Solargis Solar Resource Database

Solargis Solar Resource Database Description and Accuracy

Last updated: 13 October 2016

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SOLAR M

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http://solargis.com/

Solargis: Data description and accuracy solargis.com

© 2016 Solargis page 2 of 36

1 TABLE OF CONTENTS

1 Table of contents ....................................................................................................................................... 2

2 Acronyms ................................................................................................................................................... 3

3 Glossary...................................................................................................................................................... 4

4 Introduction ................................................................................................................................................ 6

5 Solargis solar resource data ...................................................................................................................... 8

5.1 Key features ...................................................................................................................................... 8

5.2 Solargis calculation scheme ............................................................................................................10

6 Accuracy of Solargis .................................................................................................................................13

6.1 Model performance ..........................................................................................................................13

6.2 Methods for accuracy calculation ....................................................................................................15

7 Combined uncertainty ...............................................................................................................................17

7.1 Components of uncertainty ..............................................................................................................17

7.2 Comparison of models .....................................................................................................................19

8 About Solargis ...........................................................................................................................................20

List of figures .......................................................................................................................................................21

List of tables ........................................................................................................................................................22

References ...........................................................................................................................................................23

Annex .......................................................................................................................................................... 25

List of validation sites ...............................................................................................................................25

GHI Validation statistics ............................................................................................................................29

DNI Validation statistics ............................................................................................................................33

Independent comparison with other satellite-based models .....................................................................36



2 ACRONYMS

AERONET The AERONET (AErosol RObotic NETwork) is a ground-based remote sensing network dedicated to measure atmospheric aerosol properties. It provides a long-term database of aerosol optical, microphysical and radiative parameters.

AOD Aerosol Optical Depth at 670 nm. This is one of atmospheric parameters derived from MACC database and used in Solargis. It has important impact on accuracy of solar calculations in arid zones.

CFSR Climate Forecast System Reanalysis. The meteorological model operated by the US service NOAA.

CPV Concentrated PhotoVoltaic systems, which uses optics such as lenses or curved mirrors to concentrate a large amount of sunlight onto a small area of photovoltaic cells to generate electricity.

DIF Diffuse Horizontal Irradiation, if integrated solar energy is assumed. Diffuse Horizontal Irradiance, if solar power values are discussed.

DNI Direct Normal Irradiation, if integrated solar energy is assumed. Direct Normal Irradiance, if solar power values are discussed.

ECMWF European Centre for Medium-Range Weather Forecasts is independent intergovernmental organisation supported by 34 states, which provide operational medium- and extended-range forecasts and a computing facility for scientific research.

EUMETSAT European Organisation for the Exploitation of Meteorological Satellites

GFS Global Forecast System. The meteorological model operated by the US service NOAA.

GHI Global Horizontal Irradiation, if integrated solar energy is assumed. Global Horizontal Irradiance, if solar power values are discussed.

GTI Global Tilted (in-plane) Irradiation, if integrated solar energy is assumed. Global Tilted Irradiance, if solar power values are discussed.

MACC Monitoring Atmospheric Composition and Climate – meteorological model operated by the European service ECMWF (European Centre for Medium-Range Weather Forecasts)

Meteosat MFG Meteosat satellite operated by EUMETSAT organization. MFG: Meteosat First Generation.

Meteosat MSG Meteosat satellite operated by EUMETSAT organization. MSG: Meteosat Second Generation.

NOAA National Oceanic and Atmospheric Administration

NCEP National Centre for Environmental Prediction

PVOUT Photovoltaic electricity output, often presented as percentage of installed DC power of the photovoltaic modules. This unit is calculated as a ratio between output power of the PV system and the cumulative nominal power at the label of the PV modules (Power at Standard Test Conditions).

SRTM Shuttle Radar Topography Mission

TEMP Air Temperature at 2 metres

WRF Weather Research and Forecasting model



3 GLOSSARY

Aerosols Small solid or liquid particles suspended in air, for example clouds, haze, and air pollution such as smog or smoke.

All-sky irradiance The amount of solar radiation reaching the Earth's surface is mainly determined by Earth-Sun geometry (the position of a point on the Earth's surface relative to the Sun which is determined by latitude, the time of year and the time of day) and the atmospheric conditions (the level of cloud cover and the optical transparency of atmosphere). All-sky irradiance is computed with all factors taken into account

Bias Represents systematic deviation (over- or underestimation) and it is determined by systematic or seasonal issues in cloud identification algorithms, coarse resolution and regional imperfections of atmospheric data (aerosols, water vapour), terrain, sun position, satellite viewing angle, microclimate effects, high mountains, etc.

Bias values will be positive when satellite modelled values are overestimating and negative when underestimating (in comparison to ground measurements).

𝐵𝑖𝑎𝑠 = 𝑋𝑘𝑚𝑜𝑑𝑒𝑙𝑒𝑑 − 𝑋𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑

𝑘

Clear-sky irradiance The clear sky irradiance is calculated similarly to all-sky irradiance but without taking into account the impact of cloud cover.

Frequency of data (15 minute, hourly, daily, monthly, yearly)

Period of aggregation of solar data that can be obtained from the Solargis database.

Long-term average Average value of selected parameter (GHI, DNI, etc.) based on multiyear historical time series. Long-term averages provide a basic overview of solar resource availability and its seasonal variability.

Alternative terminology: long-term prediction, long-term forecasts.

Root Mean Square Deviation (RMSD)

Represents spread of deviations given by random discrepancies between measured and modelled data and is calculated according to this formula:

𝑅𝑀𝑆𝐷 = √∑ (𝑋𝑘

𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑 − 𝑋𝑚𝑜𝑑𝑒𝑙𝑒𝑑𝑘 )2𝑛

𝑘=1

𝑛

On the modelling side, this could be low accuracy of cloud estimate (e.g. intermediate clouds), under/over estimation of atmospheric input data, terrain, microclimate and other effects, which are not captured by the model. Part of this discrepancy is natural - as satellite monitors large area (of approx. 3 x 4 km), while sensor sees only micro area of approx. 1 sq. centimetre. On the measurement side, the discrepancy may be determined by accuracy/quality and errors of the instrument, pollution of the detector, misalignment, data loggers, insufficient quality control, etc.

Alternative terminology: Root Mean Square Error (RMSE)

Site adaption Application of accuracy-enhancement methods that are capable to adapt satellite-derived DNI and GHI datasets (and derived parameters) to the local climate conditions that cannot be recorded in the original satellite and atmospheric inputs. The data adaptation is important especially when specific situations such as extreme irradiance events are important to be



correctly represented in the enhanced dataset. However, the methods have to be used carefully, as inappropriate use for non-systematic deviations or use of less accurate ground data leads to accuracy degradation of the primary satellite-derived dataset.

Alternative term: correlation, calibration.

Solar irradiance Solar power (instantaneous energy) falling on a unit area per unit time [W/m2]. Solar resource or solar radiation is used when considering both irradiance and irradiation.

Solar irradiation Amount of solar energy falling on a unit area over a stated time interval [Wh/m2 or kWh/m2].

Solar radiation The term embraces both solar irradiance and solar irradiation terms. Solar radiation, selectively attenuated by the atmosphere, which is not reflected or scattered and reaches the surface directly, is beam (direct) radiation. The scattered radiation that reaches the ground is diffuse radiation. The small part of radiation that is reflected from the ground onto the inclined receiver is reflected radiation. These three components of radiation together create global radiation.

Spatial grid resolution In digital cartography the term applies to the minimum size of the grid cell or in the other words minimal size of the pixels in the digital map

Uncertainty Is a parameter characterizing the possible dispersion of the values attributed to an estimated irradiance/irradiation values. The best estimate or median value is also called P50 value. For annual and monthly solar irradiation summaries it is close to average, since multiyear distribution of solar radiation resembles closely normal distribution.

Uncertainty assessment of the solar resource estimate is based on a detailed understanding of the achievable accuracy of the solar radiation model and its data inputs (satellite, atmospheric and other data), which is confronted by an extensive data validation experience. The second important source of uncertainty information is the understanding of quality issues of ground measuring instruments and methods, as well as the methods correlating the ground-measured and satellite-based data.

For instance, the range of uncertainty may assume 80% probability of occurrence of values, so the lower boundary (negative value) of uncertainty represents 90% probability of exceedance, and it is also used for calculating the P90 value (normal distribution is assumed). Similarly, other confidence intervals can be considered (P75, P95, P99 values, etc.)

Water vapour Water in the gaseous state. Atmospheric water vapour is the absolute amount of water dissolved in air.



4 INTRODUCTION

The quality of solar resource data is critical for economic and technical assessment of solar power plants. Understanding uncertainty and managing weather-related risk is essential for successful planning and operating of solar electricity assets. High quality solar resource and meteorological data are available today, and they can be obtained by two approaches:

By diligent operation of high-accuracy solar instruments installed at a meteorological station. Well-maintained solar instruments offer higher accuracy and high-frequency data for a given site. Typically such data is available only for limited period of time, form few months to few years. The number of high-quality solar measuring stations, deployed worldwide is relatively limited.

By complex solar meteorological models that read satellite, atmospheric and meteorological data on the input. Such models are typically less accurate, compared to the good quality measurements. But their advantage is continuous geographical coverage and ability to serve data for any location with a continuous history of 10 to more than 20 recent years. Advantage of the models is their ability to serve data in real time for monitoring and forecasting. To achieve high reliability and low uncertainty the models are calibrated and validated using high quality ground measurements.

Solargis represents a modelling approach, based on the use of modern and verified solar algorithms. The model offers long and continuous history and systematic update of primary solar resource parameters (GHI and DNI) as well as all derived parameters and data products needed by solar energy industry.

