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Atmósfera 30(1), 1-10 (2017)

doi: 10.20937/ATM.2017.30.01.01

© 2017 Universidad Nacional Autónoma de México, Centro de Ciencias de la Atmósfera.

This is an open access article under the CC BY-NC-ND License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Efcient prediction of total column ozone based on support vector regression

algorithms, numerical models and Suomi-satellite data

Leo CARRO-CALVO,a Carlos CASANOVA-MATEO,b Julia SANZ-JUSTO,b

José Luis CASANOVA-ROQUEb and Sancho SALCEDO-SANZa*

a Departmento de la Teoría de la Señal y Comunicaciones, Universidad de Alcalá, carretera Madrid-Barcelona, km 33.6,

28805 Alcalá de Henares, Madrid, España

b LATUV, Laboratorio de Teledetección, Universidad de Valladolid, Edicio I+D, Paseo de Belén 11, 47011 Valladolid,

España

* Corresponding author: sancho.salcedo@uah.es

Received: January 25, 2016; accepted: November 10, 2016

RESUMEN

Se propone un nuevo método de pronóstico para la columna total de ozono (CTO) basado en la combinación

de algoritmos de vectores de soporte para regresión (VSR) y variables de predicción provenientes del saté-

lite de colaboración nacional en órbita polar Suomi, así como de modelos numéricos del Sistema Global de

Predicción (SGP) y mediciones directas. Los datos de satélite incluyen perles de temperatura y humedad

a diferentes alturas, y mediciones de CTO realizadas en los días anteriores al pronóstico. El modelo SGP

proporciona datos de temperatura y humedad para el día del pronóstico. El sistema también considera los

datos alternos de mediciones in situ, p. ej. de la profundidad óptica de aerosoles a diferentes longitudes de

onda. Mediante la metodología VSR se puede obtener un pronóstico exacto de la CTO a partir de estas va-

riables de predicción, con mejores resultados que los obtenidos con otros métodos de regresión, p. ej. redes

neuronales. También se efectúa un análisis del mejor subconjunto de características del pronóstico de CTO.

La parte experimental de la investigación consiste en la aplicación de VSR a datos de observación directa

obtenidos en el laboratorio radiométrico de Madrid, España, donde están disponibles mediciones de ozono

adquiridas por medio de un espectrofotómetro Brewer, lo que posibilita el entrenamiento del sistema y la

evaluación de sus resultados.

ABSTRACT

This paper proposes a novel prediction method for Total Column Ozone (TCO), based on the combination

of Support Vector Regression (SVR) algorithms and different predictive variables coming from satellite

data (Suomi National Polar-orbiting Partnership satellite), numerical models (Global Forecasting System

model, GFS) and direct measurements. Data from satellite consists of temperature and humidity proles at

different heights, and TCO measurements the days before the prediction. GFS model provides predictions of

temperature and humidity for the day of prediction. Alternative data measured in situ, such as aerosol optical

depth at different wavelengths, are also considered in the system. The SVR methodology is able to obtain

an accurate TCO prediction from these predictive variables, outperforming other regression methodologies

such as neural networks. Analysis on the best subset of features in TCO prediction is also carried out in this

paper. The experimental part of the paper consists in the application of the SVR to real data collected at the

radiometric observatory of Madrid, Spain, where ozone measurements obtained with a Brewer spectropho-

tometer are available, and allow the system’s training and the evaluation of its performance.

Keywords: Total column ozone, daily forecasting, satellite data, numerical models, support vector regression.

2L. Carro-Calvo et al.

1. Introduction

Ozone is a gas naturally present in the Earth’s at-

mosphere. In the upper atmosphere, ozone is able

to absorb some of the harmful ultraviolet radiation

coming from the Sun, creating thus a protective cover

to our planet. In the troposphere, ozone is formed

through chemical reactions between volatile organic

components, nitrogen oxides and sunlight. In the

lower atmosphere, it is a harmful pollutant that may

cause respiratory problems to humans, and different

damages in plants and other living systems. For this

twofold behavior, ozone variability and prediction

studies have been a major issue in the last decades

(Anton et al., 2011a, b; Varotsos et al., 2004). The

interest in modeling ozone variability started on the

early 1970s, when changes of stratospheric ozone

were attributed to catalytic reactions in the strato-

sphere that caused losses in the total amount of ozone

(Crutzen, 1970, 1971).

