Cloud Shadows in Satellite-based Solar Irradiance Estimation: Improved Correction using EUMETSAT's Cloud Top Height Data

The estimation of solar surface irradiance at high spatio-temporal resolution from geo-stationary satellite images is a well-established technique, for example by using the Heliosat method. The method has widely reduced the need for expensive ground measurements, especially in remote regions. However, the location of cloud shadows at the ground is difficult to determine and thus a significant source of errors when either the distance from the sub-satellite point or the cloud top height (CTH) increases. Although several methods have been proposed in the literature to reduce these errors, it is still an issue. We present a novel approach to correct the cloud shadow location based on the satellite-cloud-sun geometry using the CTH maps from the EUMETSAT data archive. It uses satellite viewing angles and solar position angles to determine the correct cloud shadow location for each cloudy pixel. The method is tested on cloud index (CI) maps for the months of July, August and September 2018 derived by applying the Heliosat method on the 0.6 um visible channel images from Meteosat-8 located at 41.5°E. Convective clouds with large CTHs are frequently observed over the Indian subcontinent in these three months due to the Indian summer monsoon. The global horizontal solar irradiance (GHI) obtained from the corrected CI image is validated at two BSRN stations. The normalized root mean square error (nRMSE) is reduced from 23.2% to 20.9% for the Gurgaon station and from 15.4% to 13.9% at Tiruvallur. In general, correcting the cloud shadow location on CI map improved the accuracy of the estimated GHI. Nonetheless, the method is sensitive to the accuracy of the CTH dataset and individual cases were found for which the correction reduced the accuracy

Impact of tropical convective conditions on solar irradiance forecasting based on cloud motion vectors

Intra-day forecasts of global horizontal solar irradiance (GHI) are widely produced by displacing existing clouds on a geo-stationary satellite image to their future locations with cloud motion vectors (CMVs) derived from preceding images. The CMV estimation methods assume rigid cloud bodies with advective motion, which performs reasonably well in mid-latitudes but can be strained for tropical and sub-tropical climatic zones during prolonged periods of seasonal convection. We study the impact of the South Asian monsoon time convection on the accuracy of CMV based forecasts by analysing 2 years of forecasts from three commonly used CMV methods—Block-match, Farnebäck (Optical flow) and TV-L1 (Optical flow). Forecasted cloud index (CI) maps of the entire image section are validated against analysis CI maps for the period 2018–2019 for forecast lead times from 0 to 5.5 h. Site-level GHI forecasts are validated against ground measured data from two Baseline Surface Radiation Network stations—Gurgaon (GUR) and Tiruvallur (TIR), located in hot semi-arid and tropical savanna climatic zones respectively. The inter-seasonal variation of forecast accuracy is prominent and a clear link is found between the increase in convection, represented by a decrease in outgoing longwave radiation (OLR), and the decrease in forecast accuracy. The GUR site shows the highest forecast error in the southwest monsoon period and exhibits a steep rise of forecast error with the increase in convection. The highest forecast error occurs in the northeast monsoon period of December in TIR. The impact of convection on the number of erroneous time blocks of predicted photovoltaic production is also studied. Our results provide insights into the contribution of convection to errors in CMV based forecasts and shows that OLR can be used as a feature in future forecasting methods to consider the impact of convection on forecast accuracy

High resolution hybrid forecast based on the combination of satellite and an all sky imager network forecasts

A method to combine a highly resolved All sky imager (ASI) network forecast with a satellite based forecast was developed. The ASI network forecast input is based on the data from the DLR's Eye2Sky network. This network is installed in NortWest Germany and includes 29 ASIs, 10 Rotating Shadowband Irradiometers (RSIs) and 2 reference meteorological stations (based on thermal irradiometers) in an extent of 100 km2. This forecast was developed by our colleges from DLR-SF (Publication in preparation). It has a forecast horizon of 30 minutes and a step of 1 min with an update of 30 seconds on a domain of 40 km2. The satellite based input forecast is based on our operational satellite forecast at DLR-VE and has a horizon of 6 hours with a step and update of 15 minutes. The satellite domain is reduced to the same 40 km2 area. The method consists on 3 blocks, forecasts homogenization, regression and prediction. In the homogenization block the satellite forecast is interpolated in space and time to the resolutions of the ASI network forecast. We applied linear interpolation for both resolutions as first test case. In the second block, a linear regression is applied to find the optimal weights of the linear combination of the forecast inputs, including a bias term. The regression is based on timeseries extracted from the historical forecasts (features) where the reference are taken from the historical timeseries of ground measurements (samples). Historical data is used in order to indirectly characterize the mean actual local weather conditions on the domain. It is important to note that the regression is performed independently for every lead time. In the third block, we use the optimized weights and biases along with the present (not historical) forecasts to produce the hybrid forecasts. The hybrid forecasts resolutions are the same as the ASI based forecast. The output product can be given as maps or timeseries. For the test case, we are limited from the ASI network side to a dataset of 2 full months of forecasts (July and August 2020). The highly resolved hybrid forecast was validated against the individual input sources and satellite persistence. We found that this newly developed forecast outperforms the RMSE of persistence and the individual input forecasts for all lead times calculated. It shows an improvement on RMSE of 5.07% to 13.97% with respect to satellite forecasts and 7.55% to 15.09% with respect to the ASI network forecast on lead times going from 5 to 30 minutes. It also shows a lower RMSE under high variability conditions

