21 research outputs found

    Sources of uncertainty in annual global horizontal irradiance data

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    The major sources of uncertainty in short-term assessment of global horizontal radiation (G) are the pyranometer type and their operation conditions for measurements, whereas the modeling approach and the geographic location are critical for estimations. The influence of all these factors in the uncertainty of the data has rarely been compared. Conversely, solar radiation data users are increasingly demanding more accurate uncertainty estimations. Here we compare the annual bias and uncertainty of all the mentioned factors using 732 weather stations located in Spain, two satellite-based products and three reanalyses. The largest uncertainties were associated to operational errors such as shading (bias = - 8.0%) or soiling (bias = - 9.4%), which occurred frequently in low-quality monitoring networks but are rarely detected because they pass conventional QC tests. Uncertainty in estimations greatly changed from reanalysis to satellite-based products, ranging from the gross accuracy of ERA-Interim (+ 6.1(-6.7)(+)(1)(8.)(8)%) to the high quality and spatial homogeneity of SARAH-1 (+ 1.4(-5.3)(+)(5.6)%). Finally, photodiodes from the Spanish agricultural network SIAR showed an uncertainty of (+6.)(9)(-5.4)%, which is far greater than that of secondary standards (+/- 1.5%) and similar to SARAH-1. This is probably caused by the presence of undetectable operational errors and the use of uncorrected photodiodes. Photodiode measurements from low-quality monitoring networks such as SIAR should be used with caution, because the chances of adding extra uncertainties due to poor maintenance or inadequate calibration considerably increase.Peer reviewe

    Analysis of Spanish Radiometric Networks with the Novel Bias-Based Quality Control (BQC) Method

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    Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations.Peer reviewe

    Quantifying the amplified bias of PV system simulations due to uncertainties in solar radiation estimates

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    Solar radiation databases used for simulating PV systems are typically selected according to their annual bias in global horizontal irradiance (G(H)) because this bias propagates proportionally to plane-of-array irradiance (G(POA)) and module power (P-DC). However, the bias may get amplified through the simulations due to the impact of deviations in estimated irradiance on parts of the modeling chain depending on irradiance. This study quantifies these effects at 39 European locations by comparing simulations using satellite-based (SARAH) and reanalysis (COSMO-REA6 and ERAS) databases against simulations using station measurements. SARAH showed a stable bias through the simulations producing the best Pp c predictions in Central and South Europe, whereas the bias of reanalyses got substantially amplified because their deviations vary with atmospheric transmissivity due to an incorrect prediction of clouds. However, SARAH worsened at the northern locations covered by the product (55-65 degrees N) underestimating both G(POA) and P-DC. On the contrary, ERAS not only covers latitudes above 65 degrees but it also obtained the least biased P-DC estimations between 55 and 65 degrees N, which supports its use as a complement of satellite-based databases in high latitudes. The most significant amplifications occurred through the transposition model ranging from +/- 1% up to +/- 6%. Their magnitude increased linearly with the inclination angle, and they are related to the incorrect estimation of beam and diffuse irradiance. The bias increased around + 1% in the PV module model because the PV conversion efficiency depends on irradiance directly, and indirectly via module temperature. The amplification of the bias was similar and occasionally greater than the bias in annual G(H), so databases with the smallest bias in G(H) may not always provide the least biased PV simulations.Peer reviewe

    Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data

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    This study examines the progress made by two new reanalyses in the estimation of surface irradiance: ERAS, the new global reanalysis from the ECMWF, and COSMO-REA6, the regional reanalysis from the DWD for Europe. Daily global horizontal irradiance data were evaluated with 41 BSRN stations worldwide, 294 stations in Europe, and two satellite-derived products (NSRDB and SARAH). ERAS achieves a moderate positive bias worldwide and in Europe of + 4.05 W/m 2 and + 4.54 W/m 2 respectively, which entails a reduction in the average bias ranging from 50% to 75% compared to ERA-Interim and MERRA-2. This makes ERAS comparable with satellite-derived products in terms of the mean bias in most inland stations, but ERAS results degrade in coastal areas and mountains. The bias of ERAS varies with the cloudiness, overestimating under cloudy conditions and slightly underestimating under clear-skies, which suggests a poor prediction of cloud patterns and leads to larger absolute errors than that of satellite-based products. In Europe, the regional COSMO-REA6 underestimates in most stations (MBE = -5.29 W/m(2)) showing the largest deviations under clear-sky conditions, which is most likely caused by the aerosol climatology used. Above 45 degrees N the magnitude of the bias and absolute error of COSMO-REA6 are similar to ERAS while it outperforms ERA5 in the coastal areas due to its high-resolution grid (6.2 km). We conclude that ERAS and COSMO-REA6 have reduced the gap between reanalysis and satellite-based data, but further development is required in the prediction of clouds while the spatial grid of ERAS (31 km) remains inadequate for places with high variability of surface irradiance (coasts and mountains). Satellite-based data should be still used when available, but having in mind their limitations, ERAS is a valid alternative for situations in which satellite-based data are missing (polar regions and gaps in times series) while COSMO-REA6 complements ERA5 in Central and Northern Europe mitigating the limitations of ERA5 in coastal areas.Peer reviewe

