June 2011 Wind Speed Prediction using Global and Regional Based Virtual Towers in CFD Simulations
|
|
- Basil Bailey
- 5 years ago
- Views:
Transcription
1 June 2011 Wind Speed Prediction using Global and Regional Based Virtual Towers in CFD Simulations Master of Science in Wind Power Project Management By Roger Moubarak Energy Technology at Gotland University Supervisor: Dr. Bahri Uzunoglu Examiner: Dr. Stefan Ivanell
2 ABSTRACT Gotland University Faculty of Energy Technology By Roger Moubarak Energy Production Prediction using Global and Regional based Virtual Towers in CFD Simulations. Master s thesis, pages, 13 figures, 3 tables and 5 appendices Examiners/Supervisors: Dr Stefan Ivanell, Dr Bahri Uzunoglu Keywords: Global-Regional Global Model, Era Interim, StormGeo, WRF, ECMWF, Data Series, WindSim, Hindcast. Wind farm assessment is a costly and time consuming process when it is planned by traditional methods such as a met mast. Therefore, new models have been established and used for the wind farm assessment to ease the process of wind farm planning. These models are Global-regional models which add to cost efficiency and time saving. There are several types of these models in the market that have different accuracy. This thesis discusses and uses in simulations Global regional model data outputs from European Centre for Medium- Range Weather Forecasts (ECMWF), Weather Research Forecast WRF and ECMWF, which is currently producing ERA-Interim, global reanalysis of the data-rich period since The goal of the master's thesis is to see whether it is useful and efficient to use Global regional weather model data such as the Era Interim Global Reanalysis Model data for wind assessment by comparing it with the real data series (met mast) located in Maglarp, in the south of Sweden. The comparison shows that in that specific area (hindcast) at Maglarp, in the south of Sweden, very promising results for planning a wind farm for a 100m, 120m and 38m heights. 2
3 Acknowledgements: I would like to thank my supervisor at the University of Gotland Dr. Bahri Uzunoglu for giving me help and support during the entire period of my thesis work. Many grateful thanks to CEO of StormGeo in Sweden Johan Groth and Olav Erikstad, Key Account Manager, in Norway for giving me an opportunity for doing this thesis work for their company StormGeo. Also a lot and grateful thanks to Daniel Fredriksson from the company StormGeo for his enormous assistance and for giving me his valuable time in answering my questions and providing me with all necessary data during the period of the thesis work. I would also like to give thanks to Karin Bengtsson the vice rector of the Gotland University for helping me do the presentation of my thesis work. A lot of thanks to Ola Eriksson for his help in the Wasp and Windpro software. Many thanks to Hans Bergström for giving me data series of several locations in Sweden. Many thanks to the University of Gotland and Stefan Ivanell in particular for listening to my presentation. Last but not least, I greatly appreciate and thanks to my wonderful girlfriend Asta Glinskaite for helping me edit the English text of this thesis work. Sincerely, Roger Moubarak Visby, June
4 Table of Contents 1. Introduction.5 2. Limitations / Delimitations Models CFD models WindSim Global- Regional weather model Differences / Advantages ECMWF How numerical weather prediction works ERA The WRF model StormGeo The WRF Model (StormGeo) The ECMWF Model (StormGeo) Methodology Terrain module Map Wind fields module Object Wind resources StormGeo Results Differences in wind speed between two models with initial shooting height of ERA Interim based on WindSim of 38m Differences in wind speed between two models with initial shooting height of ERA Interim based on WindSim of 38m Differences in wind speed between two models with initial shooting height of ERA Interim based on WindSim of 38m Uncertainty Discussion Conclusion References
5 1. Introduction: Wind farm planning is a time consuming process which requires a major financial investment for wind resource assessment, particularly in rural areas and complex terrain sites where it is difficult to reach and build met masts. Wind resource simulation models are important for feasibility studies in various areas and especially in complex terrain sites since measurements can only be affordable at selected positions. For micro-scale (resolution of 1km to 1m), the CFD-models such as WindSim can be used in the wind resource analysis, especially at complex terrain and forest areas. Global-Regional Model (ECMWF- WRF) that uses historical global weather data and handles real-time meteorological data from the synoptic-scale down to the micro-scale have also been used and tested by researchers; [1] The aim of this study is to compare global real-historical data (provided by the company StormGeo) that use real weather data model such ERA Interim-model reanalysis with a real data series from a met mast. Both comparisons are fulfilled by the means of WindSim model. The measurement of comparison included is wind speed. In global weather case wind variations based on the European Centre for Medium-Range Weather Forecasts ECRF and ERA-Interim Model are provided in the thesis and the area where the measurements have taken place is in Maglarp, the south of Sweden. Moreover, one year measurements of 38m, 75m and 120m measuring heights have been provided with the mast as well. The aim of the thesis is to compare the Global-Regional Scale Model ERA-Interim reanalysis data for the wind energy assessment to virtual towers for feasibility studies and wind energy assessments for preliminary studies. 5
6 2. Limitations/Delimitations: Obtaining a data series of the met mast for recent years in order to compare it with the WRF model of same year was a major problem which was solved by taking an alternative model such as ERA Interim with the purpose to compare it with the met mast. The period of data series of ERA Interim starts with the year 1989 until current date, while the met mast data series takes the 1980s until Therefore, the data series of 1989 was chopped from both met mast data series and ERA Interim and was compared with each other. On the other hand, long time simulations in WindSim with limited computer capacity were also a big obstacle faced at the beginning of this thesis. Limited availability of hindcast data in required locations and required time period was a problem as well since the data series were only available in the south of Sweden during the time of study. 3. Models: 3.1 CFD Models: WindSim: A CFD Wind Farm Design program is used to forecast wind speeds. This can be performed by calculating numerical wind fields over a digitalized terrain called micrositing. This CFD model local wind fields are significantly influenced by terrain and topography. The 6 modules that is model use have different aspects of calculations and can be used in a different length of scales ranging from meso-scale to a smaller scale micro- scale wind resource assessments. WindSim needs at least one observation point within the modeled area necessary for meteorological data. With these initial inputs the wind resources for the whole area can be calculated and the energy production from any number of wind turbines can be obtained. Computational Fluid Dynamics (CFD) is used to perform the wind field simulations in WindSim. CFD is a numerical method for solving the fundamental equations of fluid flow and CFD has become a very efficient method within many industries and even universities. 6
