Validation of OMI satellite erythemal daily dose retrievals using ground-based measurements from fourteen stations

Remote Sensing of Environment 128 (2013) 1–10

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Validation of OMI satellite erythemal daily dose retrievals using ground-based measurements from fourteen stations D. Mateos a,⁎, J. Bilbao a, A.I. Kudish b, c, A.V. Parisi d, G. Carbajal e, f, A. di Sarra g, R. Román a, A. de Miguel a a

Atmosphere and Energy Lab, University of Valladolid, Valladolid, Spain Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, ED Bergmann Campus, Beer Sheva, 84105, Israel Dead Sea and Arava Science Centre, Neve Zohar, 86910, Israel d Australian Centre for Sustainable Catchments, University of Southern Queensland, Toowoomba, Australia e Servicio Meteorológico Nacional, Vigilancia de la Atmósfera y Geofísica, Buenos Aires, Argentina f Pontificia Universidad Católica Argentina, PEPACG, Buenos Aires, Argentina g ENEA/UTMEA-TER, Rome, Italy b c

a r t i c l e

i n f o

Article history: Received 27 February 2012 Received in revised form 30 August 2012 Accepted 15 September 2012 Available online 18 October 2012 Keywords: Solar radiation Erythemally-weighted ultraviolet radiation Satellite data Effects of ozone and aerosol Absorbing aerosol correction

a b s t r a c t The satellite Ozone Monitoring Instrument (OMI) erythemal daily dose (EDD) product is validated through an inter-comparison with ground-based measurements at 14 ground-based stations distributed worldwide between 43°N and 64°S in 5 different countries of both hemispheres: Argentina, Australia, Italy, Israel, and Spain. The results show that OMI data overestimate ground-based EDD measurements except in stations with high surface albedo (e.g., covered by snow), in agreement with the results reported by previous studies. The average differences between satellite and ground-based data reached a maximum of ~ 25% for all-sky cases. When cloudless conditions are selected, removing intra-daily changes in cloudiness, the agreement improves; although average differences between 10 and 20% still appear for seven low-albedo stations. The influences of ozone and aerosol on the observed differences show opposite trends: viz., high ozone column values result in a decrease whereas high turbidity conditions produce an increase in the differences. A correction factor based on the aerosol absorbing optical thickness was applied to correct for this effect, which is not considered in the OMI algorithm. After applying this correction, the agreement between OMI and ground-based EDD measurements improves and the difference between them falls below 20% for more than 90% of the cases at 8 stations. A dependence on station altitude is also noted for both all-sky and clear-sky cases. © 2012 Elsevier Inc. All rights reserved.

1. Introduction Solar ultraviolet (UV) radiation levels reaching the Earth's surface, in the spectral range between 280 and 400 nm, are tempered by atmospheric ozone absorption together with the effects of other gases, clouds and aerosol particles. Measurements taken at the Earth's surface require detailed instrument characterization and accurate calibration in order to provide high quality UV radiation data. There are different types of UV monitoring instruments, including spectroradiometers, multichannel radiometers, and broadband radiometers. While the spectrometers present the highest resolution, the multichannel and broadband pyranometers can reproduce the integrated quantities as accurately as the spectrometers if they are well calibrated (e.g., Dahlback, 1996). Consequently, due to the higher cost of the spectrometers, it is common to find multichannel or broadband instruments in the measuring sites.

⁎ Corresponding author. Tel.: +34 983423133. E-mail address: [email protected] (D. Mateos). 0034-4257/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.rse.2012.09.015

In the last decades, UV surface irradiance retrievals from satellites have provided wider spatial coverage than is afforded by groundbased stations. Several studies have compared space-borne and ground-based UV measurements under different atmospheric conditions in an effort to pinpoint which factors most impact the encountered differences (e.g., Arola et al., 2005; Cede et al., 2004; Kallistoka et al., 2000; Meloni et al., 2005). In some studies, new techniques for mapping the UV erythemal daily dose on a horizontal plane at the surface (EDD) from satellite (EDDs) and ground-based (EDDg) data were obtained taking into account, mainly, ozone, aerosol and surface albedo values (e.g., Janjai et al., 2010). EDD is the time integral over daylight hours of the erythemal irradiance, which in turn can be directly measured from broadband UV biometers or by weighting spectral UV irradiance measurements at the ground with the erythemal action spectrum (McKinlay & Diffey, 1987). This spectrum defines the efficiency of UV radiation to produce erythema (sunburn). The Ozone Monitoring Instrument (OMI) is a spectrometer designed to monitor ozone and other chemical species (Levelt et al., 2006). The OMI covers the 270 to 500 nm spectral interval. It is installed on board the EOS Aura satellite and its orbit is synchronized with the

