Regional scale evaluation of a MSG solar radiation product for evapotranspiration modeling

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Introduction
Knowledge of spatiotemporal distributions in solar radiation (R s ) is essential in many disciplines such as ecology, agronomy or hydrology, and plays a key role in the modeling of evapotranspiration (ET), both actual and potential, as well as air temperature.These variables are of high importance in monitoring and understanding the Figures ecohydrological properties of terrestrial ecosystems and for agricultural support (Pons et al., 2012).Together with precipitation, ET is an essential variable in the hydrological cycle, and its modeling has been a research challenge over the last 20 yr.Currently, there are a wide variety of remote sensing models for calculating ET at regional or global scales that require R s as input.These include methods based on remote sensing of land-surface temperature (LST), such as Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC, Allen et al., 2007), Surface Energy Balance Algorithm for Land (SEBAL, Bastiaanssen et al., 1998), Two-Source Energy Balance (TSEB, Kustas and Norman, 2000), Atmosphere-Land Exchange Inverse and its associated disaggregation technique (ALEXI and DisALEXI, respectively, Anderson et al., 2004), Simplified Surface Energy Balance Index (S-SEBI, Roerink et al., 2000), Simplified Two-Source Energy Balance (STSEB, S ánchez et al., 2008b), the B-Method (Jackson et al., 1977;Seguin and Itier, 1983;Crist óbal et al., 2011), and other LST-based methodologies described by Schmugge et al. (2002Schmugge et al. ( ), S ánchez et al. (2008a) ) or Kalma et al. (2008).Other methods use R s data to reference ET using Priestley-Taylor (Priestley and Taylor, 1972) or Food and Agriculture Organization (FAO) Penman-Monteith (Allen et al., 1998) approaches.Because all these methods need R s as essential input, inaccurate sources of R s can lead in considerable errors in ET retrieval.While LST-based methods typically retrieve ET directly only under clear sky conditions, accurate R s is required under cloudy conditions as well to support upscaling of fluxes to daily and longer timescales.
For operational applications, most of these methods try to minimize use of data from ground-based meteorological stations.Therefore, ET algorithms operating at regional to global scales can benefit from R s surfaces retrieved using satellite imaging.Most of these ET methods have been validated in homogeneous covers (crops or natural vegetation) and flat areas, using a single value of R s from a meteorological station record to describe a large area.However, in more complex terrain conditions, a single meteorological record may not be accurate enough to reasonably estimate ET spatially, Introduction

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Full considering gradients in the spatial distribution of R s due to variable topography and cloud cover.R s is typically estimated using one of three different methodologies: empirical models, based on statistical correlations between R s and other parameters; parametric models, based on the physics of interactions of R s with the atmosphere (Martínez-Durbar án et al., 2009); and hybrid models that combine both approaches.Some of these models use GIS-based techniques and a digital elevation model, DEM (Pons and Ninyerola, 2008) to compute R s at regional and global scales in both simple and complex areas offering high accuracy and high spatial resolution, but relying on a well developed meteorological station network.In many regions, the density of meteorological stations is sparse and only satellites can realistically provide R s data, especially at global scales (Journ ée and Bertrand, 2010;Olseth and Skartveit, 2001;Pinker et al., 2005).
Operational satellite systems provide valuable information on atmospheric parameters at regular intervals on a global scale.This satellite-based information greatly enhances our knowledge and understanding of the processes and dynamics within the Earth-atmosphere system.Nowadays, there is a wide variety of satellites, both geostationary and sun-synchronous, from which R s can be retrieved regionally or globally such as Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), the National Oceanic and Atmospheric Administration's (NOAA) Advanced Very High Resolution Radiometer (AVHRR), the Geostationary Operational Environmental Satellites (GOES) or Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager sensor (SEVIRI).Unlike sun-synchronous sensors, geostationary sensors are especially interesting because of their high temporal resolution, which facilitates mapping of R s at intervals of 15-30 min over large areas.In the case of Europe, there are currently three facilities that produce and offer R s products from 30min to monthly time steps derived from MSG SEVIRI data that can be used as input data in ET modeling: the Satellite Application Facility on Climate Monitoring, CM-SAF (http://wui.cmsaf.eu/), the Ocean and Sea Ice Satellite Application Facility, OSI SAF Introduction

