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Marine Data Literacy

Proudly published in the United States of America for environmental scientists around the world.  Providing instruction for managing, converting, analyzing and displaying oceanographic station data, marine meteorological data, GIS-compatible marine and coastal data or model simulations, and mapped remote sensing imagery



2.1 Preliminaries
2.2 Project Area
2.3 Project Map
2.4 Map Frames
___2.4.1 Frames Check
2.5 Grid Templates
___2.5.1 Grid Check
2.6 GEBCO Contours
2.7 Sediment Thickness
2.8 Boundaries & Coast
2.9 Marine Regions
2.10 Text Spreadsheet
2.11 Number Spreadsheet
2.12 Cutting a Shape
___2.12.1 Cutting Check
2.13 0-360 System
2.15 Shape from XYs
2.18 Navy Waves
2.20 Land Stations
2.21 HDF Chloro/Sal
2.22 HDF SST
2.24 Coastal Survey
2.25 NetCDF with NaN
2.26 Google Digitizing
2.27 UTM->WGS84
2.28 WGS84->UTM
2.29 Nav Charts
2.30 Argo MLDs
2.31 SST/Ice Climate
2.32 EU Wave Climate
2.33 GSHHG Vectors
2.34 GlobWave Grids
2.35 MGD77 Surveys
2.36 ColorWeb T/Chl/S
2.37 Named Places/Features
2.38 Giovanni Chlorophyll
2.39 Set Properties
2.40 Adding Graticules

Home > 2. Marine GIS > 2.31 SST/Ice Climate

2.31 Importing Satellite SST and Sea Ice Climatologies into Marine GIS: NCEI LAS

  • Exercise Title:  Importing Satellite Sea Surface Temperature (SST) and Sea Ice Climatologies into Marine GIS: US National Centers for Environmental Information (NCEI) Live Access Server (LAS)

  • Abstract:  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 2.21 Importing 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):

    • N/A

  • Required Software:

  • Other Resources: 

  • Author:  Murray Brown

  • Version:  5-13-2015

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. 

  • AQUARIUS - NCEI-binned L3 Sea Surface Salinity from Aquarius level-2 products (compare this possibility with 9.22); Versions 2 & 3

  • 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 products

  • Jason-2

    • Jason-2 Granule Statistics-Per Pass - Explore on your own; not all L3

    • Jason-2 Gridded Cycle Mean-Per Pass - Explore on your own; not all L3

  • 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 (!).

  • Pathfinder

    • Sea 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 conditions

    • Version 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

  • Regional Climatologies - S and T climatology grids for sub-global areas

  • SMOS - 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 needed.

8.  Just below the geographic choices, you can select the product type.  In this exercise we'll focus on data maps.


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 author.]

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, including Saga

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 filename
13.  Run Saga (preferably the latest version!).

Then make these selections.

Then click OK.

NOTE:  If 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 specifications.

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 that below.

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.
  • 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 calculations below.

Click OK.

20.  Now you're back at the module:
  • 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 in C
23.  Take a moment to save the final grid in the folder PRODUCTS > SAGA > GRIDS with the filename sst_feb4_liberia_avhrr_pathfinder_NCEI_las.sgrd
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, later.

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 left).
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.

The 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 find it.
The exercises, notes and graphics in this website are copyrighted, and may not be copied or abstracted in any way, without my explicit permission (in writing).  Making one copy for your personal use is allowed.   Please report any copyright infringement to me. Murray Brown m.brown.nsb <at>

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