Technically, good solar resource data should meet the following criteria:

Computation should be based on scientifically proven methods

Outputs should be systematically validated and traceable

Data should represent at minimum 10 years of harmonized history, optimally 20 or more

Data should be available fast and for any location

Outputs should include information about solar resource uncertainty

Data should be supported by an analytical technical report with metadata

Service should be supported by dedicated professional team of experts

Solargis database is designed to help effective development of solar energy strategies and projects at all stages of their lifetime, i.e. for:

Prospection: strategical planning, site identification, and prefeasibility of projects

Evaluation: technical design, financial and technical due diligence

Monitoring: systematic site evaluation, performance assessment and asset management

Forecasting: for optimised management of power production, balancing, and energy trade

Solargis is a product of more than 16 years of development. The database first developed for Europe has been step-wise extended to cover all land territories between latitudes 60N and 55S. Solargis is a unique database, incorporating a number of innovative features:

High quality and reliability, systematically monitored

High-resolution (temporal and spatial), geographically stable

Harmonized combination of solar, meteorological and geographical data

Computed by the best available methods and input data sources, continuously improved

The data represent a long history updated in near-real time

The models are extensively validated by Solargis and by external organizations.

Solar resource availability determines how much electricity will be generated and in what time. Analysis of the solar radiation components makes it possible to understand the performance of solar power plants (Tab. 1). While solar irradiance refers to solar power (instantaneous energy) falling on a unit area per unit time [W/m2], solar irradiation is the amount of solar energy falling on a unit area over a stated time interval [Wh/m2 or kWh/m2]. Solargis offers solar irradiation and irradiance, depending on a data product.



Tab. 1: Solar resource parameters provided by Solargis to solar power industry

Parameter Acronym

Description Unit

Global Horizontal Irradiance (Irradiation)

GHI Sum of diffuse and direct components and it is considered as a climate reference as it enables comparing individual sites or regions

W/m2

(Wh/m2 or kWh/m2)

Direct Normal Irradiance (Irradiation)

DNI Component that directly reaches the surface, and is relevant for concentrating solar thermal power plants (CSP) and photovoltaic concentrating technologies (CPV)

Diffuse Horizontal Irradiance (Irradiation)

DIF Part of the irradiation that is scattered by the atmosphere. Higher values of DIF/GHI ratio represent higher occurrence of clouds, higher atmospheric pollution or higher water vapour

Global Tilted Irradiance (Irradiation)

GTI Sum of direct and diffuse solar radiation falling on a tilted surface. Unlike the horizontal surface, the tilted surface also receives small amount of ground-reflected radiation. It determines performance characteristics of photovoltaic (PV) technology.



5 SOLARGIS SOLAR RESOURCE DATA

5.1 Key features

Solargis database is organised in grid (raster) data layers. Tab. 2 shows technical features Solargis solar resource data. Temporal coverage varies by region and depends on the history and features of each particular satellite mission. Presently we have been processing data from three satellite data providers with geostationary satellites operating at five key positions, to cover entire world (except polar regions). Please see Chapter 5.2 for the calculation scheme.

Tab. 2: Features of Solargis solar resource data

Parameters Description

Spatial coverage Land surface and coastal sea between latitudes 60°N to 50°S

Time representation Since 1994/1999/2006 depending on the satellite data coverage

Spatial (grid) resolution Primary data resolution 3 to 6 km (depending on the latitude)

Enhanced resolution by downscaling up to ~250 m (~90 m) in some regions

Temporal resolution (time step) Original 10/15/30 minutes depending on the satellite region

Aggregated into hourly, daily, monthly and yearly data products

Since 1994 Since 1999 Since 2006 Mixed, depending on the site position

Fig. 1: Historical data availability

Fig. 2 and Fig. 3 show geographic distribution of long-term yearly sums of solar radiation worldwide. The maps show aggregated values of Solargis historical database.



Fig. 2: Long-term average of yearly GHI

Fig. 3: Long-term yearly average of yearly DNI



5.2 Solargis calculation scheme

The solar radiation retrieval in Solargis is basically split into three steps. First, the clear-sky irradiance (the irradiance reaching ground with assumption of absence of clouds) is calculated using the clear-sky model. Second, the satellite data are used to quantify the attenuation effect of clouds by means of cloud index calculation. Then the clear-sky irradiance is coupled with cloud index to retrieve all-sky irradiance. All this process is represented in Fig. 4.

The outcome of the procedure is direct normal and global horizontal irradiance, which is used for computing diffuse and global tilted irradiance. The data from satellite models are usually further post-processed to get irradiance that fits the needs of specific applications (such as irradiance on tilted or tracking surfaces) and/or irradiance corrected for shading effects from surrounding terrain or objects.

Fig. 4: Scheme of the semi-empirical solar radiation model (Solargis)

Clear-sky model SOLIS calculates clear-sky irradiance from a set of input parameters. Sun position is a deterministic parameter, and it is described by algorithms with good accuracy. Three constituents determine geographical and temporal variability of clear-sky atmospheric conditions:

Aerosols are represented by Atmospheric Optical Depth (AOD), which is derived from the global MACC-II database. The model uses daily variability of aerosols to simulate more precisely the instantaneous estimates of DNI and GHI. Use of daily values reduces uncertainty, especially in regions with variable and high atmospheric load of aerosols.

Water vapour is also highly variable, but compared to aerosols, it has lower impact on magnitude of DNI and GHI change. The daily data are derived from CFSR and GFS databases for the whole historical period up to the present time.

Ozone has negligible influence on broadband solar radiation and in the model it is considered as a constant value.

Cloud model estimates cloud attenuation on global irradiance. Data from meteorological geostationary satellites are used to calculate a cloud index that relates radiance of the Earth’s surface, recorded by the satellite in several spectral channels with the cloud optical transmittance. A number of improvements are introduced to better cope with complex identification of albedo in tropical variable cloudiness, complex terrain, at presence of snow and ice, etc. Other support data are also used in the model, e.g. altitude and air temperature.

Solar geometry Zenith angle Azimuth angle Extra-terrestrial irradiance

Atmospheric parameters Water vapour Aerosol optical depth Aerosol type Ozone

Environmental variables Altitude Terrain shading Air temperature …

Satellite data Visible channel Infrared channels

All-sky irradiance

Other models:

DIF, transposition, terrain

Clear-sky model

Clear-sky irradiance

Cloud model

Cloud index



To calculate Global Horizontal Irradiance (GHI) for all atmospheric and cloud conditions, the clear-sky global horizontal irradiance is coupled with cloud index.

From GHI, other solar irradiance components (direct, diffuse and reflected) are calculated. Direct Normal Irradiance (DNI) is calculated by modified Dirindex model. Diffuse horizontal irradiance is derived from GHI and DNI.

Calculation of Global Tilted Irradiance (GTI) from GHI deals with direct and diffuse components separately. While calculation of direct component is straightforward, estimation of diffuse irradiance for a tilted surface is more complex, and affected by limited information about shading effects and albedo of nearby objects. For converting diffuse horizontal irradiance for a tilted surface, the Perez transposition model is used. Reflected component is also approximated considering that knowledge of local conditions is limited.

Model for simulation of terrain effects (elevation and shading) based on high resolution altitude and horizon data. Model by Ruiz Arias is used to achieve enhanced spatial representation – from the resolution of satellite (3 to 4 km) to the resolution of digital terrain model.

A description of model inputs can be found in Tab. 3. Considering the shading from terrain, the spatial resolution of data products is enhanced up to 3 arc-seconds (which is about 90 metres at the equator, less towards the poles). Typically, SRTM3 elevation data is used for this operation. Final data can be recalculated to any other spatial resolution.

Primary time step of solar resource parameters is 15 minutes for MSG satellite, 30-minutes for MFG and MTSAT satellite, 30-minutes for GOES satellite and up to 10-minute for Himawari satellite. Atmospheric parameters (aerosols and water vapour) represent daily data.

Tab. 3: Input data used in the Solargis model

Inputs to Solargis model

Source of input data

Spatial coverage

Time representation

Original time step

Approx. grid

resolution

Atmospheric Optical Depth

MERRA-2

reanalysis NASA

Global

1994 to 2002

Daily (calculated

from 3-hourly) 55 km

MACC-II reanalysis

ECMWF

Monthly long-term calculated from reanalysis

125 km

MACC-II reanalysis

2003 to 2012 Daily

(calculated from 6-hourly)

125 km

MACC-II operational

2013 to present Daily

(calculated from 3-hourly)

85 km

45km

Water vapour

CFSR

NOAA Global

1994 to 2010 1 hour 35 km

GFS 2011 to present 3 hours 55 km

Cloud index

Meteosat MFG

EUMETSAT

Europe, Africa, and parts of Middle East and Brazil

1994 to 2004 30 minutes

3 to 4 km

Meteosat MSG

2005 to present 15 minutes

Meteosat IODC

South Asia, Central Asia, and parts of

East Asia 1999 to present 30 minutes

GOES EAST

NOAA North America and

South America 1999 to present 30 minutes

GOES WEST

MTSAT

JMA East Asia and

Western Pacific Rim Countries

2007 to 2015 30 minutes

Himawari 2016 to present 10 minutes

Altitude and horizon

SRTM3 SRTM

- - 90

metres



Spatial resolution of Meteosat, GOES, and MTSAT data considered in the calculation scheme is approximately 3 km at sub-satellite point (more details in Tab. 4). Model outputs are resampled to 2 arc-minutes (app. 4x4 km) regular grid in WGS84 geographical coordinate system.