Other studies on this topic focused on the role

of chlorine (Stolarski and Cicerone, 1974) and the

chlorouorocarbons (CFCs) (Molina and Rowland,

1974) in ozone losses in the stratosphere. These

hypotheses were conrmed by the observation of a

sharp decrease in the stratospheric ozone levels over

Antarctica, at the start of the southern spring season

in the middle 1980s over several polar bases of that

continent (Farman et al., 1985).

From these first studies, the analysis of Total

Column Ozone (TCO) (dened as the amount of

ozone contained in a vertical column of base 1 cm2 at

standard pressure and temperature) became a primary

important problem in atmospheric physics (Savastiouk

and McElroy, 2005; Silva, 2007), in connection with

atmospheric circulation and its dynamics (Khokhlov

and Romanova, 2011), climate change (Krzyscin and

Borkowski, 2008), greenhouse gases concentration

(Bronnimann et al., 2000; Steinbrecht et al., 2003) and,

of course, pollutants concentration in different zones

of the Earth (Rajab et al., 2013). TCO variability has

also been studied using remote sensing techniques,

mainly satellite data, such as in Silva (2007), where

the use of satellite measurements in the study of TCO

over Brazil in the last decades is reviewed; Latha and

Badarinath (2003), where satellite measurements are

used together with ground measurements in the study

of TCO content in the atmosphere; Jin et al. (2008),

where TCO measurements are calculated from geosta-

tionary satellite data; Christakos et al. (2004), where

remote sensing data and empirical models are mixed

with existing data bases for TCO mapping; Anton et

al. (2008), where satellite data from the Global Ozone

Monitoring Experiment (GOME) are used to study

TCO variability over the Iberian Peninsula; Rajab et

al. (2013), where satellite measurements of different

atmospheric variables are used in ozone prediction

over Malaysia; and Pinedo et al. (2014), where Total

Ozone Mapping Spectrometer (TOMS) and Ozone

Monitoring Instrument (OMI) satellite data are used

to analyze TCO over Mexico in the period 1978-2013.

Regarding TCO prediction, different systems

and approaches have been proposed, both using

numerical and classical statistical methods such as

autoregressive approaches (Chattopadhyay, 2009a).

In general, TCO prediction with numerical models

tends to be more accurate than statistical prediction,

but note that alternative statistical-based procedures

are also able to obtain a good prediction, in a fraction

of time compared to numerical models, and with a

smaller infrastructure. In the last few years, compu-

tational intelligence algorithms have been proposed,

obtaining accurate algorithms for TCO prediction.

Among other approaches, neural networks have

been intensely used in TCO estimation problems

(Monge and Medrano, 2004; Chattopadhyay, 2007,

2009b, Salcedo et al., 2010). In Monge and Medrano

(2004), a multi-layer perceptron neural network (MLP)

(Hagan and Menhaj, 1994) is applied to the prediction

of TCO series in Arosa (Switzerland), Lisbon (Por-

tugal) and Vigna di Valle (Italy). In this case, using

TCO data from 1967 to 1973, a good performance

of the approach could be demonstrated. In a more

recent work, Chattopadhyay and Bandyopadhyay

(2007) successfully apply a neural network (which

was trained using the back propagation algorithm)

to the TCO series of Arosa between 1932 and 1970.

In Salcedo et al. (2011) a neural network bank is

applied to TCO prediction in the Iberian Peninsula,

with good results. Martínez et al. (2011) describe

a methodology based on association-rules for TCO

prediction, improving the interpretability of pre-

dictions in terms of the predictive variables. More

recently Rajab et al. (2013) apply multiple regres-

sion techniques and principal component analysis

(PCA) to TCO prediction in the Malaysia Peninsula

using satellite data.

In this paper we propose a novel system for

TCO prediction in a daily time-horizon (24 h) that

3

TCO efcient prediction with SMVs, numerical models and Suomi data

combines a powerful regression methodology (sup-

port vector regression, SVR) (Salcedo et al., 2014)

with different predictive variables coming from sat-

ellite data (Suomi National Polar-orbiting Partnership

[NPP] satellite), numerical models (Global Forecast-

ing System [GFS] model) and in-situ measurements.