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sun. The measurements recorded by the OMI at the overpass time are used as input for a radiative transfer model to estimate the amount of UV solar radiation reaching the Earth's surface. The first step is to obtain radiation under clear-sky conditions. To achieve this, the total ozone column measured by the OMI is used together with surface albedo, ground elevation, solar zenith angle and latitudinal dependence of ozone and temperature climate profiles. Once the clear-sky value has been obtained, it is multiplied by a factor which takes account the UV radiation attenuation by clouds and non-absorbing aerosols. This factor is obtained from reflectance measurements at 360 nm (assuming that clouds and aerosol are non-absorbing at this wavelength). A more detailed description about the OMI algorithm is given by Tanskanen et al. (2007) and references therein. As a result, the algorithm currently used does not take into account either absorbing aerosol (organic carbon, smoke, and dust) or trace gases (such as NO2 and SO2). This leads to an overestimation of UV radiation levels obtained from OMI measurements (e.g., Arola et al., 2005; Krotkov et al., 1998). UV radiation data derived from OMI instrument have been broadly validated through comparisons with ground-based measurements. For instance, Tanskanen et al. (2007) carried out a validation of daily OMI estimations at 17 stations and 18 instruments spread mainly around northern Europe, Greece, North America, New Zealand, and southern Argentina. The study highlighted the need to consider absorbing aerosols when obtaining UV radiation and stressed the importance of properly characterizing surface albedo. The two effects lead to differing biases. Whereas extinction in the troposphere causes a positive bias, underestimating surface albedo (for instance in cases of snow cover) leads to a negative bias. Buchard et al. (2008) and Ialongo et al. (2008) validated total ozone column and UV radiation values at stations in France and Italy, respectively. In both cases, differences between satellite and ground-based daily values were always above 17% and attributed mainly to aerosol load in the atmosphere. Antón et al. (2010) and Cachorro et al. (2010) conducted an extensive validation study of UV radiation measurements at the Arenosillo station in Huelva, southern Spain, observing that the relative differences between OMI and ground-based values ranged from 8% to over 20% depending on cloud conditions, aerosol load, and solar zenith angle. In particular, with regards to the influence of aerosol, the use of a correction method, which takes account of absorbing aerosols, led to a reduction between 30 and 40% in the differences between satellite and ground-based measurements. The main aim of this study is to validate UV erythemal daily doses at the surface inferred from the satellite OMI measurements. For this purpose, we compare the satellite retrievals with ground-based measurements obtained at various international stations. This enables us to verify whether the atmospheric and climate conditions, which vary from station to station, impact the radiation measurements obtained from satellites. The descriptions of the measuring stations, the satellite databases, and the methodology utilized in this study can be found in Section 2. Section 3 presents and analyzes the differences between both data series for each station. Finally, the main results have been summarized in the conclusion section. 2. Measurements and methodology 2.1. Ground station database To validate remote sensing data, 14 stations, whose locations are shown in Fig. 1, were chosen worldwide: - Spain: six stations located at A Coruña, León, Madrid, Murcia, Valladolid, and Zaragoza; - Argentina: three stations located at Buenos Aires, Marambio and Ushuaia; - Italy: two stations located at Lampedusa and ENEA-Trisaia Centre;

- Israel: two stations located at Beer Sheva and Never Zohar; and - Australia: one station located at Toowomba. Table 1 provides the geographical coordinates, database, and main characteristics of each station. The Solar Radiometric Station of the University of Valladolid is located in a wide-open area (free of obstructions) close to Valladolid (Spain). The measurements of UV erythemal radiation (UVER) are recorded by the UVB-1 Yankee Environmental Systems Inc. (YES) radiometer which has a spectral sensitivity close to the erythemal action spectrum. This instrument was calibrated at the National Institute for Aerospace Technology (INTA) in Spain. This calibration consisted of a measurement of the spectral response of the radiometer indoors and a comparison with a Brewer MKIII spectroradiometer outdoors. For further details see Bilbao et al. (2011a). The expanded uncertainty of this type of radiometers is about 7% (Hülsen & Gröbner, 2007). The UVB-1 radiometer installed in the station of the University of Valladolid mentioned before was also used to monitor broadband UVER data in a two month (May and June 2010) measuring campaign in the ENEA-Trisaia Centre in Southern Italy. Five different radiometric stations of the Spanish Meteorological Agency (A Coruña, León, Madrid, Murcia, and Zaragoza) were selected. In these stations, YES UVB-1 radiometers record erythemal data. All sensors are calibrated every second year by a comparison with a reference instrument calibrated at the World Radiometric Centre, and the spectral response is also measured. Maintenance tasks are carried out every week following the guidelines described by Webb et al. (2006). In Lampedusa (Italy), which is a small island (22 km 2) in the southern sector of the central Mediterranean Sea, there is the Station for Climate Observations maintained by the Italian ENEA agency on the north-eastern coast of Lampedusa (for more details, see e.g. di Sarra et al., 2002). An Ultraviolet Multifilter Rotating Shadowband Radiometer (UV-MFRSR), which measures solar irradiance at seven narrowband wavelengths (centered at 299.0, 304.7, 310.7, 316.8, 323.7, 331.7, and 367.2 nm; each with a 2 nm full width at half maximum bandwidth) in the UV-B and UV-A regions, is used to obtain UVER measurements. The calculation of UVER is made using an improved version of the algorithm by Dahlback (1996) and the retrieved UVER is in very good agreement with that derived from spectra measured with a MK III Brewer spectroradiometer, operating in Lampedusa (e.g., Meloni et al., 2005), and from the University of Valladolid YES UVB-1 radiometer, which was deployed on the island for several months in 2010. The Israeli radiation data are monitored at two meteorological stations: one located in the Dead Sea basin at Neve Zohar; and the other in Beer Sheva, on the campus of the Ben-Gurion University of the Negev. Neve Zohar is situated in the Judean desert and is on the western shore of the Dead Sea. Beer Sheva is located in the southern Negev region of Israel, a semi arid zone, at a distance of ca 65 km to the west of the Dead Sea. The UVB radiation at both sites is measured by the Model 501A UV-Biometer of Solar Light Co. Inc. The accuracy of the measurement is ± 5% for the daily total. A detailed description of the stations and instruments has been reported in the literature, e.g., Kudish et al. (2011). A Model 501 UV-Biometer of Solar Light Co. Inc. measures the erythemal UV radiation at the Toowoomba, Australia site. It is temperature stabilized to 25 °C and calibrated in summer and winter relative to a calibrated UV spectroradiometer (model DTM300, Bentham Instruments, Ltd, Reading, UK). This spectroradiometer is wavelength calibrated to the UV mercury spectral lines and irradiance calibrated to a 150 W lamp with calibration traceable to the National Physical Laboratory, UK. More details about the station are described by Turnbull et al. (2010). The three instruments installed at Buenos Aires, Ushuaia, and Marambio stations are the Model 501 UV-Biometer of Solar Light Co. Inc. These stations are part of the Argentine Meteorological Service.

