Surface Solar Radiation Measurements Research Articles

Impact of the Spatio‐Temporal Mismatch Between Satellite and In Situ Measurements on Validations of Surface Solar Radiation

AbstractSatellite and in situ sensors do not observe exactly the same measurand. This introduces a mismatch between both types of measurements in the spatial or temporal. The mismatch differences can be the dominant component in their comparison, so they have to be removed for an adequate validation of satellite products. With this aim, we propose a methodology to characterize the mismatch between satellite and in situ measurements of surface solar radiation, evaluating the impact of the mismatch on validations. The Surface Solar Radiation Data Set—Heliosat (SARAH‐2) and the Baseline Surface Radiation Network are used to characterize the spatial and temporal mismatch, respectively. The mismatch differences in both domains are driven by cloud variability. At least 5 years are needed to characterize the mismatch, which is not constant throughout the year due to seasonal and diurnal cloud cover patterns. Increasing the mismatch can artificially improve the validation metrics under some circumstances, but the mismatch must be always minimized for a correct product assessment. Finally, we test two types of up‐scaling methods based on SARAH‐2 in the validation of degree‐scale products. The fully data‐driven correction removes all the mismatch effects (systematic and random) but fully propagates SARAH‐2 uncertainty to the corrections. The model‐based correction only removes the systematic mismatch difference, but it can correct measurements not covered by the high‐resolution data set and depends less SARAH‐2 uncertainty.

Analysis of sunshine duration and cloud cover trends in Lisbon for the period 1890–2018

Sunshine duration (SD) represents a valuable parameter for early years when few or none measurements of surface solar radiation (SSR) are available. In the present work, daily and monthly SD records registered in Lisbon (Portugal) for the period 1890–1940 have been digitized to expand the data series available in electronic format, which starts in 1941. The resulting series for the period 1890–2018 can be considered as the earliest one in Portugal and the second one in the Iberian Peninsula. Cloud cover (CC) data for the same period have also been digitized. The SD series exhibits a weak negative trend (without statistical significance) from the 1890s to the 1910s, which is in line with the early dimming period in SSR reported in some regions. Subsequently, no trends are obtained for the period 1910s–1950s, which indicates that the early brightening is not observed in Lisbon unlike other locations in the Iberian Peninsula. After that, two strong statistically significant trends are found for the periods 1950s–1980s and 1980s–2010s in line with the well-known global dimming and brightening periods in SSR, respectively. On the other hand, the CC series presents an increase from 1890 to the 1980s, followed by a decrease up to 2018 (both being statistically significant), which may partially explain the reported SD trends. An analysis of SD under cloudless conditions proved the utility of this quantity to track long-term changes in atmospheric aerosol load. In addition, this analysis and a seasonal one pointed out that aerosols seem to play a relevant role in SD long-term variability.

Value-Added Products Derived from 15 Years of High-Quality Surface Solar Radiation Measurements at Xianghe, a Suburban Site in the North China Plain

Surface solar radiation (SSR) is a key component of the energy budget of the Earth’s surface, and it varies at different spatial and temporal scales. Considerable knowledge of how and why SSR varies is crucial to a better understanding of climate change, which surely requires long-term measurements of high quality. The objective of this study is to introduce a value-added SSR dataset from Oct 2004 to Oct 2019 based on measurements taken at Xianghe, a suburban site in the North China Plain; two value-added products based on the 1-minute SSR measurements are developed. The first is clear sky detection by using a machine learning model. The second is cloud fraction estimation derived from an effective semi-empirical method. A “brightening” of global horizontal irradiance (GHI) was revealed and found to occur under both clear and cloudy conditions. This could likely be attributed to a reduction in aerosol loading and cloud fraction. This dataset could not only improve our knowledge of the variability and trend of SSR in the North China Plain, but also be beneficial for solar energy assessment and forecasting.

Climate change impact on photovoltaic power potential in China based on CMIP6 models

China has the largest worldwide cumulative installed photovoltaic (PV) capacity, which is expected to be 1300 GW in 2050. Industrial production, population explosion and fossil fuel combustion would reduce the surface solar radiation that could be received by PV panels. However, it is still a problem to explore the integrated effects of socio-economic and air pollutant emissions on PV power potential in China. In this study, climate change impact on PV power potential in 2023–2100 were assessed using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model, combining Shared Socio-economic Pathway (SSPs) and Representative Concentration Pathways (RCPs). The validation results with ground-based surface solar radiation measurements collected from 17 China Meteorological Administration (CMA) stations showed that the Meteorological Research Institute Earth System Model version 2–0 (MRI-ESM2-0) attained a better performance with mean correlation coefficients (R), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) of 0.85, 35.80 Wm−2 and 29.37 Wm−2, respectively. Then, the MRI-ESM2-0 model was selected to analyze the spatial and temporal variations in PV power potential. PV power potential decreased significantly in SSP585 ranging from 192.71 Wm−2 to 189.96 Wm−2 in 2023–2100 corresponding to the growing resource intensity and fossil fuel dependency. In contrast, if China continues on the path of sustainable and low-carbon development and keeps temperature rise to about 1.5 °C by 2100, PV power potential will increase by 1.36–5.90 Wm−2. Meanwhile, the effects of climatological factors on PV power potential were analyzed by Empirical Orthogonal Function (EOF) method. Results indicated that surface solar radiation had the highest contribution of >50 %, and the contribution of aerosols and cloud cover was about 20 %. This study is conducive to the full utilization of solar resources and has important implications for the future formulation of solar energy policy in China.

