Tag Archives: Irrigation

3246 – The Hottest Summer

In Austria and most of Europe this summer was the hottest in recorded history and we’ve been recording meteorological data for centuries. The next few record-breaking summers before were all on this side of the millenium. Science has established climate change as one of the best supported theories of all times. It’s not as established as the theory of evolution, but it’s close. Unfortunately some people don’t grok science.

The scientific method initially observes a fact, and from reasoning about this fact a hypothesis is drawn. The hypothesis is not a fact. It’s only supported by logic based on assumptions, axioms. It has a predictive value though, and that can be tested. If the hypothesis holds, it must have consequences, and if we’re lucky, we may be able to observe the predicted consequences as facts in the real world.

Sometimes, in fact most of the time, it is not possible to just wait for something to happen. You have to carefully create an environment where it is likely to happen if the hypothesis holds, and at the same time unlikely if it does not. That’s called an experiment.

What we get from observation and experiments is not proof, it is scientific data that supports a hypothesis. If there is enough supporting data, the hypothesis matures into a theory.

Theories explain why things in the real world happen as they do. A well-supported theory is perfectly able to predict – within its scope. Newton’s theory of gravitation is pretty perfect at predicting what happens to apples on planet Earth, and in fact it does so for any kind of small object in relation to any kind of giant object. For everything out of scope Einstein’s theory of relativity is your friend.

Most people don’t grok science. They expect science to establish facts, “truths”, but that’s not what science is about. Science analyzes to the degree that is necessary to prove that gathered data supports a hypothesis. Science proves that what we observe is not an illusion and that it can be reproduced.

It may come as a disappointment that science does not drill for final truth, but if you think about it, we’ve come pretty far that way, and we’ve done it one step at a time. With conventional mechanics we could build anything from the pyramids of old to the sky scrapers of our age. Relativity and quantum mechanics gave us predictive tools good enough to send probes to the frontiers of the Solar system, to construct tiny computers with power unthinkable only 50 years before, ready for you to surf the Internets or to call your mum.

Science has not told us the complete inner secrets of silicon, but it was good enough to let us work it and create memory chips capable of storing more literature than you could ever read, and additionally a few of your vacation snapshots. I recently returned from a trip with 1244 images or 28.7 GB on my SD card.

We all have smartphones, digital cameras, personal computers, and that mankind can build these things is due to the predictive power of theories.

Theories enable us to repeatably achieve what is within the predictive scope of the theory. The “true reason” why things happen as the theory predicts may lie much deeper, but as long as it repeatably works (and in the end we get an iPhone), the theory is “good enough”.

Denial of climate change is frequently “grounded” in observation of seemingly contradictory facts. Texas had a lot of water this summer, and that seems to compensate for the drought in California or my “hottest summer”. Isn’t that understandable? We observe and we draw conclusions. Why is this suddenly different from what Sir Isaac did?

Well, we’re pretty much past the ages where falling apples called for explanation. More or less everything that can be observed by lay people “just so” has already been subject to extensive scientific scrutinity. Thanks for your observations and anecdotal evidence, but floods in Texas are perfectly in line with the theory of climate change.

The problem is, that those who don’t grok science have false expectations, and that is due to the dimensions of the problem. Have you ever seen on TV how the winning numbers in a lottery are drawn? Here is some random video from Austrian television. It always begins with the same neat array of numbered balls that, with the press of a button are tossed into a transparent vessel where air is blown in. We perfectly accept that the outcome is random. We even bet money on it.

The collissions of those plastic balls are chaotic enough that we have no theory powerful enough to predict the numbers. On the other hand, we have powerful theories predicting exactly that unpredictability.

Weather is like a giant lotto machine. It should be perfectly obvious that we are not able to exactly predict it in all its micro-aspects. Unfortunately what means “micro” in giant systems, can be the whole observable environment for a single human observer. Therefore we should never rule out a drop of rain on a day predicted to stay dry in our larger area.

But really, regional weather forecasts have vastly improved in my lifetime. They still get it wrong occasionally, but this is in the range of a few degrees more or less or of a rain front coming a day later than predicted. For short-term vacation planning this is already “good enough”. I know that it does not make sense driving down to Italy next weekend when the weather forecast predicts low pressure over Genoa.

That precision, imprecise as it may seem from the vantage point of a single observer, is enabled by vast computer models grounded in complicated theories based on centuries of research.

It’s the same kind of models that are employed on a global scale to predict the effects of climate change, but complexity rises vastly with scale, and while we have a dense network of sensors in our urban areas, it is sparse on much of the planet. Historical data does not exist everywhere to the same degree as it does in Europe, and all that forces us to compensate by much guesswork. In other words, it’s complicated and we are far from making good predictions. We can predict a rise of global average temperatures and an increase in extreme weather situations though.

People who don’t grok science have similar problems with the theory of evolution, and again it is a problem of scale, but this time it is the enormous length of time it takes evolution to achieve change. The problem is, that this change is completely invisible during a human lifespan. That all is complicated by the conflict with naive human explanation tools, i.e. with religion. Of course even religious people can watch evolution work on bacteria, but that all does not help when they deny the analogies to other life forms. It’s all “God made the ones changing and the others unchanging”. Oh Lord!

In reality there is a place for philosophy and maybe even for religion at the side of science. At least there are or were true scientists who are or were religious. Einstein was an example. They are or were only not naively religious.

As a species, humans are mostly illogical. They deny that we’ve been on the moon, but they perfectly accept and expect that GPS receivers in their phones tell them their exact position. They don’t think of smartphones in terms of the vast knowledge necessary to produce them, they think of them in terms of magic.

As always, politics is where things get really muddy. Politicians are mostly like normal people though. They are normal people, except that they have much more power. They have no scientific education, they don’t know what to expect and what not to expect from science. They are managers and not scientists, and for some stupid reason we expect managers to make the right decisions, even though they are completely unqualified for everything but following their guts.

Still, many politicians have some higher education and they are not completely at odds with science. When they insist on unscientific nonsense anyway, their reason is what we call an agenda.

The takeaway of all this? Even if you feel you don’t trust science, you probably trust your smartphone and your digital camera, and even if you don’t, you trust on the brakes of your car. Every time. You bet your life on them.

Guess what? Shockingly you trust in science 🙂

3159 – Summer Grass

Sure, Micro Four Thirds has a smaller sensor, and the appearant DOF of a lens is as if it were two stops slower. Thus my f2.8 looks like your f5.6 (at least if you use a 35mm sensor), but that does not mean that my f2.8 does not gather as much light as yours. It may capture less light, but it concentrates it on a smaller sensor area. Light density is the same and so are my shutter speeds for the same exposure value as yours. It’s only that my lenses are smaller and lighter 🙂

And if I really crave for very shallow DOF? Well, depth of field depends at least as much on subject distance as it depends on aperture. These images have been taken at f2.8, but due to the lens’ great close-focusing capability, I can just go a little closer.

You probably know the effect best from macro lenses: once you get close enough, DOF is shallow even at small apertures.

Now, with shallow DOF clearly achieveable, bokeh is the important factor. Do the out-of-focus areas look good? Creamy? Soft? The Image of the Day is obviously as creamy as it gets. Out-of-focus highlights from the droplets are even discs with no obvious borders or onion rings, and even the second image looks pleasing to me. Its abundance of hard lines at all background distances is more or less a torture test for bokeh, and I’d say this lens behaves quite well.

The Song of the Day is “Summer Grass” by Kiyoshi Yoshida. Hear it on YouTube.