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Full (http://www.osi-saf.org/),and the Land Surface Analysis Satellite Application Facility, LSA SAF (http://landsaf.meteo.pt/).In this work we present a regional-scale evaluation of the LSA SAF R s product, generated using MSG SEVIRI images from 2008 to 2011.The product dataset is evaluated at hourly, daily and monthly time steps, both as a whole and as subsets depending on terrain class (flat and hilly areas) and atmospheric conditions (clear and cloudy skies) to determine dependencies in the model accuracy.

Solar radiation product and model overview
Since 2007, the LSA SAF has offered an operative product describing the Down-welling Surface Short-wave radiation Flux (DSSF), obtained by means of the SEVIRI sensor.
The DSSF product preserves the projection and spatial resolution of the MSG-SEVIRI images, using the ellipsoid normalized geostationary projection with a nominal spatial resolution of 3 km at nadir.This product is generated at a 30-min time step using data from the three solar spectrum channels of the SEVIRI sensor (centred on 0.6, 0.8 and 1.6 µm) and is encapsulated in an HDF5 file format.Each product file includes a set of three quality flag images (see Table 1): a land and sea mask, a cloud mask also including snow and ice cover; and the DSSF algorithm that was applied (clear or cloudy sky algorithm).
The model used to retrieve R s for the DSSF product is based on the framework of the OSI SAF (Brisson et al., 1999) using three short-wave SEVIRI channels, 0.6 µm, 0.8 µm, and 1.6 µm (LSA SAF, 2010).The model is designed to compute the effective atmospheric transmittance, applying a clear or cloudy sky retrieval method depending on cloud cover.Cloud cover estimates are provided by the cloud mask developed by the Nowcasting and Very Short-Range Forecasting, which is integrated in the LSA SAF operational system (Geiger et al., 2008b).
In the case of the clear sky method, the atmospheric transmittance and the spherical albedo of the atmosphere are calculated according to the methodology of Frouin Figures

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Full  (1989).The water vapour used to estimate the atmospheric transmittance is obtained from the European Center for Medium-Range Weather Forecasts and the ozone amount is specified according to the Total Ozone Mapping Spectrometer climatology, while the visibility is currently kept at a fixed value of 20 km.The surface albedo is taken from the LSA SAF near real time albedo product (Geiger, 2008a).
In the case of the cloudy sky method, a simplified physical description of the radiation transfer in the cloud-atmosphere-surface system according to Gautier et al. (1980) and Brisson et al. (1998) is used.The cloud transmittance and albedo may be highly variable on small time scales depending on the daily evolution of the clouds.For this purpose the measured spectral reflectances in the 0.6 µm, 0.8 µm, and 1.6 µm SEVIRI are first transformed to broad-band top-of-atmosphere albedo by applying the spectral conversion relations proposed by Clerbaux et al. (2005) and the angular reflectance model of Manalo-Smith et al. (1998).
More information about the DSSF method can be found in Geiger et al. (2008b) and LSA SAF (2010).which includes 8 automatic meteorological ground stations located over grasslands and covering high altitudes from 2200 to 2540 m.From the XEMA network, 140 meteorological stations measuring R s were selected, applying a filter criterion consisting of stations that have been in service for at least 5 yr (see Fig. 1).For each of these stations SMC applies a data quality process and produces a R s quality flag.The selected meteorological stations are located in different land uses and span a range in altitude (see Table 2), providing a broad basis for comparison with satellite retrievals under different circumstances.
In order to analyse the performance of DSSF product in different terrain conditions, the meteorological stations were separated into two classes (see Fig. 1): those situated in flat and hilly terrain.This separation was based on a slope surface derived from a 30 m spatial resolution DEM from the Institut Cartogr àfic de Catalunya (Cartographic Institute of Catalonia).The standard deviation in topographic slope was computed in a 3-km buffer area around each meteorological station, simulating the resolution of MSG SEVIRI.Slope standard deviation gives information about terrain heterogeneity, and whether or not the meteorological station is surrounded by mountains that might influence shading of the R s sensor.Based on these analyses, a threshold in slope standard deviation was selected to partition the network stations into two sets: 100 meteorological stations in relatively flat terrain, and 40 in hilly terrain.