7 The fundamental behavior of fluid flow is described by the Navier-Stokes equations. The Navier-Stokes equations are non-linear partial differential equations known to be unstable and difficult to solve. [2] 3.2 Global-Regional Weather Model Differences / advantages: 1. Traditional local method: Build a measurement mast. Measure the wind for one-two years. Use CFD to estimate the wind distribution in the rest of the area /park. 2. Global-Regional Method: Use historical data from the Global Model ECMWF (European Centre of Medium- Range Weather Forecast, located in Reading, UK) as the input to that model. Use full, meteorological models for wind distribution estimates ECMWF ECMWF is one of the most accurate medium-range global weather forecasts up to 10 days, see Figure 1. This model is provided to the European National Weather Services which use numerical weather prediction to forecast the weather from its present measured state. The calculations require constant and continual input of meteorological data, collected by satellites and earth observation systems such as met mast stations, aircraft, synops, ships, Dribus and weather balloons. ECMWF receives every day a total of about 300 million observations (see Figure 2), the largest and most important part of this data comes from satellites. The resolution of this model is 16 km. [3] 7
8 Figure1: Root Mean Square Error for 12 Global-Regional models for 5 days ahead Source: 8
9 Figure2: Observations from various resources Source: How numerical weather prediction works: Three major components are necessary for the numerical weather prediction process: A set of observations shows the current atmospheric conditions. These observations are collected directly from weather met mast stations and weather balloons, aircrafts and from satellites. A mathematical model which divides the atmosphere into grids and then calculates how the temperature, pressure, humidity and wind speed change over time. A very powerful supercomputer to execute and run the numerical weather prediction system, and to perform the calculations on each grid point of the model.[4] 9
10 3.2.3 ERA: ECMWF is currently producing ERA-Interim, a global reanalysis of the data-rich period since The resolution of this data is 70 km, and it has many improvements in forecasting model and analysis methodology. ERA-Interim is the latest ECMWF global atmospheric reanalysis of the period 1989 to present. It reanalyses multi-decadal series of past observations to study atmospheric and oceanic processes and predictability. Since reanalysis are produced using fixed and modern versions of the data assimilation systems developed for numerical weather prediction, they are more suitable than operational analyses to be used in studies of long-term variability in climate. Reanalysis products are used increasingly in many fields that require an observational record of the state of either the atmosphere or its underlying land and ocean surfaces and estimation of renewable energy resources. ECMWF has in the past produced three major reanalysis: FGGE, ERA-15 and ERA-40. ERA- Interim as initially an 'interim' reanalysis of the period 1989 to present in preparation for the next-generation extended reanalysis to replace ERA-40. The job provides with a detailed description of the ERA-Interim model and data assimilation system, the observations used, and various performance aspects. The ERA-Interim archive is more extensive than that for ERA-40. [5] The WRF Model The WRF is a full, next-generation mesoscale model which is used at research and forecasting institutions around the world. WRF can be run in a number of different resolutions and setups and uses a high resolution regional weather model. WRF uses a global weather model such as ECMWF as boundary and initial conditions (it is common to use the NCEP or GFS models instead since they are freely available). [6] 10
11 4. StormGeo: StormGeo is one of the leading providers of commercial weather forecasts and consultancy services in Europe. StormGeo s main areas of activity are the renewable energy, offshore and media businesses. Also StormGeo is doing simulations using a full, 3-dimensional atmospheric model which is run in historical, so-called hindcast mode for a period of 1 to 2 years to get a complete simulation of the atmospheric state at any time during that period. StormGeo uses the main weather models that form the basis of StormGeo s wind resource mapping, climate studies and daily forecasting which are WRF and ECMWF. StormGeo also uses ERA Interim reanalysis model. [7] 4.1 The WRF Model (StormGeo) The numerical basis for most of StormGeo s prediction and consultancy services, including the wind mapping, is the so-called WRF weather model. The WRF is a full, next-generation mesoscale model which is used at research and forecasting institutions around the world. StormGeo runs the WRF in a number of different resolutions and set-ups. The main forecast model for Europe is 9 km WRF model. The latter model runs twice a day, 6 days ahead, and provides all relevant weather parameters from ground level to the top of the atmosphere. The 9 km WRF model takes in boundary data from the global ECMWF model. see Figure 3. For detailed, local forecasting, StormGeo runs the WRF and other models in finer resolution over smaller areas, using boundary values from the 9 km model. Thus, for short-term wind power forecasting we run nested models of 9, 3 and 1 km resolution to obtain very detailed, local wind forecasts. The 3 km model uses data from the 9 km model as boundary data, and the 1 km model uses data from the 3 km model as boundary data, see Figure 4. This very fine resolution makes it possible to predict the wind energy generation for each individual wind turbine. [8] 11
12 Figure 3: StormGeo WRF Model Figure 4: WRF (StormGeo) stepping down from 9 to 1 km 12
13 The WRF model is also used in hindcast (i.e., historical) mode for resource assessment and sitting purposes. StormGeo has performed numerous studies for the wind farm areas in Scandinavia, using the 1 km WRF with calibration to local measurements, and 6 km for North Sea and surroundings see Figures 5 and 6. Figure 5: WRF 6 km area for the North Sea and surroundings Figure 6: Areas in Scandinavia covered with 1 km hindcasts, May The ECMWF Model (StormGeo) To provide boundary data for the WRF model, as well as for forecasting on a global scale, StormGeo uses the so-called ECMWF model, run by the European Meteorological Centre (ECMWF). The ECMWF model is widely recognized as the best performing global weather model in the world. [8] The model has a horizontal resolution of about 16 km and a maximum forecast length of 10 days. As one of few private weather companies, StormGeo is a member of the ECMWF and has a full access to all products from the ECMWF on a global scale. 13