Satellite-data secure very high temporal coverage (more than 99% in most of regions). Data for very low sun angles are derived by extrapolation of clear-sky index. The supplied time-series data have all the gaps filled using intelligent algorithms.

Tab. 4: Approximate pixel size for different regions covered by satellites for the cloud index calculation

Spatial coverage Satellite

area Nominal Position

Approx. pixel size

Lat. 0º (Equator)

Approx. pixel size

Lat. Máx North /South

N-S component

E-W component

N-S component

E-W component

Europe, Africa, and parts of Middle East and Brazil

PRIME 0º 3 km 3 km 7.1 km 3.2 km

South Asia, Central Asia, and parts of East Asia

IODC 63º E 2.5 km 2.5 km 5.9 km 2.7 km

North America and South America

GOES-EAST 75º W 4 km 4 km 9.5 km 4.3 km

GOES-WEST

135º W 4 km 4 km 9.5 km 4.3 km

East Asia and Western Pacific Rim Countries

PACIFIC 145º E 4 km 4 km 9.5 km 4.3 km



6 ACCURACY OF SOLARGIS

6.1 Model performance

After calculating model statistics by comparing Solargis with good quality ground measurements at more than 200 sites across all type of climates the following has been observed (see Fig. 5 and Fig. 6 for map representation and complete list of sites in Annex):

Bias for 80% of the sites is within ±3.1% for GHI and ±6.8% for DNI



An analysis on the distribution of the bias across different geographies and situations lead us to the following conclusions (summary in Tab. 5):

In most situations the expected bias for annual values will be within ±4% for GHI values and ±8% for DNI values:

o Most of Europe and North America (approx. below 50°) and Japan.

o Mediterranean region, Arabian Peninsula (except the Gulf region) and Morocco.

o South Africa, Chile, Brazil, Australia

o Regions with good availability of high-quality ground measurements

Situations where the expected bias can be as high as ±8% for GHI values and ±12% for DNI values:

o High latitudes (approx. above 50°)

o Countries in humid tropical climate (e.g. equatorial regions of Africa, America and Pacific, Philippines, Indonesia and Malaysia) and coastal zones (approx. up to 15 km from water)

o Regions with high and dynamically changing concentrations of atmospheric aerosols (Northern India, West Africa, Gulf region, some regions in China)

o High mountains regions with regular snow and ice coverage and high-reflectance deserts

o Regions with limited or no availability of high-quality ground measurements.

Based on the validation of Solargis data, a location specific uncertainty estimate can be derived on a case-by-case basis by looking at the model performance after analysing the local climatic and geographic features.

Tab. 5: Model aaccuracy statistics of Solargis annual long-term averages

GHI DNI Description

Number of validation sites 208 143 -

Number of public sites 163 102 -

Mean Bias for all sites 0% -1.7% Tendency to overestimate or to underestimate the measured values, on average

Standard deviation ±2.9% ±5.8% Range of deviation of the model estimates assuming normal distribution of bias (68% occurrence)

Expected range of bias outside validation sites (P90 uncertainty)

±4% to ±8% ±8% to ±12% Depends on specific analysis on geography and availability of ground measurements



Fig. 5: Bias for yearly GHI values at validation sites (only public sites, values in percent)

Fig. 6: Bias for yearly DNI values at validation sites (only public sites, values in percent)



6.2 Methods for accuracy calculation

The performance of satellite-based models for a given site is characterized by the following indicators, which are calculated for each site for which comparisons with good quality ground measurements are available:

Bias or Mean Bias Deviation (MBD) characterizes systematic model deviation at a given site, i.e. systematic over- or underestimation. Bias values will be above zero when satellite modelled values are overestimating and below zero when underestimating (in comparison to ground measurements).

Root Mean Square Deviation (RMSD) and Mean Absolute Deviation (MAD) are used for indicating the spread of error for instantaneous values. RMSD indicates discrepancies between short-term modelled values (sub-hourly, hourly, daily, monthly) and ground measurements.

Typically, bias is considered as the first indicator of the model accuracy, however the interpretation of the model accuracy should be done analysing all measures. While knowing bias helps to understand a possible error of the long-term estimate, MAD and RMSD are important for estimating the accuracy of energy simulation and operational calculations (monitoring, forecasting). Usually validation statistics are normalized and expressed in percentage.

Other indicators can be calculated as well, like Kolmogorov-Smirnoff Index (KSI), which characterizes representativeness of distribution of values. It may indicate issues in the model’s ability to represent various solar radiation conditions. KSI is important for accurate CSP modelling, as the response of these systems is non-linear to irradiance levels. Even if bias of different satellite-based models is similar, other accuracy characteristics (RMSD, MAD and KSI) may indicate substantial differences in their performance.

Representativeness of validation sites

Validation statistics for one site do not provide representative picture of the model performance in the given geographical conditions. This can be explained by the fact that such site may be affected by a local microclimate or by hidden issues in the ground-measured data.

Therefore, the ability of the model to characterize long-term annual GHI and DNI values should be evaluated at a sufficient number of validation sites. Good satellite models are consistent in space and time, and thus the validation at several sites within one geography provides a robust indication of the model accuracy in geographically comparable regions elsewhere.

As of today Solargis model has been validated at more than 200 sites worldwide. Although the number of reference stations is increasing with time, availability of high quality ground measurements for comparison is limited for some regions. In this case, if a number of validation sites within a specific geography shows bias and RMSD consistently within certain range of values, one can assume that the model will behave consistently also in regions with similar geography where validation sites are not available.

The accuracy of the model can be calculated provided that the absolute majority of the validation data have been collected using high-accuracy instruments, applying the best measurement practices and strict quality control procedures.

Characterization of bias distribution

If we want to characterize the bias in general for sites out of the validation locations, we can take the simplified assumption of having a normal distribution of deviations between the model and the measured values for model estimates. When describing the normal distribution curve the following facts can be observed:

Average of biases is close to zero (close to 0% for GHI and below 2% for DNI). This means that there is no systematic tendency either to overestimate or underestimate (distribution is symmetrically centered).

Standard deviation of bias is relatively low (close to 3% for GHI and 6% for DNI) which will be represented by a narrow probability distribution, i.e. the P90 value (value exceeded in the 90% of the cases) will be closer to the P50 (most expected value).

As with any other measuring approaches, a user cannot expect zero uncertainty for satellite-based solar models. However, if the physics represented by the algorithms is correctly implemented, one can expect robust and uniform behavior of the model for the geographical conditions, for which it has been calibrated and validated.



Even though distribution of validation sites is irregular, a stable and predictable performance of Solargis is observed across various climate regions. The results of the comparison are summarized in the following table and figures. A complete list with the publicly available validation sites and statistics can be found in the annex.

For a practical use, the statistical measures of accuracy had to be converted into uncertainty, which better characterizes probabilistic nature of a possible error of the model estimate.

One way of evaluating the uncertainty is to apply confidence intervals for estimating its probabilistic nature. When assuming normal distribution, statistically one standard deviation characterizes 68% probability of occurrence. From the standard deviation, other confidence intervals can be constructed (Tab. 6).

Tab. 6: Construction of uncertainty intervals from normal distribution

Probability of occurrence Formula

One standard deviation 68.3% ± STDEV

Two standard deviations 95.5% ± 2*STDEV

Three standard deviations 99.7% ± 3*STDEV

P75 uncertainty 50% ± 0.675*STDEV



P97.5 uncertainty 95% ± 1.960*STDEV


From confidence intervals we can calculate different probability scenarios as represented in Tab. 7. The P50 value will be the most expected value (center of the probability density curve), from which various levels of confidence can be expressed. For instance, in solar resource assessment the P90 value has become a standard and it represents a number that would be exceeded in 90% of the cases.

Tab. 7: Expected values at various probability scenarios assuming normal distribution of values.

Probabilily of exceedance Probabilily of non-exceedance Formula

P50 value 50% 50% Mean

P75 value 75% 25% Mean - 0.675*STDEV



P97.5 value 97.5% 2.5% Mean - 1.960*STDEV




7 COMBINED UNCERTAINTY

7.1 Components of uncertainty

For understanding the model performance key indicators, it is important to consider the several factors that influence the accuracy of the values, provided both by satellite-based modelling and on-site ground sensors.

On the modelling side, accuracy will be determined by cloud estimate (e.g. intermediate clouds), under/over estimation of atmospheric input data, terrain, microclimate and other effects, which are not captured by the model. Part of this discrepancy is natural - as satellites monitor large area (of approx. 3x4 km) while ground sensors see only micro area of approx. 1 squared centimetre. Due to higher complexity of the model, bias of satellite-based DNI is higher than GHI.

On the measurement side, the discrepancy will be determined by accuracy/quality and errors of the instrument, pollution of the detector, misalignment, data loggers, insufficient quality control, etc. Only quality-controlled measurements from high-standard sensors should be used for reliable validation of satellite-based solar models. Any issues in the ground measured data result in a skewed evaluation.

The most sought-after value by project developers, technical consultants and finance industry is the uncertainty of the long-term yearly GHI or DNI estimate for the project site. The model uncertainty can be calculated from the validation statistics (Bias) as shown in Chapter 6.

However, the uncertainty values should be also taking into account the fact that the measurements also include an uncertainty component itself. In addition, when assessing the uncertainty of one single year, inter-annual variability due to the climate factors should be evaluated as well.

In conclusion, assuming that the solar radiation values can be described using a normal probability distribution (similarly as we have done when characterizing the model bias distribution), the total combined uncertainty is calculated from:

Uncertainty of the Solargis model estimate

Uncertainty of the ground measurements

Inter-annual weather variability.