To our knowledge, there are not previous works

dealing with the SVR methodology in TCO predic-

tion. The complete system provides an accurate TCO

prediction within a 24-h time-horizon, by combining

the prediction capabilities of SVR with satellite

data and proles predictions by numerical models.

The objective variable (TCO) to train the system is

obtained by means of a Brewer spectrophotometer.

Different experiments to evaluate the performance of

the system have been carried out at the radiometric

station of Madrid, including comparison with arti-

cial neural systems. Further analysis on the subsets

of features that provides the best results in terms of

TCO prediction is also included in the experimental

analysis of the paper.

The structure of the paper is as follows: section 2

presents the data available to face this daily TOC

prediction problem; section 2.1 describes the obser-

vational data available from satellite measurements;

section 2.2 describes the predicted variables used

in addition, obtained from the GFS, and section 2.3

gives the description of the TCO measurements used

to train the algorithm and to evaluate the predicted

TCO. Section 3 reviews the main concepts of the

SVR algorithm. Section 4 presents the experimental

part of the paper, where the performance of the pro-

posed system is shown in different experiments at the

radiometric station of Madrid. Finally, in section 5

some concluding remarks are given.

2. Data available for this study

A predictive model is proposed where satellite data,

aerosol optical depth (AOD) from a ground-installed

sunphotometer, and numerical models information

are considered. All the data sources used in the fol-

lowing subsections are reviewed.

2.1 Satellite-based and ground data

Regarding satellite data, the following information

is used:

a. Temperature and humidity proles (100 pressure

levels) obtained from the Advanced Technology

Microwave Sounder (ATMS) by means of the

CSPP-CIMSS software (http://cimss.ssec.wisc.

edu/cspp/).

b. Total column ozone derived from the Ozone

Mapping Proler Suite (OMPS).

The satellite used in this work is the Suomi NPP

polar satellite, the rst satellite of the new series of

American satellites forming the Joint Polar Satellite

System (JPSS), which will be the replacement of the

historical NOAA satellites. Suomi NPP is the result

of a joint venture of NOAA and NASA and it has

been designed to be the prototype of the future JPSS

satellite series. Suomi NPP carries ve instruments on

board with the aim of testing several key technologies

of the JPSS mission. It is one of the rst satellites

to meet the challenge of performing a wide range

of measurements over land, ocean and atmosphere

that may aid in the understanding of climate, while

it carries on with the operational needs of weather

forecasting and continuing key data records that are

essential for the study of global change, i.e., it meets

the objectives of NOAA and EOS satellites.

The instruments on board Suomi NPP are the

following:

– Advanced Technology Microwave Sounder

(ATMS), a scanner with 22 channels providing

vertical soundings of temperature and humidity

for weather forecasting.

– Visible Infrared Imaging Radiometer Suite

(VIIRS), a radiometer that measures 26 VIS and

IR channels with multiple applications for the

study of aerosols, clouds, ocean color, surface

temperature, res, albedo, etc. Its data can im-

prove the understanding of climate change. It is

considered the substitute for MODIS.

– Cross-track Infrared Sounder (CrIS), a Fourier

transform spectrometer with 1305 channels that

allows obtaining vertical proles of temperature,

pressure and humidity at a very high resolution

(100 levels). These measurements will help short

and medium term weather forecasting.

– Ozone Mapping Profiler Suite (OMPS), two

hyper-spectral instruments that measure ozone

prole with a very high vertical resolution. Due

to their high resolution, they provide insights into

the state of the ozone layer and a better under-

standing of chemical phenomena that lead to the

destruction of ozone near the troposphere.

4L. Carro-Calvo et al.

– Clouds and the Earth’s Radiant Energy System

(CERES), a three-channel spectrometer that mea-

sures solar radiation reected and emitted by the

Earth. It also analyzes cloud properties such as

thickness, height, particle size, phase of the cloud

and others.