Forecasting Solar Irradiance by looking at clouds from above and below

The energy meteorology measurement network Eye2Sky is a cloud monitoring system covering roughly 110x100 km in north-west Germany. It is equipped with 38 cloud cameras, solar radiation measurement stations and individual Lidar based cloud altitude measurements distributed throughout the region around Oldenburg. The system collects high-resolution information on solar radiation, tracks the variability at different locations and outputs forecasts for very short time scales. It covers a resolution range of fewer than 100 metres and less than 1 minute and supports forecasts of up to one hour (depending on the prevailing cloud height). A second data source for this region is given by images from the geostationary satellite MSG. With these images longer forecast horizons are achieved in a coarser resolution. The hybrid use of both data sources has only just begun in the community. This allows the development of new models with an improved quality of predictions. The presentation gives an overview on Helmholtz AI collaboration of DLR VE and AI collaboration with institutes DLR SP and DLR SF

SciGRID_gas - Data Model of the European GasTransport Network

The current transition in the European energy sector towards climate neutrality requires detailed and reliable energy system modeling. The quality and relevance of the energy system modeling highly depend on the availability and quality of model input datasets. However, detailed and reliable datasets are still missing, especially for the gas infrastructure. In this contribution, we present our approach for developing an opensource model of the gas transport network in Europe. Various freely available data sources were used to collect gas transport data. The datasets from multiple sources were merged, and further, statistical methods were used to generate missing data.As a result, we successfully created a gas transport network model only using open-source data. The SciGRID_gas model contains 206,000 km of pipeline data which is roughly in accordance to former estimations. In addition, datasets of compressor stations, LNG terminals, storages, production sites, gas power plants, border points, and demand time series are provided. Finally, we have discussed data gaps and how they can potentially be closed

Improving the satellite retrieval of surface solar irradiance during an eclipse

Solar eclipse causes high magnitude fluctuations in the Surface Solar Irradiance (SSI) for a short duration and consequently reduces the output of solar PV systems. Grid operators try to estimate the impending loss in PV power generation prior to the occurrence of an eclipse in order to schedule conventional generators for compensating the loss. The worldwide installed capacity of grid connected solar PV systems is expected to steeply rise in the coming decade as a result of the various policy initiatives aimed to tackle the climate change. In future electric supply networks with a high penetration of solar PV systems, such large ramps in generation could impact the stability of the network. Although a solar eclipse is a purely deterministic phenomenon, it’s impact on the satellite retrieval of Surface Solar Irradiance (SSI) is complicated due to the possibility of cloud presence in the regions affected by the eclipse. The extraterrestrial solar irradiance is reduced by the moon during an eclipse. On the one hand this causes clouds to appear darker and they get assigned lower reflectance values than they should have in reality. This leads to predicting higher values for the solar irradiance under these clouds than expected. On the other hand, the eclipse also reduces the clear sky irradiance reaching the earth surface. We developed a method to make corrections for both of these effects on the High Resolution Visible (HRV) channel images from Meteosat-11 The results are validated against ground measurements of irradiance provided by BSRN, IEA-PVPS, DTN and the National Weather Services networks. The validation is performed for sites with locations across Europe and for the last two eclipses

Grid Matching Tool

In this paper, a tool to compare grid models at the highest resolution possible is presented. It takes as input two georeferenced grid models and outputs an identification between clusters of nodes in each model. The tool can be used to transfer data between models, compare them and also create a new model with the highest resolution from the input models

GRID MATCHING TOOL: Enabling the interaction between georeferenced grid models

In this paper, a tool to compare grid models at the highest resolution possible is presented. It takes as input two georeferenced grid models and outputs an identification between clusters of nodes in each model. The tool can be used to transfer data between models, compare them and also create a new model with the highest resolution from the input models