In this study, the assessment of the combined wind and wave energy potential is presented for locations in the North Atlantic, that is characterized by high energy swells generated by remote westerly wind systems as a consequence of extratropical cyclones (Ponce de León and Bettencourt 2021), and in the Mediterranean Sea where extreme waves are common. The objective is investigating the feasibility of satellite altimetry-based assessments of combined wind/wave renewable energy potential in the European shelf, taking advantage of the increased time and spatial coverage of the satellite altimetry constellation, composed of 10 past and present altimetry missions compiled in the ESA Sea State Climate Change Initiative data base (Dodet et al., 2020).
The method consists of using the homogenized multi-mission altimeter data, to estimate site wind and wave power densities. We use the empirical model of Gommenginger et al. (2003) to estimate the energy period, required for the computation of the wave power density from the altimeter Ku band significant wave height and radar backscatter coefficient. Waves buoys were used to validate the method.
Using Atlantic and Mediterranean sites as comparators for wind/wave correlation (Fusco et al., 2010) we show that wind/wave energy are relatively correlated in the Mediterranean, but not in the North Atlantic, which has implications for the efficient combination of renewable energy sources to make renewable energy supply more resilient. In particular, the co-location of wind and wave farms only has a strong rationale in locations where wind and wave resources are relatively uncorrelated. To date, while significant effort has gone into individually mapping wind and wave resources, little attention has focused on their temporal correlation. With a drive to 100% renewable energy, it is important that complementarity between individual renewable (including marine) energy resources, so that the most resilient forms of renewable energy, and combinations of renewable sources, are developed.
References
Dodet, G., Piolle, J.-F., Quilfen, Y., Abdalla, S., Accensi, M., Ardhuin, F., Ash, E., Bidlot, J.-R., Gommenginger, C., Marechal, G., Passaro, M., Quartly, G., Stopa, J., Timmermans, B., Young, I., Cipollini, P., Donlon, C., 2020. The Sea State CCI dataset v1: towards a sea state climate data record based on satellite observations. Earth System Science Data 12, 1929–1951, https://doi.org/10.5194/essd-12-1929-2020
Fusco F., Nolan G., Ringwood J., 2010. Variability reduction through optimal combination of wind/waves resources – An Irish case study. Energy 35, 310-325, https://doi.org/10.1016/j.energy.2009.09.023
Gommenginger, C.P., Srokosz, M.A., Challenor, P.G., Cotton P.D., 2003. Measuring ocean wave period with satellite altimeters: a simple empirical model. Geophysical Research Letters VOL. 30, NO. 22, 2150, https://doi.org/10.1029/2003GL017743
Ponce de León S., Bettencourt J.H., 2021. Composite analysis of North Atlantic extra-tropical cyclone waves from satellite altimetry observations, Advances in Space Research 68 762-772, https://doi.org/10.1016/j.asr.2019.07.021
Abdalmenem Owda, Merete Badger
1-0 Introduction
Synthetic Aperture Radar (SAR) has been increasingly used in wide range applications ranging from climatic changes, monitoring natural phenomena and hazards, change detection, pollution up to maritime applications. Huge number of daily observations become available for users around the world on a free, full, and open basis thanks to Copernicus services. SAR systems are unique remote sensing instruments; they provide high spatial resolution data, operate day-and-night regardless of cloud coverage and weather conditions.
With the advent of SAR data from the current SAR constellations, a paradigm shift occurred in many maritime applications and especially in offshore wind energy applications. SAR data have been used studying wind wakes at far regions of the offshore wind farms (OWFs), wind energy and resource assessment, development and planning of new OWFs. This study aims to characterize the physical structure of the far-wind wake based on the OWF’ capacity.
2-0 Wind speed retrieval from SAR data and wind wake analysis
SAR sensors overpass and illuminate the ocean surfaces with their own illumination system. The recorded echoes of the backscattered signals, called the normalized radar cross section (NRCS), can be related to the wind speed using a Geophysical model function (GMF). The function relates the sea surface roughness, radar incidence angle, relative wind direction to wind speed measurements by inversion of the model itself. In this study, CMOD5.n function is used to retrieve the values of wind. The function works well for the neutral stable condition.
where is the NRCS and is the angle between wind direction and scatterometer azimuth look angle (both measured from the North). The other coefficients shape the terms , is the sea surface wind (SSW) at 10 m.s.l and is the incidence angle [1].
The far-wind wake refers to the velocity deficit at downstream side of the OWFs due to wind turbine operation. The velocity deficit is computed by subtracting the mean wind velocity at upstream from the mean velocity at downstream side.
