A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

remote sensing

Article A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

Steffen Kothe *, Uwe Pfeifroth, Roswitha Cremer, Jörg Trentmann and Rainer Hollmann Deutscher Wetterdienst, 63067 Offenbach, Germany; [email protected] (U.P.); [email protected] (R.C.); [email protected] (J.T.); [email protected] (R.H.) * Correspondence: [email protected]

Academic Editors: Dongdong Wang, George P. Petropoulos and Prasad S. Thenkabail Received: 17 March 2017; Accepted: 27 April 2017; Published: 2 May 2017

Abstract: Besides 2 m temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05◦ × 0.05◦. The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates.

Keywords: sunshine duration; satellite-based; climate monitoring; Meteosat

1. Introduction Sunshine duration has been used for decades in many different applications. It is applied in sectors such as tourism, public health, agriculture and solar energy. There are several stations worldwide with measured time series of more than 100 years [1,2]. Thus, it is a well-known and easy-to-use parameter. Current satellite-based data records reach length of more than 30 years, and retrieval algorithms deliver increasingly accurate results. This offers a great opportunity to get a spatial view of well-known parameters and to supplement station data. There are several hundreds of stations worldwide with sunshine duration observations on climatological time scales. Each of these stations has its own representativeness, instrument features and changes in instrumentation or location characteristics. To analyze sunshine duration on climatological time scales, all these features have to be considered. This is difﬁcult for one station—and it is much more difﬁcult for hundreds of stations—to get a spatial overview. This is one example where satellite-based data can supplement climate studies. For instance, a satellite-based climate data record of 30 years length for the Meteosat disk is usually based on about ten more-or-less identical instruments with very well known characteristics. This offers the opportunity to do climate studies for large regions with one single dataset of spatial and temporal homogeneous data. There are several studies to derive sunshine duration from satellite observations. For instance, Kandirmaz [3] used a statistical relationship between the daily mean cloud cover index and measured sunshine duration to derive daily global sunshine duration from geostationary satellite data.

Remote Sens. 2017, 9, 429; doi:10.3390/rs9050429 www.mdpi.com/journal/remotesensing Remote Sens. 2017, 9, 429 2 of 14

Kandirmaz [3] tested this method on data from the Meteosat First Generation (MFG). Good [4] used 15-minutes time series of cloud type data from Meteosat Second Generation (MSG) to compute daily sunshine duration for the United Kingdom. Shamim et al. [5] modeled sunshine duration with the help of geostationary satellite images for the United Kingdom. Journée et al. [6] derived sunshine duration maps for Belgium and Luxembourg from MFG global and direct solar radiation using the Ångström–Prescott equation [7,8]. In a study by Kothe et al. [9] the method used by Good [4] was extended to Europe. Additionally, Kothe et al. [9] derived sunshine duration from MSG-based solar surface incoming direct radiation by using the WMO threshold of 120 W/m2 for bright sunshine [10]. Both approaches were tested for one year of MSG-based data. Wu et al. [11] used cloud classification data from geostationary satellite data to estimate sunshine duration for a region in China. Most of these studies tested their approaches either for a small region or for a short time period. Journée et al. [6] applied their method to 11 years of MFG-based data, but only for Belgium and Luxembourg. Kothe et al. [9] derived sunshine duration for the whole of Europe, but just for one year. Methods by Kandirmaz [3], Shamim et al. [5] or Wu et al. [11] additionally used station data from a limited region for training or regression. Thus, none of these studies could answer the question of the applicability of these approaches to regions on continental scale and on climatological time scales at the same time. The significance of satellite-based solar radiation data for the evaluation of climate models has been recognized over the last few years (e.g., [12–15]). Compared to global radiation, there are many more stations worldwide that provide sunshine duration measurements. This could also increase the importance of sunshine duration for the climate modeling community. Another important scientific field of sunshine duration applications is in the field of agriculture (e.g., [16]) and vegetation (e.g., [17,18]). In this study, the retrieval technique by Kothe et al. [9] will be applied in an advanced version for a time period of 33 years and for the whole Meteosat disk, including Europe, Africa and parts of South America. The daily and monthly sunshine duration data will be evaluated against station data to get an estimate of the accuracy and temporal stability. The resulting sunshine duration product is part of the SARAH-2 climate data record, which is provided free of charge by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). The CM SAF (www.cmsaf.eu) is part of a network of Satellite Application Facilities, which is operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). The CM SAF, led by the Deutscher Wetterdienst (DWD), is deriving climate data records from geostationary and polar-orbiting satellite instruments. Sections2 and3 give a description of the applied data and retrieval methods. An evaluation of the daily and monthly satellite-based sunshine duration compared to station-based data is shown in Section4. The results of these comparisons and possible deficiencies are discussed in Section4. The‘final two sections present some examples and a conclusion.

