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Satellites vs. Buoys

By on July 9, 2013 in

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A little while back, I received the following question from a Visual Ocean visitor, and thought it would be fun to answer it as a post.

When might satellite sst data be more informative than buoy data?

The short answer is: it depends. You know, like all things in science.

Advantage: Satellites
Perhaps the biggest advantage satellites have is the ability to measure Sea Surface Temperature (SST) over large swaths of the ocean, while buoys can only measure temperatures at a single location.

The typical AVHRR sensor orbiting the Earth from 520 miles up can observe an area that’s over 1,500 miles wide with a resolution of 0.68 miles. By contrast, a single buoy can sample only within a single pixel from the satellite’s perspective. To put that into context, in the Mid Atlantic the typical satellite SST pass contains around 600,000 pixels of data, covering an area of over 250,000 square miles (that’s about the size of 35 New Jerseys). Meanwhile, there are only around a dozen buoys in the same area.

So if you want to study large-scale or regional features like fronts and eddies that occur over a large area, you’ll definitely need to use satellite data. In addition, anyone who has ever seen an SST satellite image knows there is a lot of spatial variability out there, so you’ll also need to use satellite data if you want data close to your study area (or beach house) than the nearest buoy, which could be hundreds of miles away.

Advantage: Buoys
On the other hand, buoys can see through clouds. Well, not really, but many satellite sensors can not, which is why you often see large white areas in SST imagery. Worse yet, when a large storm, like a hurricane, happens to move through an area, it can block the view from satellites for several days. And that’s a problem because the most interesting events in the ocean often occur when storms are overhead.

Similarly, many ocean-sensing instruments are placed on polar orbiting satellites, which are not able to measure the same location constantly. There are several satellites in orbit that measure SST, so this generally isn’t a problem as long as you’re okay with 4-10 measurements a day. Other sensors, like those for chlorophyll or salinity, are on fewer satellites, so it may be several days or more between measurements, and even longer if clouds are in the way.

However, a buoy that is sitting in the ocean can take measurements constantly. Every day, every hour, every second, every microsecond or whatever a scientist might need. In general, buoys that measure SST record data every hour, which is often sufficient for most investigations.

So, if you want to study high-resolution and/or local processes, such as those concerning specific habitats or ecosystems, then buoys are your best bet. Likewise, they’re also quite useful if your favorite fishing spot is nearby.

If you have a question about data visualization in oceanography you’d like me to answer, please let me know using the contact form or send a message to @visualocean on Twitter.

Sea Surface Temperature

By on July 6, 2010 in

In the first of several series I hope to start on this blog, let start off with our first installment of Better Know a Dataset. Given that New Jersey is currently in the midst of a sweltering heat wave (record highs around 103 °F were set it many places today), it seems only appropriate that we should start with temperature.

Sea Surface Temperature, or SST for short, is the most common ocean data measurement you will run across. Temperature is the easiest variable to measure in the ocean (and anywhere else for that matter), and as a result you can easily find oodles of temperature data online, often spanning back several decades.

Surface actually is a relative term, as it depends on how the temperature is measured. Surface buoys typically include a temperature probe underneath the surface, about 3 feet down. This insures the temperature measurement reflects the water temperature only, and does not bob in and out of the water.

But perhaps the most popular SST datasets are those measured from satellites, like the AVHRR sensors on NOAA’s POES satellites. The advantage of satellite SST data is that it can map out entire regions of the ocean’s surface, rather than just one point. The disadvantages are that satellites may not pass over a region every day, they have a lower spatial resolution (typically 1km^2) whereas a buoy can measures small scale changes in temperature, and if clouds are present most satellite sensors can not see through them.

Satellites are able to record the temperature of the ocean’s surface by measuring the emitted infrared radiation from the ocean. In theory, this measurement is from the top few micrometers of the surface. In actual practice, given that the ocean is a very dynamic place with a lot of wind, waves and mixing, the top few micrometers are often essentially the same as the top few feet or more. However, on a calm sunny day the skin temperature may be several degrees higher than the true surface layer. In oceanography, nothing is ever easy.

The image above, from the Rutgers Coastal Ocean Observation Lab, is a quintessential example of a SST image. It shows snapshot of sea surface temperature that was recorded by the satellite NOAA-15 as it flew over the Mid Atlantic earlier today. The white area on the left is the East Coast of the United States. You can see Cape Cod near the top right and Cape Hatteras in the bottom left. The recognizable coastlines of New Jersey, Long Island and the Chesapeake Bay are also clearly visible.

The temperature data in this image is colored using the typical scientist’s palette, that is, the color scaling uses the full rainbow of colors to show a full range in temperatures. While this is not a very intuitive color scale (a topic we’ll delve into in future posts), it is one that is commonly used by scientists to extract as much detail from the data as possible.

Red and orange areas depict the warmest areas while blue and purple areas are the coolest. Many web sites use a static color map throughout the year. However, due to the extreme range in ocean temperatures over the course of a year in the Mid-Atlantic and the desire of scientists, fisherman and others, to be able to extract all the features possible out of these images, Rutgers constantly changes the scales on their color map to keep up with the changing temperatures. This means you need to constantly check the color scale, as red areas on one image may not be the same temperature as red areas on another image.

There are several notable features in this image.

  • The white and purple patchy areas on the right are actually clouds that are obscuring the ocean surface and somewhat corrupting the data. (Scientists would flag this data as questionable and not use it in their research.)
  • The red ribbon of warm water toward the bottom of the image is the Gulf Stream.
  • Above the Gulf Stream and in the midst of the clouds, you can see a warm core eddy, that shows up as a red circle. This eddy was originally part of the Gulf Stream before it split off and started heading back towards the coast. The eddy itself rotates in a clockwise direction.
  • Off the coasts of Delaware, New Jersey and Long Island the temperatures are somewhat cooler than further offshore. This is due to coastal upwelling. Over the past week, winds in the area have been from the Southwest, which when combined with the forcing from the Earth’s rotation, drives surface waters offshore and allows cooler waters to come to the surface. Upwelling also brings nutrient-laden bottom waters to the sunny surface where phytoplankton can have a feeding frenzy.

As you can see, sea surface temperature maps provide a rich dataset to analyze ocean currents and features that impact the biology and chemistry in the ocean.

Of course, they can also help you find a cool beach on a hot day.

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