCS, RH and Temp. As observed in Table 1, the VIS and
FCS are the most critical parameters determining ∼ 90 % of the observed
AOD variance, although the maximum agreement is achieved when the RH is also
taken into account as input parameter (setup C). This configuration accounts
for 98 % of the actual AOD variability with a slope of 0.99 and intercept
of -0.01 between the measured and estimated AOD values. By applying this
setup, we have obtained the daily ANN AOD time series between 1941 and 2009
at IZO, which is displayed in Fig. on a monthly basis.
Parameters of the least-square fit (Pearson correlation coefficient,
R, slope and intercept) between the measured PFR AOD and the estimated ANN
AOD using different configurations of input parameters. The setup selected is
highlighted in bold.
To analyse how the ANN AOD estimates could be affected by uncertainties in
the input parameters used, we have performed a two-step theoretical error
estimation. Firstly, AOD estimations were conducted using the measured values
for all parameters described in the previous section, obtaining the
non-perturbed values (AOD). Secondly, the same sample was simulated again by applying the typical uncertainties of the inputs parameters
reported in the literature, ±5 % for FCS and
±2 % for RH . For the horizontal visibility we have
assumed a very conservative error of ±25 %. Note that the day of year
has been omitted from this analysis.
This strategy was applied to all cloud-free days (oktas = 0) between 2003 and
2009 in order to detect random and systematic behaviours in the error time
series (AOD ±δ) . As the theoretical error
distributions have not shown dependence either on the input parameters or on
the ANN AOD values (correlation is not significant at 95 % level of
confidence), the systematic and random errors have been calculated as the
median and the standard deviation of the corresponding error distributions.
The results of our error analysis are summarized in Table , where
the two prevalent atmospheric situations observed at IZO have been
distinguished: free-troposphere background conditions with AOD ≤ 0.10 and
α≥ 0.75, and Saharan dust events with AOD ≥ 0.20 and
α≤ 0.50. As expected, the uncertainties of the FCS and VIS
dominate the random and systematic error budgets for all the AOD ranges. For
AOD ≤ 0.10 the scatter reaches 0.12 and the systematic error is -0.02,
while for AOD ≥ 0.20 we obtain a scatter of 0.17, and a bias of 0.03. When
considering all the AOD range and all the input parameter errors, the overall
uncertainty is expected to be less than 0.15 (SD), with a positive bias of 0.03.
Validation of ANN AOD estimates
The ANN AOD estimates have been validated with coincident AERONET CIMEL
photometers of Level 2.0 AOD (cloud screened and quality ensured) from 2004 to
2009, and with a long-term AOD at 769.9 nm data series retrieved by
from a solar spectrometer Mark-I for the period 1975–2012.
CIMEL photometers retrieve AOD measurements at different wavelengths between
340–1640 nm from direct Sun observations under cloud-free conditions, with
an expected uncertainty of 0.01 at 500 nm for field instruments
. The validation procedure of Mark-I AOD time series was
performed by , showing a root-mean-square error of 0.022
(R= 0.94) and 0.034 (R= 0.92) in comparison with the PFR reference and AERONET
master instruments, respectively. In order to compare the Mark-I AOD values
at 769.9 nm and the ANN AOD estimates at 500 nm, we have extrapolated the
ANN AOD values from 500 to 769.9 nm by using the Ångström law
and the α data retrieved from PFR observations.
For Mark-I we have used the AOD records since 1984 when the observations
start to be seamlessly performed.
Statistics of the difference between non-perturbed and perturbed ANN
AOD estimates (AOD - (AOD ±δ)): Pearson correlation coefficient (R)
between the differences and ANN AOD values, standard deviation (SD) and
median of the difference time series (systematic bias). “All” represents the
error estimation considering the uncertainties of all parameters together
(VIS ± 25 %, FCS ± 5 % and RH ± 2 %).
