• Energy Research

Abstract Global horizontal irradiance (GHI) estimates and forecasts are necessary for the efficient use of a naturally fluctuating energy source like solar energy. However, few forecasting methods exist for high latitudes. In this study we present the development and validation of a satellite-based GHI forecast for southern Finland, called Solis-Heliosat. The forecast is formed by combining information from the clear sky (CS) model Pvlib Solis with consecutive geostationary weather satellite imagery, using the Heliosat method. Forecasts are produced up to 4 h with a 15-min temporal resolution. The CS model, the satellite-based all-sky estimate, and the GHI forecast have been compared and validated against other methods and in situ GHI measurements. An additional comparison was made for two datasets representing a changing cloud environment. The CS model estimates had an average rMBE (relative Mean Bias Error) of −6% to 1% and a rRMSE (relative Root Mean Square Error) of 6–10%. For the all-sky estimates the rMBE was −4% to −2%, and rRMSE 2–33%. With increasing forecast time the Solis-Heliosat rMBE descends to −9% and rRMSE reaches 50% at 4 h. Solis-Heliosat performs better than the persistence forecasts in most cases, particularly in a changing cloud environment. Our study indicates the use of satellite-based forecasts as a viable tool for forecasting GHI for the solar energy industry also in the high latitudes. In high latitudes geostationary satellite-based methods are at their limit; however, the information they can provide will enable efficient solar energy production.

<p>With the increase of solar power use, the need for solar irradiance forecasts is also increasing. Solar power is a naturally fluctuating energy source, which makes solar irradiance forecasts necessary for e.g. grid integration and management. Both the use of solar energy and the need for forecasts are present also in the Nordic countries, where the sub-Arctic latitude brings its own challenges.</p><p>Various methods exist for forecasting solar irradiance. Numerical Weather Prediction (NWP) models have been found superior for forecasting for the following days, while satellite-based models are found suitable for the first few hours of the forecast. The satellite-based model Solis-Heliosat has been found to perform well also in the high latitudes. The current operational NWP models in Finland, however, have not been yet extensively validated for this purpose.</p><p>To determine the suitability of the operational NWP models for forecasting solar irradiance in the Nordic countries, we have comparatively validated the MetCoOp Ensemble Model (MEPS), and the MetCoOp Nowcasting Model (MNWC) against in situ irradiance measurements at several stations in Finland and Sweden. We have also included the Solis-Heliosat model in the study, to improve our understanding on the differences and the relative accuracy between the NWP and satellite-based models. As a benchmark, two persistence models are included. The comparison is made for one summer, including all model runs and all MEPS ensemble members, with both hourly and 15 minute output depending on the model.</p><p>The results show all models to somewhat under predict irradiance. MEPS shows very good performance in the full length of the forecast, while Solis-Heliosat is better in the first 2-3 hours of the forecast. Solis-Heliosat has some difficulty with the forecasts starting in the morning, whereas MNWC slightly struggles in the afternoon.</p><p>Overall we find the NWP models very suitable for forecasting solar irradiance in Finland and Sweden, particularly with the full forecast horizon of MEPS, and the 15-minute time step of MNWC. Nevertheless, Solis-Heliosat brings further value to the beginning of the forecast.</p><p>Kallio‐Myers, V., Riihelä, A., Schoenach, D., Gregow, E., Carlund, T., & Lindfors, A. V. (2022). Comparison of irradiance forecasts from operational NWP model and satellite‐based estimates over Fennoscandia. <em>Meteorological Applications</em>, <em>29</em>(2), e2051.</p><p> </p>

Despite the importance of surface energy budgets (SEBs) for land-climate interactions in the Arctic, uncertainties in their prediction persist. In situ observational data of SEB components - useful for research and model validation - are collected at relatively few sites across the terrestrial Arctic, and not all available datasets are readily interoperable. Furthermore, the terrestrial Arctic consists of a diversity of vegetation types, which are generally not well represented in land surface schemes of current Earth system models.This dataset describes the data generated in a literature synthesis, covering 358 study sites on vegetation or glacier (>=60°N latitude), which contained surface energy budget observations. The literature synthesis comprised 148 publications searched on the ISI Web of Science Core Collection.

Abstract 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 ERA5) databases against simulations using station measurements. SARAH showed a stable bias through the simulations producing the best P DC 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°N) underestimating both G POA and P D C . On the contrary, ERA5 not only covers latitudes above 65° but it also obtained the least biased P DC estimations between 55 and 65°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.

AbstractDespite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm−2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

We evaluate the accuracy of the satellite-based surface solar radiation dataset called Surface Solar Radiation Data Set - Heliosat (SARAH-E) against in situ measurements over a variety of sites in India between 1999 and 2014. We primarily evaluate the daily means of surface solar radiation. The results indicate that SARAH-E consistently overestimates surface solar radiation, with a mean bias of 21.9 W/m2. The results are complicated by the fact that the estimation bias is stable between 1999 and 2009 with a mean of 19.6 W/m2 but increases sharply thereafter as a result of rapidly decreasing (dimming) surface measurements of solar radiation. In addition, between 1999 and 2009, both in situ measurements and SARAH-E estimates described a statistically significant (at 95% confidence interval) trend of approximately −0.6 W/m2/year, but diverged strongly afterward. We investigated the cause of decreasing solar radiation at one site (Pune) by simulating clear-sky irradiance with local measurements of water vapor and aerosols as input to a radiative transfer model. The relationship between simulated and measured irradiance appeared to change post-2009, indicating that measured changes in the clear-sky aerosol loading are not sufficient to explain the rapid dimming in measured total irradiance. Besides instrumentation biases, possible explanations in the diverging measurements and retrievals of solar radiation may be found in the aerosol climatology used for SARAH-E generation. However, at present, we have insufficient data to conclusively identify the cause of the increasing retrieval bias. Users of the datasets are advised to be aware of the increasing bias when using the post-2009 data.