6.5 Maps of SSHA: The DUACS dataset

As you have seen earlier in the last section, it can be difficult to infer 2D spatial patterns from a single pass of along-track data. Even taking all of the data from a 35-day satellite cycle, we are left with a grid of height measurements, with large gaps between the tracks (particularly at low latitudes).

Different satellites have different orbital characteristics. Generally speaking, satellites with short orbital repeat cycles (e.g. Topex/Poseidon and Jason-1 with 10 day repeat cycles) have larger gaps between passes than satellites with long repeat cycles (ERS-1/2 and Envisat have 35 day repeat cycles).

To build up a picture, we need to combine the data from all the available satellite altimeters, and then use interpolation techniques to fill in the gaps. To see how this approach can provide a grid of loosely spaced data take a look at the diagram of altimeter tracks over the Atlantic (147K)

This has been accomplished in the DUACS (Data Unification and Altimetry Combination System) dataset from CLS. Data similar to the images used here may now be obtained from AVISO.

Opening the DUACS data

In this part of the lesson, you will open, animate and apply a palette to a series of stacked Sea Surface Height Anomaly images. The data shows the MSLA (Maps of Sea Level Anomaly) in the Indian Ocean during February and March 2004. The length of time between images is 3.5 days.

1. Select Open from the file menu ([CTRL+O]) or ), make sure the file type is set to IMAGES, and open the file called msla_Feb&Mar04.

2. A window will open which displays the hierarchical data structure of this NetCDF file.

3. In the left hand side of the window, choose the Scientific Data folder. In the right hand side of the window, right-click the GRID_0001 folder and choose Open Connected. This will open an Extract dialogue.

4. The dialogue should have the correct numbers already, but make sure that the Extract tick box is checked, then click OK. This will open the Redisplay Image dialogue.

5. In the Stretch Settings part of the box, set the maximum and minimum SSHA values to be displayed to -30 to 30 cm and click All.

Viewing the images

All 17 individual images will now be open, along with a set of stacked images. This stacked set is what we will use to view the animation.

You will notice that the images are upside down. To correct this

1. Select View > Reflect from the menu bar and choose in Y from the drop-down menu.

2. To make things a little clearer right-click on the stack, select Zoom, and in the 'Zoom to Fit Window' section click Preserve Shape. You can now alter the size of the image by dragging at the bottom right hand corner of the image window.

To add some colour you can apply a palette.

1. Select the stack and chose File > Open fromthe menu bar.

2. In the Files of type: box, select PALETTES, and make sure that the Extract, Apply and Minimise tickboxes are checked.

3. Choose modjet3.pal and click Open.

Note the “zero position” colour of the data (128 on the colour bar). This is useful for distinguishing between negative and positive images.

To run the data as an animation click the Animate button () on the toolbar, or chose Image > Animate from the menu bar. This will run the animation in a loop. Click the button again to stop the animation.

If you want to move through the data slowly, image by image, you can do this using the [TAB] key.

Comparing SSHA data from different seasons

Repeat all of the steps above to open the DUACS data msla_jun&jul03.dat.

This data is taken from June and July 2003, during the Southwest monsoon. Viewing the animation, and comparing it to the previous one from February and March, you should be able to see a change in the circulation pattern of the Somali Current system.

Question 2.

What are the main differences in the circulation pattern?

Next: 6.6 Comparing MERIS and AATSR data