The influence of these three factors in the final uncertainty is calculated through the square root of the quadratic sum of each uncertainty:

𝑈𝑛𝑐𝑒𝑟𝑡𝑢𝑠𝑒𝑟 𝑐𝑜𝑚𝑏𝑖𝑛𝑒𝑑 = √𝑈𝑛𝑐𝑒𝑟𝑡𝑚𝑜𝑑𝑒𝑙2 + 𝑈𝑛𝑐𝑒𝑟𝑡𝑚𝑒𝑎𝑠

2 + 𝑈𝑛𝑐𝑒𝑟𝑡𝑣𝑎𝑟2

Ground measurements

Estimate of the long-term uncertainty of ground measurements can be a bit subjective – it can be based on combination of the theoretical uncertainty of the instrument, results of quality control procedures and comparison of the redundant measurements.

Considering that the absolute majority of the validation data have been collected using high-accuracy instruments and applying the best measurement practices and strict quality control procedures, it is considered that a ±2.0% should be added from GHI measurements from pyranometers and ±1.0% from DNI measurements from pyrheliometers (see Tab. 8 and Tab. 9). This could serve as a good starting point for assessing annual uncertainty of solar instruments. It is known from other comparisons that these values could be exceeded in standard operating conditions.

On the other hand, utilization of the state-of-the-art instruments does not alone guarantee good results. Any measurements are subject to uncertainty and the information is only complete, if the measured values are accompanied by information on the associated uncertainty. Sensors and measurement process has inherent features that must be managed by quality control and correction techniques applied to the raw measured data.



The lowest possible uncertainties of solar measurements are essential for accurate determination of solar resource. Uncertainty of measurements in outdoor conditions is always higher than the one declared in the technical specifications of the instrument. The uncertainty may dramatically increase in extreme operating conditions and in case of limited or insufficient maintenance. Quality of measured data has significant impact on validation and regional adaptation of satellite models.

Tab. 8: Theoretically-achievable daily uncertainty of GHI at 95% confidence level

Pyranometers RSR

Secondary standard

First class Second

class (After data post-processing)

GHI Hourly ±3% ±8% ±20% ±3.5% to ±4.5%

GHI Daily ±2% ±5% ±10% ±2.5% to ±3.5%

Tab. 9: Theoretically-achievable daily uncertainty of DNI at 95% confidence level

Pyrheliometers RSR

Secondary standard

First class (After data post-processing)

DNI Hourly ±0.7% ±1.5% ±3.5% to ±4.5%

DNI Daily ±0.5% ±1.0% ±2.5% to ±3.5%

Interannual variability

Weather changes in cycles and it has also a stochastic nature. Therefore annual solar radiation in each year can deviate from the long-term average in the range of few percent. This is expressed by interannual variability, i.e. the magnitude of the year-by-year change.

The interannual variability for selected sites is calculated from the unbiased standard deviation of the yearly values over the available period of years, considering a simplified assumption of normal distribution of the annual sums. All sites show similar patterns of variation over the recorded period. This analysis can be made for longer periods (see samples for few sites in Tab. 10), i.e. the uncertainty at different confidence levels expected for average values within more than one-year period.

𝑣𝑎𝑟𝑛 =𝑆𝑇𝐷𝐸𝑉

√𝑛

Tab. 10: Table of GHI interannual variability of a period of 1, 5, 10 and 25 years for several sample sites

Nearby city Country Variability [%]

1 year 5 years 10 years 25 years

Kosice Slovakia 3.8 1.7 0.5 0.1

Fresno United States 2.5 1.1 0.4 0.1

Kurnool India 2.3 1.0 0.3 0.1

Calama Chile 1.3 0.6 0.2 0.0

Upington South Africa 1.3 0.6 0.2 0.0

The historical period used for calculating the inter-annual variability may have some influence, although it is observed to be quite small (e.g. if we compare results from 10 years of data with results from 20 years of data). The expected difference for GHI would be less than 1% (depends on the climate zone). A higher influence may be found in data sets representing occurrence of large stratospheric volcano eruptions.



7.2 Comparison of models

Good models have known and lowest possible uncertainty. As it has been described in previous chapters, the expected uncertainty for a specific site can be derived from the analysis of validation statistics for a sufficient number of validation points.

Assuming that the solar resource estimates from two different models follow a normal distribution, the combined uncertainty can be compared and represented in charts. The most expected value (P50) and its uncertainty will determine the position of the center and the width of the probability distribution respectively.

Using a solar model with proven higher accuracy like Solargis provides more probable estimates and less weather-related risk for the project. In other words, the distance between P50 and P90 values is smaller in such case.

In the sample below (see Tab.11), the model A has a higher uncertainty than model B and therefore distribution of expected values in model A will be spread within a wider range. In other words, values expected by model B will occur with a higher probability.

Tab. 11: Uncertainty of GHI values from two models at a sample site in Kosice, Slovakia

Model A

[kWh/m2]

Model B

[kWh/m2]

Most expected value (P50) 1193 1230

Value exceeded with 90% probability (P90) 1069 1149

Uncertainty (P90 confidence interval) ±10.4% ±6.6%

Fig. 7: Distribution of GHI expected values by two different models for a sample site



8 ABOUT SOLARGIS

Solargis background

Primary business of Solargis is in providing support to the site qualification, planning, financing and operation of solar energy systems. We are committed to increase efficiency and reliability of solar technology by expert consultancy and access to our databases and customer-oriented services.

The Company builds on 25 years of expertise in geoinformatics and environmental modelling, and 15 years in solar energy and photovoltaics. We strive for development and operation of new generation high-resolution quality-assessed global databases with focus on solar resource and energy-related weather parameters. We are developing simulation, management and control tools, map products, and services for fast access to high quality information needed for system planning, performance assessment, forecasting and management of distributed power generation. Members of the team have long-term experience in R&D and are active in the activities of International Energy Agency, Solar Heating and Cooling Program, Task 46 Solar Resource Assessment and Forecasting.

Solargis operates a set of online services, which includes data, maps, software, and geoinformation services for solar energy.

http://solargis.com

Legal information

Considering the nature of climate fluctuations, interannual and long-term changes, as well as the uncertainty of measurements and calculations, Solargis cannot take guarantee of the accuracy of estimates. Solargis has done maximum possible for the assessment of climate conditions based on the best available data, software and knowledge. Solargis shall not be liable for any direct, incidental, consequential, indirect or punitive damages arising or alleged to have arisen out of use of the provided information.

© 2016 Solargis, all rights reserved

Solargis is ISO 9001:2008 certified company for quality management.

Contact and support

Solargis website: http://solargis.com/

Solargis support center: http://support.solargis.info/support/home

Email: [email protected]

Office address: Pionierska 15, 831 02 Bratislava, Slovakia

Tel: +421 2 4319 1708

Solargis s.r.o.

Registered at: M. Marecka 3, 841 07 Bratislava, Slovakia

http://solargis.info/

http://solargis.com/

http://support.solargis.info/support/home

mailto:[email protected]



LIST OF FIGURES

Fig. 1: Historical data availability .................................................................................................................. 8

Fig. 2: Long-term average of yearly GHI ........................................................................................................ 9

Fig. 3: Long-term yearly average of yearly DNI.............................................................................................. 9

Fig. 4: Scheme of the semi-empirical solar radiation model (Solargis) ....................................................... 10

Fig. 5: Bias for yearly GHI values at validation sites (only public sites, values in percent) .......................... 14

Fig. 6: Bias for yearly DNI values at validation sites (only public sites, values in percent) .......................... 14

Fig. 7: Distribution of GHI expected values by two different models for a sample site ............................... 19



LIST OF TABLES

Tab. 1: Solar resource parameters provided by Solargis to solar power industry 7

Tab. 2: Features of Solargis solar resource data 8

Tab. 3: Input data used in the Solargis model 11

Tab. 4: Approximate pixel size for different regions covered by satellites for the cloud index calculation 12

Tab. 5: Model aaccuracy statistics of Solargis annual long-term averages 13

Tab. 6: Construction of uncertainty intervals from normal distribution 16

Tab. 7: Expected values at various probability scenarios assuming normal distribution of values. 16

Tab. 8: Theoretically-achievable daily uncertainty of GHI at 95% confidence level 18

Tab. 9: Theoretically-achievable daily uncertainty of DNI at 95% confidence level 18

Tab. 10: Table of GHI interannual variability of a period of 1, 5, 10 and 25 years for several sample sites 18

Tab. 11: Uncertainty of GHI values from two models at a sample site in Kosice, Slovakia 19



REFERENCES

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[3] Cebecauer T., Suri M., Gueymard C., Uncertainty sources in satellite-derived Direct Normal Irradiance: How can prediction accuracy be improved globally? Proceedings of the SolarPACES Conference, Granada, Spain, 20-23 Sept 2011.

[4] Suri M., Cebecauer T., 2014. Satellite-based solar resource data: Model validation statistics versus user’s uncertainty. ASES SOLAR 2014 Conference, San Francisco, 7-9 July 2014.

[5] Ineichen P., A broadband simplified version of the Solis clear sky model, 2008. Solar Energy, 82, 8, 758-762.

[6] Morcrette J., Boucher O., Jones L., Salmond D., Bechtold P., Beljaars A., Benedetti A., Bonet A., Kaiser J.W., Razinger M., Schulz M., Serrar S., Simmons A.J., Sofiev M., Suttie M., Tompkins A., Uncht A., GEMS-AER team, 2009. Aerosol analysis and forecast in the ECMWF Integrated Forecast System. Part I: Forward modelling. Journal of Geophysical Research, 114.