These instruments perfectly fulll the objectives

of JPSS, contributing to the study of climate change

and providing series of critical data for understanding

climate dynamics.

Due to the fact that aerosols can absorb solar en-

ergy (Wang et al., 2009), we considered in addition

that it could be interesting to include aerosol optical

depth (AOD) in our model as another input parameter.

The daily mean aerosol optical depth product can be

obtained from the measurements of a sunphotometer,

which makes direct sun measurements at wavelengths

340, 380, 440, 500, 670, 870 and 1020 nm with a eld

of view of 1.20 nm. Fortunately, a Cimel CE318 sun-

photometer is installed at the radiometric observatory

of Madrid. This instrument is part of the NASA Aerosol

Robotic Network (AERONET) (Holben et al., 1998).

2.2 Model predicted variables

Regarding numerical model information, daily mean

predicted temperature and humidity proles obtained

from the GFS numerical weather prediction model

(Kanamitsu et al., 1991) were used. Although its

horizontal resolution is quite coarse, the GFS model

has the advantage that its data are freely available on

the Internet. In this case, the variables were taken at

the grid point closest to the region of interest.

2.3 Target variable: TCO control measurements

Currently the World Meteorological Organization’s

Global Atmosphere Watch (WMO/GAW) program

suggests that the most relevant instrument to mea-

sure column ozone from the ground is the Brewer

spectrophotometer. This instrument allows to derive

the total ozone amount from the ratios of measured

sunlight intensities at ve wavelengths between 306

and 320 nm with a resolution of 0.6 nm, where the

absorption by ozone presents large spectral struc-

tures (Anton et al., 2008). As a result, in this study

we used the daily mean ground-based total ozone

amount derived from the Brewer spectrophotometer

in Madrid as the objective variable to be predicted

from the predictive variables described above. The

Agencia Estatal de Meteorología (Meteorological

State Agency, AEMET) of Spain operates a national

Brewer spectrophotometer network, having one

of its instruments located at the radiometric station of

Madrid (40.8º N, 4.01º W). This Brewer instrument

is part of the WMO/GAW Global Ozone Monitoring

Network. Total ozone data cover the period from

March 1, 2013 to February 28, 2014, which represents

one year of daily measurements. Note that both Brew-

er and Cimel networks are managed under a quality

management system certied to ISO 9001:2008,

which guarantees their accuracy, and it ensures the

compliance of the measurements with international

standards on ozone and aerosol optical depth mea-

surements, particularly those stated by WMO. Table I

summarizes all the predictive (inputs) and objective

(target) variables considered in this paper.

3. Support vector regression algorithms

SVR (Smola and Scholkopf, 2004) is one of the

state-of-the-art algorithms for regression and

function approximation, which has yielded good

results in many different regression problems.

SVR algorithms are adequate for a large variety of

regression problems, since they do not only take

Table I. Input variables used for this study on TCO prediction.

Variable Source Previous Day Target day Units Spatial Coverage

Temperature prole ATMS X K 100 pressure levels

Humidity prole ATMS X % 100 pressure levels

Total Ozone OMPS X Dobson Atmospheric column

Aerosol Optical Depth Cimel sunphotometer X - Atmospheric column

Temperature prole forecast GFS X K 11 pressure levels

Humidity prole forecast GFS X % 11 pressure levels

Total Ozone (target to verify

the prediction)

Brewer

spectrophotometer X Dobson Atmospheric column

5

TCO efcient prediction with SMVs, numerical models and Suomi data

into account the error estimates of the data, but

also the generalization of the regression model (the

capability of the model to improve the prediction

when a new dataset is evaluated). Although there

are several versions of SVR, the e-SVR classical

model described in detail by Smola and Scholkopf

(2004), which has been used in a large number of

applications in science and engineering (Salcedo et

al., 2014), is considered in this work.

The SVR method for regression uses a given a

set of training vectors 𝕋 = {(xi, oi), i = 1,...l}, where

xi stands for the inputs, and oi stands for the TCO

variable to be predicted. For obtaining a regression

model of the form o(x) = f(x) + b = wT ϕ(x) + b, to

minimize a general risk function:

R

[ƒ]=

1

2w2+C

l

i=1

L(oi,ƒ(xi

))

(1)

where C is a hyper-parameter of the model, the norm

of w controls the smoothness of the regression model,

ϕ(x) is a function of projection of the input space to

the feature space, b is a parameter of bias for the

model, xi is a feature vector of the input space with

dimension N (training of the new input vector), yi is

the output value to be estimated and L (yi, f[xi]) is the

loss function selected (Smola and Scholkopf, 2004).