14 5. Methodology of CFD calculations at mesoscale: This chapter describes in details how the simulations went through WindSim and how the data series were extracted from the StormGeo`s web based database. The whole process of ERA Interim data series simulation was fully completed using the WindSim program consisting of 6 modules as shown below, see Figure 7. Figure 7: WindSim 6 modules The comparison was made between a met mast data series and the Era Interim (Global Weather Reanalyze Model) both locations are in Maglarp. The aim of the comparison was to see how close the value of the Era Interim to the met mast is (both of the same year of 1989). 5.1 Terrain Module: The terrain module generates a 3D model of the area around the wind farm based on elevation and roughness data. However, firstly the basis for the 3D model must be available which is a 2D dataset with elevation and roughness data in gws format Map: The map was created first by Google earth to get a background for the terrain map which will be used in WindPro/Wasp. This was done by deciding on 3 coordinates in the area around Maglarp in south of Sweden. The location of the site is at Maglarp (coordinates of (SWEREF 99 TM) N= and E ). The area considered plain and with no terrain complexity, see Figure 8. This background map was manipulated in Wasp and has been prepared for implementing the roughness area and height contours. Both roughness area file and height contour file were created by importing the data online. These two files were merged/pasted together into one file and converted to gws format that is adapted to the 14
15 WindSim program. The gws file of Maglarp with specific coordinates, roughness area and height contour were used in the terrain module and created the base for the next module. Figure 8: Location of Maglarp, the red colour is the actual met mast 5.2 Wind Fields Module: After the generation of the 3D model in the previous Terrain module the simulation of the wind fields can begin. The wind fields are determined by solving the Reynolds Averaged Navier-Stokes Equations (RANS). Since the equations are non-linear, the solution process is iterative; therefore the solution is resolved by iteration until solution reaches a convergence state, see Fig 9 and 10. Since this module is most time consuming; therefore, only some sectors were chosen due to the limited time of this thesis work. The log profile is defined from ground up to the boundary layer height; above this height the profile is constant. Therefore, default value is 500 m. This simulation includes 700 iterations done. 15
16 Figure 9: convergence after about 300 iterations, spot value Figure 10: convergence after about 300 iterations, Residual values 16
17 5.3 Objects: This module is used to position turbines, climatologies and transferred climatologies. Alternatively objects can also be read from ows files by using the Tools->Import objects menu item. This thesis used the ows format to position both the turbine and the climatology. The turbine was positioned in the ows file just in order to use the energy module and get the wind speed file in wind resources module. Additionally, this module used the file of both met mast and the ERA interim and uploaded by changing the format that is adapted to windsim as tws or wws format. The tws format is a type of 8 column of the tws windsim format. 5.4 Wind resources: This module is based on weighting the wind database against measurements. If several measurements are available, the wind resource map will be based on all of them by interpolation. This model is the basis for the energy optimization. In this module the wind speed of the specific coordinate was obtained by a wind resources file after running the wind resources module. Also in this module the shooting for different hub height was obtained and selected with various wind resources file with respective height. 5.6 StormGeo: After getting the access (password and user name) from StormGeo company then it was possible to obtain the ERA interim reanalysis data series for the year 1989 until present. This process is quite easy and simple, see Figure 11. The only thing one must do is to choose the hindcast region, then the coordinates of the Maglarp location with its Zone area number, then the altitude, hub height, and finally clicking on the start processing. After a few minutes of waiting all the data online were obtained, three files were created; first for Era Interim, second file is in Windsim format, third the WRF 1 km resolution of data series for year
18 Figure 11: The Stormgeo page for importing data series 6. Results: After running ERA Interim in WindSim and comparing it with both the ERA Interim raw data and met mast data series, the following results were obtained in the next section. 18
19 6.1 Differences in wind speed between two models with initial calculation height of ERA Interim based data on WindSim of 38m: After using the WindSim Model and Global Weather Model (ERA Interim Reanalyze Model) for comparing, the following results were obtained: the measurements of the wind speed for the Maglarp location between the met mast, Era Interim data series and Era Interim based on WindSim with calculations from 38 m height. See Table 1. The comparison shows that the value between ERA Interim raw data series is very close to the value of the met mast, especially at height of 120m. Furthermore, the Era Interim based on WindSim make the value even much better and closer to the value of the met mast. See Figure 12. Met mast WindSim ERA (WindSim based on (ERA/ Height based on Met mast results Interim ERA/Met mast)-1 Mast)-1 (m) WindSim based on % % ERA No data 9.10 No data Table 1: Computation of ERA Interim based data on WindSim of 38m 19
20 9,4 9,2 9 8,8 8,6 8,4 8,2 8 7,8 7,6 7,4 7,2 7 6,8 6,6 6,4 6,2 6 5,8 5,6 5,4 5,2 5 4,8 4,6 4,4 4,2 4 Met Mast ERA Windsim results based on ERA Figure 12 : Computation of ERA Interim based data on WindSim of 38m 6.2 Differences in wind speed between two models with initial calculation of ERA Interim based data on WindSim of 10m: The comparison, see Table 2, shows that the value between ERA Interim based on WindSim with the met mast value is not accurate since shooting the initial height value is of 10m as the following table shows. See Figure
21 Height Met Mast WindSim results based on ERA Interim (WindSim based on ERA/ Met Mast )-1 % Table2 : Computation of ERA Interim based data on WindSim of 10m 21
22 10,4 10,2 10 9,8 9,6 9,4 9,2 9 8,8 8,6 8,4 8,2 8 7,8 7,6 7,4 7,2 7 6,8 6,6 6,4 6,2 6 5,8 5,6 5,4 5,2 5 4,8 4,6 4,4 4,2 4 Met Mast ERA Windsim results based on ERA Fig 13: Computation of ERA Interim based data on WindSim of 10m 6.3 Differences in wind speed between two models with initial calculation of ERA Interim based on WindSim of 75m: The comparison, see Table 3, shows that the value between ERA Interim based on WindSim with the met mast value have regained its accurate value since calculating the initial height value starts from height of 75m. 22
23 Height (m) Windsim results based on ERA 10m % (Met Mast - Windsim based on ERA)-1 % Table 3: Computation of ERA Interim based data on WindSim of 75m 7. Uncertainty: Even the met mast value of two different anemometers that are put together at same height and location, gives different values in wind speed per year, according to Daniel Fridriksson from StormGeo, similar measurements have been taken in Norway and showed an average difference of wind speed with 0.5 m/s per year. So differences between the ERA Interim and the met mast with highest 7% are acceptable and lowest 1.7 % is very good. On other hand all measurements have mechanical and systematical uncertainty with some percent margin error of uncertainty, therefore differences between 2 models is reasonable when the differences are not so high. 23
