What Does it Take to Trigger a Snowfall Warning in Canada?

train-going-through-huge-snow-wall-in-canada

It’s interesting to note that Environment Canada believes in and promotes discrimination. i.e. They have a different set of rules for different parts of the country (when it comes to issuing weather warnings).

In terms of snowfall amounts, a mere 5 cm forecast for Vancouver will trigger a warning while 20cm of forecast snow is required in the far northwest corner of the province.

Up in northern Quebec, they’re even tougher where Environment Canada does not issue snowfall warnings at all. The Quebec Inuit are apparently the toughest people in the country… or perhaps they know that the traditional methods of reading the weather are still more accurate than the highest powered computer in the nation crunching numbers down in Ottawa.

Here is the map for the entire nation. Note that areas in white do not receive forecasts and warnings of any kind because they are uninhabited.

ecwarningmap

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The Sunshine Tax

 

okrailSince moving to the Okanagan Valley area, I’ve been made aware of something called the “sunshine tax.”  It’s a sort of catchall explanation for getting screwed, and perhaps the most idiotic and pretentious excuse for not understanding basic economics ever invented.

  • Gas prices just went up today — sunshine tax!
  • House prices are rising — sunshine tax!
  • Income is stagnating — sunshine tax!
  • Someone in Tuktoyaktuk makes more money than you for doing the same work — sunshine tax!
  • Traffic is bad — sunshine tax!
  • Your wife left you — sunshine tax!

The “sunshine tax” stipulates that people in Kelowna and the Okanagan Valley  get paid less and/or “enjoy” a higher cost of living simply because of the desirable climate. The sunshine as it were.

The first hint of something serious wrong with this theory appears in the ironic name itself. It’s not a tax. Nor is the Okanagan all that sunny. Sure, it’s sunnier than Prince Rupert, but the 1923 hours of sunshine per year in the south Okanagan city of Penticton is a far cry from the 2544 hours in Medicine Hat, Alberta. It’s also less than almost all major cities in Canada including Vancouver, Victoria, Calgary, Edmonton, Yellowknife, Saskatoon, Regina, Winnipeg, Thunder Bay, Toronto, Ottawa, and Montreal. If sunshine were a taxable commodity, Penticton and Kelowna would be overdue for a tax refund if anything!

The lack of sunshine aside, the climate is quite mild by Canadian standards, and that makes it one of the more desirable places to live. However, the sunshine tax explanation is not used in other cities with mild winters like Kamloops or Nanaimo or Vancouver or Halifax. They just know basic economics — a company looking for workers in northern and remote parts of Canada will need to offer higher wages to entice people to move there.

The most annoying part about someone invoking the “sunshine tax” to complain about their economic situation is that they are essentially demanding their cake and to eat it too. They think that they should get the same wages and cost of living as someone living in some remote outpost that they themselves would never live at without a significant wage increase.

So it’s not a tax, has nothing to do with sunshine, and represents nothing unique about the economic situation of the valley compared to other places that don’t have this supposed tax. Maybe it’s time to retire the self-righteous phrase, and join the realities of  planet earth.

Incidentally enough, this disconnect between reality and expectations is what has likely given rise to the “sunshine tax” phenomenon in the first place. Perhaps it’s not so much a misunderstanding of economics or a self-righteous attitude, but a failure to realize that we’ve been lied to.

Similar to the false marketing claim that the only desert in Canada resides the Okanagan Valley, marketers have also been able to project a climate with nicer weather than reality dictates. This reputation for great weather increases demand for housing and supply of workers. In turn, housing costs are driven higher while wages are simultaneously lowered.

If the Okanagan’s reputation matched reality, everyone would probably be more understanding of market conditions like they are in Kamloops and Halifax where newcomers aren’t duped into thinking they’re entering a paradise unequaled in the rest of the country.

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How to Beat Wikipedia at Their Own Game

bc_castlegar

Wikipedia’s standard of proof dictates that all information must be sourced, and not from blogs. Nor can original research be used. Interestingly enough, Wikipedia becomes an incredibly valuable resource because so many pages ignore this rule by supplying original research and local knowledge. For example, this table listing the world’s mountains by prominence was produced by a bunch of mountaineering nerds who sat down and calculated out most of these values. There’s simply no other source out there for such information. Thank you, Wikipedia.