DSSF product
A total of 1096 days of DSSF products for 2008 to 2010 were downloaded from the LSA SAF web site.A standard day consists of 48 files in HDF5 format, an image every 30 min, although there are days that have fewer files.In total, 52 608 files were downloaded and processed.
To import the HDF5 files (Rs and quality flag data), an IDL routine was implemented to read the DSSF product including all metadata.DSSF images were imported into MiraMon (Pons, 2004) file format, which allows complete metadata documentation.To Introduction

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Full minimize impacts of data re-sampling due to reprojection, the analysis was carried out in the original projection and spatial resolution of the DSSF product.

Solar radiation extraction and evaluation criteria
Once the DSSF product was imported, data extraction was performed using a bilinear interpolation in time between images and in space to meteorological station locations.
Recent work in the literature suggests that averaging over a block of pixels centered on the location of a pyranometer significantly decreases the error compared to use of a single pixel, although there is no agreement on what is the optimal block size (Pinker and Laszlo, 1991;Rigollier et al., 2004;Journ ée and Bertrand, 2010).Nevertheless, in this work, we are interested in a pixel-based analysis to better capture effects of heterogeneity in the mountainous areas with narrow valleys found in our study area.
In order to manage data efficiently through the use of SQL statements, a database was built for product evaluation.This database consists of two parts: a DSSF record every 30 min, which incorporates both R s and quality flags, and 1 h meteorological records that include measured R s , data quality from the SMC and meteorological station terrain classes (flat or hilly).
DSSF evaluation was conducted using only pixels flagged as clear or cloudy (contaminated and cloud filled conditions) in the DSSF cloud mask and processed by clear and cloudy methods in the DSSF algorithm (see Table 1).Data under undefined and unprocessed categories in the DSSF cloud mask as well as algorithm failed, beyond specified view angle limit and not processed (cloud mask undefined) categories in the DSSF algorithm (see Table 1), representing less that 0.7 % for the whole dataset, were excluded from the analysis to avoid introducing errors in the evaluation analysis due to unreliable data.
In addition, images outside the interval between dawn and dusk (zero insolation) were also excluded from analysis in order not to magnify accuracy statistics.The Introduction

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Full Based on these criteria, the DSSF product was evaluated with respect to ground observations at hourly, daily, and monthly timescales.This required aggregation of the DSSF product, available at 30 min intervals, and the SMC R s data, available only at hourly intervals.For hourly evaluation, DSSF algorithm performance was analyzed under both clear and cloudy sky conditions and snow and ice cover.To focus on pixels where cloud conditions were relatively stable over the hourly sampling interval of the SMC dataset, the hourly evaluation was conducted using only pixels reporting the same quality flags during each hourly interval in terms of cloud mask and DSSF algorithm (i.e., a pixel masked as clear both at 12:00 and 12:30 UTC).On the other hand, pixels with different quality flags for a specific hour were excluded in the analysis (i.e., a pixel masked as clear at 12:00 and cloudy at 12:30 UTC).
The relative performance of the clear and cloudy sky algorithms used in the DSSF process was also explored.Currently, there is no agreement on how to best define a clear sky day in terms of amount of cloud-free time.In this study, a clear-sky day at a given pixel was defined such that ≥ 80 % the time samples between dawn and dusk were cloud free.Moreover, as criteria for computation of daily average R s from the DSSF product, we specified that at least 90 % of the potential images within a day must be available.A similar criterion of completeness was applied to the pyranometer data, but requiring all data samples from dawn to dusk to have a good quality flag.Finally, in the case of monthly aggregation, no distinction was made between clear or cloudy conditions given the length of the averaging interval.A criterion of having daily aggregates in both satellite product and pyranometer datasets for more than 25 days per month was enforced to ensure that these data were representative of monthly conditions.Introduction