24 8. Discussion: The ERA Interim and especially ERA Interim based on WindSim has a very close value to the met mast at height such as 100 or 120 m. Therefore, in the area with plain terrain close to the sea in hindcast of Skåne the Era Interim can be used for feasibility study in order to build wind farms.however, Era Interim has not got an accurate value at the height of 50 m and below since the ERA Interim has a low quality and less accurate value at these heights. The ERA Interim based on WindSim makes the value of the ERA even much better and close to the met mast value if the calculation starts at height of 38 even higher. 9. Conclusion: It raises the question if it is feasible to use ERA Interim for wind energy assessment, and if these results be generalized in all areas. Building a windfarm with hub height such as 100 or 120 m is feasible with Era Interim with such area like Skåne with no terrain complexity and close to the sea. On other hand it might not be feasible to build wind farm if the wind farm is with low hub height such as hub 50 m and below. This research cannot be generalized in all areas since this thesis work was based on a specific area with a specific topographic character, thus, further research and studies need to be completed in various areas with different terrains in order to get a final conclusion. If this comparison in that hindcast that Era has used efficient it saves money and time. 24
25 References: [1] Sciencedirect, Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications, 2011 April 15 < [2] WindSim AS, WindSim Technical Basics, 2011 April 30, < [3] Wikipedia, European Centre for Medium-Range Weather Forecasts, 2011 May 03, < [4] ECMWF, Forecasting by computer, 2011 May 05, < [5] ECMWF, ERA-Interim, 2011 May 03, < [6] WRF, About the Weather Research & Forecasting Model, 2011 May 08, < [7] Stormgeo, About Stormgeo, 2011 April 08, < [8] Stormgeo, Modellning, 2011 May 12, < 25
26 Appendix Wind Speed at 38 m height for met mast 26
27 Wind Speed at 75 m height for ERA based on Windsim 27
28 Wind Speed at 120 m height for met mast 28
29 Wind Speed at 38 m height for ERA Interim based on Windsim 29
30 30
Descripiton of method used for wind estimates in StormGeo
Descripiton of method used for wind estimates in StormGeo Photo taken from http://juzzi-juzzi.deviantart.com/art/kite-169538553 StormGeo, 2012.10.16 Introduction The wind studies made by StormGeo for HybridTech
More informationThe use of high resolution prediciton models for energy asessment -challenges in cold climate. Gard Hauge
The use of high resolution prediciton models for energy asessment -challenges in cold climate Gard Hauge gard.hauge@stormgeo.com Outline Models and methods Wind Resource Mapping Challenges in cold climate
More informationSpeedwell High Resolution WRF Forecasts. Application
Speedwell High Resolution WRF Forecasts Speedwell weather are providers of high quality weather data and forecasts for many markets. Historically we have provided forecasts which use a statistical bias
More informationVindkraftmeteorologi - metoder, målinger og data, kort, WAsP, WaSPEngineering,
Downloaded from orbit.dtu.dk on: Dec 19, 2017 Vindkraftmeteorologi - metoder, målinger og data, kort, WAsP, WaSPEngineering, meso-scale metoder, prediction, vindatlasser worldwide, energiproduktionsberegninger,
More informationWind Forecasts in Complex Terrain Experiences with SODAR and LIDAR
Wind Forecasts in Complex Terrain René Cattin, Saskia Bourgeois, Silke Dierer, Markus Müller, Sara Koller Meteotest, Switzerland Private company founded in 1981 28 employees Any kind of meteorological
More informationGenerating Virtual Wind Climatologies through the Direct Downscaling of MERRA Reanalysis Data using WindSim
Generating Virtual Wind Climatologies through the Direct Downscaling of MERRA Reanalysis Data using WindSim WindSim Americas User Meeting December 4 th, 2014 Orlando, FL, USA Christopher G. Nunalee cgnunale@ncsu.edu
More informationWind Atlas for South Africa (WASA)
Wind Atlas for South Africa (WASA) Andre Otto (SANEDI), Jens Carsten Hansen (DTU Wind Energy) 7 November 2014 Outline Why Wind Resource Assessment Historical South African Wind Atlases Wind Atlas for South
More informationWind Resource Assessment Practical Guidance for Developing A Successful Wind Project
December 11, 2012 Wind Resource Assessment Practical Guidance for Developing A Successful Wind Project Michael C Brower, PhD Chief Technical Officer Presented at: What We Do AWS Truepower partners with
More informationWind Assessment & Forecasting
Wind Assessment & Forecasting GCEP Energy Workshop Stanford University April 26, 2004 Mark Ahlstrom CEO, WindLogics Inc. mark@windlogics.com WindLogics Background Founders from supercomputing industry
More informationWind Resource Analysis
Wind Resource Analysis An Introductory Overview MGA/NWCC Midwestern Wind Energy: Moving It to Markets July 30, 2008 Detroit, Michigan Mark Ahlstrom 1 WindLogics Background Founded 1989 - supercomputing
More informationECMWF global reanalyses: Resources for the wind energy community
ECMWF global reanalyses: Resources for the wind energy community (and a few myth-busters) Paul Poli European Centre for Medium-range Weather Forecasts (ECMWF) Shinfield Park, RG2 9AX, Reading, UK paul.poli
More informationClimate Variables for Energy: WP2
Climate Variables for Energy: WP2 Phil Jones CRU, UEA, Norwich, UK Within ECEM, WP2 provides climate data for numerous variables to feed into WP3, where ESCIIs will be used to produce energy-relevant series
More informationImportance of Numerical Weather Prediction in Variable Renewable Energy Forecast
Importance of Numerical Weather Prediction in Variable Renewable Energy Forecast Dr. Abhijit Basu (Integrated Research & Action for Development) Arideep Halder (Thinkthrough Consulting Pvt. Ltd.) September
More informationKommerciel arbejde med WAsP
Downloaded from orbit.dtu.dk on: Dec 19, 2017 Kommerciel arbejde med WAsP Mortensen, Niels Gylling Publication date: 2002 Document Version Peer reviewed version Link back to DTU Orbit Citation (APA): Mortensen,
More informationValidation n 1 of the Wind Data Generator (WDG) software performance. Comparison with measured mast data - Complex site in Southern France
Validation n 1 of the Wind Data Generator (WDG) software performance Comparison with measured mast data - Complex site in Southern France Mr. Tristan Fabre* La Compagnie du Vent, GDF-SUEZ, Montpellier,
More informationThe benefits and developments in ensemble wind forecasting
The benefits and developments in ensemble wind forecasting Erik Andersson Slide 1 ECMWF European Centre for Medium-Range Weather Forecasts Slide 1 ECMWF s global forecasting system High resolution forecast
More informationIntegration of WindSim s Forecasting Module into an Existing Multi-Asset Forecasting Framework
Chad Ringley Manager of Atmospheric Modeling Integration of WindSim s Forecasting Module into an Existing Multi-Asset Forecasting Framework 26 JUNE 2014 2014 WINDSIM USER S MEETING TONSBERG, NORWAY SAFE
More informationAn evaluation of wind indices for KVT Meso, MERRA and MERRA2