D. Mateos et al. / Remote Sensing of Environment 128 (2013) 1–10

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Fig. 1. Location of the 14 stations whose data are used in this work. The numbers correspond to those in Table 1.

The radiometers were initially calibrated by the manufacturer and were later on re-calibrated by using a comparison procedure with either a standard radiometer calibrated by a reference lamp or a precision spectroradiometer (indoor calibration), in Buenos Aires (Hülsen & Gröbner, 2007). The comparison outdoors was carried out for a week in November 2010, since Buenos Aires may be cloudy for several

days (Gröbner, 2010). Further details about these stations have been described by e.g., Cede et al. (2004). The EDDg was calculated by integrating the highest resolution time data in each station (i.e., from minute/s or hourly values). Fig. 2 shows the EDDg temporal evolution at the 14 stations used in this study. The higher levels of EDDg incident at the lower

Table 1 Geographical locations and database used in the study. CC (cloud cover) and SW (shortwave) point out the methodology to determine the cloud-free days: by cloud cover observations or by total shortwave measurements, respectively. Station

Latitude

Longitude

Altitude a.s.l. (m)

Time interval

Instrument

Recording Frequency

Cloud Observation

Type

TOC average (DU)

1-A Coruña 2-León 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

43.36°N 42.58°N 41.81°N 41.67°N 40.45°N 40.16°N 38.00°N 35.50°N 31.25°N 31.20°N 27.60°S 35.59°S 54.85°S 64.24°S

8.42°W 5.63°W 4.93°W 1.06°W 3.72°W 16.64°E 1.16°W 12.60°E 34.75°E 35.37°E 151.90°E 58.48°W 68.31°W 56.63°W

58 916 848 260 664 40 62 40 315 −375 693 25 17 20

2008–2009 2008–2009 2004–2009 2008–2009 2008–2009 2010 2008–2009 2010–2011 2007–2008 2006, 2008 2006–2007 2005–2006 2009 2007–2008

YES UVB-1 YES UVB-1 YES UVB-1 YES UVB-1 YES UVB-1 YES UVB-1 YES UVB-1 UV-MFRSR SL UVB 501 SL UVB 501 SL UVB 501 SL UVB 501 SL UVB 501 SL UVB 501

1-h 1-h 10-min 1-h 1-h 1-min 1-h 1-min 1-h 1-h 5-min 1-min 1-min 1-min

SW SW CC SW SW CC SW CC SW SW CC SW SW SW

Atlantic coast Urban Urban Urban Urban Mediterranean coast Mediterranean coast Mediterranean Island Mediterranean coast Mediterranean coast Pacific coast Atlantic coast Pristine Pristine

316 ± 35 313 ± 35 319 ± 36 316 ± 34 313 ± 32 359 ± 24 314 ± 32 327 ± 33 288 ± 23 292 ± 21 274 ± 20 285 ± 26 314 ± 44 280 ± 50

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latitudes (Argentina, Australia and Israel), maximums around 8 kJ m − 2, are observed in Fig. 2. To understand the annual cycles, the effect of ozone on the EDDg must be considered, being the higher ozone values associated with the northern hemisphere stations. The relationship between the ozone and UV cycles has been studied in detail. de Miguel et al. (2011) carried out an exhaustive analysis of the ozone-UVER relationship in the Valladolid station, which is one of the stations of this study. Another parameter that affects annual cycles of Fig. 2 is the aerosol load which seems to be relevant in Lampedusa, Israel, and Southern Spain because these stations are affected by the intrusions of air masses coming from Northern Africa. 2.2. Satellite data The EDDs was retrieved from the OMI sensor. Data were obtained thanks to the Giovanni web application (http://disc.sci.gsfc.nasa.gov/ giovanni/overview/index.html) over an area of 0.2° latitude× 0.2°

longitude around each measurement site (approximately 22 km × 22 km). “Daily Level 3 Global Gridded Products” were used. The time period for each site is shown in Table 1. The total ozone column (TOC), radiative cloud fraction, and aerosol absorption optical thickness at 340 nm (AAOT) were required to complete the study from the algorithms OMDOAO3e.003, OMTO3e.003, and OMAEROe.003 at 342.5 nm, respectively (see, e.g., Hassinen et al., 2008). The three parameters are available in the same collection of data in Giovanni, and were downloaded for each location. For the aerosol extinction data, it was decided to use “MODIS Terra and Aqua Daily Level-3 Data”. This collection of data provides aerosol optical thickness values at 550 nm (AOT550 nm) in 1° × 1° grids and it is also available in the Giovanni application. The choice of MODIS for characterizing the extinction of aerosol (scattering plus absorption) is done because it is an independent retrieval with respect to OMI, from which both TOC and AAOT are derived. MODIS data are widely used in studies on analyzing the aerosol optical and microphysical properties (e.g., Christopher & Jones, 2010).

Fig. 2. Time series of the EDDg at the fourteen stations.

