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  2. Hello Alexandre, Thanks for introducing me to Lib Interpolator. I have 289 wind atlases to add to get the interpolated wind atlas for the whole area. 289 Wind atlases cover the whole area on interest so there is no interpolation happening basically adding them together to get one wind atlas.
  3. Hello Shehan If you have LIB files for each "point" you can use the LIB interpolator tool in order to generate one wind atlas (LIB) for the entire region. In this tool you may prescribe your own weighting function or use the automatic weightings applied by the LIB Interpolator. The interpolated LIB file can be imported into WAsP and used like a normal wind atlas. Please read the LIB Interpolator help file for more information.
  4. I have with me Wind Atlases covering 2km area. Since the area I am examining is larger and to get one wind atlas covering whole area I have to combine these individual wind atlases to one. These are the points. Each point is a square 2km x 2km wind atlas. In the background is my area of interest. Now the challenge is to combine all these wind atlases covering the area of interest into one wind atlas.
  5. Explaining IBZ + dRIX correction procedure: From Predicted Climate at Mast position we obtain the following parameters: U also called U(IBZ) in the exercise RIX dRIX Using the dRIX we obtain a prediction error, P, from the provided graph (in the exercise). We apply the dRIX correction with expression: U(RIX) = U(IBZ) / (P+1) Example: prediction of Mast 10 from Mast 6 U(IBZ) = 4.63 RIX = 13,4 dRIX = -20 P = ~21% U(RIX) = 5.86 Regards Alexandre
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  7. Hi Rogier, Thanks again for the detailed answer and for the advice 🙂
  8. What I actually meant to say was "the vertical profile models are different": Vortex and their models will give you one vertical profile model, WAsP will given you another one. For the last profile model I can give you some numbers: for typical vertical extrapolations between 20 and 200 m you can see some results of the WAsP model here: https://www.wasptechnical.dk/forum/topic/687-validation-report-wasp128/ So vertical extrapolation errors are typically around 1-2% on wind speed using the latest WAsP (the report above also contains horizontal extrapolations which have larger errors). This is only under best model set-up conditions where you use measurements as input. Whether you trust the vertical profile model of Vortex more than WAsP depends on your site. There is large uncertainty on the data from vortex because they are from numerical weather prediction models for a coarse resolution (1 km? can't really judge what model it is from your message). They typically also switch off parametrizations to make it run faster I think. You may be able to get a generalized wind climate that is independent of height by obtaining the roughness, elevation and atmospheric stability information from Vortex, but even when everything is setup perfectly it might still not match, because you are mixing two models. In other words, if you trust the vortex vertical profile model I would use the 135 m hub height, if you trust the WAsP vertical profile model you will have to pick your 50 m height as input :) I would say definitely use the 135 m output, simply because you don't have the materials to setup a model chain here to make WAsP perform well (i.e. roughness map, elevation map, stability info from mesoscale model + the same for microscale modelling). If you had measurements and good model inputs, I would trust the WAsP method more than Vortex.
  9. Hi Rogier, Thanks for your reply. Could you elaborate more regarding the sentence "the vertical profile models are imperfect"? More in detail, how good or how bad does WAsP work in the vertical direction? Is the vertical wind profile only dependent on the log law, or are other components contributing as well? My issue is that I am trying to calculate the power of a wind turbine at 135 m by using .tab files from VORTEX extracted at different heights (from 50m to 130 m with a step of 10m), and I got a power production that is inversely proportional to the height at which I get the data from. In other words, the AEP at 135 m calculated with a .tab file extracted at 50 m is by far higher than the AEP at 135 m calculated with the data extracted at 130 m. So I was wondering how the wind speed is calculated in height. BR Andreas Wolf Ciavarra
  10. Hi Andreas, WAsP will try to clean your input .tab file from local effects and then apply local effects where you do your prediction. If your input height is different your are not guaranteed to get the same generalized wind climate out, simply because the vertical profile models are imperfect. Only when you use a fixed height and let WAsP (with stability set to neutral) and calculate at some heights it will be a log profile. You would also need different roughness and elevation maps and stability info from the WRF simulation for your generalization step. So in other words, you can't easily use WAsP this way to downscale WRF results..
  11. I am sorry for some reason I couldn't delete this topic, so please if could do this. I will open another one BR Andreas
  12. Hello, I would like to have a deeper understanding of the vertical development of wind speed in WAsP. More in detail my company bought some data (MAST product - .tab file) from VORTEX which I extracted at different heights (from 50 m to 130 m with a step of 10m) and I used them in WAsP to generate the GWC. What I experience is a decreasing behavior of the AEP the higher is the elevation at which that the predicted wind speed at 130 m from the data extracted at 50 m is 7,94 m/s and the predicted wind speed at 130 m from the data at 60 m is 7,81. The decreasing behavior goes all the way to 130 m where the wind speed is 7.29 . I experience the same decreasing development also for the AEP and for the percentage difference between a predicted wind speed from a given height to 130 m and the actual wind speed at 130 m. . I created then a reference site that matches the met mast but with a height of 130 m I know that the vertical development of the wind speed is expressed with the log-law, but I was wondering if it could be possible to have some deeper insight into this. BR Andreas Wolf Ciavarra
  13. You could use the windkit package (pip install windkit) to convert to latitude longitude using the function reproject. However, it won't be a raster anymore because the coordinate spacing varies in both south-north and west-east. So you can only plot it if you keep the original projection, which is given in the variable "crs". import windkit as wk import matplotlib.pyplot as plt import xarray as xr test=xr.open_dataset("/home/rofl/newa_elev.nc").set_coords("crs") test.elevation.plot() test_latlon=wk.spatial.reproject(test,"EPSG:4326") test_latlon.elevation.plot()
  14. Hello, I had a couple of questions I hope someone can help with. I have been able to download some data from the mesoscale atlas, but am struggling with getting the data to position correctly. How do the south_north and west_east values relate to lat/lon, and how can we interpret them as such? I can view the associated CRS in Panoply and display the data there but when I try in anything else (using the CRS defined) everything plots in the wrong place. Best regards, Julian
  15. I'm asking about the Exercise in WR-9: WAsP in complex terrain exercise, I couldn`t understand how to calculate 'IBZ+ΔRIX correction procedure' plz explain.