KVT/TPM/2016/RO96 An evaluation of wind indices for KVT Meso, MERRA and MERRA2 Comparison for 4 met stations in Norway Tuuli Miinalainen Content 1 Summary... 3 2 Introduction... 4 3 Description of data
More informationHigh resolution regional reanalysis over Ireland using the HARMONIE NWP model
High resolution regional reanalysis over Ireland using the HARMONIE NWP model Emily Gleeson, Eoin Whelan With thanks to John Hanley, Bing Li, Ray McGrath, Séamus Walsh, Motivation/Inspiration KNMI 5 year
More informationA Comparative Study of virtual and operational met mast data
Journal of Physics: Conference Series OPEN ACCESS A Comparative Study of virtual and operational met mast data To cite this article: Dr Ö Emre Orhan and Gökhan Ahmet 2014 J. Phys.: Conf. Ser. 524 012120
More informationWind speed and direction predictions by WRF and WindSim coupling over Nygårdsfjell
Journal of Physics: Conference Series PAPER OPEN ACCESS Wind speed and direction predictions by WRF and WindSim coupling over Nygårdsfjell To cite this article: M Bilal et al 1 J. Phys.: Conf. Ser. 753
More informationW I N D R E S O U R C E A S S E S S M E N T
W I N D R E S O U R C E A S S E S S M E N T Annual Energy Production Project: Hundhammer_WS_Express Layout: Layout1 Customer: WindSim 2014-01-29 WindSim AS Fjordgaten 15, N- 3125 Tønsberg, Norway phone.:
More informationAN ASSESSMENT OF THE DISCREPANCY BETWEEN OPERATIONAL ASSESSMENT AND WIND RESOURCE ASSESSMENT FOR A WIND FARM IN IRELAND
AN ASSESSMENT OF THE DISCREPANCY BETWEEN OPERATIONAL ASSESSMENT AND WIND RESOURCE ASSESSMENT FOR A WIND FARM IN IRELAND Thesis in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE
More informationWASA WP1:Mesoscale modeling UCT (CSAG) & DTU Wind Energy Oct March 2014
WASA WP1:Mesoscale modeling UCT (CSAG) & DTU Wind Energy Oct 2013 - March 2014 Chris Lennard and Brendan Argent University of Cape Town, Cape Town, South Africa Andrea N. Hahmann (ahah@dtu.dk), Jake Badger,
More informationMicroscale Modelling and Applications New high-res resource map for the WASA domain and improved data for wind farm planning and development
Microscale Modelling and Applications New high-res resource map for the WASA domain and improved data for wind farm planning and development Niels G. Mortensen, Jens Carsten Hansen and Mark C. Kelly DTU
More informationThe Global Wind Atlas: The New Worldwide Microscale Wind Resource Assessment Data and Tools
ICEM 2015, Boulder, Colorado USA The Global Wind Atlas: The New Worldwide Microscale Wind Resource Assessment Data and Tools Jake Badger, Neil Davis, Andrea Hahmann, Bjarke T. Olsen Xiaoli G. Larsén, Mark
More informationGlobal reanalysis: Some lessons learned and future plans
Global reanalysis: Some lessons learned and future plans Adrian Simmons and Sakari Uppala European Centre for Medium-Range Weather Forecasts With thanks to Per Kållberg and many other colleagues from ECMWF
More informationReducing Uncertainty of Near-shore wind resource Estimates (RUNE) using wind lidars and mesoscale models
Downloaded from orbit.dtu.dk on: Dec 16, 2018 Reducing Uncertainty of Near-shore wind resource Estimates (RUNE) using wind lidars and mesoscale models Floors, Rogier Ralph; Vasiljevic, Nikola; Lea, Guillaume;
More informationWhat is a wind atlas and where does it fit into the wind energy sector?
What is a wind atlas and where does it fit into the wind energy sector? DTU Wind Energy By Hans E. Jørgensen Head of section : Meteorology & Remote sensing Program manager : Siting & Integration Outline
More informationRe-dimensioned CFS Reanalysis data for easy SWAT initialization
Re-dimensioned CFS Reanalysis data for easy SWAT initialization Daniel R Fuka, Charlotte MacAlister, Solomon Seyoum, Allan Jones, Raghavan Srinivasan Cornell University IWMI East Africa Texas A&M Re-dimensioned
More informationMesoscale meteorological models. Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen
Mesoscale meteorological models Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen Outline Mesoscale and synoptic scale meteorology Meteorological models Dynamics Parametrizations and interactions
More informationInvestigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis
Investigation on the use of NCEP/NCAR, MERRA and NCEP/CFSR reanalysis data in wind resource analysis Sónia Liléo, PhD Wind resource analyst - R&D manager O2 Vind AB Stockholm, Sweden sonia.lileo@o2.se
More informationWhy WASA an introduction to the wind atlas method and some applications
Why WASA an introduction to the wind atlas method and some applications Jens Carsten Hansen and Niels G. Mortensen DTU Wind Energy (Dept of Wind Energy, Technical University of Denmark) Eugene Mabille,
More informationWASA Project Team. 13 March 2012, Cape Town, South Africa
Overview of Wind Atlas for South Africa (WASA) project WASA Project Team 13 March 2012, Cape Town, South Africa Outline The WASA Project Team The First Verified Numerical Wind Atlas for South Africa The
More informationNew applications using real-time observations and ECMWF model data
New applications using real-time observations and ECMWF model data 12 th Workshop on Meteorological Operational Systems Wim van den Berg [senior meteorological researcher, project coordinator] Overview
More informationJay Lawrimore NOAA National Climatic Data Center 9 October 2013
Jay Lawrimore NOAA National Climatic Data Center 9 October 2013 Daily data GHCN-Daily as the GSN Archive Monthly data GHCN-Monthly and CLIMAT messages International Surface Temperature Initiative Global
More information4.5 Comparison of weather data from the Remote Automated Weather Station network and the North American Regional Reanalysis
4.5 Comparison of weather data from the Remote Automated Weather Station network and the North American Regional Reanalysis Beth L. Hall and Timothy. J. Brown DRI, Reno, NV ABSTRACT. The North American
More informationLondon Heathrow Field Site Metadata
London Heathrow Field Site Metadata Field Site Information Name: Heathrow src_id (Station ID number): 708 Geographic Area: Greater London Latitude (decimal ): 51.479 Longitude (decimal ): -0.449 OS Grid
More informationDevelopment and applications of regional reanalyses for Europe and Germany based on DWD s NWP models: Status and outlook
Development and applications of regional reanalyses for Europe and Germany based on DWD s NWP models: Status and outlook Frank Kaspar 1, Michael Borsche 1, Natacha Fery 1, Andrea K. Kaiser-Weiss 1, Jan
More informationNumerical Modelling for Optimization of Wind Farm Turbine Performance
Numerical Modelling for Optimization of Wind Farm Turbine Performance M. O. Mughal, M.Lynch, F.Yu, B. McGann, F. Jeanneret & J.Sutton Curtin University, Perth, Western Australia 19/05/2015 COOPERATIVE
More informationERA5 and the use of ERA data
ERA5 and the use of ERA data Hans Hersbach, and many colleagues European Centre for Medium-Range Weather Forecasts Overview Overview of Reanalysis products at ECMWF ERA5, the follow up of ERA-Interim,
More informationIntroduction to Weather Data Cleaning
Introduction to Weather Data Cleaning Speedwell Weather Limited An Introduction Providing weather services since 1999 Largest private-sector database of world-wide historic weather data Major provider
More informationImplementation of SWAN model with COSMO-CLM and WRF-ARW wind forcing for the Barents Sea storm events (case study).