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Accuracy and error estimation
The performance of the DSSF product was evaluated using several statistical indices and measures of error.The coefficient of determination (R 2 ) indicates the precision of the estimates in relation to measured R s , the root mean square error (RMSE, Eq. 1) is used to measure the differences between values predicted by a model or an estimator and the values actually observed and is a measure of accuracy, the mean absolute error (MAE, Eq. 2) indicates the magnitude of the average error, the mean bias error (MBE, Eq. 3) indicates cumulative offsets between measured and observed values, and the percentage of error (PE, Eq. 4) expresses the magnitude of the error between observed and estimated values relative to the observed mean value.
where e i refers to the estimated value of the variable in question (satellite-derived R s ), o i is the observed value (in situ R s measurement provided by the meteorological station), n is the number of datapoints, and X is the average of the n o i values.Introduction

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Full   A literature review reveals only three comparative studies that clearly address the issue of terrain conditions on R s product evaluation.The use of different accuracy and error estimators, as well as differences in temporal extent of analysis and thresholding criteria, complicates detailed comparison of the results presented here with those in other references.Nevertheless, a qualitative comparison with prior results provides some useful context for the current study.
In the case of flat conditions (column 1 in Table 3), the evaluation results presented here are in agreement with those found in the literature.In the DSSF product validation performed by Geiger et al. (2008b) We have not found previous R s comparisons over snow and ice cover using Meteosat data.In general terms, the presence of snow or ice yielded in higher RMSE and lower correlation, especially in hilly sites.In this case, the snow and ice surface albedo may be difficult to define, leading to higher errors compared to other land covers.Still, R 2 values of ∼ 0.8 indicate that useful data are being generated even for these difficult landcover situations.
It is worth remarking that the mean PE obtained in this study at hourly timesteps for all atmospheric conditions in flat and hilly classes was 19 and 27 %, respectively, and according to Zelenka et al. (1999) this value compares favorably with the value of 20-25 % reported world-wide.While PE during cloudy sky conditions exceeds this range, with values of 36 and 42 %, respectively, this is mostly a function of lower mean observed R s during periods of cloud cover.Finally, DSSF values over snow and ice cover also yield a PE in this interval, ranging from 23 to 27 %.

Daily evaluation
Table 4 shows   Figure 3 shows examples of daily R s dynamics from dusk to dawn at two meteorological stations located in flat (V8) and hilly terrain (UI) during a clear sky day (both in 6 April 2008) and a cloudy sky day (24 September 2008).The upper left plot shows the best-case scenario, for the site in flat terrain under clear sky, while the upper right shows data for the same clear day at the site in hilly terrain.The hilly site shows evidence of topographic shadows between 16:00 and 19:00 UTC, while the 3-km average does not show a strong diurnal shadowing effect.According a sensitivity analysis by Oliphant et al. (2003) to isolate the role of spatial variability of surface characteristics in generating variance in the radiation budget, one of most important characteristics was found to be slope aspect.This fact suggests that in hilly sites, the DSSF algorithm could be enhanced to reproduce subpixel variability in shadowing effects by accounting for topography using a DEM.The lower plots in Fig. 3 show daily R s dynamics under cloudy sky conditions for both flat and hilly sites.As seen in Table 4, the accuracy of the DSSF algorithm is lower under cloud cover relative to the clear sky case.Still, in this case the DSSF algorithm reproduces the meteorological station R s dynamics with reasonable fidelity at the flat site.
As in the hourly evaluation case, the MBE is negative in almost all cases meaning that the DSSF algorithm underestimates R s at the daily timescale on average, although the bias determined for both terrain classes for the averaged 2008-2010 period does not exceed −0.2 MJ m −2 .
Results presented here at the daily timestep are consistent with those found in the literature.Bois et al. (2008)