Solar Radiation Climatology in Camagüey, Cuba (1981–2016)

The transition to renewable energies is an unavoidable step to guarantee a peaceful and sustainable future for humankind. Although solar radiation is one of the main sources of renewable energy, there are broad regions of the planet where it has not been characterized appropriately to provide the necessary information for regional and local planning and design of the different solar powered systems. The Caribbean, and Cuba in particular, lacked until very recently at least one long-term series of surface solar radiation measurements. Here we present the first long-term records of solar radiation for this region. Solar radiation measurements manually conducted and recorded on paper were rescued, reprocessed and quality controlled to develop the solar radiation climatology at the Actinometrical Station of Camagüey, in Cuba (21.422°N; 77.850°W; 122 m a.s.l.) for the period 1981–2016. The diurnal cycle based on the average hourly values of the global, direct and diffuse horizontal variables for the entire period have been determined and analyzed showing the dependence on solar zenith angle (SZA) and clouds. The annual cycle of global solar component given by the mean monthly daily values presents two maxima, one in April and another one in July with values of 5.06 and 4.91 kWh m−2, respectively (18.23 and 17.67 MJ m−2 per day for insolation), and the minimum in December (3.15 kWh m−2 or 11.33 MJ m−2). The maxima are governed by the direct solar components and are modulated by cloudiness. Both, diurnal and annual cycles of the diffuse solar component show a smoothed bell shaped behavior. In general solar radiation at this station presents a strong influence of clouds, with little seasonal variation but with higher values during the rainy season. Daily global radiation annual averages showed its maximum value in the year 1983, with 17.45 MJ m−2 explained by very low cloudiness this year, and the minimum value was reported in 2009 with a value of 12.43 MJ m−2 that could not explained by the cloud coverage or the aerosols optical depths registered that year. The effects of the 1982 El Chichón and 1991 Mount Pinatubo volcanic eruptions on the solar radiation variables at Camagüey are also shown and discussed. The results achieved in this study shown the characteristics of solar radiation in this area and their potential for solar power applications.

Characterizing Temporal Variability in Measurements of Surface Solar Radiation and its Dependence on Climate

The intrinsic temporal scales of the variability of the surface solar radiation are addressed by means of the empirical mode decomposition. High quality measurements of the solar radiation impinging on a horizontal plane at ground level, from different BSRN ground stations, are analysed. By first extracting all the embedded oscillations that share a common local time-scale, followed by Hilbert spectral analysis, the characteristic scales of variability, along with the fluctuations in the intensity of the pyranometric signal, are revealed. It is shown that data from stations with different local climates share some common features, most notably a high-frequency plateau of variability whose amplitude is found to be modulated by the seasonal cycle. The study has possible implications on the modelling and the forecast of the surface solar radiation, at different local time-scales.

The Zugspitze radiative closure experiment for quantifying water vapor absorption over the terrestrial and solar infrared – Part 2: Accurate calibration of high spectral-resolution infrared measurements of surface solar radiation

Abstract. Quantitative knowledge of water vapor absorption is crucial for accurate climate simulations. An open science question in this context concerns the strength of the water vapor continuum in the near infrared (NIR) at atmospheric temperatures, which is still to be quantified by measurements. This issue can be addressed with radiative closure experiments using solar absorption spectra. However, the spectra used for water vapor continuum quantification have to be radiometrically calibrated. We present for the first time a method that yields sufficient calibration accuracy for NIR water vapor continuum quantification in an atmospheric closure experiment. Our method combines the Langley method with spectral radiance measurements of a high-temperature blackbody calibration source (< 2000 K). The calibration scheme is demonstrated in the spectral range 2500 to 7800 cm−1, but minor modifications to the method enable calibration also throughout the remainder of the NIR spectral range. The resulting uncertainty (2σ) excluding the contribution due to inaccuracies in the extra-atmospheric solar spectrum (ESS) is below 1 % in window regions and up to 1.7 % within absorption bands. The overall radiometric accuracy of the calibration depends on the ESS uncertainty, on which at present no firm consensus has been reached in the NIR. However, as is shown in the companion publication Reichert and Sussmann (2016), ESS uncertainty is only of minor importance for the specific aim of this study, i.e., the quantification of the water vapor continuum in a closure experiment. The calibration uncertainty estimate is substantiated by the investigation of calibration self-consistency, which yields compatible results within the estimated errors for 91.1 % of the 2500 to 7800 cm−1 range. Additionally, a comparison of a set of calibrated spectra to radiative transfer model calculations yields consistent results within the estimated errors for 97.7 % of the spectral range.