In this paper, we use the L1-SVRr (L1 support vector

regression), characterized by an ε-insensitive loss

function (Smola and Scholkopf, 2004):

L(o

i

,f (x

i

))

0if|oi−f(xi)| ≤

|oi−f(xi)| −otherwise

=

(2)

Figure 1 shows an example of an SVR-process

in a two-dimensional regression problem, with an

ε-insensitive loss function.

In order to train the above presented model, it is

necessary to solve the following optimization prob-

lem (Smola and Scholkopf, 2004):

min 1

2w

2

+C

l

i=1

*

(ξ

i

+ξ

i

)

(3)

subject to

oi−w

Tϕ(xi)− b≤ + ξi,i=1,...,l

(4)

−o

i

+wTϕ(x

i

)+ b*

≤ + ξi,i=1,...,l

(5)

*

ξ

i

,ξi≥0,i=1,...,l

(6)

The dual form of this optimization problem is

usually obtained through the minimization of the

Lagrange function, constructed from the objective

function and the problem constraints. In this case,

the dual form of the optimization problem is the

following:

ma

x−

1

2

l

*

**

*

i,j =1

(αi−αi)(αj−αj)K(xi,xj

)−

−

l

i=1

(αi+αi)+

l

i=1

oi(αi−αi)

(7)

l

*

i=1

(αi−αi)=

0

(8)

α

i

*

,αi[0,C]

∈

(9)

In addition to these constraints, the Karush-Kuhn-

Tucker conditions must be fullled, and also the

bias variable, b, must be obtained. The interested

reader can consult Smola and Scholkopf (2004) for

reference. In the dual formulation of the problem the

function K(xi, xj) is the kernel matrix, which is formed

by the evaluation of a kernel function, equivalent to

the dot product (ϕ[xi], 0[xj]). A usual election for this

kernel function is a Gaussian function, as follows:

Kernel space

Input space

xi

*

xj

L(e)

Φ

ϕ(xi)

ξi

ξj

ϕ(xj)

+ε

0

–ε

0

*

ξiξj

+ε e–ε

Fig. 1. Example of a SVR-process in a two-dimensional

regression problem, with an e-insensitive loss function.

6L. Carro-Calvo et al.

K

(x

i

,x

j

)=exp( −γx

i

−x

j

2).

(10)

The nal form of function f(x) depends on the

Lagrange multipliers αiαi

*, as follows:

(x)=

l

i=1

(αi*

−αi)K(xi,x

)

f (11)

In this way it is possible to obtain a SVR model

by means of the training of a quadratic problem for a

given hyper-parameters C, ϵ and γ. One of the most

used free SVR codes is the C implementation of the

algorithm described in Chang and Lin (2011), available

at https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/.

4. Experiments and results

This section presents the experimental part of the

paper. First it is shown how the initial data are prepro-

cessed to keep a reduced number of predictive vari-

ables for the SVR. The methodology carried out to

evaluate the SVR performance is also described in the

next subsection. After this, the results obtained by

the SVR are presented, together with a comparison

with an MLP.

4.1 Data preprocessing and methodology

The input data set is huge, including 100 levels

of humidity and temperature from the satellite,

TCO measurement (from the previous days to

the one to be predicted), aerosol optical depths

at seven different wavelengths, and humidity and

temperature forecasts (11 different pressure levels:

925, 850, 700, 500, 400, 300, 250, 200, 150, 100

and 50 hPa), from the GFS model. A rst prepro-

cessing step is needed in order to reduce the size

of the data set. This is done by means of a features

extraction process using PCA, a technique that has

been used before in ozone analysis (Rajab et al.,

2013). After this preprocessing step, PCA vari-

ables that contain 99.5% of the variance are kept,

which results in a reduced number of variables, as

described in Table II.