And yet on many other pages, you have editors who will deleted every edit that does not provide a “valid source.” This detracts from Wikipedia’s value. The temptation to be too strict (for non-controversial topics) means that common knowledge and common sense are thrown out the window in the name of verification. Wikipedia is an invaluable source of information in large part because much of the information is original research and local knowledge that cannot be found anywhere else on the internet.

Their policy of not allowing blog posts as valid references falls apart when we consider the fact that this blog post is actually true while this government source is clearly wrong.

Rigidly following the rules means that anyone can find an error in the “official sources,” and permanently insert errors into the Wikipedia pages. And nothing but the use of common sense will be able to reverse the falsehood.

It’s much like how you might have a Math textbook that shows the answers in the back. You swear the answer to 2×5 should be 10, not 100 as the back shows. A good teacher is going to side with you because the “official answer” is clearly not right. A dogmatically rigid teacher will still insist the answer is 100.

You might be thinking that no such teacher exists, and hopefully you’d be right, but the same cannot be said about the editors on Wikipedia. For example, Canada has this completely meaningless variable called the Humidex, which is supposed to tell you what the humidity feels like — it doesn’t.

Those who use common sense like Environment Canada’s chief climatologist will look at the data, and know that 53.4°C (128°F) humidex in Castlegar, BC, is an error. They know that British Columbia has very dry air, so you never get humidex values like that. Plus, looking at other places in the area from that day shows that this was indeed an error that made it into the database. So instead, they will refer to the places with the highest humidex values in Canada as those in Ontario, Quebec, and Manitoba.

But not Wikipedia. All that you have to do is source the Environment Canada data for Castlegar, and voilà!  You have just re-written history with a source that can’t be refuted. On Wikipedia, Castlegar is listed as the place with the highest humidex.

When the editors demanding proper sources look at the humidex record on Wikipedia, they conclude that it must be true . Anyone with a little local knowledge or some common sense knows it’s wrong intuitively, but those are not qualities they bestow upon Wikipedia editors, or at least not all of them.

There’s really no way to scrub the false humidex value off the Wikipedia site. They can’t link to this blog because it’s a blog, and thus not reliable, and they can’t point to the articles listing Windsor, Ontario, or Carman, Manitoba, as the record holders because the Environment Canada value for Castlegar is considered a more reliable source. Heck, I’ve even seen instances where someone tried to add a note that the Castlegar record is suspect and unreliable, and within minutes, a Wikipedia editor goes in and reverses the change such as this bogus record remains unchallenged.

This is but one example of Wikipedia kiboshing truth in favour of fiction where the who of the source trumps reason. Wikipedia is a great website, but they could be even better if common sense were more common.

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Top 12 Musicians That Died in 2016

At the end of 2015 I took us through 10 black musicians we lost. It’s another year, and we are all another year older and another year closer the end of our own earthly journeys.

Over the year of 2016 we lost many of the great musicians. A sad reality of life, but we can celebrate the talent they graced us with over the decades. Here are 12 of the best who left us during the year.

12) George Michael. This was his last Christmas.

11) Paul Kantner of Jefferson Airplane

10) David Bowie

9) All 64 members of the Red Army Choir died on Christmas Day as their plan crashed into the Black Sea.

8) Fred Hellerman, the last surviving member of the folk group, the Weavers, passed away at the age of 89. Hellerman is the guitar player.

7) Bobby Vee

6) Ralph Stanley

5) Joey Feek, the wife of the husband-wife duo of Joey + Rory

4) Prince. It’s more than his guitar weeping this year.

3) Leonard Cohen

2) Merle Haggard

1) Glenn Frey from the Eagles, age 67. Take it easy, Mr. Frey.

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Yes, Last Month was the Wettest October on Record

bcairport

CBC Radio in Kelowna expressed the sentiment today that we’ve all been thinking: surely this is one of the wettest falls on record. It’s hard to argue against that! September was wet, and November is shaping up to be extremely wet down the home stretch — and yet, they have nothing on October.

