In this work, the effect that two basic air quality indexes, aerosols and tropospheric NO2, exert on surface solar radiation (SSR) is studied, along w…In this work, the effect that two basic air quality indexes, aerosols and tropospheric NO2, exert on surface solar radiation (SSR) is studied, along with the effect of liquid and ice clouds over 16 locations in Greece, in the heart of the Eastern Mediterranean. State-of-the-art satellite-based observations and climatological data for the 15-year period 2005–2019, and a radiative transfer system based on a modified version of the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model are used. Our SSR simulations are in good agreement with ground observations and two satellite products. It is shown that liquid clouds dominate, with an annual radiative effect (RE) of −36 W/m2, with ice clouds (−19 W/m2) and aerosols (−13 W/m2) following. The radiative effect of tropospheric NO2 is smaller by two orders of magnitude (−0.074 W/m2). Under clear skies, REaer is about 3–4 times larger than for liquid and ice cloud-covered skies, while RENO2 doubles. The radiative effect of all the parameters exhibits a distinct seasonal cycle. An increase in SSR is observed for the period 2005–2019 (positive trends ranging from 0.01 to 0.52 W/m2/year), which is mostly related to a decrease in the aerosol optical depth and the liquid cloud fraction.more
Cyprus plans to drastically increase the share of renewable energy sources from 13.9% in 2020 to 22.9% in 2030. Solar energy can play a key role in th…Cyprus plans to drastically increase the share of renewable energy sources from 13.9% in 2020 to 22.9% in 2030. Solar energy can play a key role in the effort to fulfil this goal. The potential for production of solar energy over the island is much higher than most of European territory because of the low latitude of the island and the nearly cloudless summers. In this study, high quality and fine resolution satellite retrievals of aerosols and dust, from the newly developed MIDAS climatology, and information for clouds from CM SAF are used in order to quantify the effects of aerosols, dust, and clouds on the levels of surface solar radiation for 2004–2017 and the corresponding financial loss for different types of installations for the production of solar energy. Surface solar radiation climatology has also been developed based on the above information. Ground-based measurements were also incorporated to study the contribution of different species to the aerosol mixture and the effects of day-to-day variability of aerosols on SSR. Aerosols attenuate 5–10% of the annual global horizontal irradiation and 15–35% of the annual direct normal irradiation, while clouds attenuate 25–30% and 35–50% respectively. Dust is responsible for 30–50% of the overall attenuation by aerosols and is the main regulator of the variability of total aerosol. All-sky annual global horizontal irradiation increased significantly in the period of study by 2%, which was mainly attributed to changes in cloudiness.more
This study investigates the light absorption properties of organic aerosols in PM10 collected at a high-altitude location (2700 m a.s.l.) in the easte…This study investigates the light absorption properties of organic aerosols in PM10 collected at a high-altitude location (2700 m a.s.l.) in the eastern Himalayas from March 2019 to February 2020. The analysis reveals an enhanced light-absorbing signature of methanol-soluble brown carbon (MeS-BrC) extracts compared to water-soluble brown carbon (WS-BrC) within the optical wavelength range of 300–700 nm. MeS-BrC exhibits approximately twice the absorption compared to that of WS-BrC at 365 nm. The highest light absorption coefficients at 365 nm (babs365) are observed during spring for both MeS-BrC (9 ± 4.6 Mm−1) and WS-BrC (5.9 ± 4.2 Mm−1). Notably, the contribution of absorption from the water-insoluble fraction is relatively higher during the summer monsoon (45.2 ± 19.5%) and autumn (44.1 ± 18.4%). A significant linear relationship between WSOC and WS-BrC as well as OC and MeS-BrC at 365 nm suggests similar sources for BrC and WSOC (OC). Furthermore, significant positive correlations of babs365 (WS-BrC and MeS-BrC) with the water-soluble fraction of total nitrogen (WSTN) and organic nitrogen (WSON) indicate the presence of nitrogenous organic chromophores playing a crucial role in BrC absorption during spring and autumn. The mass absorption efficiency at 365 nm (MAE365) reveals that BrC in spring aerosols (WS-BrC: 1.5 ± 0.6 m2 g−1; MeS-BrC: 2.07 ± 0.8 m2 g−1) absorbs UV-visible light more efficiently compared to aerosols collected during other seasons. The enhanced MAE365 during spring resulted the highest simple forcing efficiency of 8.7 ± 3.9 W g−1 and 10.8 ± 5.2 W g−1 for WS-BrC and MeS-BrC, respectively, for a specific solar geometry and surface properties. This may be attributed to intense biomass burning followed by atmospheric processing of organic aerosols in the aqueous phase. These findings confirm the significant role of anthropogenic sources in enhancing BrC light absorption and radiative effects in this highly sensitive region of the eastern Himalayas. Such insights are crucial for devising effective strategies for mitigating climate change impacts in the Himalayan ecosystem.more