3-0 Study Area and selection criteria
The northern European sea has been enriched with the global offshore wind farms. It has about 5566 wind turbines extracting about 26 MW for 5 European countries (UK, Germany, Netherlands, Denmark, Belgium), according to the mid-year report 2021 [2]. This study will take OWFs with different capacities ranging between 200 and 1000 MW. Table 1 shows the information of the 4 selected OWFs.
Table 1: the selected OWFs for the study with their characteristics.
* We take the commissioning date of the last installed OWF in the cluster which refers to Meerwind Süd/Ost .
We have setup the following criteria before processing of the selected OWFs:
1)Only the winds blow from the land are considered for this study with width sector angle 90.
2)The wind direction sector angle is 90.
3) Only SAR wind speed scenes between cut-in and cut-out speed were taken in the study, since the wind wakes appeared well within this range of speed.
4)All scenes after the commissioned date of the selected OWFs were considered in the processing.
4-0 Preliminary and Expected results
Iinvestigate the ability of SAR to monitor the impact of the OWFs’ capacities on the wind wakes’ characteristics (area, length, maximum wind deficit, wind recovery length, e.g.), the loss on power production at far wake regions, and provide useful information for OWF developer. It is expected that the physical characteristics of wind wakes will be proportional with the capacity of OWFs. Figure 1a, shows the deficit areas at the far-wake region of the Nordsee cluster, it is obvious that the close background area of the cluster experienced the highest wind deficit (more than 5%) along a distance about 20 km. Additionally, about 2 % deficit were observed in the adjacent areas of the main deficit, afterward, the wind deficit percentile decreased which means the wind speed started to recover after 20 km away from the outer edge of the cluster. For the Butendiek OWF (figure 1b), the smallest capacity in the study, the situation was completely different in terms of the magnitudes of the physical wind wakes’ characteristic, smaller deficit areas and shorter wind wakes (less than 10km) were measured. Lastly, East Angila ONE‘s capacity is little bit than Nordesee cluster, it shows considerable amount of deficit ,but in term of magnitudes, the deficit area is smaller than what we observed in Nordsee cluster.
Figure 1: mean velocity deficit for all Sentinel scenes after the assigned commissioning date of the cluster in table 1. a, b, and c refers to Nordsee cluster, Butendiek and East Angila ONE, respectively. The percentiles refer to the average velocity deficit inside the polygon. The black arrows refer to the wind direction sector angle.
Any new installation for any future OWFs close to these processed OWFs and clusters will face the severe consequences of wind wakes of the neighbor OWFs. In this study, we are going to process the OWFs, the relationships and the magnitude of power loss for any future OWF close to these selected OWFs.
5-0 References
[1] H. Hersbach, A. Stoffelen, and S. De Haan, “An improved C-band scatterometer ocean geophysical model function: CMOD5,” J. Geophys. Res. Ocean., vol. 112, no. 3, pp. 1–18, 2007, doi: 10.1029/2006JC003743.
[2] Offshore wind energy 2021 mid-year statstics, accessed online at 10/12/2021 14:20 am, https://windeurope.org/intelligence-platform/product/offshore-wind-energy-2021-mid-year-statistics/
Atmospheric aerosols have the highest impact on surface solar irradiance under cloudless conditions. Aerosols scatter and absorb solar irradiance, resulting in an attenuation of the direct normal irradiance (DNI) and an increase in the diffused horizontal irradiance (DHI). Ultimately this causes the total global horizontal irradiance (GHI) at the ground surface to reduce.
The pre-monsoon period in the arid and semi-arid regions of the Indian subcontinent is conducive to dust storm events due to the intense surface heating and steep atmospheric pressure gradients in summer. This study analyzes the degradation in accuracy of satellite-retrieved GHI under situations of high atmospheric dust aerosol content.
The Heliosat method [1] is used to derive cloud index (CI) maps from Meteosat-8 visible channel images for a period in June 2018 during which heavy dust storms were reported in Northern India. Two sources of clear sky data are utilized for transforming CI to GHI. They are the Dumortier model [2] with climatological turbidity values and the McClear method [3] within CAMS with measured or modelled aerosol optical depth (AOD) values. The satellite estimates are validated against ground measured GHI from a BSRN station. Measurement of particulate matter from a nearby air quality monitoring station is used to analyze the dust AOD modelled by CAMS. The results show that there is a large under-estimation of satellite retrieved GHI derived using the McClear model, while the GHI derived with the Dumortier model shows an over-estimation.
