Importing Satellite Sea Surface Temperature (SST) and Sea Ice
Climatologies into Marine GIS: US National Centers for Environmental
Information (NCEI) Live Access Server (LAS)
In this exercise you'll learn how to find and inspect the massive data
resources available through a relatively new Live Access Server,
published by the US National Centers for Environmental Information. The NCEI
is the final depository for a number of satellite imagery products from
other programs, and they have boldly taken on the job of organizing huge
amounts of data into an easily usable archive. In addition, they
have produced some statistical products, using up-to-date formats. This
flexible new NCEI product can be considered a companion to the HDF SST climatology products in
Satellite Chlorophyll and SST Climatologies into Marine GIS: Color Web. It makes unnecessary the awkward de-scaling of
the integral raster values to true temperatures, but a Kelvin-to-Celsius
conversion is still required. Alongside the SST climatology, a
climatology of sea ice presence is also provided, and similar procedures
can be applied to visualize those products.
Preliminary Reading (in
OceanTeacher, unless otherwise indicated):
|1. Open the NCEI LAS and take
some time to explore the interface.
NOTE: To avoid this menu, just make a random parameter selection,
and ignore the resulting graphic. That lets you see the underlying
functions. Click on CHOOSE DATASET to get back to this exact menu.
2. The author has explored
the main menu items, and offers the following summary.
NCEI-binned L3 Sea Surface Salinity from Aquarius level-2 products
(compare this possibility with
9.22); Versions 2
CoRTAD - Tiled rasters from the Coral Reef
Temperature Anomaly Database (1982-2012); Versions 4 & 5
Experimental Datasets, Under Testing
- Explore on your own
GCOS - Global Climate Observing System datasets;
numerous historical global SST rasters at 5-deg and 1-deg resolutionref; "pilot study" of reconstructed global SST rasters for
selected other periods
GHRSST Aggregation - Group for High
Resolution SST; L2 through L4 SST global analysis grids for 1981-2011 or
later in some cases; includes nearly all of the Group's archived
NODE - Regional raster products related to NOAA's
studies of impacts on coral reefs from acidification; includes a few
related global products also available elsewhere; a single tide dataset
for Port Aransas, TX is included (!).
Surface Temperature Climatology at 9 km
- Global rasters of sea temperature- and ice-related parameters;
1 grid for each of the 366 year-days (1962-2008 data)
Version 5.0/5.1 Sea Surface
Temperature - Global rasters of sea temperature- and ice-related
parameters for 1962-2008; various time periods, quality flags, and
5.2 Rich Inventory Statistics -
Multi-year (1981-2012) daily global grids of temperature-, ice-, and
wind-related parameters; daily regionally-averaged time-series of
temperature-, ice-, and wind-related parameters
Version 5.2 Sea Surface Temperature
- Global rasters of sea temperature- and ice-related parameters for
1962-2008; various time periods, quality flags, and conditions
- S and T climatology grids for sub-global areas
Level 3 Sea Surface Salinity Data (3-day and monthly averages)
World Ocean Atlas - Familiar global
multi-depth grids used in
3.4 Obtaining Standard-Depth Analyzed Parameters from the World Ocean
Atlas (WOA); WOA 2009 and 2013 included here
This exercise will
focus on the first Pathfinder item above (in yellow).
|3. In the various
data product categories, you nay find references to Quality Flags.
The author provides this direct quote from the Pathfinder User Guide to help
explain these flags:
"The overall quality flag is a relative assignment of SST
quality based on a hierarchical suite of tests. The Quality Flag varies from
0 to 7, with 0 being the lowest quality and 7 the highest. For most
applications, using SST observations with quality levels of 4 to 7 is
typical. For applications requiring only the best-available observations (at
the expense of the number of observations), use quality levels of 7 only."
Pathfinder User Guide
You will also find data products identified
as DAY or NIGHT or sometimes DAY-NIGHT. The NCEI has reported that
DAY-NIGHT means the average of the daytime and nighttime values. They
recommend this value for routine use, unless you have a special reason to
use only the DAY or NIGHT values.
|4. On the main page, select
CHOOSE DATASET > PATHFINDER > SST AT 9 KM to see this short list of products.
Select the first item, as shown.
|5. The LAS should quickly
show you a map similar to this. Don't worry if yours is not the same.
Note that the units are degrees Kelvin, not Celsius.
Now, one by one,
we'll choose settings for our desired product.
|6. In the upper left corner's
drop-down menu, you can change the specific product, as you see here.
Just make sure you've picked the sea surface skin temperature.
Beside UPDATE PLOT, make sure you
check the small box. Once checked, the map will always be updated
after any setting change.
|7. Just below the product
choice, there is a geographic choice. Enter these values for Liberia.
NOTE: You can slide the entire map left or right with the PAN tool, if
|8. Just below the geographic
choices, you can select the product type. In this exercise we'll focus
on data maps.
MAPS > LATITUDE-LONGITUDE
|9. You can pick any date.
But notice there is no year. This means that there is a separate
climatology for each day of the year (366 in all) constructed from many
years of data.