[7] Benedictow A. et al. 2012. Validation report of the MACC reanalysis of global atmospheric composition: Period 2003-2010, MACC-II Deliverable D83.1.

[8] Cebecauer T., Šúri M., Accuracy improvements of satellite-derived solar resource based on GEMS re-analysis aerosols. Conference SolarPACES 2010, September 2010, Perpignan, France.

[9] Cebecauer T., Perez R., S ̌úri M., Comparing performance of Solargis and SUNY satellite models using monthly and daily aerosol data. Proceedings of the ISES Solar World Congress 2011, September 2011, Kassel, Germany.

[10] Climate Forecast System Reanalysis (CSFR), NOAA. http://cfs.ncep.noaa.gov/cfsr/

[11] Global Forecast System (GFS), NOAA. http://www.emc.ncep.noaa.gov/index.php?branch=GFS

[12] Meteosat satellites MFG and MSG, EUMETSAT. http://www.eumetsat.int/website/home/Satellites/CurrentSatellites/Meteosat/index.html

[13] Hammer A., Heinemann D., Hoyer C., Kuhlemann R., Lorenz E., Müller R., Beyer H.G., 2003. Solar energy assessment using remote sensing technologies. Rem. Sens. Environ., 86, 423-432.

[14] Perez R., Ineichen P., Maxwell E., Seals R. and Zelenka A., 1992. Dynamic global-to-direct irradiance conversion models. ASHRAE Transactions-Research Series, pp. 354-369.

[15] Perez, R., Seals R., Ineichen P., Stewart R., Menicucci D., 1987. A new simplified version of the Perez diffuse irradiance model for tilted surfaces. Solar Energy, 39, 221-232.

[16] Ruiz-Arias J. A., Cebecauer T., Tovar-Pescador J., Šúri M., Spatial disaggregation of satellite-derived irradiance using a high-resolution digital elevation model. Solar Energy, 84, 1644-1657, 2010.

[17] Cebecauer T., Šúri M., 2012. Correction of Satellite-Derived DNI Time Series Using Locally-Resolved Aerosol Data.. Proceedings of the SolarPACES Conference, Marrakech, Morocco, September 2012.

[18] AERONET: NASA Aerosol Robotic Network. http://aeronet.gsfc.nasa.gov/

[19] Ineichen P., 2014. Long Term Satellite Global, Beam and Diffuse Irradiance Validation. Energy Procedia, Volume 48, 1586–1596. http://dx.doi.org/10.1016/j.egypro.2014.02.179

[20] Climate Forecast System Version 2 (CFS v2), NOAA. http://www.nco.ncep.noaa.gov/pmb/products/CFSv2/

[21] Meteonorm handbook, Version 6.12, Part II: Theory. Meteotest, 2010

[22] Surface meteorology and Solar Energy (SSE) release 6.0, Methodology, Version 2.4, 2009.

[23] SWERA web site. NREL monthly and annual average global data at 40 km resolution for South America from NREL, 2006.http://maps.nrel.gov/SWERA

http://cfs.ncep.noaa.gov/cfsr/

http://www.emc.ncep.noaa.gov/index.php?branch=GFS

http://www.eumetsat.int/website/home/Satellites/CurrentSatellites/Meteosat/index.html

http://aeronet.gsfc.nasa.gov/

http://dx.doi.org/10.1016/j.egypro.2014.02.179

http://www.nco.ncep.noaa.gov/pmb/products/gfs/

http://maps.nrel.gov/SWERA



[24] Šúri M., Huld T., Cebecauer T., Dunlop E.D., 2008. Geographic Aspects of Photovoltaics in Europe: Contribution of the PVGIS Web Site. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1, 1, 34-41

[25] Huld T., Müller R., Gambardella A., 2012. A new solar radiation database for estimating PV performance in Europe and Kuwait, Solar Energy, 86, 6, 1803-1815.

[26] Lohmann S., Schillings C., Mayer B., Meyer R., 2006. Long-term variability of solar direct and global radiation derived from ISCCP data and comparison with reanalysis data, Solar Energy, 80, 11, 1390-1401.

[27] Gueymard C., Solar resource e assessment for CSP and CPV. Leonardo Energy webinar, 2010. http://www.leonardo-energy.org/webfm_send/4601

http://www.leonardo-energy.org/webfm_send/4601



ANNEX

List of validation sites

Site Name Country GHI DNI Latitude

[°] Longitude

[°] Elevation [m a.s.l.]

Source

Bergen Norway x - 60.38380 5.33190 7 -

Lerwick United Kingdom x x 60.13330 -1.18330 9 In. 2013 (GAW)

Tartu Toravere Estonia x x 58.26530 26.46610 70 -

Zoseni Latvia x - 57.13300 25.91700 188 -

Liepaja Rucava Latvia x - 56.48300 21.01700 4 -

Zilani Latvia x x 56.31000 25.55000 107 In. 2013 (GAW)

Schleswig Germany x - 54.51810 9.57040 12 -

Hamburg Germany x x 53.63330 10.00000 14 -

Leeuwarden Netherlands x - 53.21667 5.75000 0 -

Loughborough United Kingdom x - 52.77000 -1.23000 70 -

Heino Netherlands x - 52.43333 6.26667 4 -

Potsdam Germany x - 52.38100 13.06200 85 -

Lindenberg Germany x x 52.21000 14.12200 125 In. 2013 (BSRN)

Cabauw Netherlands x x 51.97000 4.93000 2 In. 2011 (BSRN-KNMI)

Valentia Ireland x x 51.93800 -10.24800 14 In. 2013 (GAW)

Kassel Germany x x 51.31200 9.47800 173 In. 2013 (FhG)

Westdorpe Netherlands x - 51.21667 3.86667 2 -

Ell Netherlands x - 51.20000 5.76667 30 -

Wroclaw Poland x - 51.12630 17.01380 111 -

Doksany Czech Republic x - 50.45800 14.16900 156 CHMU

Camborne United Kingdom x x 50.21670 -5.31670 88 -

Hradec Kralove Czech Republic x x 50.18300 15.83300 236 -

Luka Czech Republic x - 49.65000 16.95000 510 CHMU

Kocelovice Czech Republic x - 49.46600 13.83300 519 CHMU

Ganovce Slovakia x - 49.03330 20.31670 706 WRDC

Weihenstephan Germany x x 48.40000 11.70000 472 -

Fort Peck, MT United States x x 48.31670 -105.10000 643 SURFRAD

Wien Austria x x 48.24850 16.35560 203 In. 2013 (GAW)

Bratislava Slovakia x x 48.16950 17.07150 195 In. 2013 (CIE)

Freiburg Germany x x 47.97920 7.83110 275 -

Hurbanovo Slovakia x - 47.87330 18.19000 113 -

Seattle, WA United States x x 47.68685 -122.25667 20 NOAA ISIS

Zurich Switzerland x - 47.47500 8.53000 558 In. 2011 (ANETZ)

Nantes France x x 47.25420 -1.55360 30 In. 2013 (CSTB)

Kishinev Moldova x x 47.00130 28.81560 205 -

Weissfluhjoch Switzerland x - 46.83320 9.80530 2690 -

SLF Versuchsfeld Switzerland x - 46.82790 9.80940 2540 -

Payerne Switzerland x x 46.81380 6.94270 490 -

Davos Switzerland x x 46.81320 9.84450 1586 -

Bismarck, ND United States x x 46.77179 -100.75955 503 NOAA ISIS




[°] Longitude


Source

Mannlichen Switzerland x - 46.61310 7.94110 2230 -

Jungfraujoch Germany x - 46.54880 7.98500 3580 -

Eggishorn Switzerland x - 46.42730 8.09270 2895 -

Sion Switzerland x - 46.22000 7.33000 489 In. 2011 (ANETZ)

Cimetta Switzerland x - 46.20110 8.78990 1670 -

Geneve Switzerland x x 46.20030 6.13160 420 -

Locarno-Monti Switzerland x x 46.17260 8.78740 370 -

Gornergrat Switzerland x - 45.98420 7.78510 3110 -

Zagreb Croatia x - 45.81880 16.01290 119 -

Ispra Italy x - 45.81200 8.62710 220 -

Vaulx un Velin France x x 45.77860 4.92250 170 -

Bozeman, MT United States x - 45.66200 -111.04500 1507 SOLRADNET

Gospic Croatia x - 44.54860 15.36130 565 -

Carpentras France x x 44.08300 5.05900 100 -

Sioux Falls, SD United States x x 43.73000 -96.62000 473 SURFRAD

A Coruna Spain x x 43.36590 -8.42150 58 -

Oviedo Spain x x 43.35360 -5.87330 336 -

San Sebastian Spain x x 43.30750 -2.03940 252 -

Madison, WI United States x x 43.07250 -89.41133 271 NOAA ISIS

Sapporo Japan x x 43.06000 141.32830 17 BSRN

Val Alinya Spain x - 42.15200 1.44900 1770 In. 2011 (FluxNet)

Soria Spain x x 41.76670 -2.46670 1082 -

Valladolid Spain x x 41.65000 -4.76670 735 -

Lleida Spain x x 41.62580 0.59500 192 -

Barcelona Spain x - 41.38580 2.11690 74 -

Trinidad Obs., CA United States x x 41.05410 -124.15100 107 NOAA ESRL

Salt Lake City, UT United States x x 40.77220 -111.95495 1228 NOAA ISIS

Rock Springs, PA United States x x 40.72000 -77.93330 376 SURFRAD

Thessaloniki Greece x - 40.63240 22.95910 60 -

Madrid Spain x x 40.45280 -3.72420 664 -

Boulder, CO United States x x 40.12500 -105.23700 1689 SURFRAD

Bondville, IL United States x x 40.06670 -88.36670 213 SURFRAD

Las Majadas Spain x - 39.94200 -5.77300 260 In. 2011 (FluxNet)