2. Data

2.1. SARAH-2 The CM SAF provides a climate data record of solar surface radiation, called SARAH (Surface Solar Radiation Data Set—Heliosat). The second release of this climate data record (SARAH-2) is published free of charge via the CM SAF web user interface (wui.cmsaf.eu) [19]. SARAH-2 includes the parameters Effective Cloud Albedo (CAL), Surface Incoming Shortwave radiation (SIS), Surface Direct Irradiance (SDI), Spectrally Resolved Irradiance (SRI) and Sunshine Duration (SDU). The SDI includes two direct radiation products, the Surface Incoming Direct radiation (SID) and the Direct Normalized Irradiance (DNI), which is the basis for the retrieval of SDU. To derive SARAH-2 surface radiation parameters, the Heliosat algorithm is used [20]. SARAH-2 data are based on the MVIRI (Meteosat Visible and InfraRed Imager) and SEVIRI (Spinning Enhanced Visible and Infrared Imager) instruments onboard EUMETSAT’s Meteosat satellites of the first and second generation. The retrieval Remote Sens. 2017, 9, 429 3 of 14 of CAL is based on the MVIRI broadband channel (0.45 to 1 µm) and an artificial SEVIRI broadband channel, which is derived by the two visible channels centered at 0.6 and 0.8 µm. The Heliosat method offers the opportunity to derive a continuous dataset of CAL from a combination of MVIRI and SEVIRI measurements. For that, Heliosat includes an integrated self-calibration parameter that minimizes the impacts of satellite changes and artificial trends due to degradation of satellite instruments [21]. The first step in the Heliosat method is the retrieval of CAL by using the normalized relation between all sky and clear sky reflection in the visible channel of the Meteosat instruments. CAL is used to derive the cloud index, which is a measure for the impact of clouds on the clear-sky irradiance. The clear-sky irradiance is calculated using the all sky model SPECMAGIC [22]. Then a combination of cloud index and clear-sky irradiance gives SIS. SID is derived using the diffuse model of Skartveit et al. [23] and the cloud index. The direct normalized irradiance, DNI, is derived from SID by normalization with the cosine of the solar zenith angle (SZA):

SID DNI = (1) cos(SZA)

A detailed description of the retrieval of SARAH surface radiation parameters can be found in Mueller et al. [24] and the ATBD [25]. SARAH radiation parameters were investigated in several publications [26–29]. The CM SAF validation report shows for SARAH-2 DNI a mean absolute difference of 33.4 W/m2 for daily averages and 16.4 W/m2 for monthly averages in comparison with station data from the Baseline Surface Radiation Network (BSRN) [30].

2.2. Station-Based Sunshine Duration Data For the evaluation of sunshine duration, data from the European Climate Assessment & Datasets (ECA&D) and CLIMAT observation station network were used in this study. ECA&D [31] is gathering long-term daily observational series from meteorological stations all over Europe. Some automatic quality control and homogeneity checks are applied to the data. The sunshine duration has to be between 0 and 24 h, otherwise it is flagged as suspect [32]. Only ECA&D data were used, which were available in a predefined time subset from 1979 to 2016. Due to some national restrictions, only a part of the ECA&D data is downloadable. In contrast, the main application for CLIMAT [33] data is climate analysis and these data are therefore monthly totals. CLIMAT is a collection of monthly climate reports of more than 2.500 stations worldwide. The CLIMAT data undergo routine quality control at DWD. This includes an automatic comparison with long-term means and the percentage of sunshine duration (if these values are known), and a manual check for values, which were flagged as problematic. As no tests for homogeneity are applied for CLIMAT sunshine duration data, additionally some basic visual checks were applied to extract suspicious stations with obvious jumps or breaks in the time series. Only CLIMAT stations with at least 120 months of data were used for evaluation purposes. The metadata, which are provided by ECA&D and CLIMAT, give no information about the type of instrument or artificial changes, such as relocations or instrument changes. Since the 1980s, several national meteorological services changed from instruments, such as Campbell-Stokes, to automatic sunshine duration recorders [34]. This change can introduce inhomogeneities in the time series, which are not documented for all cases [35–38]. CLIMAT and ECA&D sunshine duration data are only available for land-based stations. ECA&D and CLIMAT station data are available for a relatively large number of stations, but despite quality checks, there is no guarantee that these data are bias free. Stations were removed from the analysis if they reported apparently erroneous data, such as fixed zeros, permanently high values throughout the year or obvious jumps in the time series. CLIMAT data were accessed via the DWD Climate Data Centre.

3. Approach Basis for the retrieval of satellite-based SDU are the SARAH-2 30 min instantaneous DNI data and the World Meteorological Organization (WMO) threshold for bright sunshine, which is deﬁned by Remote Sens. 2017, 9, 429 4 of 14

DNI ≥ 120 W/m2. Daily SDU is derived using the ratio of Meteosat observations (slots) exceeding the DNI threshold, and hence are considered as sunny slots, to all slots during daylight:

Remote Sens. 2017, 9, 429 #daylight slots 4 of 14 ∑ Wi SDU = daylength × i=1 (2) #daylight slots by DNI ≥ 120 W/m2. Daily SDU is derived using the ratio of Meteosat observations (slots) exceeding the DNIThe threshold, day length and is calculated hence are dependingconsidered as on sunny the date, slots, longitude to all slots and during latitude. daylight: The day length is ◦ restricted by a threshold of the solar elevation angle (SEA)#푑푎푦푙𝑖𝑔 of 2.5ℎ푡⁡푠푙표푡푠[9]. Wi is a number between 0 and 1, ∑ 푊𝑖 which indicates the influence of푆퐷푈 a single= 푑푎푦푙푒푛푔푡 slot dependingℎ × 𝑖=1 on the number of surrounding cloudy and(2) #푑푎푦푙𝑖푔ℎ푡⁡푠푙표푡푠 sunny grid points. The derivation of Wi is discussed in more detail in Section 3.1. The number of daylightThe slotsday length (#daylight is calculated slots) describes depending the maximumon the date, number longitude of Meteosatand latitude. observations The day length per grid is ◦ pointrestricted and perby a day threshold during of daylight. the solar Daylight elevation is angle defined (SEA) by the of time2.5° [9]. where Wi is the a number SEA exceeds between 2.5 .0 and 1, which indicates the influence of a single slot depending on the number of surrounding cloudy and 3.1.sunny Weighting grid points. of Sunny The Slots derivation of Wi is discussed in more detail in Section 3.1. The number of daylightA grid slots point (#daylight at a specific slots) describes time slot thei is maximum regarded number as sunny of ifMeteosat DNI is observations 120 W/m2 or per larger grid (seepoint Equation and per day (3)). dur SARAH-2ing daylight. provides Daylight instantaneous is defined DNIby the data time every where 30 the min. SEA Therefore, exceeds 2.5°. without weighting, in Equation (2) one sunny slot would correspond to a 30 min time window. In reality this is 3.1. Weighting of Sunny Slots only the case in bright weather situations. If there are clouds in the surrounding area of a grid point, thereA is grida probability point at that a specific not the time whole slot 30 i min is regarded are sunny. as This sunny is also if DNI valid is in 120 the W/m opposite2 or case, larger when (see aEquation cloudy grid(3)). point SARAH has- sunny2 provides grid points instantaneous in its near DNI surroundings. data every This 30 fact min. is accounted Therefore,for without in the retrievalweighting, of SDUin Equation by using (2 the) one information sunny slot ofwould the 24 correspond surrounding to grida 30 min points time (see window. Figure1 ).In In reality addition this theis only information the case in of bright two successive weather situations. time steps If is there incorporated. are clouds in the surrounding area of a grid point, there is a probability that not the whole( 30 min are sunny. This is also valid in the opposite case, when a cloudy grid point has sunny grid points1 ifin DNI its near(x, y )surroundings.≥ 120 W/m2 This fact is accounted for in the SIni = (3) retrieval of SDU by using the information0 ofif the DNI 24(x surrounding, y) < 120 W/ grmid2 points (see Figure 1). In addition the information of two successive time steps is incorporated.