References:
1. Rigollier, C., Lefèvre, M., Wald, L., 2004. The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy 77(2) 159-169.
2. Dumortier, D., 1995. Modelling global and diffuse horizontal irradiances under cloudless skies with different turbidities. Daylight II, jou2-ct92-0144, final report vol, 2.
3. Lefèvre, M., Oumbe, A., Blanc, P., Espinar, B., Gschwind, B., Qu, Z., Wald, L., Schroedter-Homscheidt, M., Hoyer-Klick, C., Arola, A., Benedetti, A., Kaiser, J.W., Morcrette, J.-J., 2013. McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions. Atmospheric Measurement Techniques 6 2403-2418
The modern mining industry is of considerable importance to the global economy as it delivers a great range of mineral products for industry and house-hold consumers. The consequence of the considerable significance of the mining and mineral processing industry is not only a great diversity of provided mineral resources but also a massive amount of wastes generated. In fact, the mining sector is one of the largest, if not the largest waste producer on Earth (~25-50 Gt per year). The wastes of the mineral industry are generally useless at the time of production, yet they can still be rich in resource ingredients. Unfavourable economics, inefficient processing, technological limitations or mineralogical factors may not have permitted the entire extraction of resource ingredients at the time of mining and mineral processing. In the past, inefficient mineral processing techniques and poor metal recoveries produced wastes with relatively high metal concentrations. Thus, old tailings and waste rock piles that were considered worthless years ago are now “re-mined”, feeding modern mining operations (e.g. gold tailing piles in South Africa). Feasibility studies on the possible re-processing of such discarded materials require detailed site information. To date such feasibility studies commonly rely on time-consuming and costly ground surveys.
The AuBeSa project ("Automated identification, measurement and mineralogic classification of mining heaps and tailings ponds via satellite remote sensing") aims to create a database containing the location, material volume and mineralogical composition of mining heaps and tailings ponds using satellite remote sensing data and artificial intelligence (AI) computing systems. During the project, AI algorithms will be programmed and trained based on satellite images to identify mining locations and collect aforementioned wastes properties. To achieve this, the following activities are pursued:
1. Training of a machine learning (ML) model that extracts mining tailings and heaps from Sentinel-2 satellite images and classifies them automatically;
2. Volume estimation of the identified objects via topographic satellite data;
3. Mineralogical and chemical analysis of the waste material based on hyperspectral PRISMA-satellite data.
The methodology has been initially developed for mine sites in arid and semi-arid areas of Chile and Peru, because these areas allow an easier analysis of the land surface area and have existing ground-truth data sets. In future, other mines sites in areas of different climate and vegetation zones as well as unknown waste characteristics will be investigated. The acquired database is to provide information on waste dumps and tailings heaps to the mining sector so that well-informed decisions can be made on the possible extraction of remaining resources from these materials. It is expected that automated detection, surveying and classification of mine wastes using satellite remote sensing and AI systems will (a) allow faster decisions on the likely resource potential of individual waste repositories, and (b) reduce the dependence on ground surveys.
Acknowledgements:
This work was supported by the German Federal Ministry of Education and Research and is part of the AuBeSa project (grant number 01|S20083B).
Net-zero commitments require a transition to cleaner transport and renewable energy storage, but this poses many challenges for energy and mineral supply chains. Low-carbon technology is intensive from a mineral perspective. If our planet is to remain within the COP21 Paris Agreement commitment of a global average temperature increase of well below 2°C, the World Bank estimates that more than three billion tons of minerals and metals will be needed to deploy wind, solar and geothermal power, as well as energy storage [1]. Against this background, the demand for battery metals, such a lithium and cobalt, is set to reach 500% of current production levels by 2050 [1]. Mineral supply will be critical in determining the speed and scale at which green energy technologies can lower greenhouse gas emissions and enable climate-resilient development. At the forefront of green technologies are electric vehicles, where Li-ion rechargeable batteries are a fundamental component. Lithium is also used for the production of other batteries, such as cell phones and laptops. The battery market currently takes the 71% of global lithium production [2]. Worldwide lithium production rose more than 300% from 25300 tons in 2010 to 82000 tons in 2020 [2,3].
Within this context, the search and extraction of lithium is becoming an important revenue source for many world economies. In particular, the U.S. Geological Survey [2] estimates that the first three producers lie within the South American ‘Lithium Triangle’, i.e. Bolivia, with 21 million tons resources, Argentina, with 19.3 Mt, and Chile, with 9.6 Mt. Security of supply of lithium to the global markets and increasing expectations by consumers for responsibly sourced raw materials result in a growing global interest in lithium resources and their extraction.