The straightforward comparisons between AOD observations and estimates show a
good agreement for the daily values with ∼ 94 % (R= 0.97) of the
variance in agreement between AERONET AOD and ANN AOD, and 85 % (R= 0.93)
between Mark-I AOD and ANN AOD values (Fig. a and b). When
considering monthly values the agreement increases, achieving a correlation
of 96 and 98 % with Mark-I/ANN and AERONET/ANN, respectively. Although
the comparison with the Mark-I AOD records shows a poorer agreement, both
inter-comparisons behave similarly. We observe that the ANN AOD estimates
have been shown to be dependent on the AOD range (see Table ),
confirming the results obtained in the theoretical error estimation
(Table ). For low AOD, the ANN AOD values tend to overestimate
compared with the observed AOD values (median bias of ∼ 0.01–0.02), but
the contrary behaviour is observed for high AOD (underestimation by
0.01–0.03). However, the overall ANN AOD/ Mark-I AOD scatter (0.06)
duplicates that observed for the ANN AOD/AERONET AOD comparison (0.03). This
agreement is within the AOD uncertainty of Mark-I and
within our error estimation (Table ). Notice that the
experimental scatter is significantly smaller than the theoretical one,
suggesting that our assumed uncertainties could be very conservative.
Therefore, in summary, we consider that the ANN AOD values capture well the
day-to-day AOD variability and successfully identify Saharan mineral dust
events at IZO.
Statistics for the difference between AOD observations and ANN AOD
estimates for different AOD ranges. The series of differences between Mark-I
AOD and ANN AOD is at 769.9 nm in the period 1984–2009 and between AERONET
AOD and ANN AOD at 500 nm in the period 2004–2009. N is the number of data
and R is Pearson correlation coefficient. The bold values represent the R,
random bias and systematic bias considering all AOD range for AERONET and Mark-I,
respectively.
Scatterplot of ANN AOD estimates vs. (a) daily Mark-I AOD at
769.9 nm for all the cloud-free days (oktas = 0) and (b) daily AERONET AOD at
500 nm for the periods 1984–2009 and 2004–2009, respectively. The black
solid lines are the least-square fits and the dotted lines are the diagonals.
The least-square fit parameters are shown in the legend (Pearson correlation
coefficient, R, slope and intercept). (c) Time series of monthly medians of
Mark-I AOD and ANN AOD estimates in July (on the left axis) and time series
of the differences between these AOD values (on the right axis). Shadings
show the range of ±1 SEM (standard error of the monthly
median).
(a) Time series of the number of days grouped into ANN AOD intervals
(AOD ≥ 0.05; AOD≥ 0.10; AOD ≥ 0.20) on the left axis, while on the
right axis, the bars indicate the number of days with SYNOP data reporting
dust in suspension (05–06 SYNOP codes) for the period 1941–2009. The
5-year running mean is shown in black. (b) Scatterplot of number of days with ANN
AOD ≥ 0.20 and number of days with 05–06 SYNOP codes. The least-square fit
parameters are shown in the legend. (c) Time series of the ANN AOD monthly
medians (blue line) and monthly percentage of time the wind blows from the
second quadrant (E–S; 90–180∘) (black line) at IZO in July
in the period 1941–2009. (d) Percentage of time (y axis) the wind blows from
in each one of the four quadrants vs. the ANN AOD monthly medians
(x axis). R indicates the Pearson coefficient.
The long-term Mark-I AOD time series also allows us to analyse the temporal
consistency of the ANN AOD estimations by examining possible drifts and
discontinuities in the monthly time series of the differences between ANN AOD
and Mark-I AOD for July, August and September. A drift is defined as the
linear trend of monthly median bias (measurements–estimations), while the
change points (changes in the median of the bias time series) are analysed by
using a robust rank order change-point test . The
Lanzante's procedure is an iterative method that applies a (single)
change-point test, based on summing the ranks of the values from the
beginning to each point in the series, and followed by an adjustment step
(the median computed for the segments enclosed by the identified change
points is used to adjust the series). In the subsequent iteration the
change-point test is applied to the adjusted series and the iterative process
finishes when the significance of each new change point is less than an a
priori specified level.
By applying this change-point test we identify 1997 as the change point in
the monthly median bias time series (see Fig. c), caused by the
horizontal visibility records. Although this discontinuity is significant at
99 % confidence level, the difference of median bias is rather small
(-0.013 ± 0.001 for the 1984–1997 period and +0.006 ± 0.003 for the 1998–2009
period) and within the ANN AOD and Mark-I AOD expected uncertainties.
Furthermore, we observe that there are no significant drifts in the bias time
series either before or after this systematic change point at 99 % of
confidence level. For the other months, August and September, the monthly
median bias time series have shown neither significant systematic change
points nor temporal drifts. These findings indicate that the ANN AOD
estimates are consistent over time and, thus, valid to reconstruct the AOD
time series at IZO.