Figure 1.1. Demonstration for accounting for surrounding grid points.points. The target grid point is marked inin thethe center.center.

ퟐ In a ﬁrst step for each grid point theퟏ number⁡풊풇⁡푫푵푰( of풙, sunny풚) ≥ ퟏퟐퟎ grid⁡퐖 points/퐦 in an environment of 24 grid 푺푰풏풊 = { ퟐ (3) points plus the center grid point is summedퟎ⁡풊풇 up⁡푫푵푰 (see(풙, Equation풚) < ퟏퟐퟎ (4)).⁡퐖/퐦 ⁡ In a first step for each grid point the number of sunny grid points in an environment of 24 grid m=y+2 n=x+2 points plus the center grid point is summed up (see Equation (4)). #SIni(x, y) = ∑ ∑ SIni(m, n) (4) m푚==y푦−+22n푛==x푥−+22

This is done for each daytime#푆퐼푛 slot𝑖(i푥., The푦) = number∑ of∑ each푆퐼푛 time𝑖(푚, 푛 step) is combined with the number(4) of the previous time step for each pixel to incorporate푚=푦−2 푛 also=푥−2 the temporal shift of clouds in a simpliﬁed way (seeThis Equationis done for (5)). each daytime slot i. The number of each time step is combined with the number of the previous time step for each pixel toN incorporate= # SIn ×also0.04 the temporal shift of clouds in a simplified 1 1 (5) way (see Equation (5)). Ni = (# SIni + # SIni−1) × 0.02

By using a factor of 0.04 (in case of i 푁=1 1)= or# 0.02⁡푆퐼푛1 (i ×> 1)0.04 the resulting number N is 1 in case that all 25 grid points are sunny, and 0 in the case that no grid point is sunny. (5) 푁𝑖 = (#⁡푆퐼푛𝑖 + #⁡푆퐼푛𝑖−1) × 0.02 By using a factor of 0.04 (in case of i = 1) or 0.02 (i > 1) the resulting number N is 1 in case that all 25 grid points are sunny, and 0 in the case that no grid point is sunny. In a second step the impact of cloudy and sunny grid points on the temporal length of one time slot is estimated. The fraction of time, which slot i contributes to the daily SDU is derived by Equation (6):

Remote Sens. 2017, 9, 429 5 of 14

In a second step the impact of cloudy and sunny grid points on the temporal length of one time slot is estimated. The fraction of time, which slot i contributes to the daily SDU is derived by Equation (6): ( 2 max(Ni, C1)i f DNI(Cgp) ≥ 120 W/m Wi = 2 (6) Ni·C2 i f DNI(Cpg) < 120 W/m

2 If at the center grid point (Cgp) DNI ≥ 120 W/m , the grid point is regarded as sunny, and Wi is 2 equal to Ni, but not smaller than C1. If at Cgp DNI < 120 W/m Wi is the product of Ni and C2. The constants C1 and C2 are derived empirically by sensitivity tests, where the bias compared to reference station data in Germany was minimized. C1 is set to 0.4 and indicates the minimum fraction that a single sunny slot can contribute. C2 is set to 0.05 and deﬁnes the weight for the contribution of a non-sunny slot. The ﬁnal SDU in hours is then derived by Equation (2).

4. Evaluation Results The uncertainty of SDU was estimated in comparison to station-based sunshine duration measurements from ECA&D and CLIMAT (see Section 2.2). Depending on the data availability ECA&D data were used as reference for comparisons of daily SDU and CLIMAT data were used as reference for comparisons of monthly SDU. Measures for the accuracy are the bias, mean absolute difference (MAD) and the correlation following Pearson. A detailed evaluation of SARAH-2 SDU for the UK against gridded station data is available by Zeder et al. [39]. Bias, MAD and Pearson correlation r were calculated as follows:

1 n Bias = (SDU − SDU ) ∑ SARAHi Stationi n i=1

n 1 MAD = SDU − SDU ∑ SARAHi Stationi n i=1 n − − ∑i=1 SDUSARAHi SDUSARAH SDUStationi SDUStation r = q q n − 2· n − 2 ∑i=1 SDUSARAHi SDUSARAH ∑i=1 SDUStationi SDUStation