Xianghe China x x 39.75400 116.96200 32 BSRN

Palma Spain x x 39.56670 2.74390 4 -

Caceres Spain x x 39.47220 -6.33940 405 -

El Saler Spain x - 39.34600 -0.31900 10 In. 2011 (FluxNet)

Sterling, VA United States x x 38.97673 -77.48379 85 NOAA ISIS

Badajoz Spain x x 38.88610 -7.01170 175 -

Penteli Greece x - 38.08770 23.86320 736 -

Murcia Spain x x 38.00280 -1.16940 62 -

Athens Greece x - 37.97180 23.71830 107 -

Cordoba Spain x x 37.84440 -4.85060 91 -

Seoul, Yonsei Univ. South Korea x - 37.56440 126.93490 88 SOLARFLUX

Almeria, PSA Spain x x 37.09279 -2.36240 491 DLR

Malaga Spain x x 36.71920 -4.48030 60 -

Desert Rock, NV United States x x 36.62600 -116.01800 1007 SURFRAD




[°] Longitude


Source

Hanford, CA United States x x 36.31357 -119.63164 73 NOAA ISIS

Tsukuba, Tateno Japan x x 36.05000 140.13330 25 BSRN

Crete, TEI Greece x - 35.29970 25.10000 90 -

Albuquerque, NM United States x x 35.03796 -106.62211 1617 NOAA ISIS

Goodwin Creek, MS United States x x 34.25000 -89.87000 98 SURFRAD

Fukuoka Japan x x 33.58170 130.37500 3 BSRN

Amman Jordan x - 32.02470 35.87890 1041 -

Yatir Forest Israel x - 31.34700 35.05200 650 In. 2011 (FluxNet)

Ouarzazate Morocco - x 31.01200 -6.86500 1230 -

Sede Boqer Israel x x 30.85500 34.78190 480 -

Al-Jouf Saudi Arabia x x 29.79000 40.10000 669 KACST/NREL

Pantnagar India x x 29.04580 79.52080 241 SolRadNet

Tabouk Saudi Arabia x x 28.38000 36.61000 768 KACST/NREL

Al-Qaisumah Saudi Arabia x x 28.32000 46.13000 358 KACST/NREL

Izana Spain - x 28.30890 -16.49940 2371 -

San Bartolome de T. Spain x x 27.75810 -15.57560 50 -

Kanpur India x x 26.51270 80.23190 123 SolRadNet

Qassim Saudi Arabia x x 26.31000 43.77000 647 KACST/NREL

Doha Qatar x x 25.32857 51.42571 20 QEEERI

Al-Ahsa Saudi Arabia x x 25.30000 49.48000 178 KACST/NREL

Riyadh, Solar Vil. Saudi Arabia x x 24.91000 46.41000 757 KACST/NREL

Al-Madinah Saudi Arabia x x 24.55000 39.70000 626 KACST/NREL

Ishigaki Japan x x 24.33670 124.16330 11 BSRN

Minamitorishima Japan x x 24.28830 153.98330 7 BSRN

Tamanrasset Algeria x x 22.78330 5.51370 1378 -

Wadi Al-Dawaser Saudi Arabia x x 20.44000 44.68000 701 KACST/NREL

Kailua-Kona, HI United States x - 19.72747 -156.05901 4 NREL

Manua Loa, HI United States x x 19.53620 -155.57630 3397 NOAA ESRL

Abha Saudi Arabia x x 18.23000 42.66000 2101 KACST/NREL

Bovoni, VI United States x - 17.70799 -64.69328 28 NREL

Sharurah Saudi Arabia x - 17.47000 47.11000 725 KACST/NREL

Bamba Mali x - 17.09900 -1.40180 272 AMMA

Gizan Saudi Arabia x x 16.90000 42.58000 7 KACST/NREL

Agoufu Mali x - 15.34450 -1.47910 290 AMMA

M Bour Senegal x - 14.39400 -16.95900 5 AMMA

Silpakorn Thailand x - 13.81880 100.04080 72 SOLARFLUX

Banizoumbou Niger x - 13.53110 2.66130 211 AMMA

El Nido Airp. Philippines x - 11.20500 119.41300 4 SOLARFLUX

Djougou Benin x - 9.69200 1.66200 438 AMMA

Kwajalein Marshall Islands

x x 8.72000 167.73100 10 BSRN

USM Penang Malaysia x - 5.35800 100.30200 51 SOLARFLUX

Kosrae Micronesia x - 5.35290 162.95700 0 -

Bukit Kototabang Indonesia x x -0.20190 100.31810 864 WRDC, GAW

Nauru Island Nauru x x -0.52100 166.91670 7 BSRN

Nairobi Kenya x - -1.33888 36.86530 1650 SolRad-net

Momote Papua New G. x x -2.05800 147.42500 6 BSRN




[°] Longitude


Source

Palangka Raya Indonesia x - -2.22800 113.94600 27 SOLARFLUX

Petrolina Brazil x x -9.06800 -40.31900 387 -

Keeling Cocos Islands x x -12.18920 96.83440 3 BSRN

Darwin Airp. Australia x x -12.42390 130.89250 30 BoM

Samoa Obs. Amer. Samoa x x -14.24740 -170.56440 42 NOAA ESRL

Brasilia Brazil x x -15.60100 -47.71300 1023 BSRN

Broome Airp. Australia x x -17.94750 122.23530 7 BoM

Pampa Camarones Chile x - -18.85840 -70.21730 798 Ministerio de Energía

Pozo Almonte Chile x - -20.25680 -69.77500 1033 Ministerio de Energía

Learmonth Airp. Australia x x -22.24060 114.09700 5 BoM

Crucero Chile x - -22.27460 -69.56610 1185 Ministerio de Energía

Salar Chile x - -22.34090 -68.87660 2521 Ministerio de Energía

S. Pedro de A. Chile x - -22.97670 -68.16010 2379 Ministerio de Energía

Puerto Angamos Chile x - -23.07360 -70.38560 28 Ministerio de Energía

Rockhampton Airp. Australia x x -23.37530 150.47750 10 BoM

Alice Springs Airp. Australia x x -23.79510 133.88900 546 BoM

Cerro Armazones Chile x - -24.63460 -70.24260 2576 Ministerio de Energía

Salvador Airp. Chile x - -26.31270 -69.75040 1609 Ministerio de Energía

Inca de Oro Chile x - -26.75320 -69.90600 1580 Ministerio de Energía

Tellerie South Africa x - -27.37500 21.29700 931 -

Florianopolis Brazil x x -27.66800 -48.54200 5 BSRN

Upington South Africa - x -28.46830 21.07150 864 Stellenbosch

Geraldton Airp. Australia x x -28.79530 114.69800 33 BoM

Paulputs South Africa - x -28.88020 19.56500 823 -

Aggeneys South Africa - x -29.29530 18.80530 789 -

S. Martinho da S. Brazil x x -29.44280 -53.82310 489 BSRN

Durban South Africa x x -29.90000 30.98000 151 UKZN

De Aar South Africa x x -30.66670 24.00000 1331 BSRN

Kalgoorlie-B. Airp. Australia x x -30.78470 121.45300 365 BoM

Stellenbosch South Africa x x -33.92800 18.86500 120 Stellenbosch

Adelaide Airp. Australia x x -34.95240 138.52040 2 BoM

Wagga Wagga Airp. Australia x x -35.15830 147.45730 212 BoM

Melbourne Airp. Australia x x -37.66550 144.83210 113 BoM

Cape Grim Australia x x -40.68170 144.68920 95 BoM

Lauder New Zealand x x -45.04500 169.68900 350 BSRN



GHI Validation statistics

Site Name Country ISO code

Valid data pairs

Bias GHI Root Mean Square Deviation GHI

[W/m2] [%] Hourly

[%]

Daily

[%]

Monthly

[%]