D. Mateos et al. / Remote Sensing of Environment 128 (2013) 1–10

2.3. Methodology The ratio ρ is used to estimate the agreement between satellite and ground-based measurements of EDD, and it is calculated as: ρ¼

EDDs : EDDg

ð1Þ

This ratio is unstable for low daily dose values, and a minimum of 0.2 kJ m −2 was established as the daily threshold dose at the surface for the comparison (Tanskanen et al., 2007). Another parameter used to evaluate the agreement between EDDs and EDDg is the root mean square error (RMSE): vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uX 2 u n  u EDDs −EDDg t 100 1 RMSEð% Þ ¼ EDDg n

ð2Þ

5

instruments. For instance, Kallistoka et al. (2000) and Cede et al. (2004) found an underestimation of TOMS-derived UV erythemal irradiance in sites of high surface reflectivity due to the misinterpretation of high surface albedo as cloud cover. The coefficients of the linear fits shown in the solid lines in Fig. 3 are reported in Table 2. It is observed that the slopes of the curves range between 0.93 and 1.20, viz., around unity, with the exception of the sub-Antarctic stations, which have slopes below 0.8. Similarly, the determination coefficient exhibits high values (>0.8) for all the stations with the exception of the sub-Antarctic ones (around 0.7). Meloni et al. (2005) reported for the previous generation of TOMS instrument a linear fit between the EDD obtained by a BREWER spectroradiometer, placed at the same Lampedusa station used in this study, and the versions 7 and 8 of TOMS estimates showing slopes less than 1 (opposite to that reported in Table 2) and intercepts of the same order of magnitude as those in this study (0.372 and 0.544 for the v7 and v8 versions, respectively). 3.2. Validation statistical analysis

where n is the number of data and EDD g is the average of the ground-based values in each station. In order to isolate the effects caused by ozone and aerosol, cloud-free days are selected. Cloud cover observations and total shortwave solar radiation data, utilizing both visual criteria and clearness index, are used to define cloud-free days. When using cloud cover observations, only periods showing between 0 and 2 octas were considered as cloudless (http://worldweather.wmo.int/oktas. htm). When, at least, 95% of the periods are classified as cloudless during an interval of 8 h centered at midday, the whole day is considered as cloud-free. With the total shortwave radiation data, as a first criterion, the clearness index is calculated, which is defined as the ratio between the total shortwave radiation measured at the surface and the corresponding extraterrestrial solar radiation for the same interval of time (more details about the calculation of the clearness index were given by, e.g., Bilbao et al., 2011b). A cloudless day was defined as that having a clearness index greater than 0.7 (Utrillas et al., 2007). After this test, the intra-daily evolution was plotted for each day to corroborate or to reject the selection as a cloud-free day. Once the ground-based measurements (by cloud cover observations or by total shortwave radiation) detect a cloudless day, OMI overpass reflectance data were also analyzed. When the satelliteinferred cloud fraction is smaller than 10% (e.g., Kallistoka et al., 2000) the day under study is classified as cloud-free. The latter criterion is applied to all the stations with the exception of Ushuaia and Marambio, where the high reflectivity measured by the satellite can be due to the presence of cloud or snow cover. The ground-based and satellite data corresponding to cloudless conditions were obtained using this methodology and possible errors due to cloudiness within an OMI pixel were minimized. 3. Results 3.1. Ground-based versus satellite measurements This section describes the “scatter plots” of the daily erythemal doses obtained from the satellite in terms of the corresponding surface measurements. The scatter plots for the 14 sites are reported in Fig. 3. These dispersion diagrams resemble those reported previously, e.g., by Antón et al. (2010) and Ialongo et al. (2008), for other Spanish or Mediterranean regions. With regards to the sub-Antarctic stations of Ushuaia and Marambio, a clear underestimation in the daily values provided by the OMI sensor is observed. This is consistent with the findings of other studies (e.g., Tanskanen et al., 2007). The importance of surface albedo linked to snow-cover was also shown by comparison of ground-based and satellite estimates from the previous generation of total ozone mapping spectrometer (TOMS)

As pointed out in Section 2.3, the ratio between satellite and ground measurements was used to assess agreement between the two series (Eq. 1). Tables 3 and 4 show the results of the main statistical parameters for all-sky cases and for clear-sky conditions, respectively. The number of stations showing a ratio smaller than 10% increases from 3 (León, Toowoomba and Valladolid) to 7 (A Coruña, Beer Sheva, León, Madrid, Toowoomba, Valladolid and Zaragoza) when clear-sky days only are taken into account. The standard deviation (SD) is lower when the cloudless conditions are analyzed. In fact, for all data it varies in the range (0.16, 0.40) whereas, for cloudless conditions, it does so in the range (0.04, 0.25). The average value is similar to the median in all stations for the cloudless database. Hence, a smaller dispersion in the ratio values under these conditions is expected. This behavior is confirmed by looking at the first and third quartiles (q1 and q3). For instance, the maximum values (max) do not exceed 2 units for cloudless conditions, while maximum values around 5 are observed for all conditions. Although the statistics obtained for clear skies are noticeably better than those obtained for all-sky cases, there are still satellite values which differ significantly from actual surface values. Possible seasonal trends in the differences are analyzed evaluating the statistics for each month of the year and for all-sky and clear-sky cases. Fig. 4 shows the average of ρ for five stations (three in the Southern hemisphere and two in the Northern one). The stations of Valladolid, Beer Sheva, and Toowoomba do not show clear dependence with the month of the year for all-sky cases, with the effect even weaker for clear-sky data. However, Ushuaia and Marambio stations evidence strong seasonal dependence, although Ushuaia station does not have sufficient clear-sky data to evaluate the monthly dependence. These stations present the smallest values of ρ average in April, August and September. For instance, at the Marambio station, the ρ average is below 0.6 for all-sky cases while the value increases up to 0.7 when cloud-free conditions are analyzed. There are no EDDs data during most of the summer period at these latitudes. The low ρ values are associated with the months with the highest frequency of snow. Therefore, the relevance of surface albedo for the EDDs retrievals found by previous studies (e.g., Cede et al., 2004; Kallistoka et al., 2000; Tanskanen et al., 2007) is confirmed in this study. 3.3. Effects of ozone and aerosol on the validation The effect of ozone on the comparison between OMI and ground based station was studied by selecting only clear skies and the stations were divided into two groups: a first group containing sufficient cloudless data to establish several TOC groups and to allow the evaluation of RMSE (Eq. 2) with, at least, 6 points in each interval;