4.1. Evaluation of Daily Data For the comparison of daily SDU only ECA&D stations with data between 1983 and 2015 were chosen. All CM SAF SDU ﬁles contain information about the number of available slots per day. As the uncertainty of SDU increases with decreasing number of available slots per day, only days with 24 or more slots were used for the comparison with ECA&D data. The CM SAF SARAH-2 SDU climate data record contains 12,028 days with SDU data and 11,911 days were derived from 24 or more available slots per day. The reasons for missing slots were mainly maintenances or failures of the satellite instrument or data storage issues. Most of the missing slots were in the operation time of Meteosat 2 (till 1988). The comparison of SDU and ECA&D daily sunshine duration measurements showed a positive bias of 0.41 h (Table1). Figure2 shows that the found biases are lowest in Germany, which might be partly due to the determination method for the constants C1 and C2. Higher biases were found in Spain and especially on the Balearic and Canary Islands. The highest seasonal bias was in the months September to November (SON) with 0.53 h. Table1 shows for the comparison of daily SDU and ECA&D a MAD of 1.33 h. The lowest MAD values were found in northern Germany (Figure3). The MAD was highest in the Alpine region, northern Spain and the Canary Islands. In the summer season (June to August, JJA) the MAD is a little higher with 1.43 h, which is probably due to the higher absolute SDU values in this season. Remote Sens. 2017, 9, 429 6 of 14

Remote Sens. 2017, 9, 429 6 of 14 Remote Sens. 2017, 9, 429 6 of 14 Table 1. Bias, MAD and correlation for the comparison of daily SDU and ECA&D for the time period Table 1. Bias, MAD and correlation for the comparison of daily SDU and ECA&D for the time period 1983–2015.Table The 1. Bias, mean MAD includes and correlation 2,472,762 for comparedthe comparison daily of values.daily SDU Seasons and ECA&D are divided for the time into period March to 1983–2015. The mean includes 2,472,762 compared daily values. Seasons are divided into March to May (MAM),1983–2015. June The to mean August includes (JJA), 2 September,472,762 compared to November daily values. (SON) Seasons and December are divided to into January March (DJF).to May (MAM), June to August (JJA), September to November (SON) and December to January (DJF). May (MAM), June to August (JJA), September to November (SON) and December to January (DJF). Bias (h) MAD (h) Correlation Bias (h)Bias (h) MADMAD (h)(h) Correlation Correlation Mean 0.41 1.33 0.91 MeanMean 0.410.41 1.331.33 0.91 0.91 MAMMAMMAM 0.320.320.32 1.381.38 0.920.92 0.92 JJAJJAJJA 0.410.410.41 1.431.43 0.930.93 0.93 SONSONSON 0.530.530.53 1.261.26 0.880.88 0.88 DJFDJFDJF 0.370.370.37 1.231.23 0.810.81 0.81

Figure 2. Spatial distribution of the bias (h) between daily SDU and ECA&D for the time period 1983 Figure 2. Spatial distribution of the bias (h) between daily SDU and ECA&D for the time period 1983 Figureto 2015. 2. Spatial distribution of the bias (h) between daily SDU and ECA&D for the time period 1983 to 2015.to 2015.

Figure 3. Spatial distribution of the MAD (h) between daily SDU and ECA&D for the time period 1983 to 2015. Figure 3. Spatial distribution of the MAD (h) between daily SDU and ECA&D for the time period Figure 3. Spatial distribution of the MAD (h) between daily SDU and ECA&D for the time period 1983 1983 to 2015. to 2015.

The correlation between SDU and ECA&D is high with 0.91 (Table1), but shows a lower value of

0.81 in winter (December to February, DJF). Figure4 shows the temporal evolution of the normalized bias in SDU. This ﬁgure illustrates that the bias is very stable in time and has no signiﬁcant trend. Remote Sens. 2017, 9, 429 7 of 14 Remote Sens. 2017, 9, 429 7 of 14

Figure 4. Temporal evolution of the normalized bias (h) between daily SDU and ECA&D for the time Figure 4. Temporal evolution of the normalized bias (h) between daily SDU and ECA&D for the time period 1983 to 2015. The normalized bias is the mean value for all stations. period 1983 to 2015. The normalized bias is the mean value for all stations. 4.2. Evaluation of Monthly Data 4.2. Evaluation of Monthly Data The evaluation of monthly sums of SDU was done with reference to measurements of CLIMAT groundThe evaluation stations. A of requirement monthly sums to the of chosen SDU was CLIMAT done withstations reference was the to availability measurements of at least of CLIMAT ten groundyears stations. of data between A requirement 1983 and to 2015. the chosen To account CLIMAT for som stationse missing was days the availability of SDU the ofmonthly at least sum ten of years of dataSDU betweenwas derived 1983 by and multiplying 2015. To the account monthly for mean some SDU missing with daysthe number of SDU of thedays monthly per month. sum There of SDU wasare derived 18 missing by multiplying days in the time the monthlyperiod from mean 1983 SDU to 1991, with most the of number them (6) of in days 1983. per month. There are 18 missingThe days comparison in the time of monthly period sums from of 1983 SDU to against 1991, mostCLIMAT of them data (6)showed in 1983. a good agreement with a Thepositive comparison bias of 7.5 of h monthly (Table 2). sums The ofspatial SDU distribution against CLIMAT of the bias data shows showed much a good higher agreement bias values with a positivein West bias Africa of 7.5 with h (Table partly2 ).more The than spatial 50 h distribution overestimation of the of SDU bias compared shows much to CLIMAT higher bias (Figure values 5). in WestThere Africa is also with some partly seasonal more thanvariation 50 h in overestimation the bias with the of highest SDU compared value from to September CLIMAT (Figureto November5). There (SON) of 10.3 h (Table 2). However, as the CLIMAT stations are heterogeneously distributed on the is also some seasonal variation in the bias with the highest value from September to November (SON) northern and southern hemisphere, the seasonal variation is difficult to interpret. To see the seasonal of 10.3 h (Table2). However, as the CLIMAT stations are heterogeneously distributed on the northern impact on SDU differences, Table 2 shows the bias and MAD divided by stations north of 23°N (NH) and southern hemisphere, the seasonal variation is difﬁcult to interpret. To see the seasonal impact on and south of 23°S (SH). The bias on the NH varies between 4.7 h from March to May ◦(MAM) and 10.3 SDUh for differences, SON and Tableon the2 SHshows between the bias 0.9 h and for MADDJF and divided −3.8 h for by SON. stations north of 23 N (NH) and south of 23◦S (SH). The bias on the NH varies between 4.7 h from March to May (MAM) and 10.3 h for SON and onRemote theTable Sens. SH 2. betweenBias,2017, 9MAD, 429 0.9 and h correlation for DJF and for the−3.8 comparison h for SON. of daily SDU and CLIMAT for the time period 8 of 14 1983–2015. The mean includes 117.933 compared monthly values. The Northern Hemisphere (NH) is defined by latitude >23°, the Southern Hemisphere (SH) by latitude <−23°. Seasons are divided into March to May (MAM), June to August (JJA), September to November (SON) and December to January (DJF).