IGU Regional Conference Moscow 2015 Implementation of SWAN model with COSMO-CLM and WRF-ARW wind forcing for the Barents Sea storm events (case study). Stanislav Myslenkov 1, Vladimir Platonov 2 and Pavel
More informationUniResearch Ltd, University of Bergen, Bergen, Norway WinSim Ltd., Tonsberg, Norway {catherine,
Improving an accuracy of ANN-based mesoscalemicroscale coupling model by data categorization: with application to wind forecast for offshore and complex terrain onshore wind farms. Alla Sapronova 1*, Catherine
More informationSwedish Meteorological and Hydrological Institute
Swedish Meteorological and Hydrological Institute Norrköping, Sweden 1. Summary of highlights HIRLAM at SMHI is run on a CRAY T3E with 272 PEs at the National Supercomputer Centre (NSC) organised together
More informationThe MSC Beaufort Wind and Wave Reanalysis
The MSC Beaufort Wind and Wave Reanalysis Val Swail Environment Canada Vincent Cardone, Brian Callahan, Mike Ferguson, Dan Gummer and Andrew Cox Oceanweather Inc. Cos Cob, CT, USA Introduction: History
More informationAn Integrated Approach to the Prediction of Weather, Renewable Energy Generation and Energy Demand in Vermont
1 An Integrated Approach to the Prediction of Weather, Renewable Energy Generation and Energy Demand in Vermont James P. Cipriani IBM Thomas J. Watson Research Center Yorktown Heights, NY Other contributors
More informationWind Flow Modeling The Basis for Resource Assessment and Wind Power Forecasting
Wind Flow Modeling The Basis for Resource Assessment and Wind Power Forecasting Detlev Heinemann ForWind Center for Wind Energy Research Energy Meteorology Unit, Oldenburg University Contents Model Physics
More informationGlobal Wind Atlas validation and uncertainty
Downloaded from orbit.dtu.dk on: Nov 26, 20 Global Wind Atlas validation and uncertainty Mortensen, Niels Gylling; Davis, Neil; Badger, Jake; Hahmann, Andrea N. Publication date: 20 Document Version Publisher's
More informationCombining Deterministic and Probabilistic Methods to Produce Gridded Climatologies
Combining Deterministic and Probabilistic Methods to Produce Gridded Climatologies Michael Squires Alan McNab National Climatic Data Center (NCDC - NOAA) Asheville, NC Abstract There are nearly 8,000 sites
More informationWake modeling with the Actuator Disc concept
Available online at www.sciencedirect.com Energy Procedia 24 (212 ) 385 392 DeepWind, 19-2 January 212, Trondheim, Norway Wake modeling with the Actuator Disc concept G. Crasto a *, A.R. Gravdahl a, F.
More informationIntegrated Electricity Demand and Price Forecasting
Integrated Electricity Demand and Price Forecasting Create and Evaluate Forecasting Models The many interrelated factors which influence demand for electricity cannot be directly modeled by closed-form
More informationS e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r
S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r C3S European Climatic Energy Mixes (ECEM) Webinar 18 th Oct 2017 Philip Bett, Met Office Hadley Centre S e a s
More informationExtreme wind atlases of South Africa from global reanalysis data
Extreme wind atlases of South Africa from global reanalysis data Xiaoli Guo Larsén 1, Andries Kruger 2, Jake Badger 1 and Hans E. Jørgensen 1 1 Wind Energy Department, Risø Campus, Technical University
More informationData Short description Parameters to be used for analysis SYNOP. Surface observations by ships, oil rigs and moored buoys
3.2 Observational Data 3.2.1 Data used in the analysis Data Short description Parameters to be used for analysis SYNOP Surface observations at fixed stations over land P,, T, Rh SHIP BUOY TEMP PILOT Aircraft
More informationAN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS
AN INTERNATIONAL SOLAR IRRADIANCE DATA INGEST SYSTEM FOR FORECASTING SOLAR POWER AND AGRICULTURAL CROP YIELDS James Hall JHTech PO Box 877 Divide, CO 80814 Email: jameshall@jhtech.com Jeffrey Hall JHTech
More informationwind power forecasts
wind power forecasts the user friendly forecast studio about aiolos users Aiolos is Vitec s market-leading tool for effective management for all of your forecasts. With Aiolos it is possible to predict
More informationSuperPack North America
SuperPack North America Speedwell SuperPack makes available an unprecedented range of quality historical weather data, and weather data feeds for a single annual fee. SuperPack dramatically simplifies
More informationIncreasing Transmission Capacities with Dynamic Monitoring Systems
INL/MIS-11-22167 Increasing Transmission Capacities with Dynamic Monitoring Systems Kurt S. Myers Jake P. Gentle www.inl.gov March 22, 2012 Concurrent Cooling Background Project supported with funding
More informationImproving Gap Flow Simulations Near Coastal Areas of Continental Portugal
Improving Gap Flow Simulations Near Coastal Areas of Continental Portugal 11th Deep Sea Offshore Wind R&D Conference Trondheim, 22-24 January 2014 Section Met Ocean Conditions paulo.costa@lneg.pt antonio.couto@lneg.pt
More informationApplication and verification of ECMWF products 2009
Application and verification of ECMWF products 2009 Hungarian Meteorological Service 1. Summary of major highlights The objective verification of ECMWF forecasts have been continued on all the time ranges
More informationNesting large-eddy simulations within mesoscale simulations in WRF for wind energy applications
Performance Measures x.x, x.x, and x.x Nesting large-eddy simulations within mesoscale simulations in WRF for wind energy applications Julie K. Lundquist Jeff Mirocha, Branko Kosović 9 WRF User s Workshop,
