Bias correction of a novel European reanalysis data set for solar energy applications

Several sectors need global horizontal irradiance (GHI) data for various purposes. However, the availability of a long-term time series of high quality in situ GHI measurements is limited. Therefore, several studies have tried to estimate GHI by re-analysing climate data or satellite images. Validation is essential for the later use of GHI data in the regions with a scarcity of ground-recorded data. This study contributes to previous studies that have been carried out in the past to validate HelioClim-3 version 5 (HC3v5) and the Copernicus Atmosphere Monitoring Service, using radiation service version 3 (CRSv3) data of hourly GHI from satellite-derived datasets (SDD) with nine ground stations in northeast Iraq, which have not been used previously. The validation is carried out with station data at the pixel locations and two other data points in the vicinity of each station, which is something that is rarely seen in the literature. The temporal and spatial trends of the ground data are well captured by the two SDDs. Correlation ranges from 0.94 to 0.97 in all-sky and clear-sky conditions in most cases, while for cloudy-sky conditions, it is between 0.51–0.72 and 0.82–0.89 for the clearness index. The bias is negative for most of the cases, except for three positive cases. It ranges from −7% to 4%, and −8% to 3% for the all-sky and clear-sky conditions, respectively. For cloudy-sky conditions, the bias is positive, and differs from one station to another, from 16% to 85%. The root mean square error (RMSE) ranges between 12–20% and 8–12% for all-sky and clear-sky conditions, respectively. In contrast, the RMSE range is significantly higher in cloudy-sky conditions: above 56%. The bias and RMSE for the clearness index are nearly the same as those for the GHI for all-sky conditions. The spatial variability of hourly GHI SDD differs only by 2%, depending on the station location compared to the data points around each station. The variability of two SDDs is quite similar to the ground data, based on the mean and standard deviation of hourly GHI in a month. Having station data at different timescales and the small number of stations with GHI records in the region are the main limitations of this analysis.

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“…Those rates are quite reasonable for hourly GHI [42]. Those rates of bias can be corrected or modified in some ways [14,18,56,57].…”

Section: All-sky Conditionsmentioning

confidence: 86%

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

et al. 2018

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“…Another possible source of negative bias during clear-sky situations would be too much water vapour in the atmosphere. There are earlier studies showing similar results elsewhere (Ahlgrimm and Forbes, 2012;Frank et al, 2018). Overcast situations occur more frequently than clear sky (23 % of the time), resulting in the overall positive bias in the solar radiation forecast.…”

Section: How Do Errors In Cloud Cover Impact the Solar Radiation Foresupporting

confidence: 69%

“…Schroedter-Homscheidt et al, 2017) and accuracy of the solar radiation data obtained from the reanalysis (e.g. Frank et al, 2018;Urraca et al, 2018) at different locations based on solar radiation measurements, satellite-derived radiation products and numerical model data. These studies focus on solar radiation forecast error, and do not investigate the possible source of the error.…”

Section: Forecasting Solar Radiation and Cloudsmentioning

confidence: 99%

“…Observed climatologies can be obtained from surface-based instrumentation (Ohmura et al, 1998) and from satellite (Posselt et al, 2012;López and Batlles, 2014;Müller et al, 2015). Climatologies can also be derived from Numerical Weather Prediction (NWP) forecasts and reanalyses, which are attractive from a cost perspective but may display larger uncertainties than observations (Jia et al, 2013;Boilley and Wald, 2015;Frank et al, 2018;Urraca et al, 2018). Climatologies require that the correct amount and type of cloud is predicted on average, whereas a…”

Section: Introductionmentioning

confidence: 99%

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Meteorological conditions affecting renewable energy