Bias (h) MAD (h) Correlation Mean 7.5 18.4 0.96 Mean NH 6.4 17.4 0.97 Mean SH −2.2 15.2 0.95 MAM 7.8 20.1 0.92 MAM NH 4.7 18.6 0.94 MAM SH −0.4 14.6 0.95 JJA 8.6 20.6 0.93 JJA NH 6.9 20.5 0.93 JJA SH −3.2 13.6 0.97 SON 10.3 18.0 0.96 SON NH 10.3 17.0 0.97 SON SH −3.8 15.0 0.95 DJF 9.1 20.2 0.96 DJF NH 6.8 18.5 0.94 DJF SH 0.9 17.6 0.93 FigureFigure 5. Spatial 5. Spatial distribution distribution of the of the bias bias (h) (h) between between monthly monthly SDU SDU andand CLIMAT for for the the time time period period 1983 to 2015. 1983 to 2015. The MAD is 18.4 h (Table 2). As the long-term mean of SDU monthly sums for the whole Meteosat disk is about 215 h, this results in an uncertainty of less than nine percent (18.4 h/215 h = 8.6%). The spatial distribution of MAD (Figure 6) shows the highest uncertainties in West Africa. The seasonal variation of MAD is between 18.0 h for SON to 20.6 h for JJA. For the NH the MAD is highest (20.5 h) for JJA and lowest (17.0 h) for SON, while for the Southern Hemisphere there is a MAD of 17.6 h for DJF and 13.6 h for JJA. The correlation coefficient between SDU and CLIMAT monthly sums is very high with 0.96 (Table 2). This value is even higher than for the daily evaluation against ECA&D and has only minor variations throughout the seasons. The temporal evolution of the normalized bias (Figure 7) shows only a very low positive trend, which is not significant. The graph (Figure 7) illustrates that the SDU time series is stable, but there is a conspicuous peak in 1992 and an increase after 2005, which have to be evaluated.

Figure 6. Spatial distribution of the MAD (h) between monthly SDU and CLIMAT for the time period 1983 to 2015.

Remote Sens. 2017, 9, 429 8 of 14

Table 2. Bias, MAD and correlation for the comparison of daily SDU and CLIMAT for the time period 1983–2015. The mean includes 117.933 compared monthly values. The Northern Hemisphere (NH) is deﬁned by latitude >23◦, the Southern Hemisphere (SH) by latitude <−23◦. Seasons are divided into March to May (MAM), June to August (JJA), September to November (SON) and December to January (DJF).

Bias (h) MAD (h) Correlation Mean 7.5 18.4 0.96 Mean NH 6.4 17.4 0.97 Mean SH −2.2 15.2 0.95 MAM 7.8 20.1 0.92 MAM NH 4.7 18.6 0.94 − MAM SH 0.4 14.6 0.95 JJA 8.6 20.6 0.93 Figure 5. SpatialJJA NH distribution of the bias 6.9 (h) between monthly 20.5 SDU and CLIMAT for 0.93 the time period 1983 to 2015.JJA SH −3.2 13.6 0.97 SON 10.3 18.0 0.96 The MADSON is NH18.4 h (Table 2). As 10.3 the long-term mean 17.0 of SDU monthly 0.97 sums for the whole Meteosat diskSON is about SH 215 h, this results−3.8 in an uncertainty 15.0 of less than nine percent 0.95 (18.4 h/215 h = DJF 9.1 20.2 0.96 8.6%). The spatial distribution of MAD (Figure 6) shows the highest uncertainties in West Africa. The DJF NH 6.8 18.5 0.94 seasonal variationDJF of SH MAD is between 18.0 0.9 h for SON to 20.6 17.6 h for JJA. For the NH 0.93 the MAD is highest (20.5 h) for JJA and lowest (17.0 h) for SON, while for the Southern Hemisphere there is a MAD of 17.6 h for DJF and 13.6 h for JJA. TheThe MAD correlation is 18.4 h (Table coefficient2). As between the long-term SDU meanand CLIMAT of SDU monthly monthly sumssums foris very the whole high withMeteosat 0.96 disk(Table is about 2). This 215 h,value this isresults even higher in an than uncertainty for the daily of lessevaluation than nine against percent ECA&D (18.4 and h/215 has only h = minor 8.6%). The spatialvariations distribution throughout of MADthe seasons. (Figure The6) shows temporal the highestevolution uncertainties of the normalized in West bias Africa. (Figure The 7) seasonal shows variationonly a of very MAD low is positive between trend, 18.0 hwhich for SON is not to significant. 20.6 h for JJA.The Forgraph the (Figure NH the 7) MADillustrates is highest that the (20.5 SDU h) for JJAtime and series lowest is stable, (17.0 but h) for there SON, is a while conspicuous for the Southernpeak in 1992 Hemisphere and an increase there after is a MAD 2005, ofwhich 17.6 have h for DJF andto be 13.6 evaluated. h for JJA.

