How is the weather?: data, observation, and the generation gap
This past weekend, my parents were out of town, and unforeseen circumstances made it necessary for me to spend a lot of time (all but overnight really — although night starts pretty early…) with my maternal grandmother, better known as Bubby.
For those of you who may not know, I’m a bit of a weather nerd, so when a tornado watch went up Friday afternoon, I was pretty excited. After taking Bubby out to dinner, I put on a movie (Fiddler on the Roof, just to be stereotypical), but, of course, I had to keep abreast of any potentially severe weather conditions. Out of this came my favorite interaction of the whole weekend.
As we were watching the movie, I pulled out my iPhone to check the latest watch/warning/advisory and mesoscale discussion issues from the Storm Prediction Center, the latest statements from the local National Weather Service office, and, of course, radar. I explained to Bubby that I was checking the weather, at which point she simply looked out the window, listened to a peal of thunder, and shrugged her shoulders, saying, “It’s bad,” as if to say What do you want to do about it?.
And that, to me, is representative of the difference between my data-driven generation and previous generations. On one hand, having data can be both insightful and actionable. But on the other hand, is our reliance on sensors, data, and computer modeling enabling our detachment from the observable world? What has been gained — and what has been lost — by my getting weather data that was collected by ground-based and satellite sensors, sent to the Storm Prediction Center in Norman, Oklahoma, run through computer models, and sent over fiber optic cables to servers that let me retrieve aggregate and interpreted data on my phone, when looking out the window can clearly tell us that the weather is bad?
Even today, the NWS recognizes the fallibility of sensors, relying on storm reports from thousands of trained weather spotters, most of whom use amateur radio, a technology that probably deserves its own blog post for its incredible power despite — and because — it does not rely on any large communications infrastructure.
To be sure, forecasting saves many lives. But was forecasting of acute severe weather events really that bad before humans had even urbanized? I heard it said on the BBC the other day that at one time, some people could tell what species a tree belonged to just by listening to the wind rustling its leaves. I bet those people knew when a storm was coming, too.
The Cost of Text Messaging
A couple weeks ago there was a post in a New York Times blog about the cost of text messaging that I would like to briefly recap here. A British space scientist, Nigel Bannister, ran some quick numbers and concluded that
“The maximum size for a text message is 160 characters, which takes 140 bytes because there are only 7 bits per character in the text messaging system, and we assume the average price for a text message is [about 10 cents]. There are 1,048,576 bytes in a megabyte, so that’s 1 million/140 = 7490 text messages to transmit one megabyte. At 10 cents each, that’s [$734] per MB – or about 4.4 times more expensive than the ‘most pessimistic’ estimate for Hubble Space Telescope transmission costs [of $166 per megabyte].”
So basically, consumers have allowed mobile phone companies to charge literally astronomical rates to send a text message: we pay at least 4.4 times as much to send a text message than NASA does to download data from the Hubble Space Telescope (in cost per unit data, anyway). I can’t believe we put up with that. Disgusting, I think.