Comparison of long-term ANN AOD with meteorological records
We have analysed the long-term variability of ANN AOD time series by
comparing with long-term meteorological records identifying Saharan dust
events at IZO. On the one hand, we have compared the number of days in which
estimated ANN AOD values fall within different AOD intervals with the number
of days in which the meteorological observers reported presence of dust in
suspension (05–06 SYNOP codes, ) at IZO during the dust season
(July–September) since 1941 (see Fig. a and b). On the other
hand, locally at the observatory, when haze or dust in suspension is reported
by the observers, the wind normally blows from the second sector
(90–180∘) (Fig. c). Therefore, we have analysed
the relation between the monthly AOD medians in July (month with the maximum
frequency of Saharan dust events at IZO in the study period) and the monthly
percentage of time the wind is blowing from each of the four quadrants for
the period 1941–2009. Both analyses provide consistent results. On the one hand,
we found that the number of days with 05–06 Synop codes time series agrees
with the number of days with ANN AOD ≥ 0.20 time series (R= 0.89). On the
other hand, a high correlation (R= 0.86) between the ANN AOD monthly medians
and the percentage of time the wind blows in the second quadrant is observed,
whilst no correlation at all is found in the other three quadrants (R of
0.24, 0.16 and 0.14, for the first, third and fourth quadrants, respectively)
(see Fig. c and d). These results show that the reconstructed ANN
AOD series correlates well with other series of independent atmospheric
parameters, confirming its consistency in this long period (1941–2009), and
probing its capability for tracking inter-annual variations of dust-laden
Saharan air mass outbreaks. The ANN AOD series is suitable to be used in
climate analysis.
Summary and conclusions
This paper presents, for the first time, the AOD time series of Saharan
mineral dust outbreaks over the subtropical North Atlantic between 1941 and
2013. This has been done at the Izaña Atmospheric Observatory, frequently
affected by the Saharan Air layer during the summer months, and by combining
AOD estimates from artificial neural networks between 1941 and 2001, and AOD
measurements during the period 2003–2013.
The ANN method has proved to be a very useful tool for the reconstruction of
daily AOD values at 500 nm from meteorological input data, such as the
horizontal visibility, fraction of clear sky, and relative humidity, recorded
at IZO. ANN AOD estimates adequately capture the day-to-day AOD variations
and the long-term trends when compared to coincident AOD measurements from
Mark-I solar spectrometer (1984–2009) and AERONET (2004–2009). The results
show a good agreement for the daily values, with Pearson coefficients of 0.97
(AERONET/ANN) and 0.93 (Mark-I/ANN). At the longest timescale (1941–2009),
we found a good agreement between ANN AOD monthly medians and the percentage
of time the wind blows from the Sahara desert (SE) (R= 0.86), and also a good
correlation between the number of days with AOD ≥ 0.20 and the number of
days in which synoptical observations reported mineral dust events (R= 0.89).
These results show the reliability of the reconstructed ANN AOD series,
confirming its consistency in this long period (1941–2009), and capability
for tracking inter-annual variations of dust-laden Saharan air mass
outbreaks.
Finally, this paper also highlights the potential of ANN to estimate AOD
values and probe its suitability for long-term AOD series reconstruction.
Thereby, the ANN methodology developed here for AOD series reconstruction
might be suitable to be applied in Synoptic stations of North Africa, the
Middle East and Asia, in which the reduced visibility is primarily due to the
presence of mineral dust, and where recent AOD observations are available for
validation.
Acknowledgements
This work was developed under the Specific Agreement of Collaboration between
the Meteorological State Agency (AEMET) of Spain and the University of
Valladolid regarding radiometry, ozone and atmospheric aerosol programmes
conducted at Izaña Atmospheric Observatory (IZO), and for the adaptation
and integration of the AEMET CIMEL network following the AERONET-RIMA
standards. This study is also part of the activities carried out within the
WMO CIMO Testbed for Aerosols and Water Vapor Remote Sensing instruments at
Izaña Observatory. The AERONET Cimel sun photometer at Izaña has been
calibrated by AERONET-EUROPE Calibration Service, financed by the Aerosol
Cloud and TRace gas InfraStructure (ACTRIS) European Research Infrastructure
Action (FP7/2007-2013 no. 262254). Financial support from the Spanish
Ministry of Economy and Competitiveness (MINECO) and from the “Fondo Europeo
de Desarrollo Regional” (FEDER) for project CGL2012-33576 is gratefully
acknowledged. We thank the AERONET-GSFC, PHOTONS-LOA, RIMA-UVa, and
RIMA-CIAI (AEMET) staff for their scientific and technical support. The
authors are grateful to the IZO team and especially all observers who have
worked in the past at Izaña Atmospheric Observatory. We also acknowledge
our colleague Celia Milford for improving the English language of the
paper.
