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Data Sources for Offshore Renewable Energy

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Data Sources for Offshore Renewable Energy Data Sources for Offshore Renewable Energy July 20ll Note: All website links in this document were accessed and proved working on August 1st 201. If links change in future, datasets can be found by an internet search of their title in full. Authors: David Woolf, Jason Mcilvenny A report by Environmental Research Institute, University of the Highlands and Islands – North Highland College (ERI, UHI-NHC) contributing to Work Package 2 of the ORECCA project Contents Introduction 1 Global Atmospheric Re-analysis and Instrument Data Sets 3 ERA-40 4 NCEP/NCAR Reanalysis 7 Hadley Centre Sea Level Pressure dataset 2 (HadSLP2) 9 Twentieth Century Reanalysis (V1) & (V2) 10 ICOADS 11 NOAA Blended Sea Winds 12 Global Atlas of Ocean Waves 13 Public Naval Oceanography Portal (NOP) 13 DTU National Space Institute: DTU10 14 GHCN Monthly Station Data 15 Forecasting System 16 Global Forecasting Systems (GFS) 16 Regional Climatic Models 18 REMO 18 NOAA Wavewatch III 19 WAM: Wave Prediction Model 21 ALADIN 22 PRECIS 23 Satellite Data 24 Local & National datasets 30 NORSEWIND 30 CoastDat 31 Royal Dutch Shell plc: Oil Platform data 32 MIDAS land surface station data 33 Crown Estates Data 34 BODC (British Oceanographic Data Centre) 35 Ocean weather Inc. 36 NOAA Wave Buoy Network 36 Channel Coast 37 Wavenet 37 ABPmer :Atlas of UK Marine Renewable Energy Resources 37 Sustainability Development Commission 38 Commercially available products 39 BMT Fluid Mechanics 39 FUGRO Oceanor 40 Metadatabases 43 UKDMOS 43 EDIOS 43 Local resources (Pentland Firth) 44 References 46 Introduction The purpose of this document is to collate information on data sets on ―resources‖ that may be useful to the development of the offshore renewable energy industry. Sources of data with varying levels of details and accuracy are available depending on the scale and type of the study. Reanalysis data is generally used for global resource assessments. National and regional scale resource assessments rely on synoptic scale data or regional scale model data scaled from reanalysis data. Local site specific data is derived from on site wind measurements at specific locations to predict the power production of a single wind turbine or wind farm or to establish the power curve of a wind turbine (Monahan, 2006; Petersen et al., 1997). Here, data sets are divided into global datasets and regional/ local scale datasets. The data and model list is not exhaustive; many other models and data sources exist. This list however describes the principal data sources and models that might realistically be used in offshore renewable energy resource assessment. The data of primary interest to the ORECCA project is wind data, but wave and tidal data are also of interest. Within ORECCA we are primarily interested in data for resource estimates and site identification in the following regions: Area 1: North Sea + Baltic Sea Area 2: Atlantic Ocean Area 3: Mediterranean and Black Sea High quality data are an essential source of information during a resource assessment process. For wind energy, many published studies used data from existing weather station networks operated by meteorological departments. Acceding to World Meteorological Organization WMO, wind measurement should ideally be mounted on wind mast 10 m above the ground; somewhere distortion of the wind field is not significant. Failing that, data needs to be corrected for measurement height and/or flow distortion to a 10-meter-elevation standard. Offshore wind energy companies also need to be able to calculate wind speeds at the height 1 of the wind turbines (often greater than 100 metres). For wind energy resource assessment applications, minimum the amount of wind data should cover a minimum period of one year. Longer periods (10 years) of wind data will provide more reliable results and will identify any long-term variability. The one-year data are usually sufficient to determine diurnal and seasonal variations. Even though data from weather stations were widely used in wind energy resource assessment, they have several limitations which require researchers to find alternative source of accurate data. Even though, wind measurements from weather stations provided data for wind assessment, these types of data have some constraints that disadvantage their use in the assessment process. These main constrains are as follows (Yahyai et al., 2010): Cost Wind Instrument measurements are costly. Installation of a weather station at one location requires infrastructure preparation, communication links, power, sensors and maintenance. Typically, a weather station would cost in the region of $100,000 to $300,000 installation costs depend on location and weather station type. Spatial Resolution Due to cost, weather stations are typically deployed in coarse spatial distributions that vary from one country to the next. Offshore locations include wave buoys, oil industry platforms and sporadic shipboard measurements. Measurement height Standard weather stations measure the wind speed at 10 m above ground height. Wave buoys, oil platforms and ship data differ. For wind energy resource assessment, surface data can introduce errors when using statistics for a 50 – 100 m hub height rotor. 