FigureFigure 6. Spatial 6. Spatial distribution distribution of theof the MAD MAD (h) (h) between between monthly monthly SDU SDU and and CLIMAT CLIMAT forfor thethe time period 19831983 to 2015. to 2015.

The correlation coefﬁcient between SDU and CLIMAT monthly sums is very high with 0.96 (Table2). This value is even higher than for the daily evaluation against ECA&D and has only minor variations throughout the seasons. The temporal evolution of the normalized bias (Figure7) shows only a very low positive trend, which is not signiﬁcant. The graph (Figure7) illustrates that the SDU Remote Sens. 2017, 9, 429 9 of 14 time series is stable, but there is a conspicuous peak in 1992 and an increase after 2005, which have to be evaluated.Remote Sens. 2017, 9, 429 9 of 14

FigureFigure 7. Temporal 7. Temporal evolution evolution of of the the normalized normalized biasbias (h) between monthly monthly SDU SDU and and CLIMAT CLIMAT for forthe the time period 1983 to 2015. The normalized bias is the mean value for all stations. time period 1983 to 2015. The normalized bias is the mean value for all stations.

4.3. Discussion 4.3. Discussion Overall the comparisons of SDU against ECA&D and CLIMAT station-based data show a good agreementOverall the, with comparisons small uncertainty of SDU and against high ECA&D correlation. and Compared CLIMAT to station-based other surface data radiation show a goodparameters agreement, there with are small a high uncertainty number of station and high-based correlation. measurements Compared for sunshine to other duration surface available. radiation parametersNevertheless, there there are a is high no informati numberon of about station-based the quality measurements of individual ECA&D for sunshine and CLIMAT duration sunshine available. Nevertheless,duration time there periods is no. informationEven after manual about inspection the quality and of some individual basic automated ECA&Dand quality CLIMAT checks sunshinethere is an unknown uncertainty for these station data. duration time periods. Even after manual inspection and some basic automated quality checks there is Another issue regarding the comparison of satellite-based and station-based data is the an unknown uncertainty for these station data. representativeness of each station for a satellite pixel of the size of 0.05° × 0.05°. This issue can explain someAnother higher issue uncertainties regarding in the mountainous comparison areas of or satellite-based islands. Mountain and summits, station-based where data stations is the ◦ ◦ representativenessusually are placed, of eachcan influence station the for surrounding a satellite pixel clouds, of which the size is visible of 0.05 in the× 0.05 satellite. This pixel, issue but can explainnot somein the station higher data. uncertainties Shadowing in of mountainous stations is also areas relevant, or islands. as this Mountain is not considered summits, in the where satellite stations- usuallybased are data. placed, For instance, can inﬂuence the comparison the surrounding of daily clouds,SDU sho whichws for is Spain visible a strong in the variation satellite of pixel, the bias but not in theover station short data. distances, Shadowing which is of unlikely stations due is also to small relevant,-scale asvariations this is not in consideredthe satellite- inbased the satellite-basedSDU. data. ForThe instance, correlation the comparisonof daily SDU ofagainst daily ECA&D SDU shows shows for a decrease Spain a strongin winter. variation As ECA&D of the stations bias over shortare distances, all in the whichnorthern is unlikelyhemisphere due (mainly to small-scale in Germany variations and Spain) in the the satellite-based lower correlation SDU. values for DJFThe might correlation be due ofto snow daily cover. SDU againstSnow cover ECA&D can be showsmisinterpreted a decrease by satellite in winter. retrievals As ECA&D (see Section stations are all2.1) in as the clouds. northern This leads hemisphere to an underestimation (mainly in Germany of solar surfaceand Spain) radiation the lower and accordingly correlation to values an underestimation of SDU. In addition, the uncertainty of the ground station measurements increases for DJF might be due to snow cover. Snow cover can be misinterpreted by satellite retrievals in the case of snow, because regular maintenance of the instruments is needed. (see Section 2.1) as clouds. This leads to an underestimation of solar surface radiation and accordingly Higher uncertainties in West Africa can be explained by an issue with low clouds. Hannak et al. to an underestimation of SDU. In addition, the uncertainty of the ground station measurements [40] showed that in the region of West Africa there are large and persistent low cloud fields. They increasespostulated in the that case the of selfsnow,-calibrating because method regular maintenanceof the Heliosat of algorithm the instruments leads to is an needed. underestimated effectiveHigher cloud uncertainties albedo in inthe West region Africa with persistent can be cloud explained cover, since by an the issue scenes with with minimum low clouds. Hannaknumber et al. of [40counts] showed are still that contaminated in the region with clouds of West [40]. Africa This leads there to are a strong large overestimation and persistent of low cloudsurface ﬁelds. radiation They postulated and SDU that in this the self-calibratingarea. In addition, method this effect of the could Heliosat be reinforced algorithm by leads an to an underestimatedunderestimation effective of aerosols cloud optical albedo depth in the(AOD). region In thewith retrieval persistent of SARAH cloud cover,-2 a constant since the AOD scenes withclimatology minimum was number used. Thus, of counts biomass are burning still contaminated or dust outbreaks with are clouds not regarded [40]. This in the leads retrieval to a and strong overestimationcan lead to the of observed surface radiation overestimation and SDU of surface in this radiatio area. Inn. addition, this effect could be reinforced by an underestimationThe Figures 4 and of aerosols7 confirm optical that the depth daily (AOD). and monthly In the SDU retrieval time ofseries SARAH-2 are very a stable constant from AOD climatology1983 to 2015. was used.There Thus,is a peak biomass after 1991, burning which or is dust very outbreaks likely due are to notthe regardedPinatubo eruption in the retrieval in June and 1991. The constant AOD climatology of the SARAH-2 retrieval might be the reason why the SARAH- can lead to the observed overestimation of surface radiation. 2 data record does not include the effects of the Pinatubo eruption, which reduced the solar radiation up to three years after the event. Thus, SDU is reduced in station data, but not in SARAH-2 SDU,