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Fig. 3. Comparison between the erythemal daily doses inferred from the OMI satellite instrument (EDDs) and measurements taken by ground radiometers (EDDg). The linear fit (solid line) is included in each graph. The dashed lines represent the unit slope.

and a second group to ascertain the direct dependence of the ratio ρ on TOC. An interval of aerosol optical thickness was fixed at 0.05 b AOT550 nm b 0.3 in order to reduce the aerosol effect on these results. Fig. 5a shows the RMSE values obtained for six stations as a function of the columnar ozone. Fig. 5b shows the dependence of ρ on TOC for the four remaining stations. The Madrid station was included in the second plot in order to have four stations with very different conditions in the figure. It is observed that as the TOC value increases, the differences become smaller. Therefore, the greater the TOC, the better the OMI algorithm is able to reproduce EDDg. However, the agreement between ground-based and satellite measurements is worse when TOC increases at the Marambio station; although the relationship between ρ and TOC becomes flatter without the 2 points with the highest ozone (TOC > 360 DU). That the OMI underestimates the EDDg in Marambio station may be attributed to the role of surface albedo at this location (e.g., Piacentini et al., 2002; Tanskanen et al., 2007).

The ozone column retrievals from TOMS instruments are accurate (e.g., Buchard et al., 2008; Ialongo et al., 2008; Meloni et al., 2005), except at higher latitudes where a latitudinal dependence is observed (e.g., Piacentini et al., 2002). Therefore, the dependence of the observed differences on this variable at mid-latitude stations cannot be attributed to a bad characterization of ozone column in the OMI algorithms. Hence, the relationship between ozone and other atmospheric factors, such as aerosol load, could have an impact on the EDDs retrievals. This fact is discussed in the following paragraph. The same kind of analysis is performed for the effect of aerosol as it was done for the case of ozone after removing outliers from the ozone column, i.e., TOC b 200 DU or TOC > 450 DU. Other intervals of ozone with a smaller range were also tested. The number of data is considerably reduced because each location presents different ‘typical values of ozone column’. Stations in the Southern hemisphere usually show values of TOC b 300DU, e.g., Toowoomba and Buenos Aires evidence an average TOC with the standard error below 300 DU

D. Mateos et al. / Remote Sensing of Environment 128 (2013) 1–10 Table 2 Linear fit parameters EDDs = c1 + c2 EDDg for each of the stations, where r2 is the determination coefficient and n the number of data. Station

c1 (kJ m−2)

c2

r2

n

1-A Coruña 2-Leon 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

0.19 0.09 0.02 0.08 0.10 0.45 0.15 0.47 0.00 0.19 0.28 0.41 0.05 0.10

1.05 0.99 1.07 1.04 1.02 0.97 1.11 1.04 1.12 1.20 0.93 1.07 0.68 0.75

0.94 0.97 0.98 0.97 0.98 0.69 0.96 0.85 0.97 0.88 0.89 0.79 0.92 0.68

613 595 1166 548 602 41 600 106 701 634 673 638 303 501

(see Table 1). In the Northern hemisphere values of TOC > 300 DU are achieved during most of the annual ozone cycle and all the stations with a latitude greater than 35° have an average TOC over 300 DU, cf., Table 1. Hence, this selection allows ensuring of a large dataset at all stations. The results of the analysis are presented in Fig. 6 and are split into 2 classes. Values of the AOT550 nm within a width of 0.05 were grouped, and the behavior of RMSE is shown in Fig. 6a for several stations. The ratio ρ is plotted versus the measured aerosol optical thickness for other stations in Fig. 6b. As can be seen in the two figures, the deviation between OMI and ground-based values rises with an increasing aerosol load. It is worth mentioning here that the high values of AOT around Mediterranean Sea usually occur with low values of TOC, so the decreasing trend in the errors between OMI and ground-based instruments observed for the ozone (Fig. 5) can be generally explained by the inverse relationship between ozone and aerosol (e.g., di Sarra et al., 2002). The results for Ushuaia station can be explained by the same reasoning as applied to explain the behavior of Marambio station, viz., the effect of surface albedo. The dependence of ρ on the aerosol load observed in Fig. 6b corroborates previous results of the validations of OMI (e.g., Antón et al., 2010; Ialongo et al., 2008) and TOMS products (e.g., Meloni et al., 2005).

