Let’s make Earth great again

When in November 2016 Americans played their trump card, climate scientists faced a new challenge: how to persuade conservatives to start caring about our planet? Even though this task seems to be hopeless, research is being done – and I recently came across some very interesting results.

Researchers from Cologne discovered that conservatives are more likely to act against climate change when the problem is presented with reference to the past, as opposed to the future. We usually focus on future degradation of the environment, which doesn’t appeal to them. However, conservatives tend to be more concerned about the global warming, when we point out that the planet isn’t as “great” as it used to be.

German sociologists conducted a series of experiments on self-identified liberals and conservatives. As a part of the study participants were asked to donate money to one of two fictitious environmental charities: the one preventing future degradation or the one striving to restore the past state of the Earth. In all experiments conservatives were more likely to support the second organisation.

Does it solve our problem? Of course it doesn’t! Riley Dunlap, a sociologists from Oklahoma State University, commented on this study: “If you’re a good conservative, you need to be a climate change sceptic. Global warming has joined God, guns, gays, abortion and taxes. It’s part of that ideology.”

Even though sadly I must agree with Dunlap, I also believe that we should study ways of communicating climate change effectively to various social groups. One size doesn’t fit all – but we can find a good approach for many. If someone is blind in his or her scepticism, we probably can’t do much. However, I believe that with new communication methods we can persuade many people, who are ready to at least listen.

To make Earth great again.

Source: https://paularowinska.wordpress.com/2017/04/12/lets-make-earth-great-again/

 

Money blowing in the wind

I bet you’re not looking forward to receiving your monthly electricity bills. Can you predict how much you’ll be charged this time? Short answer: assume that more than you’d be willing to pay. Long answer: spend a couple of years studying how electricity prices evolve in time. Yes, that’s exactly what my PhD is about.

Power markets are surprisingly complicated. Trading energy is a relatively new idea, increasingly important because of the gradual liberalisation of the EU electricity industry. Not only do market rules in various countries differ significantly, but relevant laws change frequently. Therefore if you’re interested in any details, please don’t rely solely on my article, but refer to the website of the appropriate market (eg. European Power Exchange).

From the mathematical point of view, modelling any financial processes is an extremely difficult task. Stock values change in unpredictable ways, they also strongly depend on political events and human behaviour. Because of that, financial mathematics attracts increasing numbers of mathematicians with different backgrounds. Actually, not only mathematicians. For example, a building block for many financial models is a so-called Brownian motion, first used by physicists to describe chaotic movement of particles. The tools we can use are limited only by our imagination!

Energy markets are problematic, as they behave differently than traditional stock exchanges, so we have to come up with completely new ideas to model them. The main difference is that the supply and demand for electricity must always match. Storing electricity is almost impossible, in the best case very expensive, so we cannot produce (or buy) more and leave it for later. On the other hand, the supply is inelastic, because industry and citizens require a specific amount of power for their regular activities. You don’t like blackouts, do you? And they happen exactly as a result of a significant imbalance in the energy market.

Thankfully many people work very hard (this is how I like to think about myself) to make sure that you don’t have to dig out these candles too often. Mathematical models help producers decide how much energy to generate and traders to buy and sell its appropriate amounts. Most of trading takes place in electricity markets.

Two main types of contracts are traded. First, spot contracts (traded at noon) oblige producers to deliver a specified amount of energy for 24 hours, from midnight of the following day. Second, one can also trade futures contracts for a specified delivery period: a week, a month or a year. For example, if a producer signs a “2 months ahead” contract today (June 2017), she or he would have to deliver the electricity between 01/08/2017 and 31/08/2017.

However, predicting the prices so far in the future is a difficult task. We don’t know the general state of economy or if Donald T. decides to build a *huge* bridge from New York to the Moon (which would require a lot of power, I guess). And, what interests me most, what the weather will be.

Weather conditions significantly influence electricity prices, both the demand and supply. In countries like the UK or Germany, in general the demand is higher in cold months, when we need to heat our houses and offices, as well as use more light due to shorter days. In warmer places, also in the summer a lot of energy is needed for air-conditioning. On the other hand, renewable energy production strongly relies on the weather. We can’t generate wind energy without wind!

This is why in my models I have to take into account weather forecasts. As you know, they’re pretty useless a few weeks in advance, not to mention a few years. Therefore the models need to account for uncertainty related to these forecasts.

You might wonder if these factors really matter. They actually do! Now many markets even allow the prices to be negative, which means that we get paid for the electricity usage. It happens rather rarely, so you won’t even notice. However, it’s interesting to note that almost all negative prices are observed in early morning hours after a windy night. This means that a high level of wind energy generation, combined with a low demand (most factories are closed at night, we also tend to sleep), can significantly decrease electricity prices.

In other words, the chance for a lower electricity bill is literally blowing in the wind.

Source: https://paularowinska.wordpress.com/2017/06/11/money-blowing-in-the-wind/

A tale of a statistician without an umbrella

You should have seen our office today – our cycle to work resembled swimming rather than biking, so wet clothes were hanging everywhere. Well, I can blame only myself, since a normal human being would assume that 98% probability of rain means “it WILL rain, take a bus”. However, being an incurable optimist, I counted on these 2%. In the end, improbable things happen a lot, as Jordan Ellenberg (a mathematician, of course) says. But how improbable was a bit of sunshine in London today? And why on Earth do I try to squeeze some maths even in the weather prediction?!

If you live in the UK, chances are that you begin your day checking your favourite weather app. Based on the information you gained, you know what to wear – unless you’re a proper Briton, then you wear shorts, no matter what, don’t you? Regardless of the website you use, the forecast is probably provided either by Met Office or ECMWF, who constantly compete to produce the most reliable weather prediction in the world. Despite, or maybe because of this rivalry, their forecasts are more and more accurate. So again, why can’t they just get it right, why do they give us percentages?

Weather is chaotic. You might associate chaos with the butterfly effect, a term coined by Edward Lorenz. If you think that it means that a butterfly in Asia can create a tornado in America, then please, please forget this concept; or, even better, read an excellent book by Ian Stewart, Does God Play Dice?, or request an article about it in the comments. The bottom line is that weather forecasting centres will never be able to predict the weather with the accuracy of 100%, no matter how hard they try. Never. Ever.

lorenz
A chaotic system: a slight change in the intial conditions can lead us to a different “wing” (by Computed in Fractint by Wikimol [Public domain], via Wikimedia Commons)
Instead, they calculate their confidence in a particular weather forecast. For example, 98% PoP (probability of precipitation) between 8 am and 11 am in London means that there is 98 in 100 chance that in this period we’ll get at least 0.1 mm of some precipitation. Basically, if you kept going back in time and went for a walk in London today between 8 and 11 one hundred times, twice you would be lucky and came home completely dry.

What?! It doesn’t make any sense! We can’t go back in time! But computers can. Even better, they can look into the future. Because weather is chaotic, small changes in the initial conditions (so the temperature, pressure, clouds etc. in the moment when we start simulations) can lead to big changes in the outcome. This is why Met Office and ECMWF use so-called ensemble forecasting. They run many simulations starting from slightly different conditions and look at their outcomes; it’s a bit like in Groundhog Day, but less creepy. And this means that today I believed in these 2 measly forecasts out of 100 – and that’s why my neighbours asked me if I cycled into the Thames on my way home (not funny).

Learn your probabilities and figure out your chances for a dry day. Or take an umbrella, just in case.

Source: https://paularowinska.wordpress.com/2017/08/09/a-tale-of-a-statistician-without-an-umbrella/