Remote Sens. 2017, 9, 429 10 of 14

The Figures4 and7 confirm that the daily and monthly SDU time series are very stable from 1983 to 2015. There is a peak after 1991, which is very likely due to the Pinatubo eruption in June 1991. The constant AOD climatology of the SARAH-2 retrieval might be the reason why the SARAH-2 data record does not include the effects of the Pinatubo eruption, which reduced the solar radiation upRemote to three Sens. years2017, 9, after429 the event. Thus, SDU is reduced in station data, but not in SARAH-210 of SDU, 14 which leads to an overestimation during that time. Figure7 shows a slight increase in the normalized SDUwhich bias leads after to 2005, an over whichestimation might beduring attributed that time. to the Figure change 7 shows from a the slight Meteosat increase MVIRI in the tonormalized the SEVIRI SDU bias after 2005, which might be attributed to the change from the Meteosat MVIRI to the SEVIRI instruments. The transition between MVIRI and SEVIRI as well as the implementation of improved instruments. The transition between MVIRI and SEVIRI as well as the implementation of improved aerosols will be further investigated for future SARAH releases. aerosols will be further investigated for future SARAH releases. An issue that might have an influence on the results of the evaluation in some regions is artificial An issue that might have an influence on the results of the evaluation in some regions is artificial patterns, which are most obvious in cloud-free areas with a high solar insolation, such as deserts. patterns, which are most obvious in cloud-free areas with a high solar insolation, such as deserts. TheseThese artifacts artifacts are are a resulta result of of the the combination combination ofof 3030 min instantaneous data, data, where where the the threshold threshold of of ≥ 2 DNIDNI 120≥ 120 W/m W/m2 isis alreadyalready reachedreached at at the the twilight twilight zone. zone. Figure Figure 88 shows shows an an example example of of daily daily SDU, SDU, wherewhere artificial artificial patterns patterns are are visible visible in in the the KalahariKalahari andand Sahara deserts. deserts. These These spatial spatial jumps jumps of ofSDU SDU cancan be be up up to to 30 30 min min differences, differences, which whichmay may havehave a slight impact impact on on the the re resultssults of of the the comparisons comparisons in in SectionSection 4.2 4.2.. For For longer longer sampling sampling periods, periods, suchsuch as monthly averages averages or or monthly monthly sums sums of ofSDU, SDU, these these patternspatterns disappear. disappear.

FigureFigure 8. 8.Daily Daily sum sum SDU SDU (h) (h) for for 25 25 JulyJuly 2015.2015. The colorbar highlights highlights artificial artiﬁcial patterns patterns in inAfrica. Africa.

5. Applications5. Applications FigureFigure9 shows 9 shows the the mean mean annual annual sum sum of SDUof SDU for for 1983 1983 to 2015to 2015 for for different different regions. regions. This This ﬁgure figure also demonstratesalso demonstrates that the that high the spatial high spatial resolution resolution of 0.05 of◦ 0.05°× 0.05 × ◦0.05°is suitable is suitable for manyfor many applications applications from continentalfrom continental to local to scales. local scales. The main The impactsmain impacts on sunshine on sunshine duration duration are atmosphericare atmospheric parameters, parameters, such as clouds,such as waterclouds, vapor water and vapor aerosols. and aerosols However,. However, also topographic also topographic features features and local and processes local processes inﬂuence SDUinfluence and can SDU be seen and incan Figure be seen9. For in Figure instance 9. For the gradientinstance the in SDU gradient between in SDU the between northern the and northern southern sideand of southern the Alps orside the ofPyrenees the Alps isora the good Pyrenees example is a for good the blockingexample for impact the blocking of these mountainimpact of these ranges. mountain ranges.