Monthly evaluation
Table 5 and Fig. 4 show results from comparison of satellite retrievals and pyranometer data aggregated to monthly timesteps.Comparisons at sites in flat terrain yield an averaged RMSE, MAE, PE and R 2 for 2008-2010 period of 0.9 and 0.7 MJ m −2 , 5.6 % and 0.99, respectively; while hilly sites yield an averaged RMSE, MAE, PE and R 2 of 1.4 and 1 MJ m −2 , 9.3 % and 0.97, respectively.As with the hourly and daily results, better agreement was obtained at sites in flat terrain, although dependency of accuracy on terrain condition was not as marked at the monthly timestep.In both cases, MBE is negative in all cases meaning that the DSSF algorithm underestimates R s at the monthly timescale on average, although the bias determined for both terrain classes for the averaged 2008-2010 period does not exceed −0.2 MJ m −2 .Using the DSSF product, Geiger et al. (2008) found no clear seasonal bias dependence in the results.However, seasonal trends in MBE show that MBE is generally more positive during summer months, from June to September, and negative for the rest of the year in flat and hilly sites (see Fig. 5).Pinker et al. (2003) and Otkin et al. (2005) also found similar seasonal trends in MBE using GOES to model R s .According to Geiger et al. (2008) and Ineichen et al. (2009) this bias may be related to the atmospheric transmission inputs such as the atmospheric turbidity that could be addressed by considering the temporal and spatial variability of the aerosol concentration in more detail.The removal of this bias would further decrease the RMSE and the MBE in model estimates in both terrain classes.general, the DSSF product reproduces the behavior measured at meteorological station located in these two sites displaying a good seasonal agreement in both flat and hilly sites.These monthly results are in good agreement with previous work, although there are few studies that have aggregated R s on a monthly basis.Rigollier et al. (2004) report a PE from 5 to 24 % based on their dataset as well as for other studies using the same retrieval method.Using Meteosat retrievals, Pereira (1996) reported a PE of 13 % during a 2-yr period (1985)(1986) and using 22 meteorological stations.Finally, using data from GOES, GMS and MTSAT from 1995to 2008, Janjai et al. (2011) found a PE of 6.3 % with 5 meteorological stations in flat areas in Cambodia.According to Pinker et al. (2005), several attempts to compute R s with remote sensing data at a monthly time step and at a global scale yielded RMSE between 11.7 and 31.5 W m −2 .
It is interesting to remark that similar results were found by Pons and Ninyerola ( 2008) using an hybrid model, applying DEM-based corrections to R s retrievals and comparing to 5 yr series of monthly data from meteorological stations.They found a PE ranging from 7.3 to 13.1 % in four months, with a RMSE from 0.98 to 1.4 MJ m −2 .
If we take also into account that 77 % of the meteorological stations analyzed present a complete 3-yr monthly R s record from the DSSF, this means this product can be also useful for mapping R s from a climatic perspective (Crist óbal et al., 2008;Ninyerola et al., 2000).

