Aerosols play a primary role in the global climate system and the solar radiation budget at the Earth surface. Here we analyze the role of spaceborne aerosol observations and their uncertainties in ...the expected accuracy of the modeled cloudless surface shortwave downward radiation using a state‐of‐the‐art numerical weather prediction model over the continental U.S. We compare five different modeling approaches for the aerosol optical effects with differing sophistication. Overall, we show that, counterintuitively, the direct and diffuse irradiances predicted by solar radiation models that use empirically adjusted fixed aerosol extinction may be more accurate than more sophisticated radiative transfer models that require inputs of aerosols. We conclude that, compared to ground observations, the mean absolute error in satellite‐retrieved aerosol optical depth over the U.S., and possibly elsewhere, should be reduced to less than 0.025 aerosol optical depth unit to assure improvement over the predictions of a simpler, aerosol‐insensitive radiation model.
Key Points
Aerosols are the major atmospheric driver of cloudless surface solar fluxes
Spaceborne sensors monitor aerosols over vast areas at short timescale
Aerosol retrievals must be improved for surface solar radiation modeling
The ability of six microphysical parameterizations included in the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model to represent various macroscopic cloud ...characteristics at multiple spatial and temporal resolutions is investigated. In particular, the model prediction skills of cloud occurrence, cloud base height, and cloud cover are assessed. When it is possible, the results are provided separately for low‐, middle‐, and high‐level clouds. The microphysical parameterizations assessed are WRF single‐moment six‐class, Thompson, Milbrandt‐Yau, Morrison, Stony Brook University, and National Severe Storms Laboratory double moment. The evaluated macroscopic cloud properties are determined based on the model cloud fractions. Two cloud fraction approaches, namely, a binary cloud fraction and a continuous cloud fraction, are investigated. Model cloud cover is determined by overlapping the vertically distributed cloud fractions following three different strategies. The evaluation is conducted based on observations gathered with a ceilometer and a sky camera located in Jaén (southern Spain). The results prove that the reliability of the WRF model mostly depends on the considered cloud parameter, cloud level, and spatiotemporal resolution. In our test bed, it is found that WRF model tends to (i) overpredict the occurrence of high‐level clouds irrespectively of the spatial resolution, (ii) underestimate the cloud base height, and (iii) overestimate the cloud cover. Overall, the best cloud estimates are found for finer spatial resolutions (1.3 and 4 km with slight differences between them) and coarser temporal resolutions. The roles of the parameterization choice of the microphysics scheme and the cloud overlapping strategy are, in general, less relevant.
Key Points
WRF cloud estimates reliability depends on cloud parameter and type
WRF reliability results higher for finer spatial and coarser temporal resolution
Role of microphysics parameterization and overlapping scheme are less relevant
Machine learning is routinely used to forecast solar radiation from inputs, which are forecasts of meteorological variables provided by numerical weather prediction (NWP) models, on a spatially ...distributed grid. However, the number of features resulting from these grids is usually large, especially if several vertical levels are included. Principal Components Analysis (PCA) is one of the simplest and most widely-used methods to extract features and reduce dimensionality in renewable energy forecasting, although this method has some limitations. First, it performs a global linear analysis, and second it is an unsupervised method. Locality Preserving Projection (LPP) overcomes the locality problem, and recently the Linear Optimal Low-Rank (LOL) method has extended Linear Discriminant Analysis (LDA) to be applicable when the number of features is larger than the number of samples. Supervised Nonnegative Matrix Factorization (SNMF) also achieves this goal extending the Nonnegative Matrix Factorization (NMF) framework to integrate the logistic regression loss function. In this article we try to overcome all these issues together by proposing a Supervised Local Maximum Variance Preserving (SLMVP) method, a supervised non-linear method for feature extraction and dimensionality reduction. PCA, LPP, LOL, SNMF and SLMVP have been compared on Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI) radiation data at two different Iberian locations: Seville and Lisbon. Results show that for both kinds of radiation (GHI and DNI) and the two locations, SLMVP produces smaller MAE errors than PCA, LPP, LOL, and SNMF, around 4.92% better for Seville and 3.12% for Lisbon. It has also been shown that, although SLMVP, PCA, and LPP benefit from using a non-linear regression method (Gradient Boosting in this work), this benefit is larger for PCA and LPP because SMLVP is able to perform non-linear transformations of inputs.
This paper presents a portfolio-based approach to the harvesting of renewable energy (RE) resources. Our examined problem setting considers the possibility of distributing the total available ...capacity across an array of heterogeneous RE generation technologies (wind and solar power production units) being dispersed over a large geographical area. We formulate the capacity allocation process as a bi-objective optimization problem, in which the decision maker seeks to increase the mean productivity of the entire array while having control on the variability of the aggregate energy supply. Using large-scale optimization techniques, we are able to calculate – to an arbitrary degree of accuracy – the complete set of Pareto-optimal configurations of power plants, which attain the maximum possible energy delivery for a given level of power supply risk. Experimental results from a reference geographical region show that wind and solar resources are largely complementary. We demonstrate how this feature could help energy policy makers to improve the overall reliability of future RE generation in a properly designed risk management framework.
•Comparison of four forecasting models following mostly independent approaches.•Models performance highly dependent of the season and synoptic weather conditions.•Smart persistence model difficult to ...beat, especially at lead times lower than 2 h.•Break-even point between satellite and NWP models differs for GHI and DNI.•Notably higher errors at a coastal station, caused with sea-land breezes.