Remote Sens. 2017, 9, 429 11 of 14 Remote Sens. 2017, 9, 429 11 of 14

Remote Sens. 2017, 9, 429 11 of 14

FigureFigure 9. 9. MeanMean annualannual sum (h) (h) of of SDU SDU for for the the time time period period 1983 1983 to to2015 2015 for forthe the Meteosat Meteosat disk disk (a), (a), EuropeEuropeFigure and 9.and Mean North North annual AfricaAfrica sum ((bb),), (h) and of Central SDU for Europe Europe the time (c (c).). period 1983 to 2015 for the Meteosat disk (a), Europe and North Africa (b), and Central Europe (c). Trend5.1. Trend in Sunshine in Sunshine Duration Duration The temporal coverage of 33 years and the temporal stability of the data allow climatological 5.1.The Trend temporal in Sunshine coverage Duration of 33 years and the temporal stability of the data allow climatological investigations. For instance, trend analysis can be a valuable tool to investigate impacts of a changing investigations.The temporal For instance, coverage trend of 33 analysisyears and can the be temporal a valuable stability tool to of investigate the data allow impacts climatological of a changing climate.investigations. Figure For10 shows instance, the trendlinear analysis decadal cantrend be for a valuable SDU. Except tool to for investigate some regions impacts in the of tropicsa changing, the climate. Figure 10 shows the linear decadal trend for SDU. Except for some regions in the tropics, decadalclimate. trendFigure in 10 SDU shows for the 1983 linear to 2015decadal (Figure trend 10a) for SDU.is slightly Except positive for some or regionsaround inzero. the tropicsSignificant, the the decadal trend in SDU for 1983 to 2015 (Figure 10a) is slightly positive or around zero. Signiﬁcant trendsdecadal were trend found in SDU mainly for south1983 toof 2015the equator. (Figure A10a) split is ofslightly the time positive series orfrom around 1983 tozero. 1999 Significant and 2000 trends were found mainly south of the equator. A split of the time series from 1983 to 1999 and 2000 totrends 2015 wereshows found very mainly different south trend of thepatterns equator. (Figure A split 10b of,c). the In time many series regions from the 19 83found to 1999 trends and were 2000 to 2015 shows very different trend patterns (Figure 10b,c). In many regions the found trends were evento 2015 opposite. shows veryFor instance, different theretrend is patterns a strong (Figure positive 10b trend,c). In from many 2000 regions to 2015 the infound Eastern trends Europe, were even opposite. For instance, there is a strong positive trend from 2000 to 2015 in Eastern Europe, whileeven opposite.there is a Forslightly instance, negative there tren is da instrong this regionpositive from trend 1983 from to 1999. 2000 Thereto 2015 is ina positiveEastern trendEurope, in whilethewhile Atlantic there there is isOcean a a slightly slightly near negative negativethe Canary trendtren Islandsd in this from region region 1983 from from to 1999, 1983 1983 whileto to 1999. 1999. there There There is ais slightly a is positive a positive decreasing trend trend in in thetrendthe Atlantic Atlantic in this Ocean regionOcean nearfromnear the2000the CanaryCanary to 2015. Islands A study from from by Pfeifroth1983 1983 to to 1999, et 1999, al. while[41] while shows there there that is isa trendsslightly a slightly o f decreasingSARAH decreasing-2 trendsurfacetrend in in thisradiation this region region are from from slightly 20002000 underestimated toto 2015.2015. A A study study compared by by Pfeifroth Pfeifroth to the et et correspondingal. al. [41] [41 shows] shows that trends that trends trends of station of SARAH of SARAH-2-based-2 surfacesurfacesurface radiation radiation are data.are slightly slightly A possible underestimatedunderestimated reason for thiscompared compared underestimation to to the the corresponding corresponding of trends is trends the trends usage of ofstation of station-based constant-based surfaceAODs.surface radiation Thus,radiation most data. data. of the AA possible possiblefound trends reason in for forSARA this this Hunderestimation underestimation-2 SDU are due of to of trends changes trends is isthein the cloudsusage usage ofand constant of water constant AODs.vapor.AODs. Thus, Nevertheless,Thus, mostmost ofof the the foundfound trends in are in SARA SARAH-2 comparableH-2 SDU SDU to are trends are due due into to changes solar changes radiation, in inclouds clouds which and and water were water vapor.recentlyvapor. Nevertheless, Nevertheless, investigated the by thefound Arturo found trends Sanchez trends are - areLorenzo comparable comparable et al. to [42], trends to Alexandri trends in solar in et solar radiation, al. [29] radiation, and which Pfeifroth which were et wererecently al. investigated[41]recently for Eu investigatedropean by Arturo station by Sanchez-Lorenzo Arturodata. Sanchez-Lorenzo et al. [42 et], al. Alexandri [42], Alexandri et al. [29et ]al. and [29] Pfeifroth and Pfeifroth et al. et [41 al.] for European[41] for Eu stationropean data. station data.

Figure 10. Linear decadal trend in SDU for the time period 1983 to 2015 (a), 1983 to 1999 (b), 2000 to 2015 (c). FigureFigure 10. 10.Linear Linear decadal decadal trendtrend in SDU for for the the time time period period 1983 1983 to to 2015 2015 (a), (a 1983), 1983 to to1999 1999 (b), ( b2000), 2000 to to 20152015 (c ().c).

Remote Sens. 2017, 9, 429 12 of 14

6. Conclusions The CM SAF SARAH-2 SDU is the first climate data record for daily and monthly sunshine duration covering Europe, Africa and parts of South America for the time period 1983 to 2015. The retrieval is based on the WMO threshold for sunshine of DNI ≥ 120 W/m2 and additionally estimates the impact of surrounding grid points. SARAH-2 SDU has a spatial resolution of 0.05◦ × 0.05◦ and is therefore highly suitable for regional analysis. The comparison against daily ECA&D station-based measurements showed a good agreement with a bias of 0.41 h, a MAD of 1.31 h and a correlation of 0.91. The highest MADs were found in mountainous regions and on islands, which might be an issue of representativeness. In comparison with CLIMAT station-based monthly sums of sunshine duration, the CM SAF SDU slightly overestimated the sunshine duration. The evaluation against CLIMAT gave a bias of 7.5 h, MAD of 18.4 h and a correlation of 0.96. Thus, the uncertainty of SDU compared to CLIMAT is well below 9% in most regions of the Meteosat disk. Besides the high accuracy of SDU it was shown that the time series is very stable. There are still some deficiencies, such as minor artifacts in some scenes, an issue with low clouds in West Africa, a constant AOD climatology or cases of a misinterpretation of snow coverage as clouds, which will be investigated for a next version. The CM SAF SARAH-2 SDU climate data record is freely available via wui.cmsaf.eu. The combination of a well-known meteorological parameter with high-quality, high spatial resolution for a homogeneous time series of 33 years is qualified for many climate applications, including variability and trend analysis. For instance, the combination of SARAH-2 SDU and station-based data by merging could be a highly beneficial application for the near future.

Acknowledgments: The work performed was done by using data from EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF). CLIMAT data were accessed via the DWD Climate Data Centre (http://www.dwd.de/EN/climate_environment/cdc/cdc_node.html). ECA&D data were accessed via http: //www.ecad.eu/. Author Contributions: Steffen Kothe designed and performed the study and analyzed the data. Uwe Pfeifroth and Jörg Trentmann contributed SARAH-2 DNI data and gave feedback. Roswitha Cremer supported the data analysis. Rainer Hollmann is the scientific manager of the project and gave feedback. Conflicts of Interest: The authors declare no conflict of interest.

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