The Role of the Effective Cloud Albedo for Climate Monitoring and Analysis

Expected Impacts of Mixing European Beech with Silver Fir on Regional Air Quality and Radiation Balance

The anticipated climate change during the next decades is posing crucial challenges to ecosystems. In order to decrease the vulnerability of forests, introducing tree species’ mixtures are a viable strategy, with deep-rooting native Silver fir (Abies alba) being a primary candidate for admixture into current pure stands of European beech (Fagus sylvatica) especially in mountainous areas. Such a change in forest structure also has effects on the regional scale, which, however, have been seldomly quantified. Therefore, we measured and modeled radiative balance and air chemistry impacts of admixing Silver fir to European beech stands, including changes in biogenic volatile organic compound emissions. An increased fraction of Silver fir caused a smaller albedo and a (simulated) larger evapotranspiration, leading to a dryer and warmer forest. While isoprene emission was negligible for both species, sesquiterpene and monoterpene emissions were larger for fir than for beech. From these differences, we derived that ozone concentration as well as secondary organic aerosols and cloud condensation nuclei would increase regionally. Overall, we demonstrated that even a relatively mild scenario of tree species change will alter the energy balance and air quality in a way that could potentially influence the climate on a landscape scale.

The Seamless Solar Radiation (SESORA) Forecast for Solar Surface Irradiance—Method and Validation

Due to the integration of fluctuating weather-dependent energy sources into the grid, the importance of weather and power forecasts grows constantly. This paper describes the implementation of a short-term forecast of solar surface irradiance named SESORA (seamless solar radiation). It is based on the the optical flow of effective cloud albedo and available for Germany and parts of Europe. After the clouds are shifted by applying cloud motion vectors, solar radiation is calculated with SPECMAGIC NOW (Spectrally Resolved Mesoscale Atmospheric Global Irradiance Code), which computes the global irradiation spectrally resolved from satellite imagery. Due to the high spatial and temporal resolution of satellite measurements, solar radiation can be forecasted from 15 min up to 4 h or more with a spatial resolution of 0.05 ∘ . An extensive validation of this short-term forecast is presented in this study containing two different validations based on either area or stations. The results are very promising as the mean RMSE (Root Mean Square Error) of this study equals 59 W/m 2 (absolute bias = 42 W/m 2 ) after 15 min, reaches its maximum of 142 W/m 2 (absolute bias = 97 W/m 2 ) after 165 min, and slowly decreases after that due to the setting of the sun. After a brief description of the method itself and the method of the validation the results will be presented and discussed.

Validation of All-Sky Imager Technology and Solar Irradiance Forecasting at Three Locations: NREL, San Antonio, Texas, and the Canary Islands, Spain

Increasing photovoltaic (PV) generation in the world’s power grid necessitates accurate solar irradiance forecasts to ensure grid stability and reliability. The University of Texas at San Antonio (UTSA) SkyImager was designed as a low cost, edge computing, all-sky imager that provides intra-hour irradiance forecasts. The SkyImager utilizes a single board computer and high-resolution camera with a fisheye lens housed in an all-weather enclosure. General Purpose IO pins allow external sensors to be connected, a unique aspect is the use of only open source software. Code for the SkyImager is written in Python and calls libraries such as OpenCV, Scikit-Learn, SQLite, and Mosquito. The SkyImager was first deployed in 2015 at the National Renewable Energy Laboratory (NREL) as part of the DOE INTEGRATE project. This effort aggregated renewable resources and loads into microgrids which were then controlled by an Energy Management System using the OpenFMB Reference Architecture. In 2016 a second SkyImager was installed at the CPS Energy microgrid at Joint Base San Antonio. As part of a collaborative effort between CPS Energy, UT San Antonio, ENDESA, and Universidad de La Laguna, two SkyImagers have also been deployed in the Canary Islands that utilize stereoscopic images to determine cloud heights. Deployments at three geographically diverse locations not only provided large amounts of image data, but also operational experience under very different climatic conditions. This resulted in improvements/additions to the original design: weatherproofing techniques, environmental sensors, maintenance schedules, optimal deployment locations, OpenFMB protocols, and offloading data to the cloud. Originally, optical flow followed by ray-tracing was used to predict cumulus cloud shadows. The latter problem is ill-posed and was replaced by a machine learning strategy with impressive results. R2 values for the multi-layer perceptron of 0.95 for 5 moderately cloudy days and 1.00 for 5 clear sky days validate using images to determine irradiance. The SkyImager in a distributed environment with cloud-computing will be an integral part of the command and control for today’s SmartGrid and Internet of Things.

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

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.

A Satellite-Based Sunshine Duration Climate Data Record for Europe and Africa

Besides 2 m - temperature and precipitation, sunshine duration is one of the most important and commonly used parameter in climatology, with measured time series of partly more than 100 years in length. EUMETSAT’s Satellite Application Facility on Climate Monitoring (CM SAF) presents a climate data record for daily and monthly sunshine duration (SDU) for Europe and Africa. Basis for the advanced retrieval is a highly resolved satellite product of the direct solar radiation from measurements by Meteosat satellites 2 to 10. The data record covers the time period 1983 to 2015 with a spatial resolution of 0.05° × 0.05°. The comparison against ground-based data shows high agreement but also some regional differences. Sunshine duration is overestimated by the satellite-based data in many regions, compared to surface data. In West and Central Africa, low clouds seem to be the reason for a stronger overestimation of sunshine duration in this region (up to 20% for monthly sums). For most stations, the overestimation is low, with a bias below 7.5 h for monthly sums and below 0.4 h for daily sums. A high correlation of 0.91 for daily SDU and 0.96 for monthly SDU also proved the high agreement with station data. As SDU is based on a stable and homogeneous climate data record of more than 30 years length, it is highly suitable for climate applications, such as trend estimates.