While last month failed to set many precipitation records in terms of total precipitation, the number of days with precipitation shattered records all over the southern third of British Columbia.

Environment Canada defines a precipitation day as one that sees at least 1 mm of precipitation. British Columbia is a diverse province that varies from an average of about 4.5 days/October in the South Okanagan to over 24 days of precipitation on Haida Gwaii (Queen Charlotte Islands).

Interestingly enough, the north and central coast along with the far northwest part of the province saw well below average precipitation days. For example, the Langara lighthouse on the northern tip of Haida Gwaii, which typically averages 24.3 days of precipitation in October, only recorded 15 days in 2016 — the 3rd fewest days since records began in 1936.

They can’t afford to be smug up there, however, for Langara is one of the few places to ever experience 30 joyous days of precipitation in October. Now that is wet!

The map below shows the entire list of locations in BC matching Langara’s October record. The most recent occurrence of 30 October precipitation days comes from 2005 when both the Nootka Lightstation and Estevan Point managed just a single day of relief from the rain. But that’s not the worst of it because Estevan Point in 1963 and Pine Island in 1990 recorded 1mm or more for the entire 31 days of October.

precipitation

Locations that have received 30 or 31 days of precipitation in their wettest October (none in 2016).

While the remote, super wet locations were not setting new records in 2016, the more populated areas of the province were. Courtney, Powell River, Campbell River and a number of other stations in the area recorded 27 days of precipitation. All of them were new records. Estevan Point also recorded 27 days, but fell well short of its record.

The south coast and the southern interior were hit the hardest in October. Even places that average under 5 days per October managed to record more than double that amount.

Here are some places that set new records in October:

Vancouver Island:

  • Chemainus with 22 days (old record set in 1967).
  • The Campbell River airport managed to receive 25 days with precipitation, 2 more than the 1967 record. It was even wetter on the outskirts of the city with 27 days.
  • Comox with 26 days (old record of 21 days set in 1967).
  • Malahat with 24 days (old record of 19 days set in 2014).
  • Nanaimo with 23 days of rain in October (old record of 22 set in 968).
  • North Cowichan with 23 days (old record of 19 set in 2014).
  • Saanichton with 21 days (tied the 1967 record).
  • Shawnigan Lake, with records stretching back to 1910, recorded 22 days of precipitation — 1 more than the previous record set in 1967.
  • Victoria (YXX) with 21 days (tied the 1975 record).

Islands around Vancouver Island:

  • Galiano with 24 days of precipitation (old record of 19 days set in 1975).
  • Saturna Island with 9 days (old record of 18 days set in 1985).
  • Ballenas Island with 25 days (old record of 22 days set in 1975)
  • Fanny Island and Cortez Island both recorded 27 days of precipitation.
  • Merry Island with 24 days of precipitation (old record of 23 set in 1967).

Sunshine Coast:

  • Pender Harbour with 26 days (old record of 22 days set in 2005).
  • Powell River with 27 days (old record of 25 days set in 1967).
  • Sechelt with 21 days (old record of 18 days set in 1967).
  • Whistler with 24 days (old record of 23 days set in 2005).

Greater Vancouver – Fraser Valley:

  • Cloverdale with 24 days (old record of 19 days set in 2007).
  • Tsawwassen with 23 days (tied the old record set in 1967).
  • Fort Langley with 23 days (old record of 20 days set in 2007).
  • Mission with 24 days (tied the old record from 1967).
  • Vancouver (YVR) with 23 days (tied old record from 1967).

Okanagan-Similkameen:

  • Hedley with 15 days (old record of 13 days set in 1997).
  • Okanagan Centre with 15 days (tied old record set in 1985).
  • Osoyoos with 12 days (tied old record set in 1967).
  • Peachland with 15 days (beat old record set in 2012).
  • Penticton with 12 days (tied old record set in 1967).
  • Summerland with 13 days (tied old record set in 1950).

