The two smaller images on the right are of the same thing — infrared images that indicate temperature. The top one is a color-enhanced version of the lower; the colors used in the top do not necessarily indicate storms, but high, cold cloudtops that can be associated with storms and other weather events; the infrared representation in the bottom looks like clouds, and is closely associated with clouds, but it fails to show low, warmer clouds; and the daily pulsing in the top and bottom insets are due to the ground heating during the day and cooling at night. The main radar image uses a simple noise-reduction method that will remove some light preciptiation but leaves the interesting bits; the diurnal flashing around the radar sites is due to ground clutter that atmospheric conditions can exacerbate ovenight in certain seasons. See the previous articles for details.
The URL from which I get the black-and-white infaredimages changed in June, which I did not notice until August, so the better part of two months went missing. Fortunately I had also been collecting black-and-white infrared images from another source that I composited with the radar images. I used those to fill in the gap. So for a spell in the summer months the size and presentation changes, but the show goes on. I could not use those for the whole video because I hadn’t started generating them until April, and there is a gap in their production for a few weeks later in the year. There’s also the issue with the satellite images, the western one in paticular, being unreliable at times. That causes really annoying results, as can be seen in another video made from just those images.
Below are some nitty-gritty details of how I generated this video.
How guilty should you feel for buying your lunch every day instead of brown-bagging it? Very.
Amount saved per day:
It’s hard to find a reasonable lunch anywhere for much under $6 or $7, and it’s easy to spend much more. But a good home-made lunch can be made for only a dollar or two, so buying your lunch instead of bringing it from home can easily cost you $5 or more every day.
That’s only $25 per week, though, and what’s that in the grand scheme of things? A lot over the course of a 40-year work career.
It adds up even more — a lot more — if you consider the amount you could accumulate by investing the savings and compounding the earned interest. A bank savings account earns a percentage point or so these days. The total return on the S&P 500 stock index from 1950 to 2009 came out to 11% annually. A few savvy investors do better, and many do worse. Pick a rate of return, a term, and how much you can save each lunch, and let the calculator tell you what that burger and fries is really costing you.
This uses the formula for periodic compounding found on Wikipedia and makes the assumption that you do not save extra lunch money on weekends. It is rather simple-minded in that it does not figure out the number of days until the next compounding period. That is to say, if you choose to compound quarterly (4 times a year), it just plugs that interval into the formula without trying to figure out on which calendar days the interest will be calculated. Your actual return may vary slightly, but you can still see that it pays well to save your lunch money.
What this doesn’t tell you is what years of inflation will do to your savings, nor does it consider taxes. Those stories are not nearly so nice.
Here is an animation of US radar and GOES satellite images for all of 2011. There is a progress bar at the bottom of the animation with certain notable events highlighed. When the video reaches those points, the name of the event is shown right above the bar. Most of the event names came from an article from The Weather Channel titled 2011: Weather by the Billions.
The image sources are the same as last year’s, so the same explanations and caveats still apply. Notably, the monochrome satellite image does not show clouds, and the color satellite image does not show storms — though there is a strong correlation between those images and clouds and storms. Those are two different renderings of infrared images that basically record temperature. Clouds are generally colder as they get higher, so they cause cool areas in the infrared images, which are rendered white in the monochrome images. Storm clouds generally are tall clouds, and their tops are cooler (that’s where hail forms, e.g.), and those cold tops are highlighted in the color satellite image. But lower, warmer clouds may not show up distinctly in these images at all.
As for the radar, those are modified from the NOAA originals with a simple-minded noise reduction method — all radar returns below a certain level are simply removed. That gets rid of noise around the radar stations that, when animated, is rendered as really annoying pulses.This causes light precipitation to be absent from some frames. The interesting weather is there, though. (As is some of the noise, still.)
The data includes the estimated depth of the quakes as well as the geographic coordinates. Below is my first effort to use that data to visualize the fault zone in 3 dimensions. There is a lot of data that gets in its own way, so an animation that tilts and rotates was chosen as a a means to see it from different angles.
In order to view this animation you need a modern browser that supports HTML5 videos in Ogg Theora format. The current versions of FireFox and Chrome work., though if you insist there is another version on YouTube.
I’m fairly well pleased with how it turned out, but I’m concerned the reliability of some of the data. That is discussed below along with notes on how it was produced.