Using DSSF as input data in ET modeling
While few analyses of ET model sensitivity to R s accuracy have been published, Diak et al. (2004) claim that a PE about 10 % or less for daily solar radiation is acceptable for reasonable model performance.When retrieving net radiation, an essential variable for estimating ET, Kustas et al. (1994) scheduling in Wisconsin, USA.Stewart et al. (1999) used GOES data to retrieve hourly and daily R s in a comparison among three evapotranspiration formulations applied over an agricultural area in northwest of Mexico.They found that hourly PE of 9.1 and 16.8 % for clear sky and all averaged conditions, respectively, and daily PE of 4.7 and 9.2 for clear sky and all averaged conditions, respectively, produced reasonable estimates of ET.Garautza-Payan et al. (2001) and Garautza-Payan and Watts (2005) estimate crop water requirements of irrigated vegetation, cotton and wheat, in the same area also using GOES data as R s input for ET modeling.The GOES derived R s data displayed a PE from 9-14 % to 7-14 % for hourly and daily periods, allowing retrieval of daily cotton and wheat ET with a RMSE of 23 and 7 W m −2 , respectively.In the work conducted by Jacobs et al. (2002), GOES data was used to estimate wetlands potential ET in Florida, USA, during a growing season under non-water-limited conditions.R s evaluation with ground data showed a PE of 28.3 and 9.9 % for 30-min and daily R s time steps yielding an ET error at 30-min timesteps around 30 % and a R 2 of 0.67 but lower error of 3.1 % and higher R 2 of 0.90 in daily ET retrievals.When comparing four potential ET methods at a daily time step over a wetland area in Florida, USA, Jacobs et al. (2004) found dramatic improvements in the efficiency of ET-radiation based models using GOES R s , with a RMSE of 19.4 W m −2 .During the Soil-Moiture Atmospheric Coupling Experiment (SMACEX) carried out in Iowa, USA, in 2002, Su et al. (2005) reported good agreement between modeled ET using GOES Rs data and in situ observations of instantaneous ET, with a RMSE of 60 W m −2 in 8 corn and soybean plots.
In evaluating crop reference ET estimation at a daily time step in two study areas in Southern France, Bois et al. (2008), found better results from methods using Meteosat R s compared to methods only using air temperature, ranging the relative annual RMSE from 22 % to 28 %, according to the method and the type of climate, humid-Oceanic or In general terms, evaluation results derived from this study show that the DSSF product has a relative error of about 10 % in comparison with pyranometer data at daily timesteps, and therefore can be used as a functional input to radiation-based ET models.However, it is important to remark that DSSF product may produce errors in models running at finer spatial scales (sub 3-km) over regions of hilly terrain unless topographic corrections are applied.This may be important for ET modeling applied to Landsat thermal imagery at 60-100 m spatial resolution.Errors are significantly higher over regions of snow cover, and this will affect studies monitoring energy fluxes and snow melt in cold land regions.At daily time steps, the DSSF product performs within the 10 % error, except for the most difficult modeling scenario involving hilly terrain under cloudy skies.Satellite-based insolation retrievals can therefore be of significant utility in extrapolating instantaneous clear-sky ET retrievals to daily, monthly and seasonal estimates (Anderson et al., 2012).