Since only one year of data is available (see

section 2.3), the direct partition of the data into

training and test data (as usually performed) could

lead to misleading results. Instead, a 20-fold

cross validation procedure is proposed, i.e., the

available data are split into 20 subsets (with 13

or 14 days per subset), and the performance of the

SVR is analyzed by the average that results from

training the SVR in 19 subsets and testing in the

remaining one.

For comparison purposes an MLP with Lev-

enberg-Marquardt training algorithm (Hagan and

Menhaj, 1994) is used. MLPs have been previously

applied to TCO prediction, and are considered as the

state-of-the-art in this eld.

4.2 Results

First of all, the performance of the proposed SVR

was tested vs. the MLP approach using all variables

described in Table II. In addition, to establish the

most important features in TCO prediction, both

approaches were evaluated using each prediction

variable separately. Results are shown in Table

III. As can be seen, SVR outperforms MLP in all

the cases, with improvements in the range of 5

to 11%. TCO prediction by means of the SVR,

considering all the variables, is accurate, with a

mean absolute error (MAE) of about 28 Dobson

units. TCO prediction, with the input data taken

separately, reveals that the accurate prediction of

temperatures given by the GFS (10 variables after

the PCA pre-processing) is crucial to obtain good

TCO predictions. In contrast, neither aerosols and

water content (in situ measurements), nor humid-

ity given by satellite measurements, contribute to

improve the TCO prediction. It is also interesting

that the TCO measurement of the previous day is

Table II. Input variables considered for TCO prediction after a rst data extraction preprocessing step.

Variable # initial variables # nal variables Method

(HS) Humidity (Suomi) 100 3 PCA (99.5%)

(TS) Temperature (Suomi) 100 7 PCA (99.5%)

(AW) Aerosole+water content (Cimel) 7+1 2 PCA (99.5%)

(TCO) TCO measurements (Suomi) - 3 t-1,t-2,t-3

(HG) Humidity prediction (GFS) 11 9 PCA (99.5%)

(TG) Temperature prediction (GFS) 11 10 PCA (99.5%)

7

TCO efcient prediction with SMVs, numerical models and Suomi data

not a very good input variable for predicting TCO

for the following day.

The next issue is whether a subset of data can

provide a more accurate TCO prediction than the

complete set. Table IV shows the results of using

different subsets of predictive variables in TCO

prediction. Four subsets are investigated in this case,

and compared to the case where all variables are

considered. The rst subset analyzed is TS + TCO +

TG (temperature proles [Suomi] + TCO measure-

ment [Suomi] + temperatures prediction [GFS], in

all 20 predictive variables). The second, third and

fourth cases are subsets considering combinations of

two of these variables. As can be seen in Table IV,

TCO prediction using the TS + TCO + TG variables

and SVR is the best obtained in all the experiments

carried out, with a MAE of about 25 Dobson units.

Subsets of two of these variables with the SVR show

different behavior: the TCO + TG case (13 predic-

tive variables) also gives good results, only slightly

inferior to the case with three variables. The third

worse case is TS + TG, but it is still better than the

TCO prediction obtained considering all variables.

Note that the last case (TS + TCO, 10 predictive

variables) leads to much poorer results in terms of

TCO prediction, which highlights the importance of

the TG variables to obtain a good TCO prediction

with a daily time-horizon.

These results can be better visualized by means

of depicting TCO prediction graphs. Figures 2, 3, 4

and 5 show TCO prediction using the SVR approach

(temporal prediction and scatter plot), corresponding

to the predictive variables TS + TCO + TG, TCO

+ TG, TG + TS and TCO + TS, respectively. Note

the good prediction obtained by using SVR with

TS + TCO + TG, which follows the TCO peaks and

provides a very accurate prediction in all the cases

considered. In contrast, the input variables TCO + TS

provide a worse TCO prediction, in which the TCO

peaks are not completely resolved. This shows the

importance of temperature prediction variables (TG)

in TCO prediction, and how the rest of the satellite

variables provide a slightly more accurate prediction.

Note also that humidity variables (either the satellite

Table III. Results in TCO prediction (mean absolute

error, in Dobson units) obtained with the different input

variables considered.