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the look-up table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

Effective aerosol optical depth from pyranometer measurements of surface solar radiation (global radiation) at Thessaloniki, Greece

Abstract. Pyranometer measurements of the solar surface radiation (SSR) are available at many locations worldwide, often as long time series covering several decades into the past. These data constitute a potential source of information on the atmospheric aerosol load. Here, we present a method for estimating the aerosol optical depth (AOD) using pyranometer measurements of the SSR together with total water vapor column information. The method, which is based on radiative transfer simulations, was developed and tested using recent data from Thessaloniki, Greece. The effective AOD calculated using this method was found to agree well with co-located AERONET measurements, exhibiting a correlation coefficient of 0.9 with 2/3 of the data found within ±20% or ±0.05 of the AERONET AOD. This is similar to the performance of current satellite aerosol methods. Differences in the AOD as compared to AERONET can be explained by variations in the aerosol properties of the atmosphere that are not accounted for in the idealized settings used in the radiative transfer simulations, such as variations in the single scattering albedo and Ångström exponent. Furthermore, the method is sensitive to calibration offsets between the radiative transfer simulations and the pyranometer SSR. The method provides an opportunity of extending our knowledge of the atmospheric aerosol load to locations and times not covered by dedicated aerosol measurements.

Absolute high spectral resolution measurements of surface solar radiation for detection of water vapour continuum absorption

Solar-pointing Fourier transform infrared (FTIR) spectroscopy offers the capability to measure both the fine scale and broadband spectral structure of atmospheric transmission simultaneously across wide spectral regions. It is therefore suited to the study of both water vapour monomer and continuum absorption behaviours. However, in order to properly address this issue, it is necessary to radiatively calibrate the FTIR instrument response. A solar-pointing high-resolution FTIR spectrometer was deployed as part of the 'Continuum Absorption by Visible and Infrared radiation and its Atmospheric Relevance' (CAVIAR) consortium project. This paper describes the radiative calibration process using an ultra-high-temperature blackbody and the consideration of the related influence factors. The result is a radiatively calibrated measurement of the solar irradiation at the ground across the IR region from 2000 to 10 000 cm(-1) with an uncertainty of between 3.3 and 5.9 per cent. This measurement is shown to be in good general agreement with a radiative-transfer model. The results from the CAVIAR field measurements are being used in ongoing studies of atmospheric absorbers, in particular the water vapour continuum.

Retrospective cloud determinations from surface solar radiation measurements

Surface solar radiation measurements have been made at many meteorological sites for long periods. These data potentially provide higher temporal resolution cloud amount information than traditional estimations of cloud amount recorded in eighths of the sky obscured (oktas). To utilise existing solar radiation datasets for this, two quantities are derived from the surface measurements of global ( G) and diffuse ( D) solar irradiance, and top of atmosphere solar irradiance ( E) — the Diffuse Fraction (DF = D/ G) and the Opaqueness (Op = 1 - G/ E). These are compared with subjective cloud observations made at Reading daily during 1997 to 2006. This shows that DF measurements are sensitive to cloud amount, particularly for low and moderate cloud coverage. Complimentary information is available in Op, which is particularly sensitive to cloud amount in moderate to overcast cloud coverage. In overcast conditions, DF = 1, during which Op provides a measure of overcast cloud thickness. As well as cloud amount, the variability in DF and Op provides a basis for discriminating between cumuliform and stratiform cloud: large variability indicates convective cloud whereas only small variability occurs under stratiform cloud.

On the Space–Time Scales of the Surface Solar Radiation Field

Abstract The characteristic space–time scales of surface solar radiation fields measured by the 111-instrument MESONET in Oklahoma are estimated after removal of the diurnal cycle. These estimates of “within-day” variability are used to deduce the representativeness of surface solar radiation measurements made at the central ARM measurement site as a function of time-averaging interval. Nomograms of the relation between point measurements and area averages are given for different space–time-averaging intervals. Examples from the nomograms show, for instance, that under conditions of low mean radiation (cloudy days), the central site point measurements are representative of a spatial area the size of a T42 GCM grid box (280 km × 280 km) if one uses hourly averages and is willing to accept a correlation of 0.45 between area average and point measurement. The point data represent a 60 km × 60 km region at a 0.90 correlation level if a 5-min time average is used. The characteristic timescale for the within-da...

Stratospheric Temperature Change from the Mt. Agung Volcanic Eruption of 1963

The volcanic dust from the Mt. Agung eruption on 17 March 1963 strongly influenced the solar radiation reaching the ground, so much so that surface solar radiation measurements could be used to track the spread of the cloud over the globe. This paper outlines fairly strong circumstantial evidence that the presence of the aerosol caused increases of the stratospheric temperature of approximately 5C.