Conclusions
Hourly, daily and monthly solar radiation estimates derived from the DSSF product produced by LSA SAF using MSG SEVIRI imagery were compared to pyranometer data in two terrain classes (flat and hilly) and for two atmospheric conditions (clear and cloudy sky), as well as for snow and ice cover.In general terms, hourly results compared favorably with the RMSE value of 20-25 % reported previously for global evaluation studies  Full  Full  Full  Full  Full Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | et al.
Hourly meteorological data were downloaded from the Catalan Meteorological Service (SMC) web (meteorological data are available at http://www.meteocat.com).SMC currently manages a network of 165 meteorological ground stations in Catalonia called Meteorological Automatic Stations Network (XEMA).At its origin, XEMA combined several existing networks: the Agroclimatic Network, starting in 1996 and including 90 meteorological ground stations mainly covering crop field areas and with elevation ranging from 0 to 1571 m; the Automatic Station Network, starting in 1988 and including 56 automatic meteorological ground stations covering natural vegetation and urban areas, ranging from 0 to 1971 m; and starting in 1997, the Snow Meteorological Network Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | calculation of dawn and dusk for each day and each meteorological station was carried out using the methodology proposed by Or ús et al. (2007).
Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | hilly sites.Averaged over the period 2008-2010, for flat terrain differences between cloudy and clear sky conditions in terms of RMSE, MAE and PE are 44, 28 W m −2 and 26 %, respectively.In the case of hilly terrain these differences are 44, 32 W m −2 and 37 %, respectively.Finally, for snow and ice covers these differences between hilly and flat classes in terms of RMSE, MAE and PE are 21, 17 W m −2 and 4 %, respectively.It is interesting to note that in most of the cases MBE is negative, meaning that the DSSF algorithm underestimates R s measured at the pyranometer, although mean Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | MBE values for the averaged 2008-2010 period and for all conditions do not exceed −6 W m −2 .
, with 6 meteorological stations data from 2004 to 2006, a RMSE of 40 W m −2 and 110 W m −2 was found for clear sky and cloudy sky conditions, respectively; and a MBE of 9 W m −2 and 5 W m −2 , respectively.Journ ée and Bertrand (2010) reported an overall RMSE for clear sky and cloudy sky conditions of 110 W m −2 using 13 meteorological stations in Belgium from 2008 to 2009 when using DSSF data.In comparison, focusing on stations in flat terrain, RMSE of 41 W m −2 and 85 W m −2 are found for clear and cloudy conditions, with MBE of 5 W m −2 in both cases.Improvement in RMSE in the current study may be due to further product improvement in 2010, or to differences in data rejection criteria.Comparing all satellite application facilities irradiance products (CM-SAF, OSI SAF and LSA SAF) at 15 km of spatial resolution, including also the DSSF product, Ineichen et al. (2009) found a RMSE of 80-100 W m −2 and a PE ranging from 15 to 32 % using 8 meteorological stations over Europe.According to Rigollier et al. (2004), the obtained hourly results are within the error displayed by Heliosat-1 and Heliosat-2 models, also designed for Meteosat images, and reported RMSE errors from 64 to 120 W m −2 and a PE from 7 to 16 % in the case of Heliosat-1 hourly irradiation.In the same work, they reported and hourly irradiation RMSE and PE from 62 to 103 W m −2 and from 18 to 45 %, respectively, using the Heliosat-2 algorithm in three months from 1994 to 1995 and using 35 stations in flat areas, ranging the bias from −31 to 1 W m −2 .Using Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | GOES-W and GOES-E data, Otkin et al. (2005) report a PE, a RMSE and a MBE of 19 %, 62 and −2 W m −2 , respectively, using observations from 11 meteorological stations from the US Climate Reference Network over a continuous 15-month period at 20 km of spatial resolution; and Garautza-Payan et al. (2001) reported similar results in Northern Mexico of about 13 % of PE and 69 W m −2 of RMSE in a one year experiment using data from 2 flux towers.
evaluation results on daily timesteps, depending on terrain class and sky conditions averaged by year and from 2008 to 2010.Scatterplot comparisons for 2008-2010 are shown in Fig. 2. Results at the daily interval show similar general behavior with the hourly results.DSSF data retrieved over flat sites in both clear and cloudy sky conditions show better agreement with observations than retrievals over hilly sites.At flat sites, clear sky conditions yield an averaged RMSE, MAE, PE and R 2 for the 2008-2010 period of 1.2 and 1 MJ m −2 , 5.7 % and 0.98, respectively; and cloudy sky conditions an averaged RMSE, MAE, PE and R 2 for 2008-2010 period of 1.7 Discussion Paper | Discussion Paper | Discussion Paper | reported a RMSE of 2.16 MJ m −2 and a PE of 14 % using a Meteosat R s product obtained by means of the Heliosat-2 method in comparison with daily data from 19 meteorological stations in flat areas from 2000 to 2004.Discussion Paper | Discussion Paper | Discussion Paper | the same method applied to Meteosat data, Rigolier et al. (2004) found a PE between 9 and 20 % in his dataset as well as for other works using the same method.Using GOES, Otkin et al. (2005) found a RMSE of 1.3 MJ m −2 and a MBE of −0.1 over the US, Garautza-Payan et al. (2001) a PE of 11.7 %, and Paech et al. (2009) a PE of 10 %.