Boundary-West Kootenay:

  • Midway with 16 days (old record of 14 days set in 2009).
  • Castlegar with 19 days (tied old record set in 1975).
  • Nelson with 21 days (old record of 18 set in 1997).

East Kootenay:

  • Cranbrook with 17 days (old record of 16 days set in 1947).
  • Sparwood with 17 days (old record of 15 days set in 1990).
  • Wasa with 15 days (old record of 14 days set in 1950).

Thompson -Nicola:

  • Ashcroft with 9 days (old record of 8 days set in 1967).
  • Merritt with 10 days (tied old record of 10 days set in 1997).
  • Red Lake with 13 days (tied old record from 2009).
  • Blue River with 25 days (old record of 22 days set in 1967).

Oct2016records.png

In addition to these records, many other areas achieved near record setting precipitation days in October, from Fort St. John southward. The truly impressive figure from October in the Peace River area around Fort St. John was the snowfall. Chetwynd, for example, was the snowiest place in the entire country with almost 100cm of snow, but that’s for another discussion.

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No, Voter Turnout Did Not Change the Election

I’ve seen several media outlets claiming that Trump only won because of reduced voter turnout — even though voter turnout was up 0.4% from 2012. The argument goes that turnout was down in some key areas and among some key demographics. In addition, this idea that there was a “white surge” while minority races stayed home also seems to be rubbish. I mean, states like Florida and Texas saw a YUGE increase in turnout where there are large Hispanic populations while whiter states like Wisconsin saw reduced turnout. This also simultaneously debunks the intellectually bankrupt theory that Trump won because of racism — he won in spite of racist overtures. In fact, demographically white Wisconsin was one of only five states were fewer voters turned out in 2016 than 2012.

In Florida, Trump captured 454,000 more votes than Romney while Clinton gained 267,000 votes over Obama.

By contrast, Wisconsin saw an overall drop in voter participation, and was one of only two states in which Trump won despite receiving fewer votes than Romney did in 2012 (the other was Mississippi, which Trump took in a landslide).

I thought that it would be interesting take an extreme case just to see if voter turnout changed the election (just to satisfy the critics). Let’s look at the 10 states where the difference between the two candidates was under 5% points. Any margin of victory beyond that could not be overcome with turnout alone.

10closeststates

As the above graph demonstrates, only little Wisconsin saw a drop in voter turnout. We don’t know the political views of those who stayed home. It could be that they had no preference, and thus didn’t vote, meaning that their votes would cancel each other out, giving Trump the states anyway. But let’s assume that every single voter that never showed up to vote would have cast a ballot for Clinton.

This is a completely insane proposition because it means that Alaska would have gone Democrat in 2016. Yes, that’s right, a state that repudiates Hillary Clinton more often than the Democrats would go for Clinton in 2016. The only time Alaska ever voted for the Democratic presidential candidate was 1964 when they voted against the wishes of Hillary Clinton and the Republican candidate she was campaigning for, Goldwater, and instead went with Johnson. That’s how much Alaskans don’t like Clinton. Or maybe they weren’t thinking straight after dealing with the massive 9.2 earthquake. Or maybe that just like Johnsons. I know a few of them in Alaska, and they seem like nice people. After all, they voted in high numbers for Gary “where’s Aleppo” Johnson who managed to capture 5.9% of the Alaskan vote. Only New Mexico and North Dakota beat that.

Making the crazy assumption with the 10 closest states gives Wisconsin to Clinton, but that still means Trump ends up with 296 electoral votes, 26 more than he needed.

At this point, I think it’s safe to say that poor voter turnout did not give Trump the victory.

Another one making the rounds is this idea of a whitelash: “Trump enjoyed a huge increase of white supporters.”

Again, the data does not show this. Trump did see a huge increase in supporters from poor whites, but this was more than offset by middle and upper-class whites. Overall, Republican support among whites was down while Republican support among blacks, Hispanics, and Asians was up.

whitelash.jpg

One more note: Trump lost ground in Arizona and Texas, while gaining huge in Florida at the same time. I wonder if it’s the case that Mexican Latinos preferred Clinton while Cuban Latinos preferred Tump. Trump never made insulting comments about Cubans (Mark Cuban aside), and Cuban Americans are more apt to want the US to be tough with the types of oppressive foreign governments like the one of which they’re most familiar.