Edited by: O. Torres
References
Ångström, A. K.: On the atmospheric transmission of sun radiation
and on the dust in the air, Geogr. Ann., 12, 130–159, 1929.Barreto, A., Cuevas, E., Pallé, P., Romero, P. M., Guirado, C., Wehrli, C.
J., and Almansa, F.: Recovering long-term aerosol optical depth series
(1976–2012) from an astronomical potassium-based resonance scattering
spectrometer, Atmos. Meas. Tech., 7, 4103–4116, 10.5194/amt-7-4103-2014,
2014.
Beale, M. H., Hagan, M. T., and Demuth, H. B.: Neural Network Toolbox, User's Guide, The MathWorks
Inc., Natick, MA, USA, 2014.
Cazorla, A., Olmo, F. J., and Alados-Arboledas, L.: Using a sky imager for aerosol
characterization,
Atmos. Environ., 42, 2739–2745, 2008.Cerdeña, A., González, A., and Pérez, J. C.: Remote sensing of water
cloud parameters using neural networks, J. Atmos. Ocean. Tech., 24, 52–63, 10.1175/JTECH1943.1, 2006.
Cuevas, E.: Estudio del Comportamiento del Ozono Troposférico en el
Observatorio de Izaña (Tenerife) y su Relación con la Dinámica
Atmosférica, Thesis, Univ. Complutense de Madrid, Madrid, Spain, 1996.Cuevas, E., González, Y., Rodríguez, S., Guerra, J. C., Gómez-Peláez, A. J.,
Alonso-Pérez, S., Bustos, J., and Milford, C.: Assessment of atmospheric
processes driving ozone variations in the subtropical North Atlantic free
troposphere, Atmos. Chem. Phys., 13, 1973–1998, 10.5194/acp-13-1973-2013,
2013.
Cuevas, E., Milford, C., Bustos, J. J., del Campo-Hernández, R., García,
O. E., García, R. D., Gómez-Peláez, A. J., Ramos, R., Redondas, A.,
Reyes, E., Rodríguez, S., Romero-Campos, P. M., Schneider, M., Belmonte,
J., Gil-Ojeda, M., Almansa, F., Alonso-Pérez, S., Barreto, A.,
Guirado-Fuentes, C., López-Solano, C., Afonso, S., Bayo, C., Berjón, A.,
Bethencourt, J., Camino, C., Carreño, V., Castro, N. J., Cruz, A. M.,
Damas, M., De Ory-Ajamil, F., García, M. I., Fernández-de Mesa, C. M.
González, Y., Hernández, C., Hernández, Y., Hernández, M.A.,
Hernández, B., Jover, M., Kühl, S. O., López-Fernández, R.,
López-Solano, J., Peris, A., Rodríguez-Franco, J. J., Sálamo, C.,
Sepúlveda, E., and Sierra-Ramos, M.: Izaña Atmospheric Research Center
Activity Report 2012–2014, edited by: Cuevas, E. and Milford, C., NIPO:
281-15-004-2, State Meteorological Agency (AEMET), Madrid, Spain, 2015a.Cuevas, E., Camino, C., Benedetti, A., Basart, S., Terradellas, E.,
Baldasano, J. M., Morcrette, J. J., Marticorena, B., Goloub, P., Mortier, A.,
Berjón, A., Hernández, Y., Gil-Ojeda, M., and Schulz, M.: The MACC-II
2007–2008 reanalysis: atmospheric dust evaluation and characterization over
northern Africa and the Middle East, Atmos. Chem. Phys., 15, 3991–4024,
10.5194/acp-15-3991-2015, 2015b.De Bruin, H. A. R., Van den Hurk, B. J. J. M., and Welgraven, D.: A
series of global radiation at Wageningen for 1928–1992, Int. J. Climatol.,
15, 1253–1272, 10.1002/joc.3370151106, 1995.