  16. Hi Kwak, I see that we were able to help you in the direct support system. We see the problem a few times per year and we don't know why it happens but we can usually get things working again. Duncan.
  17. Error downloading URL:.... I used the latest version of WAsP suite (12), however, it cannot access the GWA data still. Could you please give some suggestions? Thanks in advance. The GWA URL is: http://mapserver.globalwindatlas.info/
  18. Hi Kim, The dZ values for the RIX number evaluation in IEC 61400-12-2 are calculated by terrain elevation at points distributed with 30m separation along the centerline of each 10 degree sector. The dZ values are defined by elevation differences of neighboring points, and the slopes are the elevation differences divided by the point separation (30m). This measure is not related to the distance from the turbine, so I am not sure the conversion you suggest will makes sense. The RIX number evaluation depends on a threshold, which is scaled by H+D (hub height plus rotor diameter) and we evaluate a RIX value by several thresholds. There is an explanation in the WAT help file section IEC 61400 standards>IEC 61400-12-2>terrain assessment but it is very brief, so I recommend that you read section 6.3 of the IEC 61400-12-2 standard for a full explanation.
  19. Thank you so much, it worked! I actually simplified the code even more, I'm not great at programming so I tried to use only the stuff I understood, I'll leave it here in case it proves useful to anyone: import xarray as xr # Open the NetCDF file using xarray DS = xr.open_dataset(r'input-file.nc') # Convert the data to CSV DS.to_dataframe().to_csv('output-file.csv')
  20. Hi, there. If RIX0.04 is 9m, (in the effective wind vane) it is converted to the ratio of the distance where the delta Z is more than 9m out of 20 times the hub height. How is delta Z calculated here?😄 Is it just : sum of (exceed distans)/ 20times HH ? And, about as this limit RIX0.04>16 : Is 16 the maximum value? or It means average 16% of the total range (or the effective vane) ? Thanks and regards, NY Kim
  21. Hi We are aware of some performance issues. We are currently focusing on processing the remaining 20 years of NEWA data, and then will revisit performance. For now, it seems that best performance is found when downloading data in 6-month intervals, as this matches best with the resources on the server. You have the correct URL. I am not sure why data would be provided with some data missing, as it should either succeed or fail. Could you provide a list of the variables that you are using so that I can do some testing? I will be on holiday starting Wed, so won't get a chance to look until the new year unfortunately.
  22. Hi Gyeongil, We are currently gearing up for a release of PyWAsP in Q1 next year. It will be a separate product from WAsP and will therefore require an additional license. We will announce more information when we are closer to release.
  23. Dear Team, one of my colleagues aske me if we can use or how to use Pywasp. he refers to this documents: https://wes.copernicus.org/articles/5/1679/2020/wes-5-1679-2020.pdf and want to apply his project. based on the available information: https://docs.wasp.dk/pywasp/installation.html / You will be prompted for the url and port number of your license server, which should be provided by your institution. From there, you will be able to access all of the pywasp_api from the pw namespace. / it seems like we need to first contact someone responsible for it and get and API or some information to access. my company has already use commerical WAsP/WAsP eng. if this is necessary to use. Could you please advise me how to use pywasp? Regards, Gyeongil
  24. Hi Lidia, This can be done using Python with the xarray and Pandas packages. To create a text file using the xarray package in Python, you can use the open_dataset() function to open the NetCDF file, and then use the to_dataframe() function to convert the data to a Pandas DataFrame. You can then use the drop() function from Pandas to remove the columns that you do not want to include in the output text file, and then use the to_csv() function to write the resulting DataFrame to a text file. Here is an example of how you can do this: import xarray as xr import pandas as pd # Open the NetCDF file using xarray ds = xr.open_dataset('input.nc') # Convert the data to a Pandas DataFrame df = ds.to_dataframe() # Remove the "height" and "crs" columns from the DataFrame df = df.drop(columns=['height', 'crs']) # Write the DataFrame to a text file in the preferred format for WAsP Climate Analyst df.to_csv('output.txt', sep='\t', index=False) Note that this makes a tab-separated file with "sep='\t'. This can be changed to your preferred delimiter. In this example, we are using the drop() function from Pandas to remove the "height" and "crs" columns from the DataFrame. We are then using the to_csv() function to write the resulting DataFrame to a text file with a tab-separated value (TSV) format, which is the format that is required by the WAsP Climate Analyst tool. For more information and examples of using the xarray and Pandas packages to convert NetCDF data to text files, you can refer to the xarray and Pandas documentation: http://xarray.pydata.org/en/stable/generated/xarray.Dataset.to_dataframe.html https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html
  25. + I also have NetCDF files but I still don't know how to work with them.
  26. Hello! I want to use some reanalysis data from CDS API (Copernicus), but I´ve only found it in NC or GRID files, I don't know how to convert it into a file type that Climate Analyst 3 can read. Could someone tell me how? Or perhaps where I can find the data already in a txt file. I'm currently looking for information in central Spain. Thank you 🙂 Lidia N
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