Bergen NO - 14 8.2

32.2 17.0 11.0

Lerwick GB - 3 1.5

28.5 14.1 3.3

Tartu Toravere EE - -4 -1.8

23.3 10.8 3.7

Zoseni LV - -14 -5.1

23.4 13.3 9.4

Liepaja Rucava LV - -6 -2.2

17.8 8.7 3.3

Zilani LV - -4 -1.0

- - -

Schleswig DE - -11 -4.5

22.0 12.5 7.6

Hamburg DE - 3 1.6

20.8 9.5 3.3

Leeuwarden NL 11777 -4 -1.6

18.7 8.4 3.0

Loughborough GB - -4 -1.7

24.4 11.8 4.2

Heino NL 11973 -1 -0.5

17.7 7.7 1.9

Potsdam DE - -7 -2.7

18.2 8.2 4.0

Lindenberg DE - -9 -3.0

- - -

Cabauw NL 11912 3 1.0

18.9 8.5 2.2

Valentia IE - -9 -4.0

- - -

Westdorpe NL 11910 -8 -2.9

19.0 8.4 4.1

Ell NL 3939 -15 -5.7

20.7 10.4 7.2

Wroclaw PL - 8 3.2

19.5 9.0 4.1

Doksany CZ - 1 0.5

18.5 9.0 4.5

Camborne GB - -4 -1.6

19.9 8.4 2.9

Hradec Kralove CZ - 4 1.5

22.2 10.0 3.2

Luka CZ - 0 0.0

22.1 10.8 5.0

Kocelovice CZ - -5 -2.0

18.9 8.9 4.8

Ganovce SK - -4 -1.6

26.3 12.5 3.2

Weihenstephan DE - -6 -2.3

20.4 10.1 3.7

Fort Peck, MT US 49156 0 0.0

20.8 13.0 4.1

Wien AT - 7 2.5

20.6 9.3 3.1

Bratislava SK - 7 3.0

19.2 9.7 4.2

Freiburg DE - 12 3.9

19.4 8.3 4.4

Hurbanovo SK - -1 -0.2

20.9 10.8 4.8

Seattle, WA US 8702 4 1.3

22.2 9.6 3.8

Zurich CH - 10 4.0

- - -

Nantes FR - -4 -1.3

18.3 9.4 2.7

Kishinev MD - 3 0.9

16.7 7.5 1.9

Weissfluhjoch CH - -9 -2.8

31.5 18.0 8.9

SLF Versuchsfeld CH - -7 -2.4

32.2 18.0 9.0

Payerne CH - 6 1.6

15.3 8.5 2.2

Davos CH - -12 -3.7

27.5 14.0 5.4

Bismarck, ND US 8882 -5 -1.4

21.6 14.4 2.9

Mannlichen CH - -5 -1.7

30.1 15.9 6.4

Jungfraujoch DE - -5 -1.3

32.7 20.8 11.5

Eggishorn CH - 5 1.6

42.1 27.0 15.3

Sion CH - -11 -3.0

- - -




Valid data pairs


[W/m2] [%] Hourly

[%]

Daily

[%]

Monthly

[%]

Cimetta CH - 18 6.1

27.3 14.6 8.0

Geneve CH - 17 5.7

20.0 10.0 5.8

Locarno-Monti CH - -1 -0.3

17.7 7.9 2.1

Gornergrat CH - -25 -6.6

31.1 19.3 11.2

Zagreb HR - 5 1.7

19.2 8.1 3.2

Ispra IT - 13 4.6

15.4 7.7 4.8

Vaulx un Velin FR - 17 6.3

17.8 9.5 6.7

Bozeman, MT US 8434 -6 -1.6

21.8 11.3 2.5

Gospic HR - 3 1.2

24.7 11.1 3.2

Carpentras FR - 3 0.7

12.5 5.5 1.0

Sioux Falls, SD US 33121 -1 -0.4

18.6 11.6 3.7

A Coruna ES - -5 -1.7

18.0 9.2 3.5

Oviedo ES - 19 7.1

24.2 13.6 7.5

San Sebastian ES - 4 1.4

19.4 8.0 2.9

Madison, WI US 9691 -5 -1.3

15.7 8.5 2.2

Sapporo JP 9996 -4 -1.4

29.1 17.1 3.4

Val Alinya ES - 9 2.0

- - -

Soria ES - -2 -0.7

18.2 7.2 1.5

Valladolid ES - 11 3.0

14.0 6.8 3.6

Lleida ES - -2 -0.6

13.1 7.0 3.3

Barcelona ES - 11 3.0

14.9 7.0 3.7

Trinidad Obs., CA US 20211 2 0.5

18.0 8.9 1.9

Salt Lake City, UT US 6933 -4 -1.0

19.6 10.3 2.3

Rock Springs, PA US 50432 3 1.0

20.2 10.9 2.0

Thessaloniki GR - 4 1.0

13.6 5.8 1.7

Madrid ES - 5 1.3

13.3 6.3 1.9

Boulder, CO US 48777 7 1.5

24.7 13.2 4.1

Bondville, IL US 50201 2 0.5

17.4 10.4 2.4

Las Majadas ES - 21 5.0

- - -

Xianghe CN 14891 -3 -1.0

19.9 14.5 3.9

Palma ES - -2 -0.6

13.7 5.5 1.3

Caceres ES - 10 2.5

13.7 6.7 3.1

El Saler ES - 4 1.0

- - -

Sterling, VA US 5532 -7 -1.8

17.2 9.0 3.5

Badajoz ES - 10 2.3

12.0 5.6 3.0

Penteli GR - -13 -3.0

16.6 7.5 3.2

Murcia ES - 5 1.3

12.3 5.7 1.8

Athens GR - 14 3.9

16.1 8.4 4.3

Cordoba ES - 17 3.7

12.2 7.0 4.7

Seoul, Yonsei Univ. KOR 2651 0 0.1

19.7 9.9 3.3

Almeria, PSA ES - 3 0.8

13.5 5.9 1.5

Malaga ES - 13 3.2

14.8 7.5 3.8

Desert Rock, NV US 48353 3 0.6

12.5 5.6 1.2

Hanford, CA US 5221 2 0.4

12.1 6.1 1.9

Tsukuba, Tateno JP 27734 0 -0.1

20.4 10.7 2.4

Crete, TEI GR - 8 2.2

13.4 6.8 2.6




Valid data pairs


[W/m2] [%] Hourly

[%]

Daily

[%]

Monthly

[%]

Albuquerque, NM US 8446 0 0.1

15.9 7.7 1.4

Goodwin Creek, MS US 49789 14 3.4

15.5 7.7 3.6

Fukuoka JP 9978 1 0.2

24.0 13.6 3.2

Amman JO - -10 -1.9

9.6 3.8 1.9

Yatir Forest IL - -4 -1.0

- - -

Sede Boqer IL - -6 -1.3

9.8 4.3 1.6

Al-Jouf SA - 5 1.0

9.5 5.6 2.4

Pantnagar IN 616 -6 -1.5

17.3 11.3 2.5

Tabouk SA - 19 3.8

10.0 6.2 4.0

Al-Qaisumah SA - -7 -1.4

10.7 6.6 2.4

San Bartolome de T. ES - 2 0.5

13.6 5.7 1.1

Kanpur IN 12999 -9 -2.0

14.7 8.2 2.8

Qassim SA - 4 0.8

9.8 5.5 1.6

Doha QA - 0 -1.3

- 5.3 2.0

Al-Ahsa SA - -8 -1.6

10.0 6.4 2.5

Riyadh, Solar Vil. SA - -3 -0.6

9.6 5.5 2.0

Al-Madinah SA - 15 3.1

11.9 6.4 3.4

Ishigaki JP 10141 -5 -1.3

24.2 14.3 2.3

Minamitorishima JP 10155 -2 -0.4

15.0 7.2 1.4

Tamanrasset DZ - 0 0.0

8.5 4.6 1.8

Wadi Al-Dawaser SA - 7 1.4

10.7 5.6 2.0

Kailua-Kona, HI US 3999 1 0.2

13.0 5.0 1.5

Manua Loa, HI US 49774 -40 -6.7

16.8 10.1 6.9

Abha SA - 19 3.6

15.2 6.9 3.9

Bovoni, VI US 2416 15 2.9

28.2 15.8 5.2

Sharurah SA - -3 -0.5

9.6 5.5 1.9

Bamba ML - 0 -2.2

12.0 7.7 5.1

Gizan SA - 0 -0.1

10.0 5.3 2.1

Agoufu ML - 0 -1.0

10.9 6.1 2.9

M Bour SN - 0 1.9

11.2 6.4 3.3

Silpakorn TH 7308 -9 -1.9

23.6 12.2 5.0

Banizoumbou NE - 0 -1.8

12.3 7.5 4.8

El Nido Airp. PH 686 -13 -3.1

26.4 10.7 5.7

Djougou BJ - 0 2.7

16.8 9.6 5.4

Kwajalein MH 8842 -2 -0.5

17.8 8.5 1.3

USM Penang MY 942 -11 -2.8

29.4 10.8 3.0

Kosrae FM 2593 21 5.6

35.0 15.4 6.7

Bukit Kototabang ID 22593 2 0.6

31.6 14.8 2.5

Nauru Island NR 9050 16 3.1

20.0 10.5 3.5

Nairobi KE - 10 2.0

18.0 7.3 3.5

Momote PG 21439 -13 -2.9

25.9 12.4 3.8

Palangka Raya ID 356 -20 -4.6

21.7 9.8 8.0

Petrolina BR - 0 2.4

13.5 6.4 2.9

Keeling CC 23893 -14 -2.9

18.0 7.8 3.7

Darwin Airp. AU 12993 11 2.1

18.3 8.5 2.9

Samoa Obs. AS 44032 -3 -0.6

21.2 9.0 0.9




Valid data pairs


[W/m2] [%] Hourly

[%]

Daily

[%]

Monthly

[%]

Brasilia BR 6317 19 4.2

21.3 9.0 5.1

Broome Airp. AU 14095 1 0.1

11.7 6.0 2.1

Pampa Camarones CL - 0 -0.6

9.1 3.9 1.1

Pozo Almonte CL - 3 0.6

8.1 4.0 1.6

Learmonth Airp. AU 25730 -11 -1.8

11.5 5.1 2.3

Crucero CL - 0 -2.1

6.7 3.5 1.1

Salar CL - 16 2.7

7.6 4.2 3.1

S. Pedro de A. CL - 12 2.2

9.3 4.5 2.4

Puerto Angamos CL - 5 1.0

9.1 4.3 2.0

Rockhampton Airp. AU 14889 -2 -0.5

19.1 8.9 1.7

Alice Springs Airp. AU 14499 2 0.4

12.8 5.8 1.0

Cerro Armazones CL - -18 -2.8

5.8 3.7 3.1

Salvador Airp. CL - -9 -1.5

5.9 3.2 2.0

Inca de Oro CL - -8 -1.4

6.4 3.7 1.7

Tellerie ZA - 5 1.0

15.2 9.6 6.7

Florianopolis BR 7347 10 2.8

26.1 10.7 3.5

Geraldton Airp. AU - -9 -1.8

13.7 5.4 2.1

S. Martinho da S. BR 8847 11 2.8

18.3 9.4 3.3

Durban ZA - 5 1.2

15.5 7.5 3.6

De Aar ZA - 8 1.8

11.5 6.9 2.5

Kalgoorlie-B. Airp. AU - -1 -0.2

15.4 6.0 0.9

Stellenbosch ZA - -10 -2.3

11.8 5.3 3.3

Adelaide Airp. AU 14774 -12 -2.5

15.9 8.1 3.1

Wagga Wagga Airp. AU 14722 -12 -2.7

18.3 10.0 4.1

Melbourne Airp. AU 14557 -9 -2.4

21.5 10.6 3.3

Cape Grim AU 13051 -26 -6.8

21.7 12.0 7.8

Lauder NZ 15102 -14 -4.0

31.1 17.6 5.6



DNI Validation statistics


Valid data pairs

Bias DNI Root Mean Square deviation DNI

[W/m2] [%] Hourly

[%] Daily [%]