Variables SVR MLP improvement (%)

all 28.86 31.18 7.44

HS 50.99 56.74 10.13

TS 36.69 41.27 11.09

AW 60.86 65.89 7.63

TCO 41.22 46.71 11.75

HG 44.42 49.33 9.95

TG 30.93 34.57 10.52

Table IV. Results in TCO prediction (mean absolute error

in Dobson units) obtained with selected subsets of the

input variables considered.

Variables SVR MLP improvement (%)

all 28.86 31.18 7.44

TS+TCO+TG 25.59 28.37 9.79

TCO+TG 26.92 30.02 10.32

TS+TG 27.48 29.93 8.18

TCO+TS 37.85 40.24 5.23

200 300 400 500 600 700

800

200

300

400

500

600

700

800

TOC measured (Dobson units)

(a)Scatterplot

(b) Temporal

0 50 100 150 200 250

200

300

400

500

600

700

800

Days (March 2014 - February 2014)

TOC predicted (Dubson units)

TOC (Dubson units)

Fig. 2. Prediction (scatter plot and temporal prediction)

with the SVR using TS + TCO + TG predictive variables

(20 variables); (a) scatter plot; (b) temporal prediction,

TCO measured (blue) and predicted (red).

8L. Carro-Calvo et al.

prole the day before prediction and humidity pre-

diction by GFS) do not seem to be relevant variables

for obtaining accurate daily TCO predictions.

5. Conclusions

The prediction of total column ozone (TCO) is a

difcult problem with important environmental

applications. In this paper, a novel and efcient

prediction method for TCO has been proposed,

which includes an excellent performance regression

approach (SVR) applied to a set of predictive vari-

ables from heterogeneous sources, such as satellite

data (Suomi NPP polar satellite), numerical models

(GFS) or direct measurements using devices such

as sunphotometers. Data from satellite instruments

consist of temperature and humidity proles at

different heights, and TCO measurements from the

days before the prediction. The GFS model provides

predictions of temperature and humidity for the day

of prediction. Alternative measurement data such as

aerosol optical depth at different wavelengths are

also considered in the system.

This work shows the good performance of the

proposed SVR algorithm applied to daily TCO pre-

diction, outperforming alternative algorithms such

as neural networks.

An analysis of the most suitable input data for

TCO prediction has also been carried out in this study.

The results show that temperature prediction by a

numerical model is the most important variable to be

considered in TCO prediction. We have shown that the

SVR methodology is able to provide excellent results

in daily TCO prediction, better than the previously

considered neural networks algorithms. The improve-

ment obtained with SVR over the neural networks

methodology is in the range of 5 to 11% in all the

cases evaluated. We have also shown the importance

of a good temperature prediction by numerical models

in obtaining accurate TCO predictions, which can be

200 300 400 500 600 700 80

0

200

300

400

500

600

700

800

0 50 100 150 200 250

200

300

400

500

600

700

800

(a) Scatter plot

TOC predicted (Dubson units)

TOC (Dubson units)

TOC measured (Dobson units)

Days (March 2014 - February 2014)

(b) Temporal

Fig. 3. Prediction (scatter plot and temporal prediction)

with SVR using the TCO + TG predictive variables (13

variables). (a) Scatter plot; (b) temporal prediction, TCO

measured (blue) and predicted (red).

200 300 400 500 600 700

800

200

300

400

500

600

700

800

050 100 150 200 250

200

300

400

500

600

700

800

(a) Scatterplot

TOC predicted (Dubson units)

TOC (Dubson units)

TOC measured (Dobson units)

Days (March 2014 - February 2014)

(b) Temporal

Fig. 4. Prediction (scatter plot and temporal prediction)

with SVR using the TS + TG predictive variables (17

variables). (a) Scatter plot; (b) temporal prediction, TCO

measured (blue) and predicted (red).

9

TCO efcient prediction with SMVs, numerical models and Suomi data

complemented with satellite measurements to improve

even more the accuracy of the prediction results.

Acknowledgments

This work has been partially supported by the

project TIN2014-54583-C2-2-R of the Comisión Inter-

ministerial de Ciencia y Tecnología (CICYT) of Spain.

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