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Table 4 The same as Table 3 but for clear-sky cases. Station

n

ave

SD

max

min

med

q1

q3

1-A Coruña 2-Leon 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

102 160 254 150 196 12 221 26 163 88 76 166 12 53

1.10 1.04 1.08 1.11 1.08 1.10 1.15 1.20 1.08 1.19 1.01 1.20 0.64 0.73

0.06 0.06 0.04 0.06 0.07 0.09 0.07 0.12 0.04 0.13 0.04 0.15 0.11 0.25

1.30 1.23 1.25 1.35 1.32 1.27 1.37 1.39 1.20 1.71 1.09 1.80 0.85 1.12

0.97 0.91 0.95 0.98 0.77 0.97 0.96 0.90 0.97 0.90 0.92 0.93 0.45 0.27

1.09 1.03 1.07 1.09 1.07 1.11 1.15 1.22 1.08 1.19 1.01 1.16 0.62 0.79

1.06 1.00 1.05 1.07 1.03 1.04 1.11 1.09 1.05 1.13 0.99 1.11 0.58 0.52

1.13 1.07 1.10 1.14 1.12 1.14 1.19 1.27 1.10 1.23 1.04 1.26 0.72 0.93

which takes into account the aerosol absorption. Several methods have been applied to evaluate this correction. What all of these have in common is that the correction factor, Ca, is inversely proportional to the aerosol absorption optical thickness in the following form: Ca ¼

1 : a þ bAAOT

ð3Þ

Different a and b parameter values were tested in the abovementioned studies, as well as varying approaches for obtaining aerosol absorption optical thickness. For this analysis, we plotted the EDDs/EDDg ratio as a function of the AAOT at 340 nm (obtained by the OMI sensor, see Section 2). We then calculated a linear fit between both variables assuming a = 1 (e.g., Krotkov et al., 2005).

3.4. Aerosol absorption correction Previous studies (e.g., Arola et al., 2005; Buntoung & Webb, 2010; Kazadzis et al., 2009; Krotkov et al., 2005) established that the UV product values determined from the OMI sensor require a correction

Table 3 Main statistical parameters of the ratio between satellite and ground measurements for all-sky cases for the 14 stations. n is the number of data, ave is the mean, SD is the standard deviation, max the maximum value, min the minimum value, med the median value, and q1 and q3 the first and third quartiles, respectively. Station

n

ave

SD

max

min

med

q1

q3

1-A Coruña 2-Leon 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

596 586 1156 542 598 41 598 106 701 634 672 633 256 466

1.19 1.05 1.09 1.11 1.10 1.10 1.21 1.20 1.12 1.26 1.02 1.25 0.73 0.79

0.24 0.18 0.16 0.21 0.18 0.18 0.31 0.30 0.21 0.32 0.23 0.40 0.18 0.33

2.35 2.13 1.97 2.35 2.15 1.70 4.62 3.48 3.98 3.93 4.96 4.68 1.62 2.10

0.41 0.46 0.25 0.49 0.45 0.67 0.64 0.75 0.59 0.56 0.46 0.30 0.33 0.15

1.13 1.03 1.08 1.08 1.07 1.08 1.15 1.20 1.10 1.21 1.00 1.16 0.73 0.79

1.05 0.97 1.03 1.01 1.02 1.00 1.09 1.05 1.05 1.12 0.94 1.06 0.61 0.52

1.27 1.10 1.14 1.16 1.14 1.20 1.23 1.26 1.16 1.29 1.07 1.34 0.84 1.02

Fig. 4. ρ average as a function of the month for all-sky (a, solid symbols) and clear-sky (b, open symbols) cases for five and four stations, respectively.

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D. Mateos et al. / Remote Sensing of Environment 128 (2013) 1–10

Fig. 5. a) RMSE between satellite and ground data in terms of the ozone column for six stations. The central value of each ozone group is indicated in the figure, each being 20DU wide; b) ratio between satellite and ground measurements versus the total ozone column for various stations. See text for the criterion to select the stations in each panel.

Only days with AAOT ≥ 0.01 were used in the determination of the linear fits. EDDs must be divided by the linear fit equation to get EDDg, i.e., EDDs is multiplied by Ca to obtain corrected data. A single b parameter was evaluated utilizing this method for each station, i.e., a site-dependent b, in order to reduce differences in the aerosol types on each station. Table 5 shows the values obtained from this parameter for 12 stations. It is observed that the b values are in the same range of those proposed or obtained in previous studies. For instance, Krotkov et al. (2005) proposed b = 3, Ialongo et al. (2009) used b = 1.60, and Buntoung and Webb (2010) obtained b = 0.5 and b = 3.29 for urban and maritime aerosol types, respectively. Different b values in Table 5 can be attributed to several aerosol sources in each station and different time periods used in this study. Table 6 details the statistics obtained when the aerosol absorption optical thickness correction is applied to EDDs once the cloudless conditions were ensured. It is observed from Table 4 that some stations exhibit significant improvement in the agreement between the two data series, viz.: Beer Sheva, Buenos Aires, Lampedusa, Murcia, and Neve Zohar. A calculation was also performed of the percentages of satellite data showing differences ranging between ± 10% (W10%), ±20% (W20%), and ±30% (W30%) relative to ground-based measurements (Tanskanen et al., 2007). Fig. 7 shows the number of cases falling below the 20% difference versus the mean ratio for all data for each station. The circles in the figure are those obtained for all-sky cases. According to Tanskanen et al. (2007), there are three kinds of subsets: satisfactory cases, with W20% above 60% and a mean ratio around 1%;

Fig. 6. a) RMSE between satellite and ground data in terms of the aerosol optical thickness at 550 nm for six different stations, the central value of each optical thickness group is shown in the figure, each being 0.05 wide; b) ratio between satellite and ground measurements versus the aerosol optical thickness at 550 nm for various stations. See text for the criterion to select the stations in each panel.