Energy system models rely on accurate weather information to capture the spatio-temporal characteristics of renewable energy generation. Whereas energ…Energy system models rely on accurate weather information to capture the spatio-temporal characteristics of renewable energy generation. Whereas energy system models are often solved with high abstraction of the actual energy system, meteorological data from reanalysis or satellites provides rich gridded information of the weather. The mapping from meteorological data to renewable energy generation usually relies on major assumptions as for solar photovoltaic energy the photovoltaic module parameters. In this study, we show that these assumptions can lead to large deviations between the reported and estimated energy, as shown for the case of photovoltaic energy in Germany. We propose a novel gradient-based end-to-end framework that can learn local representative photovoltaic capacity factors from aggregated PV feed-ins. As part of the end-to-end framework, we compare physical and neural network model formulations to obtain a functional mapping from meteorological data to photovoltaic capacity factors. We show that all the methods developed have better performance than commonly used reference methods. Both physical and neural network models have much better performance than reference models whereas operational use cases may prefer the neural network due to higher accuracy while interpretable, physical models are more suited to academic settings.more
To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA…To address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km).more
Random uncertainties and vertical error correlations are estimated for three independent data sets. The three collocated data sets are (1) refractivit…Random uncertainties and vertical error correlations are estimated for three independent data sets. The three collocated data sets are (1) refractivity profiles of radio occultation measurements retrieved from the Metop-A and B and COSMIC-1 missions, (2) refractivity derived from GRUAN-processed RS92 sondes, and (3) refractivity profiles derived from ERAS forecast fields. The analysis is performed using a generalization of the so-called three-cornered hat method to include off-diagonal elements such that full error covariance matrices can be calculated. The impacts from various sources of representativeness error on the uncertainty estimates are analysed. The estimated refractivity uncertainties of radio occultations, radiosondes, and model data are stated with reference to the vertical representation of refractivity in these data sets. The existing theoretical estimates of radio occultation uncertainty are confirmed in the middle and upper troposphere and lower stratosphere, and only little dependence on latitude is found in that region. In the lower troposphere, refractivity uncertainty decreases with latitude. These findings have implications for both retrieval of tropospheric humidity from radio occultations and for assimilation of radio occultation data in numerical weather prediction models and reanalyses.more
The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a tru…The net Arctic sea-ice area (SIA) can be estimated from the sea-ice concentration (SIC) by passive microwave measurements from satellites. To be a truly useful metric, for example of the sensitivity of the Arctic sea-ice cover to global warming, we need, however, reliable estimates of its uncertainty. Here we retrieve this uncertainty by taking into account the spatial and temporal error correlations of the underlying local sea-ice concentration products. As 1 example year, we find that in 2015 the average observational uncertainties of the SIA are 306 000 km2 for daily estimates, 275 000 km2 for weekly estimates, and 164 000 km2 for monthly estimates. The sea-ice extent (SIE) uncertainty for that year is slightly smaller, with 296 000 km2 for daily estimates, 261 000 km2 for weekly estimates, and 156 000 km2 for monthly estimates. These daily uncertainties correspond to about 7 % of the 2015 sea-ice minimum and are about half of the spread in estimated SIA and SIE from different passive microwave SIC products. This shows that random SIC errors play a role in SIA uncertainties comparable to inter-SIC-product biases. We further show that the September SIA, which is traditionally the month with the least amount of Arctic sea ice, declined by 105 000±9000 km2 a−1 for the period from 2002 to 2017. This is the first estimate of a SIA trend with an explicit representation of temporal error correlations.more