Sources of uncertainty in annual global horizontal irradiance data

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

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

CORRESPONDENCE BETWEEN PERSONALITY AND JOB TITLE

Does the position held by an individual in a company fit into his/her personality? The answer to this question is yes. This is because there is significant relationship between the characteristics possessed by a worker which includes his character, personality and way of life, with the current position he/she is holding in an organization. The evolution of man is in accordance with technological advancement, new cultures, social and economic developments, among other phenomena. In other words, the success in the position given to a person will depend largely on his/her personality from a mental and physical point of view. This aim of this paper is to analyze the part of clinical psychology which is associated with human talent through a series of characteristics and personality traits. Personality traits and characteristics are crucial for the proper performance of a worker in a specific job

Discussion on density-based clustering methods applied for automated identification of airspace flows

Air Traffic Management systems generate a huge amount of track data daily. Flight trajectories can be clustered to extract main air traffic flows by means of unsupervised machine learning techniques. A well-known methodology for unsupervised extraction of air traffic flows conducts a two-step process. The first step reduces the dimensionality of the track data, whereas the second step clusters the data based on a density-based algorithm, DBSCAN. This paper explores advancements in density-based clustering such as OPTICS or HDBSCAN*. This assessment is based on quantitative and qualitative evaluations of the clustering solutions offered by these algorithms. In addition, the paper proposes a hierarchical clustering algorithm for handling noise in this methodology. This algorithm is based on a recursive application of DBSCAN* (RDBSCAN*). The paper demonstrates the sensitivity of these algorithms to different hyper-parameters, recommending a specific setting for the main one, which is common for all methods. RDBSCAN* outperforms the other algorithms in terms of the density-based internal validity metric. Finally, the outcome of the clustering shows that the algorithm extracts main clusters of the dataset effectively, connecting outliers to these main clusters

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

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