    Quality control of global solar radiation data with satellite-based products

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    Several quality control (QC) procedures are available to detect errors in ground records of solar radiation, mainly range tests, model comparison and graphical analysis, but most of them are ineffective in detecting common problems that generate errors within the physical and statistical acceptance ranges. Herein, we present a novel QC method to detect small deviations from the real irradiance profile. The proposed method compares ground records with estimates from three independent radiation products, mainly satellite-based datasets, and flags periods of consecutive days where the daily deviation of the three products differs from the historical values for that time of the year and region. The confidence intervals of historical values are obtained using robust statistics and errors are subsequently detected with a window function that goes along the whole time series. The method is supplemented with a graphical analysis tool to ease the detection of false alarms. The proposed QC was validated in a dataset of 313 ground stations. Faulty records were detected in 31 stations, even though the dataset had passed the Baseline Surface Radiation Network (BSRN) range tests. The graphical analysis tool facilitated the identification of the most likely causes of these errors, which were classified into operational errors (snow over the sensor, soiling, shading, time shifts, large errors) and equipment errors (miscalibration and sensor replacements), and it also eased the detection of false alarms (16 stations). These results prove that our QC method can overcome the limitations of existing QC tests by detecting common errors that create small deviations in the records and by providing a graphical analysis tool that facilitates and accelerates the inspection of flagged values.Peer reviewe

    Strength Performance of Different Mortars Doped Using Olive Stones as Lightweight Aggregate

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    The amount of ground olive stone available in Spain surpasses the needs of the construction industry for lightweight aggregate. The objective herein is to generate a material, lightweight mortar, with different percentages of ground olive stone, and then evaluate the mechanical performance and viability of these materials for the manufacture of lightweight elements used in the construction sector. To this end, an experiment was designed with nine different dosages of ground olive stone and three types of cement. In all, 378 test pieces were produced to assess the material, its handling while fresh, and its performance. Based on an analysis of consistency, density, compressive strength, and flexural strength, we were able to determine how much ground olive stone can be successfully incorporated into the material: 30% ground olive stone achieved a decrease in density of 15% compared to mortar without ground olive stone. The compressive strength of the different dosages studied remained above 70% of that of the mortar without ground olive stone. Bending behavior was more severely compromised, the values being around 50%. Cements with a more robust strength performance proved capable of assimilating a higher percentage of ground olive stone. This study shows the technical viability of the materials produced

    Strength Performance of Different Mortars Doped Using Olive Stones as Lightweight Aggregate

    No full text
    The amount of ground olive stone available in Spain surpasses the needs of the construction industry for lightweight aggregate. The objective herein is to generate a material, lightweight mortar, with different percentages of ground olive stone, and then evaluate the mechanical performance and viability of these materials for the manufacture of lightweight elements used in the construction sector. To this end, an experiment was designed with nine different dosages of ground olive stone and three types of cement. In all, 378 test pieces were produced to assess the material, its handling while fresh, and its performance. Based on an analysis of consistency, density, compressive strength, and flexural strength, we were able to determine how much ground olive stone can be successfully incorporated into the material: 30% ground olive stone achieved a decrease in density of 15% compared to mortar without ground olive stone. The compressive strength of the different dosages studied remained above 70% of that of the mortar without ground olive stone. Bending behavior was more severely compromised, the values being around 50%. Cements with a more robust strength performance proved capable of assimilating a higher percentage of ground olive stone. This study shows the technical viability of the materials produced
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