Tag Archives | SST

The End of Upwelling

1 week change in Sea Surface Temperature from July 12 to 19, 2013fig_0719_7day_diff_cb

What a difference a week makes.

Late last week, the waters off New Jersey were between 5-15 degrees below normal thanks a persistent pattern of coastal welling in which warmer surface waters were pushed offshore and replaced by colder waters from below.

This year’s upwelling, which typically occurs this time of year, was longer than usual due to a strong Bermuda High. The High also stalled weather fronts along the eastern seaboard, carrying a lot of moisture up the East Coast from the Gulf of Mexico. But it was the upwelling that caused a lot of consternation among beachgoers in New Jersey, particularly on Long Beach Isltand where the upwelling was strongest. (Warning, the comments on that last link are a sad example of the urgent need for scientists to become more involved in communicating science.)

However, this week, the Bermuda High shifted west, causing record high temperatures across much of the East coast and odd rainstorms out west. It also reversed the coastal upwelling pattern enough so that surface waters could return to their seasonal norms.

The image above shows the change in sea surface temperature over the last week, between July 19 and July 12. (Technically, this map shows the difference between two 7-day composites, one ending on the 19th and the other on the 12th.) Almost the entire region warmed up a few degrees thanks to the strong sun and cloudless days we’ve had. But it is the coastal waters off NJ that increased the most, from 5-12 degrees Fahrenheit. The shift in winds allowed warmer waters from offshore to head back towards the coast, while at the coast, previously upwelled cooler waters were subject to downwelling.

For the ocean, and all the fish in it, this is a huge change to cope with in just a few short days. But if you’re at the beach, it means a more pleasant swim is in your future.

Image Note: I created a new colormap for this image that includes 4 color points instead of the traditional 2 for a divergent color scale (e.g. blue to white to red). Let me know what you think!


Satellites vs. Buoys

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.


The Ocean in Red, White and Blue

Red, white and blue map of SST Gradients in the Mid Atlantic on July 4, 2013

To celebrate Independence Day, I thought it would be fun to dress up the ocean in a little red, white and blue.

If you’re curious, the image above represents the gradient of sea surface temperature (SST) at each point, and is based off of today’s 7-day composite of SST collected by the AVHRR instrument on NOAA’s polar orbiting satellites.

For every point, if the temperature change from its left (or below) neighbor to its right (or above) neighbor increases, then the gradient is considered positive and is colored red. Similarly, if the temperature decreases as you go from left to right or bottom to top, then the gradient is negative and is colored in blue. If the temperature doesn’t change much, white is used. The darkest colors (red or blue) represent a temperature change of around 2-3 degrees Fahrenheit over 2km of distance.

The map above is actually the sum of the horizontal and vertical gradients (i.e. dT/dx + dT/dy), so areas that are blue, indicate areas where colder temperatures can be found towards the Northeast.

In the image, a few patterns stand out, particularly the north wall of the Gulf Stream which shows up as blue streaks, indicating colder temperatures to the North. The waters off New England show up as a dark mess of blue and red, due to the large number of clouds in the area over the past few days, not to mention all the hot weather warming up the cold waters, both of which resulted in uneven measured temperatures, and therefore chaotic calculated gradient values.

Scientists often use a gradient calculations to identify large-scale features in the ocean, like fronts and eddies, which can be seen in the image. However, to do so they generally would calculate the gradient over larger areas than the 1km pixels used above or use a combination of filtering or averaging to smooth out the features and make them stand out more.

Using raw SST data to calculate gradients results in an image that is very noisy. But then, fireworks are noisy too… and they are also quite beautiful to behold.

Happy 4th!


Painting Temperatures by Number

Today is the Solstice, and pretty soon the dog days of sumer will be here. (Though, for those of us in the Northeast, we’ve already seen our fair share.) As things begin to heat up, I figured it would be appropriate to highlight some of the ways oceanographers visualize sea surface temperature data.

Temperature is a scalar variable, and its values are represented by continuous real numbers. Other variables types include categorical, vector or arrays (like RGB images), but we’ll save those for another day.

Temperature data collected at a singular point can easily be represented as numerical value (i.e. 76 degrees C), or if collected over a period of time, a time-series graph displaying temperature values over time can be easily drawn. Satellites, though, collect data over a large spatial area, meaning the dataset is inherently 3-dimensional. Each data point consists of a latitude (X position), longitude (Y position) and temperature (Z) value for each data point. The visualization challenge is figuring out how to display this 3D dataset on a 2D graph.

The most common solution is to create a pseudo-color image based on the temperature data. The trick is choosing the best colors to represent temperature values. The following are just a few examples.

The Scientist’s Rainbow

Perhaps the most common colormap, the rainbow (know as Jet in Matlab parlance) is often a scientist’s default choice. Like a Swiss Army knife, it is a good all-purpose tool. It is especially handy in helping subtle features stand out.

Sea Surface Temperature for June 2, 2011 from RU COOL

This Mid Atlantic image demonstrates how the rainbow palette allows you to easily distinguish small features within the range of a degree, even though the temperatures in the entire image range from 41 to 80 degrees Fahrenheit.

As useful as it is, the rainbow does have some major weaknesses. It is practically useless for colorblind viewers. In addition, it is important to keep in mind that non-scientists often associate rainbow representations with temperature, and only temperature. There is something intuitive about the red=hot and blue=cold extremes. Therefore, when creating images for public audiences it is unwise to use rainbow coloring for anything other than temperature, unless you enjoy confusing users.

An Alternate Choice

Sea Surface Temperature for May 2011 from NASA Earth Observatory

Given an infinite number of available colors (or at least 4 billion on modern displays), it’s easy to create an alternative color map. But balancing the need to highlight nuances in a dataset with accessibility for colorblind users while also keeping an image aesthetically pleasing is no small task.

Luckily, the artists at NASA’s Earth Observatory have come up with good alternatives over the years.

Hurricane Ready Temperatures

Hurricane-Ready Waters in the Atlantic, July 28, 2007

There is also no reason that colors need to be evenly distributed. If there is a scientific need to represent a dataset in a particular way, then adjusting the color map to highlight the message that needs to be communicated is certainly appropriate.

For instance, Tropical Storms develop best over waters that are above 80 degrees Fahrenheit. This image from NASA’s Earth Observatory, clearly shows those portions of the Atlantic Ocean that are ripe for hurricane development. For an alternate take, check out this recent image from NOAA’s Environmental Visualization Lab.

Temperature Anomalies

Sea Surface Temperature Anomaly for May 2011 from NASA Earth Observatory

Finally, sometimes it’s not the temperature value you want to represent, but the difference between the current temperature and a historical average. This is referred to as an anomaly, and it is often graphed slightly differently as it is a divergent dataset. Anomalies are typically represented as positive and negative differences, colored using red and blue respectively. Differences close to zero are left white.

This SST Anomaly image is from May 2011. These images are used to track El Nino and La Nina patterns, among other processes.

Well, that’s a quick roundup of some common examples. Do you have your own favorite way of coloring spatial maps of temperature or other datasets?

For more, check out the excellent primer Use of Color in Data Visualization by Robert Simmon.


Sea Surface Temperature

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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