The ability of four models to provide short-term (up to 6 h ahead) GHI and DNI forecasts in the Iberian Peninsula is assessed based on two years of data collected at four stations. The models follow (mostly) independent approaches: one pure statistical model (Smart Persistence), one model based on CMV derived from satellite images (Satellite), one NWP model (WRF-Solar) and a hybrid satellite-NWP model (CIADCast). Overall, results show Smart Persistence to be the best at the first lead steps, advective models (Satellite and CIADCast) at intermediate ones and the WRF-Solar at the end of the forecasting period. The break-even point between the advective models and WRF-Solar varies between 1 and 3 h for GHI and 3 and 5 h for DNI. Nevertheless, a detailed analysis shows enormous differences between models performance related to 1) the local geographic and topographic conditions of the evaluation stations; 2) the evaluated variable (GHI vs. DNI); and 3) the sky and synoptic weather conditions over the study area. Depending on the station and lead time, rRMSE values range from 25% to 70% for GHI and from 35% to 100% for DNI. For the same stations and leading time, rRMSE values for DNI are between 50% and 100% higher than the corresponding GHI counterparts. Depending on the synoptic pattern, rRMSE values are about 10/20% for GHI/DNI (3 h lead time, during high pressure conditions) to about 80/180% for GHI/DNI (during low pressure conditions). All models show a poor performance at a coastal station, attributed to a lack of ability to forecast clouds associated with sea-land breezes. To conclude, no single model proves to be the best performing model and, therefore, results show that the four models are, somehow, complementary. The advantages attained by this complementarity are further explored in a companion paper (Part II).
The proposal of new energy systems based on renewable energies requires thorough research in order to derive technically reliable and economically sustainable systems. One of the key inputs of such ...research is constituted by reliable databases of renewable resources. Despite the great effort of the scientific community in recent years, most current databases are far from optimal. Although some databases are based on real data, they lack adequate spatial resolution and/or temporal coverage. Other databases are obtained by estimating renewable energy potential from meteorological reanalysis; however, these estimates are subject to high uncertainty. One of the main problems when building these renewable resource databases is the lack of actual values of installed capacity. In this study we present the SOlar and Wind Installed Spanish Power (SOWISP) database. SOWISP provides the actual installed capacity of wind and photovoltaic solar energy in each Spanish town, with a monthly resolution, and covering the period of 2015–2020. SOWISP has been developed and validated based on a careful and thorough compilation of different public databases. It covers the need for a publicly available database with sufficient spatial and temporal resolution suitable for the analysis of energy systems. Moreover, SOWISP, along with other freely available datasets, supports many modern applications. In addition, a Python package (available on GitHub) was developed for managing this database.
•Publicly available database of the wind/solar PV installed power at the Spanish towns.•Information from 59386 solar PV plants and 1205 wind farms are included.•Dataset validation conducted at regional (coarser) scale.•Allows for energy system modeling and regional solar/wind power generation modeling.•Python code for managing the database is publicly available on GitHub.
Solar radiation plays a key role in the atmospheric system but its distribution throughout the atmosphere and at the surface is still very uncertain in atmospheric models, and further assessment is ...required. In this study, the shortwave downward total solar radiation flux (SWD) predicted by the Weather Research and Forecasting (WRF) Model at the surface is validated over Spain for a 10-yr period based on observations of a network of 52 radiometric stations. In addition to the traditional pointwise validation of modeled data, an original spatially continuous evaluation of the SWD bias is also conducted using a principal component analysis. Overall, WRF overestimates the mean observed SWD by 28.9 W m super(-2), while the bias of ERA-Interim, which provides initial and boundary conditions to WRF, is only 15.0 W m super(-2). An important part of the WRF SWD bias seems to be related to a very low cumulus cloud amount in the model and, possibly, a misrepresentation of the radiative impact of this type of cloud.
Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve ...point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%–3%. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%–17% over QR and 10%–44% over GBR. Improvements for low-coverage intervals (0.85) are smaller.
•Multi-objective optimization using Particle Swarm to estimate Prediction Intervals.•Prediction Intervals for a solar forecasting blending approach.•Study the effects of using weather types to estimate Prediction Intervals.•Quality of Prediction intervals improves with weather type information.
An enhanced database (RetroDB) of the Spanish wind energy resources, derived from a high spatial resolution integration with the WRF model, is proposed and evaluated. RetroDB provides hourly capacity ...factor (CF) values for the Spanish regions, along the period of 2007–2020, with an unprecedented spatial resolution. RetroDB estimates were benchmarked based on the ERA5 global reanalysis. A comprehensive evaluation study of both RetroDB and ERA5 estimates was conducted using surface and tall mast measurements, along with actual CF values. The extent to which RetroDB and ERA5 reproduced the CF spatial variability, distribution, and ramp distribution were specifically addressed. The results showed no differences between the global and regional reanalysis performance regarding nationally aggregated wind energy estimates. Nevertheless, RetroDB clearly shows a superior performance reproducing the wind speeds’ and CFs’ spatial and temporal distributions. This was found to be related to the higher reliability of RetroDB reproducing the aloft winds in complex topographic areas. Overall, the results clearly indicate that, in areas such as the study region, where the wind resources are mostly associated with topographic enhancements, high spatial resolution regional reanalyses are preferable over relative coarse reanalyses (e.g., ERA5), particularly for wind energy integration studies. RetroDB database is made publicly available.
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