cases with negative bias, due to surface albedo and showing low W20% values with a mean ratio below 1%; and cases with positive bias, due to tropospheric extinction, which show low W20% values with a mean ratio above 1. Following this classification, considering only all-sky cases, six stations (Zaragoza, Madrid, Valladolid, León, Beer Sheva, and Toowoomba) may be classified as ‘satisfactory cases’ with W20% > 70% for all of them, six stations (Neve Zohar, Lampedusa, A Coruña, Murcia, Buenos Aires, and Trisaia) are classified as having ‘positive bias’ with W20% b 70% and mean ratios above 1.10, and two stations display a ‘negative bias’ (Ushuaia and Marambio) with mean ratios below 0.8 due to the high albedo effect explained above. Table 5 Parameter b values (Eq. 3) assuming a = 1. Station

b

Standard error

r2

n

1-A Coruña 2-Leon 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

1.88 0.70 2.55 1.97 1.28 7.01 4.28 5.65 0.52 1.49 0.20 1.21 – –

0.60 0.32 0.18 0.44 0.23 3.29 0.32 0.87 0.06 0.28 0.05 0.72 – –

0.23 0.05 0.43 0.15 0.20 0.60 0.55 0.57 0.13 0.09 0.31 0.01 – –

33 83 277 115 126 4 146 33 491 313 34 206 – –

D. Mateos et al. / Remote Sensing of Environment 128 (2013) 1–10

9

Table 6 The same as Table 3 but for clear-sky cases with the aerosol absorption optical thickness correction. Station

n

ave

SD

max

min

med

q1

q3

1-A Coruña 2-Leon 3-Valladolid 4-Zaragoza 5-Madrid 6-Trisaia 7-Murcia 8-Lampedusa 9-Beer Sheva 10-Neve Zohar 11-Toowoomba 12-Buenos Aires 13-Ushuaia 14-Marambio

38 124 184 97 142 8 137 16 153 58 22 122 0 0

1.11 1.04 1.05 1.08 1.07 1.05 1.08 1.12 1.03 1.06 1.02 1.14 – –

0.07 0.05 0.05 0.06 0.07 0.06 0.08 0.10 0.08 0.17 0.06 0.17 – –

1.26 1.21 1.23 1.26 1.29 1.11 1.28 1.28 1.17 1.52 1.18 1.64 – –

0.98 0.91 0.96 0.94 0.86 0.94 0.87 0.94 0.69 0.60 0.90 0.60 – –

1.09 1.03 1.04 1.08 1.06 1.06 1.09 1.15 1.05 1.09 1.01 1.15 – –

1.07 1.00 1.02 1.04 1.02 1.02 1.03 1.04 0.98 0.93 0.98 1.07 – –

1.14 1.07 1.08 1.12 1.11 1.10 1.14 1.19 1.09 1.16 1.04 1.24 – –

The aerosol absorption optical thickness correction described previously was applied to cloud-free cases at each station. Results are shown as triangles in Fig. 7. Data from Ushuaia and Marambio stations were excluded from the correction as no AAOT data were available. The remaining stations were found to be classified as ‘satisfactory cases’ after the correction was applied; the lowest W20% being around 70% at the Buenos Aires station. The overestimation of EDD due to the non-inclusion of aerosol absorption in the OMI sensor algorithm entails a collateral effect linked to the station altitude. This effect could be expected since the cloudless estimations from the OMI surface UV algorithm do not take into account aerosol information which is introduced as a correction factor derived from the measured reflectance at 360 nm (e.g., Tanskanen et al., 2007). The number of cases with a difference below 10% in terms of the station's altitude above sea level is shown in Fig. 8. It is observed, when considering both all-sky cases as well as clear-sky cases AAOT-corrected data, that there is a clear trend towards higher W10% values with increasing site altitude. This is related to the fact that the aerosol optical thickness and the associated bias in the retrieved EDD are generally larger for low altitude than for elevated sites. In the present study, station altitude varies between − 375 m at Neve Zohar (the lowest terrestrial site on the Earth's surface) and 916 m at León. In addition, the stations whose data are used in this study show different sources of aerosol particles, viz., industrialized regions where pollution reaches high levels, regions subjected to dust storms, biomass burning, and pristine areas.

Fig. 8. Percentage of cases with differences below 10% in terms of the height above sea level of the measuring station for uncorrected (solid symbols) and corrected (open symbols) satellite data.

4. Summary and conclusions In the present study, daily values of erythemal radiation obtained from the OMI sensor have been compared with measurements taken at ground stations at six Spanish, three Argentinean, two Italian, two Israeli and one Australian station. The results of the inter-comparison between OMI and ground-based measurements indicate an overestimation of satellite values (for low surface albedo conditions) with a clear dependence on atmospheric parameters such as clouds, ozone and aerosol. We have analyzed the ratio between OMI sensor measurements and those recorded at the surface for all sky conditions, and for clear-sky cases before and after correcting the satellite data for the aerosol absorption optical thickness. The observed differences between satellite and ground-based data are reduced when the correction for aerosol absorption is applied. In fact, the statistical analysis of the EEDs/EDDg ratio clearly improves with the aerosol absorption correction (Table 6): for instance, after the correction, average and median ρ values are closer to 1 than the values obtained with all-sky cases (Table 3) and only cloudless data (Table 4). If the aerosol absorption correction is not applied, between 40 and 80% of the data of each station exhibit a difference between ±20%, whereas after applying the correction 8 stations have W20% > 90%. The two sub-Antarctic stations of this study show a clearly dominant role of the surface albedo. The effect of ozone and aerosols has been evaluated after eliminating the impact of clouds by selecting only cloud-free days. It is observed that the deviation between OMI and ground-based measurements decreases with increasing ozone column, whereas the opposite is observed in the aerosol load. In other words, conditions of maximum turbidity lead to the greatest differences, probably because the algorithm for obtaining UV radiation from the OMI sensor fails to take into account tropospheric aerosol. Hence, the site altitude is a relevant parameter for the retrievals of the satellite data since the percentage of data with differences between ±10% presents a clearly increasing trend with altitude, independently of the database used (i.e., all-sky cases or with the aerosol absorption correction). Acknowledgments