Figure 6
Figure 6 show an example of the monthly DSSF solar radiation (R s DSSF) and monthly meteorological station solar radiation (R s DSSF) cycle from 2008 to 2010 at two meteorological stations located in flat conditions (DP) and hilly conditions (WQ).In 8919 , found that daily GOES R s data with a RMSE of 23 W m −2 led to acceptable basin scale estimates.In the work of Diak et al. (1998), R s derived from GOES was applied to routinely estimate daily crop ET for irrigation Figures Back Close Full Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | semi-arid Mediterranean.Using a calibrated R s product derived from GOES data with a PE of 10 % (1.7 MJ m −2 ) for daily reference and potential ET in Florida, USA, Paech et al. (2009) estimated a 5-6 % error in potential ET retrieval, generating a product useful for routine water management-related activities.Discussion Paper | Discussion Paper | Discussion Paper | of satellite-based R s retrievals.Evaluation yielded good results in flat areas with an averaged model RMSE of 65 W m −2 (19 %), 1.6 MJ m −2 (9.7 %) and 0.9 MJ m −2 (5.6 %), and good R 2 of 0.95, 0.96 and 0.99, for hourly, daily and monthly-averaged solar radiation and including clear and cloudy sky conditions and snow or ice covers.Sites in hilly terrain also yielded reasonable R 2 of 0.91, 0.93 and 0.96 for hourly, daily and monthly time steps, and averaged model RMSE of 89 W m −2 (27 %), 2.3 MJ m −2 (14.5 %) and 1.4 MJ m −2 (9.3 %).Comparisons at these sites could be improved by applied terrainbased corrections for topographic shadowing at sub-pixel levels.Hourly solar radiation Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | S ánchez, J. M., Scavone, G., Caselles, V., Valor, E., Copertino, V. A., and Telesca, V.: Monitoring daily evapotranspiration at a regional scale from Landsat-TM and ETM+ data: application to the Basilicata region, J. Hydrol., 351, 58-70, doi:10.1016/j.jhydrol.2007.11.041, 2008a.S ánchez, J. M., Kustas, W. P., Caselles, V., and Anderson, M. C.: Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy tempera-Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Screen / Esc Printer-friendly Version Interactive Discussion Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |
Table3shows results of statistical evaluation at hourly timesteps, with data segmented based on terrain classes (flat or hilly), clear or cloudy sky conditions, as well as presence of snow and ice cover, by year and averaged from 2008 to 2010.The RMSE variability for all sky conditions from 2008 to 2010 is low in both terrain classes, not exceeding more than 10 W m −2 (from 61 to 71 W m −2 ) in flat sites and 2 W m −2 (from 88 to 90 W m −2 ) in hilly sites (number of samples is similar in both cases).High R 2values (> 0.8) also indicate a strong agreement between DSSF and meteorological stations data.However, apparent satellite retrieval performance shows a significant dependence on local terrain conditions, with better agreement with observations in flat areas for all analyzed years.In terms of RMSE, the difference in accuracy between hilly and flat classes is about 24 W m −2 .This behavior is expected, and might be due either to actual errors in the retrieval or errors in representativeness of the point pyranometer observations with respect to R s levels averaged over the surrounding 3 km pixel.To fully understand remote sensing R s behavior in hilly terrain areas, further research needs to be addressed in order to evaluate the representativeness of the point pyranometer observations with remote sensing R s measurements.When datasets are segmented based on both atmospheric conditions and terrain classes, clear sky conditions show better measurement agreement than cloudy conditions(2008)(2009)(2010)in both flat and overestimation in cloudy sky conditions and especially over snow and ice cover could lead to high errors in energy fluxes monitoring in snow melting related studies.Finally, it was demonstrated that the LSA SAF solar radiation product can be used as a reliable and operative input to calculate evaporative fluxes.Authors would like to thank Miquel Ninyerola, Xavier Pons and Lluís Pesquer from the Autonomous University of Barcelona and Center for Ecological Research and Forestry Applications for his help in solar radiation data filtering.This research has been developed thanks to a grant funded by the Generalitat de Catalunya, Tal ència (2009BE200188) and GRUMETS (2009SGR1511) and financial support from the Unites States Department of Agriculture.We would like to express our thanks to the SMC (Catalan Meteorological Service) and Department of Territory and Sustainability of the Catalan Ministry that has freely provided us with the solar radiation data from the meteorological stations.The US Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of race, color, national origin, age, disability, and where applicable, sex, Acknowledgements.

Table 2 .
Land cover type and altitude classes (in percentage) of the meteorological stations used in hourly, daily and monthly R s evaluation.

Table 4 .
Daily solar radiation error and accuracy statistics depending on flat or hilly terrain and clear sky or cloudy sky conditions from 2008 to 2010.RMSE, MBE and MAE in MJ m −2 , PE in percentage and n is the number of samples.

Table 5 .
Monthly solar radiation error and accuracy statistics for 2008 to 2010 by terrain class.RMSE, MBE and MAE in MJ m −2 , PE in percentage and n is the number of samples.