Demuth, H. and Beale, M.: Neural network toolbox for use with MATLAB, Natick, MA, USA, 1993.
Dirección General del Instituto Geográfico y Estadístico: Observatorio
Atmosférico de Izaña, Anual del Observatorio Central Meteorológico,
Sumplemento al tomo III, 1915.
Dorvlo, A. S., Jervase, J. A., and Al-Lawati, A.: Solar radiation estimation
using artificial neural networks, Appl. Energ., 71, 307–319, 2002.Eck, T. F., Holben, B. N., Reid, J. S., Dubovik, O., Smirnov, A., O'Neill, N. T., Slutsker, I., and
Kinne, S.: Wavelength dependence of the optical depth of biomass burning, urban,
and desert dust aerosols, J. Geophys. Res., 104, 31333–31349, 10.1029/1999JD900923, 1999.
Feister, U. and Junk, J.: Reconstruction of daily solar UV irradiation by an
artificial neural network (ANN), Remote Sensing of Clouds and the Atmosphere, 6362, 63622–63622,
2006.Feister, U., Junk, J., Woldt, M., Bais, A., Helbig, A., Janouch, M.,
Josefsson, W., Kazantzidis, A., Lindfors, A., den Outer, P. N., and Slaper,
H.: Long-term solar UV radiation reconstructed by ANN modelling with emphasis
on spatial characteristics of input data, Atmos. Chem. Phys., 8, 3107–3118,
10.5194/acp-8-3107-2008, 2008.
Foresee, F. D. and Hagan, M. T.: Gauss-Newton approximation to Bayesian
learning, In Proceedings of the 1997 international joint conference on neural
networks, Piscataway: IEEE, 3, 1930–1935, 1997.
Foyo-Moreno, I., Alados, I., Antón, M., Fernández-Gálvez, J.,
Cazorla, A., and Alados-Arboledas, L.: Estimating aerosol characteristics
from solar irradiance measurements at an urban location in southeastern
Spain, J. Geophys. Res.-Atmos., 119, 1845–1859, 2014García, R. D., Cuevas, E., García, O. E., Cachorro, V. E., Pallé, P., Bustos,
J. J., Romero-Campos, P. M., and de Frutos, A. M.: Reconstruction of global
solar radiation time series from 1933 to 2013 at the Izaña Atmospheric
Observatory, Atmos. Meas. Tech., 7, 3139–3150, 10.5194/amt-7-3139-2014,
2014a.García, R. D., García, O. E., Cuevas, E., Cachorro, V. E., Romero-Campos, P. M.,
Ramos, R., and de Frutos, A. M.: Solar radiation measurements compared to simulations at the
BSRN Izaña station. Mineral dust radiative forcing and efficiency study, J. Geophys. Res.,
119, 179–194, 10.1002/2013JD020301, 2014b.
Gilgen, H., Wild, M., and Ohmura, A.: Means and trends of
shortwave irradiance at the surface estimated from GEBA, J. Climate, 11, 2042–2061, 1998.
González, A., Pérez, J. C., Herrera, F., Rosa, F., Wetzel, M. A., Borys,
R. D., and Lowenthal, D. H.: Stratocumulus properties retrieval method from
NOAA-AVHRR data based on the discretization of cloud parameters, Int. J.
Remote Sens., 23, 627–645, 2002.González, Y., Schneider, M., Rodríguez, S., Cuevas, E., Dyroff, C.,
Christner, E., Andrey, J., García, O., and Sepúlveda, E.: Measurements
and interpretation of the water vapor δD variability at Izaña
North Atlantic free troposphere site, Symposium on Atmospheric Chemistry and
Physics at Mountain Sites, 11–15 August 2014, Steamboat Springs, CO, USA, 2014.
Hao, Y. and Wilamowski, B. M.: Levenberg-Marquardt Training. Industrial
Electronics Handbook, vol. 5, Intelligent Systems, CRC Press, Boca Raton, FL, USA, 2nd Edition, chapter 12,
12-1 to 12-15, 2011.Holben, B. N., Tanré, D., Smirnov, A., Eck, T. F., Slutsker,
I., Abuhassan, N., Newcomb, W. W., Schafer, J. S., Chatenet, B., Lavenu, F.,
Kaufman, Y. J., Vande Castle, J., Setzer, A.,Markham, B., Clark, D., Frouin,
R., Halthore, R., Karneli, A., O'neill, N. T., Pietras, C., Pinker, C., Voss,
K., and Zibordi, G.: An emerging ground-based aerosol climatology: Aerosol
Optical Depth from AERONET, J. Geophys. Res.-Atmos., 106, 12067–12097,
10.1029/2001JD900014, 2001.