Monthly [%]

Lerwick GB - 9 7.0

- - -

Tartu Toravere EE - -29 -11.8

48.8 30.6 17.6

Zilani LV - -1 0.0

- - -

Hamburg DE - -31 -9.3

32.3 22.8 14.1

Lindenberg DE - -19 -6.0

- - -

Cabauw NL - -13 -6.0

- - -

Cabauw NL - - -

- - -

Valentia IE - -48 -22.0

- - -

Kassel DE - 4 2.0

- - -

Camborne GB - -1 -0.6

45.3 23.6 5.5

Hradec Kralove CZ - -27 -11.5

44.6 27.3 16.7

Weihenstephan DE - -15 -4.3

38.0 23.1 9.3

Fort Peck, MT US 45666 -11 -2.5

37.9 23.1 4.9

Wien AT - -6 -2.0

- - -

Bratislava SK - -27 -10.0

- - -

Freiburg DE - -9 -3.3

36.8 16.7 6.7

Seattle, WA US 8700 15 5.8

44.3 20.1 6.7

Nantes FR - -24 -8.0

- - -

Kishinev MD - -23 -7.6

34.4 20.9 12.6

Payerne CH - -10 -2.5

32.7 21.6 11.1

Davos CH - 21 7.2

58.7 27.0 10.2

Bismarck, ND US 8877 1 0.2

41.7 27.1 8.1

Geneve CH - 0 0.2

39.3 22.2 8.4

Locarno-Monti CH - -14 -4.5

48.9 30.4 6.9

Vaulx un Velin FR - -12 -4.1

35.0 20.7 10.6

Carpentras FR - -5 -1.0

24.7 14.8 6.3

Sioux Falls, SD US 29543 6 1.3

32.8 21.1 3.1

A Coruna ES - -8 -2.8

47.0 33.4 12.3

Oviedo ES - 8 3.1

50.2 27.0 8.5

San Sebastian ES - 13 4.9

38.4 20.0 7.8

Madison, WI US 9701 16 4.2

33.5 20.2 5.7

Sapporo JP 7602 5 1.4

53.4 33.8 7.0

Soria ES - 3 0.9

34.1 18.8 4.9

Valladolid ES - 18 4.3

28.1 16.3 7.7

Lleida ES - 8 2.7

33.5 20.8 10.2

Trinidad Obs., CA US 20081 29 10.0

48.0 26.3 16.5

Salt Lake City, UT US 6915 -21 -4.4

35.3 20.0 9.3

Rock Springs, PA US 41430 11 2.9

39.4 25.3 3.8

Madrid ES - -19 -4.1

25.2 15.7 5.4

Boulder, CO US 46499 24 4.5

40.3 23.8 5.6

Bondville, IL US 42296 11 2.6

31.8 20.9 4.0

Xianghe CN 12880 6 1.9

45.0 36.4 5.9

Palma ES - 6 1.4

28.6 16.2 7.9




Valid data pairs


[W/m2] [%] Hourly

[%] Daily [%]

Monthly [%]

Caceres ES - 8 1.8

30.6 18.1 6.7

Sterling, VA US 5520 -6 -1.8

36.2 21.7 7.0

Badajoz ES - 49 11.2

28.3 19.7 11.7

Murcia ES - 17 3.9

25.8 16.3 7.3

Cordoba ES - 55 14.3

35.1 23.9 15.7

Almeria, PSA ES - -12 -2.4

23.8 13.4 3.6

Malaga ES - 46 11.4

32.5 24.6 12.9

Desert Rock, NV US 47364 4 0.6

21.8 12.9 3.6

Hanford, CA US 5217 30 5.9

27.1 17.4 10.0

Tsukuba, Tateno JP 27702 -2 -0.7

39.4 22.4 3.4

Albuquerque, NM US 8425 33 5.1

26.9 14.9 5.9

Goodwin Creek, MS US 43959 13 2.8

25.9 16.0 3.2

Fukuoka JP 7788 -15 -4.4

40.3 25.7 8.2

Ouarzazate MA - -6 -1.1

22.8 14.8 5.5

Sede Boqer IL - -34 -5.5

23.9 16.0 6.4

Al-Jouf SA - -40 -7.1

24.5 19.7 9.7

Tabouk SA - -8 -1.4

19.7 13.8 5.2

Al-Qaisumah SA - -46 -9.3

29.6 25.3 12.7

Izana ES - -61 -9.0

31.9 24.2 11.6

San Bartolome de T. ES - 21 4.9

28.0 16.7 5.4

Qassim SA - -21 -4.2

25.6 20.4 6.7

Doha QA - 0 -1.9

- 15.5 4.4

Al-Ahsa SA - -22 -4.7

30.5 24.3 6.3

Riyadh, Solar Vil. SA - -34 -6.1

26.0 21.1 8.3

Al-Madinah SA - -17 -3.2

22.6 16.2 5.1

Ishigaki JP 7770 2 0.7

43.7 25.7 4.9

Minamitorishima JP 9458 -1 -0.3

28.3 14.0 2.3

Tamanrasset DZ - 24 3.9

21.6 16.4 5.6

Wadi Al-Dawaser SA - -29 -5.6

26.1 20.1 7.6

Manua Loa, HI US 49433 -63 -8.2

24.4 15.2 8.5

Abha SA - 0 -0.1

23.5 13.3 3.3

Gizan SA - -27 -6.8

25.4 19.2 10.3

Kwajalein MH 8628 -11 -2.7

33.4 16.2 3.1

Bukit Kototabang ID 15301 19 8.9

72.6 42.1 11.0

Nauru Island NR 8853 30 6.5

38.6 21.2 6.8

Momote PG 19952 5 1.5

49.2 24.1 5.2

Petrolina BR - 0 1.7

22.8 8.7 2.3

Keeling CC 22419 -14 -3.3

37.7 19.4 5.9

Darwin Airp. AU 12583 11 2.2

29.4 14.9 3.2

Samoa Obs. AS 43737 -17 -4.1

42.2 19.9 4.8

Brasilia BR 5919 36 7.8

36.1 18.0 9.3

Broome Airp. AU 13474 14 2.1

21.2 12.7 4.6

Learmonth Airp. AU 23089 -25 -3.4

17.5 9.0 3.6

Rockhampton Airp. AU 13766 17 3.3

31.2 16.5 4.7

Alice Springs Airp. AU 14150 25 3.6

20.9 11.8 4.0

Florianopolis BR 5736 -3 -0.7

44.0 24.1 5.5




Valid data pairs


[W/m2] [%] Hourly

[%] Daily [%]

Monthly [%]

Upington ZA - -41 -6.0

19.7 12.4 7.8

Geraldton Airp. AU - -3 -0.5

25.0 11.7 2.2

Paulputs ZA - -54 -7.8

18.0 12.4 9.3

Aggeneys ZA - 6 0.9

17.5 10.1 2.4

S. Martinho da S. BR 7623 18 3.9

31.0 18.2 5.5

Durban ZA - -22 -5.8

32.2 20.3 8.0

De Aar ZA - -6 -1.0

16.8 9.9 2.4

Kalgoorlie-B. Airp. AU - 20 3.4

26.7 12.1 4.4

Stellenbosch ZA - -34 -6.4

20.1 12.1 7.8

Adelaide Airp. AU 13670 4 0.8

28.3 14.9 3.2

Wagga Wagga Airp. AU 12958 7 1.2

28.6 16.7 4.0

Melbourne Airp. AU 12758 15 3.5

38.1 20.6 5.2

Cape Grim AU 11132 -15 -4.0

45.2 25.8 7.3

Lauder NZ 12195 7 1.3

45.1 26.0 5.6



Independent comparison with other satellite-based models

2013 study* 2011 study**

Solargis Helioclim3 Solemi Heliomont EnMetSol IrSOLaV Solargis 3Tier

GHI Mean Bias -0.1 1.4 1.8 1 -0.8 0.5 0.4 1.3

SD of Bias 2.1 5.1 4.8 3.6 3.6 4.2 2.6 3.3

DNI Mean Bias -1.6 5.9 -11.3 0.1 -0.1 -0.4 -2.5 8.1

SD of Bias 5.9 13.9 14.5 9.3 9.3 12 5.6 11.6

* Ineichen, P. University of Geneva 2013. Long Term Satellite Global, Beam and Diffuse Irradiance Validation.

** Ineichen, P. University of Geneva 2011. Five satellite products deriving beam and global irradiance validation on data from 23 ground stations.

More info: http://solargis.info/doc/independent-comparison

http://solargis.info/doc/independent-comparison

Documents

Solargis Solar Resource Database