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Riding the Waves of the Seasonal Roller-Coaster

By on March 29, 2013 in Visualizations

Average monthly wave heights at NDBC Buoy 44025

It is often said in Pennsylvania that March comes in like a lion and goes out like a lamb. And while this March felt more like a ride on an Arctic roller-coaster that wouldn’t end, the good news is that, based on past years, we should soon be on the downward slope towards more calmer weather.

In the Mid-Atlantic, the winter months usually bring with them strong storms and high winds, like the nor’easter we saw earlier this month. In the ocean, strong winds lead to larger significant wave heights, as can be seen in the graph above that depicts the average monthly wave heights off the coast of New Jersey over the course of a year.

This graph was created using 8 years of significant wave height data from NDBC Buoy 44025, which is a little more than 40 miles from the New Jersey coast. Each line represents a particular percentile level, indicating the percentage of measurements that fall below the indicated level. The 50% percentile level is commonly called the median average value. For each month, half of the measured data over the course of the 8 years fell above the median value while the other half fell below.

This graph shows a distinct difference between the seasons. The median wave height in December, January and February is around 4.5 feet, while in the summer months of June, July and August, the average is closer to 3 feet. While the median value is higher in winter months than summer ones, the change is even larger for the 80th percentile line. For that, we can thank those large winter storm events that turn the ocean into one rough ride.

Thankfully, spring will soon give way to summer, and if the past averages hold true, the summer months of this roller-coaster should include calmer waters.

A Treasure Trove of Buoy Data

By on March 19, 2013 in Data Sources

NDBC Homepage

Every day, the National Weather Service issues countless official forecasts and warnings, relying on a large network of land and ocean sensors to provide up-to-the-minute observations of the weather around the world. The accuracy of these forecasts depends largely on having enough data collected from the right places, all transmitted back to the forecaster in a timely manner. While it’s relatively easy to set up an instrument station on land and communicate with it, it’s far more difficult to do so in the ocean.

To meet this need, NOAA’s National Data Buoy Center is tasked with operating and maintaining a global network of over 250 buoys and shore stations that collect and relay (via satellite) real-time data on atmospheric and ocean conditions. As if that wasn’t enough, NDBC also collects and processes data from over 850 additional stations run by a number of collaborators, including the National Ocean Service, the Integrated Ocean Observing System and even the oil and gas industry.

Most importantly, NDBC provides their data to the world to use, for free. Their site may not be pretty, but it is an amazing resource for atmospheric and ocean data, (they even include a lot of background information) and it is an essential resource for oceanographers who need weather data to provide context to their experiments.

From the NDBC homepage, you can quickly navigate to any region of the world that you might be interested in. Clicking on any buoy or land station brings up the most recent real-time observations from that station, as well as links to additional information on the station (including 5-day graphs of each variable), and a full archive of data. The archived data files are relatively easy to use (though they do take some massaging – more on that soon), and they provide a virtual treasure trove of information to explore.

As someone who is interested in data about the natural world, and in particular about the ocean, I often find myself on the NDBC site. Whether you want to investigate physical processes like the correlation between winds and waves or between air and water temperatures, or study the differences and similarities between two locations, or review the events that occurred during past storms, the wealth of data on the buoy center’s site is sure to keep you busy for a long time.

If you have a favorite research subject or activity that you utilize NDBC data for, I’d love to hear about it. Please leave a note in the comments, or contact me.

Significant Waves

By on March 15, 2013 in Visualizations

Significant wave heights at Station 44025

Last week a major snowstorm travelled across the continental United states, becoming a strong nor’easter over the Mid-Atlantic. While snowfall amounts in New Jersey were far less than some had predicted, the wind and waves that battered the coast were still quite severe. Dunes in Mantoloking, NJ that were heavily damaged last fall by Hurricane Sandy were again breached, causing flooding and further hindering repairs.

Wave heights at NOAA Station 44025, just 43 miles off the coast of New Jersey, reached 18.4 feet on the night of March 6th. The blue line above shows the significant wave heights measured by the NOAA buoy over the course of the last week.

The red horizontal lines signify the percentage of hourly wave measurements recorded between 2005 and 2012 that were less than the indicated height. The top line, at 31.6 feet, represents the maximum wave height reached during the 8-year record, which occurred as Hurricane Sandy made landfall.

The maximum wave height during last week’s storm reached the 99.9th percentile. Only 1 hourly measurement in 1000 hours of measurements (the equivalent of 42 days) ever reach this level. After the peak, wave heights remained between the 90 and 99.9th percentile for 3 days, which indicates the significance of this storm.

Matlab tip: If you’re interested in calculating significant wave heights at various percentile levels at other stations or for other parameters, here’s some code to play with.

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% Load in the concatenated NDBC Datafile
fid=fopen('data/44025_8yr.txt','r');
data = textscan(fid,'%4f %2f %2f %2f %2f %f %f %f %f %f %f %f %f %f %f %f %f %f %*[^\n]','HeaderLines',2,'CommentStyle','#');
fclose(fid);
 
% Calculate time and remove bad datapoints
dtime = datenum(data{1},data{2},data{3},data{4},data{5},0);
wvht = data{9};
wvht(find(wvht==99)) = NaN;
 
% Calculate percentile levels and convert meters to feet
wvd = sort(wvht(find(~isnan(wvht))));
disp([.9 wvd(round(length(wvd)*.9))*3.28084]);
disp([.99 wvd(round(length(wvd)*.99))*3.28084]);
disp([.999 wvd(round(length(wvd)*.999))*3.28084]);
disp([1 wvd(round(length(wvd)*1))*3.28084]);

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