More informationThe Forecasting Challenge. The Forecasting Challenge CEEM,
Using NWP forecasts at multiple grid points to assist power system operators to predict large rapid changes in wind power Nicholas Cutler. n.cutler@unsw.edu.au 9 th April, 2008 CEEM, 2008 The Forecasting
More informationAdrian Simmons & Re-analysis by David Burridge (pay-back time!) Credits: Dick Dee, Hans Hersbach,Adrian and a cast of thousands
Adrian Simmons & Re-analysis by David Burridge (pay-back time!) Credits: Dick Dee, Hans Hersbach,Adrian and a cast of thousands A brief history of atmospheric reanalysis productions at ECMWF 1990 2000
More informationAlexander Abboud, Jake Gentle, Tim McJunkin, Porter Hill, Kurt Myers Idaho National Laboratory, ID USA
Dynamic Line Ratings and Wind Farm Predictions via Coupled Computational Fluid Dynamics and Weather Data Alexander Abboud, Jake Gentle, Tim McJunkin, Porter Hill, Kurt Myers Idaho National Laboratory,
More informationAtmospheric science research for numerical weather prediction and climate modeling in Slovenia: contribution of the PECS program
Atmospheric science research for numerical weather prediction and climate modeling in Slovenia: contribution of the PECS program Matic Šavli, Žiga Zaplotnik, Veronika Hladnik, Gregor Skok and Nedjeljka
More informationValidation of Boundary Layer Winds from WRF Mesoscale Forecasts over Denmark
Downloaded from orbit.dtu.dk on: Dec 14, 2018 Validation of Boundary Layer Winds from WRF Mesoscale Forecasts over Denmark Hahmann, Andrea N.; Pena Diaz, Alfredo Published in: EWEC 2010 Proceedings online
More informationComputationally Efficient Dynamical Downscaling with an Analog Ensemble
ENERGY Computationally Efficient Dynamical Downscaling with an Analog Ensemble Application to Wind Resource Assessment Daran L. Rife 02 June 2015 Luca Delle Monache (NCAR); Jessica Ma and Rich Whiting
More informationStatus of Atmospheric Winds in Relation to Infrasound. Douglas P. Drob Space Science Division Naval Research Laboratory Washington, DC 20375
Status of Atmospheric Winds in Relation to Infrasound Douglas P. Drob Space Science Division Naval Research Laboratory Washington, DC 20375 GOT WINDS? Douglas P. Drob Space Science Division Naval Research
More informationMethodology for the creation of meteorological datasets for Local Air Quality modelling at airports
Methodology for the creation of meteorological datasets for Local Air Quality modelling at airports Nicolas DUCHENE, James SMITH (ENVISA) Ian FULLER (EUROCONTROL Experimental Centre) About ENVISA Noise
More informationHow to shape future met-services: a seamless perspective
How to shape future met-services: a seamless perspective Paolo Ruti, Chief World Weather Research Division Sarah Jones, Chair Scientific Steering Committee Improving the skill big resources ECMWF s forecast
More informationPrimary author: Tymvios, Filippos (CMS - Cyprus Meteorological Service, Dpt. of Aeronautical Meteorology),
Primary author: Tymvios, Filippos (CMS - Cyprus Meteorological Service, Dpt. of Aeronautical Meteorology), ftymvios@ms.moa.gov.cy Co-author: Marios Theophilou (Cyprus Meteorological Service, Climatology
More informationForecasting of Optical Turbulence in Support of Realtime Optical Imaging and Communication Systems
Forecasting of Optical Turbulence in Support of Realtime Optical Imaging and Communication Systems Randall J. Alliss and Billy Felton Northrop Grumman Corporation, 15010 Conference Center Drive, Chantilly,
More informationImplementation of global surface index at the Met Office. Submitted by Marion Mittermaier. Summary and purpose of document
WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPAG on DPFS MEETING OF THE CBS (DPFS) TASK TEAM ON SURFACE VERIFICATION GENEVA, SWITZERLAND 20-21 OCTOBER 2014 DPFS/TT-SV/Doc. 4.1a (X.IX.2014)
More informationPower Forecasting and Dynamic Line Rating
Power Forecasting and Dynamic Line Rating WindSim User Meeting, Xiamen 24-25 October 2016 PRESENTED BY: DR. ARNE GRAVDAHL Power Forecasting and Dynamic Line Rating WindSim User Meeting, Xiamen, 24-25 October
More informationThe new worldwide microscale wind resource assessment data on IRENA s Global Atlas. The EUDP Global Wind Atlas
Downloaded from orbit.dtu.dk on: Dec 25, 2017 The new worldwide microscale wind resource assessment data on IRENA s Global Atlas. The EUDP Global Wind Atlas Badger, Jake; Davis, Neil; Hahmann, Andrea N.;
More informationClimpact2 and regional climate models
Climpact2 and regional climate models David Hein-Griggs Scientific Software Engineer 18 th February 2016 What is the Climate System?? What is the Climate System? Comprises the atmosphere, hydrosphere,
More information8-km Historical Datasets for FPA
Program for Climate, Ecosystem and Fire Applications 8-km Historical Datasets for FPA Project Report John T. Abatzoglou Timothy J. Brown Division of Atmospheric Sciences. CEFA Report 09-04 June 2009 8-km
More informationEffects of the NAO and other atmospheric teleconnection patterns on wind resources in Western Europe
Effects of the NAO and other atmospheric teleconnection patterns on wind resources in Western Europe Laura Zubiate Sologaistoa Frank McDermott, Conor Sweeney, Mark O Malley The North Atlantic Oscillation
More informationThis wind energy forecasting capability relies on an automated, desktop PC-based system which uses the Eta forecast model as the primary input.