This study uses the largest salt flat in the world, the Salar de Uyuni in Bolivia, as a test site to develop a repeatable and seamless workflow to track lithium from its source in the watershed to the salar nucleus at its highest concentration. It provides a systems-based understanding of aspects of lithium-brine deposit genesis that can contribute to broader considerations on the reporting of Li resources, such as assigning uncertainty bounds to resource estimates. For this study, open source Earth observation (EO) data is analysed to support geological and hydrological research. We explore the potentials of EO data for several research aspects, such as (1) Jointing: it may influence fracture-flow of groundwater and also be significant in terms of surface-area for water-rock interaction, i.e. potentially increasing the "leaching" rates of Li from the bedrock into the water; (2) Weathering: the degree and style of weathering may influence the liberation of Li from rocks into the water; (3) Distribution of clays: the distribution of clays that may restrict the liberation of Li from weathered rock, or may scavenge Li from passing water; (4) Water and moisture: the distribution of water-bodies and sources, including active streams, springs etc. We are building a groundwater recharge model having as input soil moisture content; (5) Geological structure: the presence of neotectonic faults that may disrupt the salar, as well as structures that may provide pathways for the flow of fluids; (6) Lithological mapping and classification: possible refinement of existing geological maps.
In conclusion, we found that by constructing a flexible and repeatable workflow the question how does lithium reach the salar de Uyuni can be addressed. This workflow will support the sustainable management of lithium in the region. Moreover, the provision of "fit for purpose" systems of tracking Li helps in filling gaps in existing methods to enable Li brines resources to be correctly reported.
[1] Kirsten Hund, Daniele La Porta, Thao P. Fabregas, Tim Laing, John Drexhage. Minerals for Climate Action: The Mineral Intensity of the Clean Energy Transition. World Bank Group Report; 2020.
[2] USGS. Lithium: Mineral Commodity Summary. 2021.
[3] USGS. Lithium: Mineral Commodity Summary. 2011.
The modern world is built upon a network of complex, globe-spanning supply chains, where critical natural resources are extracted, processed, and transported with a bare minimum of oversight. Mining of metal ores, mineral and fuel sources is fundamental to both current and future green economies but requires vast earthworks as well as intensive processing and refinement operations. A major agreement from the COP26 summit, the Glasgow Climate Pact, stipulates a “phasing down” of coal rather than a complete phase out. The question follows: how does this translate to real action to mitigate the worst polluting hydrocarbon energy resource? How many countries will continue to use coal as part of their energy mix without the technology or monitoring infrastructure to verify its continued environmental impact? Terrabotics provides easy to understand metrics, enhanced by AI, to collect, organise and make sense of the gamut of geospatial data on coal mine operation, allowing decision makers, regulators and operators to assess the environmental impact of coal mine sites objectively and reliably. Observing vast coal mining operations is highly suited to satellite earth observation owing to its wide spatial coverage, weekly-to-daily revisit times and much lower relative cost compared to ground, drone or aerial surveys. Using an array of optical, thermal and radar sensors aboard satellites, we have blended multi-sensor data analytics from this rich, ever improving data source to create a catalogue of key leading indicators pertaining to site operations, production capacity and ESG metrics.
We present a study of two major coal mines in the Unites States and Kazakhstan using Terrabotics’ Mine Monitoring platform (Minotor™) and data streams from the Energy SCOUT™ product portfolio. Using time series optical satellite data, we provide an overview of a site’s operation with change detection algorithms highlighting areas of increased activity or new construction. Thermal and emissions data help identify step changes in mining operations, the intensity of ground vehicular movements, activation of processing facilities and outgassing from extraction. Radar imagery time series detects the location of large vehicles, mining equipment and any other changes in infrastructure across the entire mine site.
Together, we have converted the numerous data feeds from independent satellite sources into valuable integrated metrics and actionable intelligence that allows operators, stakeholders, industry observers access to critical ESG performance and production data. Our goal at Terrabotics is to shine a light on critical but opaque natural resource supply chains, with the Minotor™ EO analytics platform providing real time intelligence and ESG/production forecasting. As we transition to economies free from fossil fuels, we must also meet the demands of a greener economy based on battery technologies that will inevitably require intensive mineral processing and extraction. Without comprehensive, cost-effective routine monitoring solutions of the world’s largest and most important mining facilities, we will fail to build a sustainable economy or succeed in a phased transition away from fossil fuels while managing their lasting impact on the environment.