Jain, A. K., Mao, J., and Mohiuddin, K. M.: Artificial neural networks: A
tutorial, Computer, 3, 31–44, 1996.
Junk, J., Feister, U., and Helbig, A.: Reconstruction of daily solar UV
irradiation from 1893 to 2002 in Potsdam, Germany, Int. J.
Biometeorol., 51, 505–512, 2007.
Kaufman, Y. J., Gitelson, A., Karnieli, A., Ganor, E., and Fraser,
R. S.: Size distribution and phase function of aerosol particles
retrieved from sky brightness measurements, J. Geophy. Res.-Atmos., 99, 10331–10356, 1994.
Kaufman, Y. J., Tanré, D., and Boucher, O.: A satellite view of aerosols
in the climate system, Nature, 419, 215–223, 2002.Kim, D., Chin, M., Yu, H., Eck, T. F., Sinyuk, A., Smirnov, A., and Holben,
B. N.: Dust optical properties over North Africa and Arabian Peninsula
derived from the AERONET dataset, Atmos. Chem. Phys., 11, 10733–10741,
10.5194/acp-11-10733-2011, 2011.Kudo, R., Uchiyama, A., Yamazaki, A., Sakami, T., and Ijima, O.: Decadal
changes in aerosol optical thickness and single scattering albedo estimated
from ground-based broadband radiometers: A case study in Japan, J.
Geophys. Res.-Atmos. (1984–2012), 116, D03207, 10.1029/2010JD014911 2011.Lachat, D. and Wehrli, C. : Dimming and brightening trends in direct solar
irradiance from 1909 to 2010 over Davos, Switzerland: Proportions of aerosol
and gaseous transmission, J. Geophys. Res.-Atmos., 118, 3285–3291,
10.1002/jgrd.50344, 2013
Lanzante, J. R.: Resistant, robust and nonparametric techniques for the analysis
of climate data: Theory and examples including applications to historical radiosonde
station data, Int. J. Climatol., 16, 1197–1226, 1996.
Linares-Rodríguez, A., Ruiz-Arias, J. A., Pozo-Vázquez, D., and
Tovar-Pescador, J.: Generation of synthetic daily global solar radiation data
based on ERA-Interim reanalysis and artificial neural networks, Energy,
36, 5356–5365, 2011.
Linares-Rodríguez, A., Ruiz-Arias, J. A., Pozo-Vázquez, D., and
Tovar-Pescador, J.: An artificial neural network ensemble model for
estimating global solar radiation from Meteosat satellite images, Energy, 61,
636–645, 2013.
López, G., Batlles, F. J., and Tovar-Pescador, J.: Selection of input
parameters to model direct solar irradiance by using artificial neural
networks, Energy, 30, 1675–1684, 2005.Mohandes, M., Rehman, S., and Halawani, T. O.: Estimation of global solar
radiation using artificial neural networks, Renew. Energ.,
14, 179–184, 10.1016/S0960-1481(98)00065-2, 1998.
Ohmura, A.: Observed long-term variations of solar irradiances at
the Earth's surface, Space Sci. Rev., 125, 111–128, 2006.
Ohmura, A. and Lang, H.: Secular variation of global radiation over Europe,
in: Current Problems in Atmospheric Radiation, edited by: Lenoble, J. and
Geleyn, J. F., 298–301, 1989.Ohvril, H., Teral, H., Neiman, L., Kannel, M., Uustare, M., Tee, M., Russak,
V., Okulov, O., Joeveer, A., Kallis, A., Ohvril, T., Terez, E. I., Terez, G.
A., Gushchin, G. K., Abakumova, G. M., Gorbarenko, E. V., Tsvetkov, A. V., and
Laulainen, N.: Global dimming and brightening versus atmospheric column
transparency, Europe, 1906–2007, J. Geophys. Res.-Atmos., 114,
D00D12, 10.1029/2008JD010644, 2009.