Fig. 7. Summary of the validation statistics: percentage of cases below 20% of difference in terms of the mean ratio between satellite and ground measurements. The solid symbols correspond to all-sky cases, and the open symbols to aerosol absorption correction applied to clear-sky cases. Each dot corresponds to each of the stations in Table 1.

The authors gratefully acknowledge the financial support extended by the Spanish Government under the projects CGL2010-12140E and CGL2011-25363. The Dead Sea and Arava Science Centre has funded and provided on-going support for the two Israeli meteorological stations monitoring the UVB radiation. Measurements at Trisaia site were supported by the Italian Ministry for Environment through the

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MINNI Project. The authors gratefully thank the OMI International Science Team for the satellite data used in this study. Analyses and visualizations used in this paper were produced with the Giovanni online data system, developed and maintained by the NASA GES DISC. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort. D. Mateos and R. Román would also like to thank the University of Valladolid for the Ph.D. financial support by the PIF-UVa grants. References Antón, M., Cachorro, V. E., Vilaplana, J. M., Toledano, C., Krotkov, N. A., Arola, A., et al. (2010). Comparison of UV irradiances from Aura/Ozone Monitoring Instrument (OMI) with Brewer measurements at El Arenosillo (Spain) — Part 1: Analysis of parameter influence. Atmospheric Chemistry and Physics, 10, 5979–5989, http://dx.doi.org/10.5194/ acp-10-5979-2010. Arola, A., Kazadzis, S., Krotkov, N., Bais, A., Gröbner, J., & Herman, J. R. (2005). Assessment of TOMS UV bias due to absorbing aerosols. Journal of Geophysical Research, 110, D23211, http://dx.doi.org/10.1029/2005JD005913. Bilbao, J., Mateos, D., & de Miguel, A. (2011b). Analysis and cloudiness influence on UV total irradiation. International Journal of Climatology, 31(3), 451–460, http://dx.doi.org/10. 1002/joc.2072. Bilbao, J., Román, R., de Miguel, A., & Mateos, D. (2011a). Long‐term solar erythemal UV irradiance data reconstruction in Spain using a semiempirical method. Journal of Geophysical Research, 116, D22211, http://dx.doi.org/10.1029/2011JD015836. Buchard, V., Brogniez, C., Auriol, F., Bonnel, B., Lenoble, J., Tanskanen, A., et al. (2008). Comparison of OMI ozone and UV irradiance data with ground-based measurements at two French sites. Atmospheric Chemistry and Physics, 8, 4517–4528, http://dx.doi.org/10.5194/acp-8-4517-2008. Buntoung, S., & Webb, A. R. (2010). Comparison of erythemal UV irradiances from Ozone Monitoring Instrument (OMI) and ground based data at four Thai stations. Journal of Geophysical Research, 115, D18215, http://dx.doi.org/10.1029/2009JD013567. Cachorro, V. E., Toledano, C., Antón, M., Berjón, A., Vilaplana, J. M., Arola, A., et al. (2010). Comparison of UV irradiances from Aura/Ozone Monitoring Instrument (OMI) with Brewer measurements at El Arenosillo (Spain) — Part 2: Analysis of site aerosol influence. Atmospheric Chemistry and Physics, 10, 11867–11880, http://dx.doi.org/10.5194/ acp-10-11867-2010. Cede, A., Luccini, E., Nuñez, L., Piacentini, R. D., Blumthaler, M., & Herman, J. R. (2004). TOMS derived erythemal irradiance versus measurements at the stations of the Argentine UV Monitoring Network. Journal of Geophysical Research, 109, D08109, http://dx.doi.org/10.1029/2004JD004519. Christopher, S. A., & Jones, T. A. (2010). Satellite and surface-based remote sensing of Saharan dust aerosols. Remote Sensing of Environment, 114, 1002–1007, http://dx.doi.org/10. 1016/j.rse.2009.12.00. Dahlback, A. (1996). Measurements of biologically effective UV doses, total ozone abundances, and cloud effects with multichannel, moderate bandwidth filter instruments. Applied Optics, 35, 6514–6521. de Miguel, A., Román, R., Bilbao, J., & Mateos, D. (2011). Evolution of erythemal and total shortwave solar radiation in Valladolid, Spain: Effects of atmospheric factors. Journal of Atmospheric and Solar Terrestrial Physics, 73, 578–586, http://dx.doi.org/10.1016/j.jastp. 2010.11.021. di Sarra, A., Cacciani, M., Chamard, P., Cornwall, C., DeLuisi, J. J., Di Iorio, T., et al. (2002). Effects of desert dust and ozone on the ultraviolet irradiance at the Mediterranean island of Lampedusa during PAUR II. Journal of Geophysical Research, 107(D18), 8135, http://dx.doi.org/10.1029/2000JD000139. Gröbner, J. (2010). Report on the intercomparison of UV broadband radiometers measuring erythemally weighted irradiance. PMOD/WRC, 15–24 November 2010, Buenos Aires, Argentina.

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