Home > 9. Operational/Synoptic > 9.39 PISCES Geochemistry
9.39 Visualizing Mercator/PISCES Ocean Biogeochemistry Model Products in IDV
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Open the MyOcean website and read all about the available products. Then go to the Catalog page (shown here).
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2. You can work your way down the categories along the left margin, and as you make choices the number of possible products is reduced.
Under AREA, select GLOBAL OCEAN. |
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| 3. Under PARAMETER, select OCEAN CHEMISTRY. |  |
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| 4. Under TIME COVERAGE, select NEAR-REAL-TIME. |  |
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5. Under OBSERVATIONS/MODELS select MODELS.
Now you're down to only 1 candidate, so you can stop here. |
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6. Here are the remaining categories, so you'll have an idea of how the resources are cataloged:
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7. On the main catalog page, this is the only resource remaining, after all the above choices.
Click on ADD TO CART. |
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| 8. The cart contents should appear, and you can click DOWNLOAD. |  |
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9. First, you must register in order to download data:
Now you can LOGIN and continue. |
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| 10. On this final selection screen, drop the menu down and select the GLOBAL ANALYSIS product. |  |
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11. This page of dataset filters will immediately appear.
For GEOGRAPHICAL AREA, drop down the list and select TROPICAL ATLANTIC (as close as we can get to Liberia). |
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| 12. For TIME RANGE, select what you want, but the author suggests approximately one month of data (going back from today's date = END DATE). |
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| 13. For DEPTH, you must select at least the top two ocean depths. You'll get an error message if you try to select only one depth. |  |
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14. For VARIABLES, select what you want. Here the author has selected all 6 of the available parameters.
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15. Click on APPLY FILTERS. You might see error messages here that must be dealt with. Then click DOWNLOAD. This secondary DOWNLOAD page appears, where you must click DOWNLOAD again (if your file isn't too big).
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| 16. Navigate to DATA > OCEAN > MYOCEAN and save the file as opki7777 | |||||||||
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17. For Python users, the View Script button provides the command to extract the subset via the Python/Motu Client. For these data, it is imperative to have the version 2.7.0 or 2.7.5 of Python that you can download from here. You will also need the Motu Python Client package that can be obtained here. To execute your extraction through the Python/Motu, COPY/PASTE (clicking the right button of the mouse – CTRL-C and CTRL-V don't work) the command-lines below onto your system command prompt screen (the cmd.exe you find in your windows accessories):
C:/python27/python.exe C:/motu-client-python/motu-client.py -u MyUsername -p MyPassword -m http://atoll.mercator-ocean.fr/mfcglo-mercator-gateway-servlet/Motu -s http://purl.org/myocean/ontology/service/database#GLOBAL_ANALYSIS_BIO_001_008_a-TDS -d global-analysis-bio-001-008-a -x -80 -X 20 -y -30 -Y 30 -t 2014-07-05 12:00:00 -T 2014-08-02 12:00:00 -z 0.49402 -Z 1.54138 -v PP -v NO3 -v CHL -v PHYC -v PO4 -v O2 -o D:\MyOcean -f global-analysis-bio-001-008-a_1409058589081.nc Whatever is in bold, you have to replace with your own (directories, paths and login info). Below you can see a screen capture of this process as it occurs.
For questions, please contact author Lica Krug, our PYTHON guru. |
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| 18. If you want to see how to work with these data in Saga, continue here. If not, then skip down to the IDV panels below. | |||||||||
| 19. Run Saga |  |
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20. Select TOOLS > IMPORT/EXPORT > GDAL/OGR > IMPORT RASTER.
Then click OK. |
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| 21. Now you see the "subdatasets" (i.e. the variables). Check all you want, then click OK. |  |
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22. Now you see the different dates in the file, and you must check LOAD ALL BANDS. Do this once for each of the 6 data types you selected above. When finished, click OK.
Days X variables can be a lot of clicking.
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23. When all the rasters are loaded, you can select what you want to ADD TO MAP.
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24. Here's a random example of 3 maps, with rainbow colors, etc. to look good.
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25. Depending on what you want to do, and how much you like Saga, you can do all sorts of things with the PISCES data you have. It's all up to you. We did not resample the "Tropical Atlantic" grids down to the Liberia area of interest, but that's easy; it's covered in 5.3 Resampling Gridded Data to the Project Map Extent with a "Dummy" Grid.
One useful thing from the Saga presentation is that you can click the DESCRIPTION tab for the above maps to see these units for Nitrate: millimoles no3 per cubic m which is the same thing as micromoles no3 per liter. Now, we'll move on to Integrated Data Viewer (IDV), which is a bit faster than Saga, but the visualizations are very similar. One special addition is animations. |
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| 26. Run IDV. |  |
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27. Use the DATA CHOOSER to load the NC file. It immediately can be opened to show the 6 contained variables.
These user-friendly names are a consequence of the "self-describing" nature of NetCDF. |
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| 28. On the main dashboard page, right-click on the data file, and select PROPERTIES. |  |
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| 29. Click the SPATIAL SUBSET tab at the top of this new window. You cannot enter any values in the 4 spaces, until you first draw any rectangle on the map, as you see here. |  |
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| 30. Now you can enter the standard area of interest values for Liberia. Then click OK. |  |
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31. Now what will we draw with the data? Select on of the variables, for example Nitrate. Then DISPLAY = PLAN VIEW = COLOR-SHADED PLAN VIEW.
Leave all the TIMES selected. Then click CREATE DISPLAY. |
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32. Now the first map appears. You can see from the small time control on the top that there are 5 maps. And color palette ranges from 6.6E-7 to 6.4 (or 0.00000066 to 6.4).
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33. As we often do, we used the dashboard to select the usual RADAR color setting, with a range of 0 to 6 (umoles per l)
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| 34. If you want to make an animation, then select VIEW > CAPTURE > MOVIE | |||||||||
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35. Here is the MOVIE CAPTURE window. Actually, it is usually completely ready to make a good animation, with the current settings. You simply click TIME ANIMATION and it proceeds automatically.
When it completes, it asks you what format to use, filename and save location. It's that easy. |
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| 36. You can see the author's animation of the above maps here: nitrate_animation_liberia_7_2014_8_2014.gif. It's not very sexy, but imagine how it could look if you used an entire year! Think of the possibilities. | |||||||||
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37. This should give you a basic idea of how to mine these types of multiple-variable, multiple-time data products. The possibilities are endless, and it would serve no purpose to go further ... you'll probably get better ideas of your own.
Here's a set of the initial maps for 4 of the variables, just to show you what sort of products you'll have to work with. All atlases show that there is a maximum of chlorophyll and primary productions very near the coasts of the countries shown here, in addition to the general area of higher values along the equator. This is not really apparent in the lower pair of images. So if you intend to use these data or maps, do some research into PISCES to determine how accurately these images reflect chlorophyll and primary productivity for purposes of your own inquiries.
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