Tuononen

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Synoptic situation and different meteorological phenomena can highly affect renewable energy production. Investigating different phenomena will give new information on the occurrence and characteristics of specific phenomena and their impacts on renewable energy applications. Different observational data sets and numerical models can be widely used in different phases of renewable energy projects; from planning of the project to help with the operation and the maintenance of the existing wind or solar field.In this thesis a meteorological phenomena, a low-level jet, is investigated. Thesis comprises analysis of the climatological occurrence of low-level jets, their characteristics and forcing mechanisms, as well as numerical model capability to capture the phenomena. In addition, solar radiation forecasts obtained from the operational numerical weather prediction model are evaluated and the role of cloud cover forecast skill in solar radiation forecast error is investigated. Long data sets of observational data: mainly Doppler wind lidar, ceilometer, and solar radiation observations, are used, in addition to reanalysis and operational numerical weather prediction model data.A low-level jet is a wind phenomenon that can affect wind energy production. Nighttime low-level jets are a commonly known boundary-layer phenomenon occurring during stably stratified conditions over flat terrain. In this thesis, new information on the occurrence, characteristics, and forcing mechanisms of a low-level jet was gained in different conditions: in Northern Hemisphere mid-latitude and polar regions based on reanalysis data and at two different sites in Finland and Germany based on long-term Doppler lidar observations. The low-level jet identification algorithms developed in these studies can be used to repeat the studies by using different models covering different areas or at any site operating a Doppler lidar. The low-level jet identification algorithm for Doppler lidar data can also be applied to operationally detect low-level jets, which is useful information for example from wind energy point-of-view.Solar radiation and cloud cover forecasts were evaluated at one site in Finland based on long time-series of solar radiation and ceilometer observations. The role of cloud cover forecast in solar radiation forecast error is investigated. The solar radiation and cloud cover forecasts were obtained from operational numerical weather prediction model that can be used to predict the expected power production at solar field day-ahead. It was found that there is a positive bias in the forecast incoming solar radiation even if the cloud cover forecast is correct. The study can guide model improvements as the bias is likely due to underestimation in the forecast cloud liquid water content or incorrect representation of cloud optical properties. The methods created in this study can be applied to hundreds of sites globally. In addition, the algorithms developed in this study can be further used in different applications in the field of ren...

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“…Pfeifroth et al, [7] compared the trends and variability of surface solar radiation based on measurements and satellite data sources. Frank et al, [8] proposed new data sets, which outperform ERA-Interim and MERRA-2 thanks to the bias correction.…”

Section: Introductionmentioning

confidence: 99%

Measurements and reanalysis data on wind speed and solar irradiation from energy generation perspectives at several locations in Poland

2019

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Energy system research requires input data with high temporal and spatial resolution. However, the measurements performed in meteorological stations are only available for selected locations. Currently a growing number of research papers on small and large-scale power systems utilizes data coming from satellite measurements and various reanalyses. Although many authors aimed at comparing various data sources on irradiation and wind speed there is a lack of such studies, which compare both resources simultaneously at the same location. In consequence, many studies which are entirely based on satellite/reanalysis data may not be representative. In this paper 15 locations in Poland have been selected where the National Institute of Meteorology and Water Management performs continuous measurements of wind speed, temperature and global irradiation on a horizontal surface. Hourly time series were obtained for the year 2012 and 2013. The renewable resources were converted into electrical energy, considering the performance of typical PV panels and wind turbines. The findings of this study are as follows: solar radiation (CAMS and ERA5) shows a good agreement with ground measurements, for hourly values the coefficient of correlations is greater than 0.9; for individual locations the energy yield from PV system can differ by up to 9% but on average (all locations) the simulated energy yield based on satellite data is higher by less than 0.5%; simulations for wind energy showed a large variability in results with differences in capacity factors reaching 15 percentage points.

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Bias correction of a novel European reanalysis data set for solar energy applications

Cited by 70 publications

References 37 publications

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Validation of Hourly Global Horizontal Irradiance for Two Satellite-Derived Datasets in Northeast Iraq

Meteorological conditions affecting renewable energy

Measurements and reanalysis data on wind speed and solar irradiation from energy generation perspectives at several locations in Poland

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