Özkan, C. and Erbek, F. S.: The Comparison of Activation Functions
for Multispectral Landsat TM Image Classification, Photogramm.
Eng. Rem. S., 69, 1225–1234, 2013.Pallé, E. and Butler, C. J.: Sunshine records from Ireland: Cloud factors and
possible links to solar activity and cosmic rays, Int. J. Climatol., 21, 709–729,
10.1002/joc.657, 2001.
Paoli, C., Voyant, C., Muselli, M., and Nivet, M. L.: Solar radiation
forecasting using ad-hoc time series preprocessing and neural networks, In
emerging Intelligent Computing technology and Applications, Springer Berlin Heidelberg, Germany, 898–907, 2009.Prospero, J., Ginoux, P., Torres, O., and Nicholson, S.: Environmental
characterization of global sources of atmospheric soil dust derived from the
NIMBUS7 (TOMS) absorbing aerosol product, Review Geophysical, 40, 1002, 10.1029/2000RG000095, 2002.Retalis, A., Hadjimitsis, D. G., Michaelides, S., Tymvios, F., Chrysoulakis,
N., Clayton, C. R. I., and Themistocleous, K.: Comparison of aerosol optical
thickness with in situ visibility data over Cyprus, Nat. Hazards Earth Syst.
Sci., 10, 421–428, 10.5194/nhess-10-421-2010, 2010.Rodríguez, S., Alastuey, A., Alonso-Pérez, S., Querol, X., Cuevas, E.,
Abreu-Afonso, J., Viana, M., Pérez, N., Pandolfi, M., and de la Rosa, J.:
Transport of desert dust mixed with North African industrial pollutants in
the subtropical Saharan Air Layer, Atmos. Chem. Phys., 11, 6663–6685,
10.5194/acp-11-6663-2011, 2011.Rodríguez, S., Cuevas, E., Prospero, J. M., Alastuey, A., Querol, X.,
López-Solano, J., García, M. I., and Alonso-Pérez, S.: Modulation of Saharan
dust export by the North African dipole, Atmos. Chem. Phys., 15, 7471–7486,
10.5194/acp-15-7471-2015, 2015.Sanchez-Lorenzo, A., Brunetti, M., Calbo, J., and Martin-Vide, J.:
Recent spatial and temporal variability and trends of sunshine duration
over the Iberian Peninsula from a homogenized data set, J. Geophys.
Res., 112, D20115, 10.1029/2007JD008677, 2007.
Shaw, G. E.: Aerosols at Mauna Loa, Optical properties, J. Atmos. Sci., 36, 862–869,
1979.Stanhill, G. and Cohen, S.: Global dimming: a review of the evidence
for a widespread and significant reduction in global radiation,
Agr. For. Meteorol., 107, 255–278, 10.1016/S0168-1923(00)00241-0, 2001.
Thies, A.: Instruction Manual Hygro-thermo transmitter compact, Adolf
THIES GmbH & Co. KG, Göttingen, Germany, 2011.
Wehrli, C.: Calibrations of filter radiometers for determination of
atmospheric optical depth, Metrologia, 37, 419–422, 2000.Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. G.,
Forgan, B., Kallis, A., Russak, V., and Tsvetkov, A.:From dimming to
brightening: Decadal changes in surface solar radiation, Science, 308,
847–850, 10.1126/science.1103215, 2005.Wild, M., Grieser, J., and Schär, C.: Combined surface solar
brightening and increasing greenhouse effect support recent intensification
of the global land-based hydrological cycle, Geophys. Res. Lett., 35, L17706,
10.1029/2008GL034842, 2008.Wild, M.: Global dimming and brightening: A review, J. Geophys. Res., 114,
D00D16, 10.1029/2008JD011470, 2009.
World Meteorological Organization (WMO), Guide to meteorological instruments and methods of observation.
6th ed. WMO no. 8. Secretariat of the World Meteorol. Organ., Geneva, Switzerland, 1996.
World Meteorological Organization (WMO), Manual on Codes, Regional Codes and
National Coding Practices,Volumen II, Secretariat of the World Meteorological
Organization, WMO 306, Geneva, Switzerland, 1998.
Zhang, G., Patuwo, B. E., and Hu, M. Y.: Forecasting with artificial neural
networks: The state of the art, Int. J. Forecasting, 14,
35–62, 1998.