For this example select today's date. [February 4, for the
|10. Because the LAS updates every time you make a choice, you should soon see this data
map. Take some time to compare this with other sources and
authorities. Make sure it seems reasonable; don't just accept
everything that pops out of the internet.
|11. Now let's download these data
for local management. Select SAVE AS along the top row of controls.
There are several format choices:
- NetCDF - Binary format, already familiar to you; compatible
with Saga, IDV and ncBrowse.
- CSV - Comma-separated variables (ASCII); useful to load into
Excel, or possibly also Saga
- ASCII - Complicated table structure for direct visual
inspection, but not useful otherwise
- ArcGrid - Familiar ASCII gridded format for most GIS systems,
Select NetCDF for its wide acceptance among environmental software.
[NetCDF files sometimes take a little tender care in Saga, so we'll expect to
do a little work to make sure it goes in OK.]
Then click OK.
|12. When your
file arrives, save it in the folder DATA > OCEAN > NCEI_LAS with the
|13. Run Saga (preferably the
|14. Select MODULES >
IMPORT/EXPORT-GDAL/OGR > GDAL:IMPORT RASTER
Then make these selections.
Then click OK.
you try the GDAL: IMPORT NETCDF tool, you will get a mess. Evidently
Saga and the NCEI folks just aren't on the same page yet, regarding format
|15. This new grid object
should appear. Note that it must have a grid system appropriate for
the area of interest.
|16. Here you can use ADD TO
MAP to see the new dataset. It is a gray-scale image. We can fix
The cursor has been placed at the middle of the map, and you
can see the X, Y and Z values below it along Saga's bottom margin. The
X and Y are expected lon/lat values for a point near Liberia, and the Z value is
a typical Kelvin temperature value for the open sea near the equator. So everything looks OK.
NOTE: The land area is white, so we can assume it is successfully
represented in the database with NODATA values. Put the cursor over it
to see that the Z value disappears.
|17. Before we can
do anything else, we need to change Kelvin degrees to Celsius degrees.
|18. Select TOOLS > GRID
> CALCULUS > GRID CALCULATOR.
- GRID SYSTEM - The system for the NC file
Then click on the ellipsis (...) to the right of NO OBJECTS.
|19. Select the NC file, and
move it to the right side with the > arrow. This means it is selected
for the calculation.
By Saga convention, it is now referred to as
g1 in the grid
|20. Now you're back at the
- FORMULA: g1-273.15 (converts K to C)
- NAME - Deg C
- TAKE FORMULA - Not checked
- USE NODATA - Not checked
- DATA TYPE - 4 byte floating point number
Now click OK to apply these settings and use the formula.
|21. A new grid, named as
above, has appeared.
|22. Now here is the new grid,
named Deg C. Typical values near the center of the map are indicated
along the bottom margin. Notice these changes:
- The change in units has automatically changed the former gray-scale
palette to a more useful rainbow palette
- New new temperatures are reasonable for tropical sea surface values
|23. Take a moment
to save the final grid in the folder PRODUCTS > SAGA > GRIDS with the
|24. Now you can use the above
method to go back to the LAS and select the CLIMATOLOGICAL SEA ICE AREA FRACTION product.
NOTE: There are many, many other products here, and this choice is
just for demonstration. Come back later to explore and find whatever
data you really need.
|25. Here's the global map of
a random day.
NOTE: Nearly all the useful data are polar, so a polar projection
might be better for these data. You can research that on your own,
26. We need to subset the data to a typical icy sea, such as the
Greenland-Icealand-Norwegian Sea area, called the GIN Sea. Usually you
could use the site map at the upperleft of the screen to cut a smaller
rectangle, but as you see here on this day that function was not working
well. You can make some guesses, and try it several times to get a
reasonably good approximation, as you see below. Also, if know the
exact coordinates, you could enter them in the compass.
|27. However you
do it, the color values show
the percentage of time that ice is present, on the given day. You can see that there is day-number problem with the NCEI software,
because the requested date is Feb 5 (q.v. bottom left corner), but the provided map shows Feb 4
(q.v. upper middle).
28. Take the time to save the NC file in the same folder as above,
with the filename
|29. Here are the NetCDF data
in Saga, loaded, opened and displayed as above for the SST data. The
map is a gray-scale image, again.
|30. Just for interest, let's
check the values histogram to see what we have in the grid.
|31. As expected, most of the
values are zero, for the open sea without any ice on this day. Most
other cells are either nearly completely covered (i.e. >85%) or only lightly
covered (i.e. <20%)
|32. Open the PROPERTIES
window for this data object, and you'll see that the NODATA cells have
values of -128. Change these both to 0 (zero) and click APPLY (bottom
|33. This removes all ice-free
sea from the analysis, so we can focus only on ice. This is the sort
of thing you need to do with many datasets where only marginal or
small-value data are of interest, for example chlorophyll datasets.
new histogram shows the more interesting range of non-zero values.
|34. To better "see" the
results, open the TYPE > SCALING > COLORS tool to select a rainbow palette
of 100 colors, as you see here.
Then click APPLY.
|35. Here's the resulting map
of average sea ice coverage, as a percentage, on Feb 4.
|36. And to put the analysis
back into nautical perspective, here the World Borders map has been added.
Use FILE > SHAPE > LOAD and navigate to DATA > BASEMAP > BORDERS to