2 Data integrity Instrument data sets can be incomplete due to sensor failure, equipment changes. Older data may not be in digital format. In the new IEC 61400 standard (ed. 3, draft version) there are several wind conditions, which have to be considered when designing a wind farm (Jørgensen et al 2001). The wind conditions, which are important for offshore turbines, are: 1. Extreme winds 2. Wind shear 3. Wind speed probability 4. Turbulence (ambient) 5. Park (wake) turbulence Wave and tidal energy resource assessments have the same data problems as offshore wind resource assessment. Few instrument site data are available, many with short record lengths. Models calibrated with instrument data are commonly used for resource assessment. However trials of wave and tidal devices have been plagued by the loss or damage of devices in the past, highlighting that the extremes from instrument data are as important as the mean annual power for long term commercial investment. Global Atmospheric Re-analysis and Instrument Data Sets Data are available in global datasets which cover the entire globe or substantial parts of it. These datasets are usually gridded at various spatial resolutions or are available in complete coverage via interpolation from data points. Global datasets can be modelled data calibrated by instrument data or directly from instrument data. Global datasets are generally most useful for mapping large scale features, while their resolution and uncertainties can be issues at a smaller scale. They are useful in understanding 3 and studying large-scale spatial and temporal variations in oceanic and atmospheric variables to identify areas of interest for detailed research. One source of reasonably consistent data is reanalysis products. These products are designed to eliminate some of the inconsistencies in long term data sets associated with operational Numerical Weather Forecasting, where the operational model is modified occasionally resulting in ―step changes‖. In a reanalysis, the numerical model is run in a consistent configuration, but will assimilate different data sources as satellites and in situ data sources appear or disappear. (Data assimilation is essentially a mathematical technique to ―nudge‖ numerical model output towards reality, as represented by data). Data from reanalyses are not perfectly consistent over long periods due to the varying data sources and the influence of that data on the model output. There are two large globally gridded atmospheric reanalysis datasets available which are principal sources of data in large scale investigations, the ERA- 40 and the NCEP/NCAR Reanalysis. These two reanalyses are described below, along with related and similar datasets. ERA-40 ERA-40 stands for ―ECMWF (European Centre for Medium-Range Weather Forecasts) Re- analysis-40‖. ECMWF is an international organization supported by eighteen European states and with cooperation agreements with several other European states, European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Meteorological Organization (WMO). It is responsible for producing operational global data analyses and medium-range forecasts for its member states, and undertakes a comprehensive programme of research to ensure the continued development and improvement of its product Initially the project intended to be a reanalysis of 40 years of data, hence ERA-40, however it has incorporated 45 years of data and may incorporate more if extended beyond 2002. The reanalysis involved collecting all available conventional and satellite observations (many supplied to ECMWF by DSS) and using a modern, global atmospheric model to create atmospheric analyses ( gridded fields) of many variables on many levels, four times a day, over the 45-year period. The result is an archive of gridded output data. This dataset provides 4 a highly relevant, consistent representation of weather and climate phenomena since 1957. This dataset has been the primary resource for many atmospheric studies and resource assessment studies for example, ―Europe's onshore and offshore wind energy potential: EEA Technical Report No 6/2009‖. Many sources of meteorological observations were used including radiosonde, balloons, aircraft, satellites, buoys and scatterometers. This data was run through the ECMWF computer model at a 40 km resolution. As the ECMWF's computer model is one of the more highly-regarded in the field of forecasting, many scientists take its reanalysis to have similar merit. The data is stored in GRIB format. A problem with the wave output is apparent from wave height measurements by satellite- borne radar altimeter (available since the launch of ERS-1 in 1991 and continuing indefinitely). The size of waves of low magnitude (less than 1 metre) appears to be too high and the corresponding mean periods too high. The problem is largely confined to enclosed or semi-enclosed seas such as the Mediterranean Sea, Black Sea, Baltic Sea, the Gulf of Mexico and the East China Sea.
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