A Simple Method of Forecasting Wind Energy Production at a Complex Terrain Site: An Experiment in Forecasting Using Historical Data Lubitz, W. David and White, Bruce R. Department of Mechanical & Aeronautical
More informationApplication and verification of ECMWF products 2016
Application and verification of ECMWF products 2016 RHMS of Serbia 1 Summary of major highlights ECMWF forecast products became the backbone in operational work during last several years. Starting from
More informationWIND RESOURCE MAPS AND DATA OF AMERICAN SAMOA
Wind Resource Maps and Data of American Samoa PREPARED FOR AMERICAN SAMOA POWER AUTHORITY WIND RESOURCE MAPS AND DATA OF AMERICAN SAMOA NOVEMBER 4, 2014 FINAL REPORT Wind Resource Maps and Data of American
More informationState of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO)
State of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO) thanks to S. Bauernschubert, U. Blahak, S. Declair, A. Röpnack, C.
More informationGeneration and Initial Evaluation of a 27-Year Satellite-Derived Wind Data Set for the Polar Regions NNX09AJ39G. Final Report Ending November 2011
Generation and Initial Evaluation of a 27-Year Satellite-Derived Wind Data Set for the Polar Regions NNX09AJ39G Final Report Ending November 2011 David Santek, PI Space Science and Engineering Center University
More informationEffect of Wind Turbine Wakes on the Performance of a Real Case WRF-LES Simulation
Effect of Wind Turbine Wakes on the Performance of a Real Case WRF-LES Simulation Paula Doubrawa 1, A. Montornès 2, R. J. Barthelmie 1, S. C. Pryor 1, G. Giroux 3, P. Casso 2 1 Cornell University, Ithaca,
More informationMesoscale Modelling Benchmarking Exercise: Initial Results
Mesoscale Modelling Benchmarking Exercise: Initial Results Andrea N. Hahmann ahah@dtu.dk, DTU Wind Energy, Denmark Bjarke Tobias Olsen, Anna Maria Sempreviva, Hans E. Jørgensen, Jake Badger Motivation
More informationA comparative and quantitative assessment of South Africa's wind resource the WASA project
A comparative and quantitative assessment of South Africa's wind resource the WASA project Jens Carsten Hansen Wind Energy Division Risø DTU Chris Lennard Climate Systems Analysis Group University of Cape
More informationCAPACITY BUILDING FOR NON-NUCLEAR ATMOSPHERIC TRANSPORT EMERGENCY RESPONSE ACTIVITIES. (Submitted by RSMC-Beijing) Summary and purpose of document
WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPAG on DPFS Task Team on Development of Operational Procedures for non-nuclear ERA CBS-DPFS/TT-DOP-nNERA/Doc.8 (4.X.2012) Agenda item : 8
More informationUpdate from the European Centre for Medium-Range Weather Forecasts
JSC-34 Brasilia, May 2013 Update from the European Centre for Medium-Range Weather Forecasts Adrian Simmons Consultant, ECMWF First main message ECMWF has a continuing focus on a more seamless approach
More informationKeywords: Wind resources assessment, Wind maps, Baltic Sea, GIS
Advanced Materials Research Online: 2013-10-31 ISSN: 1662-8985, Vol. 827, pp 153-156 doi:10.4028/www.scientific.net/amr.827.153 2014 Trans Tech Publications, Switzerland Mapping of Offshore Wind Climate
More informationJ17.3 Impact Assessment on Local Meteorology due to the Land Use Changes During Urban Development in Seoul
J17.3 Impact Assessment on Local Meteorology due to the Land Use Changes During Urban Development in Seoul Hae-Jung Koo *, Kyu Rang Kim, Young-Jean Choi, Tae Heon Kwon, Yeon-Hee Kim, and Chee-Young Choi
More informationRegional Climate Simulations with WRF Model
WDS'3 Proceedings of Contributed Papers, Part III, 8 84, 23. ISBN 978-8-737852-8 MATFYZPRESS Regional Climate Simulations with WRF Model J. Karlický Charles University in Prague, Faculty of Mathematics
More informationNicolas Duchene 1, James Smith 1 and Ian Fuller 2
A METHODOLOGY FOR THE CREATION OF METEOROLOGICAL DATASETS FOR LOCAL AIR QUALITY MODELLING AT AIRPORTS Nicolas Duchene 1, James Smith 1 and Ian Fuller 2 1 ENVISA, Paris, France 2 EUROCONTROL Experimental
More informationDNV GL s empirical icing map of Sweden and methodology for estimating annual icing losses
ENERGY DNV GL s empirical icing map of Sweden and methodology for estimating annual icing losses An update with further Nordic data Till Beckford 1 SAFER, SMARTER, GREENER Contents Experience from operational
More informationWorkshop on Numerical Weather Models for Space Geodesy Positioning
Workshop on Numerical Weather Models for Space Geodesy Positioning Marcelo C. Santos University of New Brunswick, Department of Geodesy and Geomatics Engineering, Fredericton, NB Room C25 (ADI Room), Head
More informationSpeedwell Weather System. SWS Version 11 What s New?
Speedwell Weather System The Open Weather Derivative Pricing and Risk Management System SWS Version 11 What s New? SWS Version 11 has been released. We are pleased to announce the following important new
More informationApplication and verification of ECMWF products 2009
Application and verification of ECMWF products 2009 RHMS of Serbia 1. Summary of major highlights ECMWF products are operationally used in Hydrometeorological Service of Serbia from the beginning of 2003.
More informationAERMOD Sensitivity to AERSURFACE Moisture Conditions and Temporal Resolution. Paper No Prepared By:
AERMOD Sensitivity to AERSURFACE Moisture Conditions and Temporal Resolution Paper No. 33252 Prepared By: Anthony J Schroeder, CCM Managing Consultant TRINITY CONSULTANTS 7330 Woodland Drive Suite 225
More informationValidation and comparison of numerical wind atlas methods: the South African example
Downloaded from orbit.dtu.dk on: Jul 18, 2018 Validation and comparison of numerical wind atlas methods: the South African example Hahmann, Andrea N.; Badger, Jake; Volker, Patrick; Nielsen, Joakim Refslund;
More informationInterim (5 km) High-Resolution Wind Resource Map for South Africa
Interim (5 km) High-Resolution Wind Resource Map for South Africa Metadata and further information October 2017 METADATA Data set name Interim (5 km) High-Resolution Wind Resource Map for South Africa
More information