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The Trump Landslide Myth

currentvotingsituation

A number of my Trump-loving friends have told me that the polls are wrong. When I inquire further, I hear that the polls are rigged or that Trump is so hated that voters tell pollsters they’re voting for Clinton while actually intending on choosing Trump.

This concept that a certain segment of the population will lie to pollsters is an old theory, but it’s still a myth. There will not be a landslide by Trump. In fact, Trump will be extremely lucky to fall one or two states short, let alone win.

The Trump landslide myth starts in Germany where the  spiral of silence theory was first proposed in 1974. The premise states that people fear isolation, and thus those who are ostracized by society will not express their opinions in public (or to pollsters apparently).

Other nations have subsequently developed similar theories to try and explain instances where the polls turned out to wrong. In the U.S.A., the Bradley effect stems from the 1982 California governor’s race where the African American candidate, Tom Bradley, was leading in the polls, but lost the election. Pundits explained away his loss as a function of social desirability bias — voters will virtue signal to pollsters that they love racial minorities, but at the poll booth pick the candidate they actually like (because they’re actually racist). This theory tends to fall apart when we consider the fact that Obama did better than the polls predicted.

In the U.K. and Canada pundits have tried to peg discrepancies in the polls on the shy Tory factor. Tories is another name for Conservatives. The theory goes that conservative voters are — for some reason — shy with pollsters.

There are no shy voters. It’s a myth invented by wishful thinking (or sometimes fear-mongering if trumpeted by opposing candidates).  There are especially no shy Trump supporters. Have you seen how many people show up at his rallies? Clinton might have 50 people at her rally, but Trump will have 100 times that amount. These are not shy voters!

The polls are incredibly accurate and scientific, and not subject to rigging. When they do miss the mark, it’s largely because voters changed their minds during the last few days of the campaign. Polls only reflect the viewpoints of voters on the day they were surveyed, and since polls are conducted over several days, they’re already behind current public open as soon as they’re released.

Trump supporters point to Brexit as an example of polls getting the results wrong. Not all pollsters got it wrong (the Telegraph nailed it). In fact, the polls were quite close to the actual result. There was some surge at the end, which most polls did pick up on. It was the pro-EU voters who suffered from a Trumpian sort of willful ignorance by denying the reality of the polls, and instead going with the betting markets.

Here in Canada we have had similar problems with the polls appearing to be wrong. During the 2013 provincial election in British Columbia, the Liberals saw a huge surge in the final week of the campaign. Because of the lag factor, it only looked like the pollsters missed the mark. Further investigation revealed that following the polling trends through to election day called the election result correctly. A similar scenario played out during the 2015 federal election. The pollsters called the correct result, but they underestimated the margin because of the rapid change in public opinion in final few days.

There is no Trump surge over the final few days of this election.

Sometimes it’s not the pollsters who get it wrong, but the political spinsters. On one side are the wishful thinking voters who insist that they are not behind in the polls, and on the other you have fear-mongers, who say “vote for me, or the bad guy will win.” In Canada, for example, you will have a politician knowing his New Democratic Party (NDP) was well back in third place telling the Green candidate who is in first place to vote NDP to stop the Conservative candidate who is in second place.

Trump supporters might be right in a certain sense in that this election is not like a typical one, and thus the pollsters might not be able to accurately gauge who will vote. I would agree that uncertainty is up this time around, and the result could fall outside of the margin of error, but the polls are more likely over-estimating Trump’s support. It could also be that the undecided voters who are largely Republican leaning come home to roost at the last minute. In the important states like Florida, one third of voters have already cast their ballots in advanced voting, so it’s more likely that a large percentage of these voters have already cast their ballots.

Republicans normally attract richer and more educated folks, but not this election. Unlike previous Republican candidates, Trump’s supporters have less education than his rival’s supporters. This is an important factor because better educated and wealthier voters are more likely to vote. Another factor determining propensity to vote is religiosity, and on that Trump’s moral failings have turned many on the religious right toward Clinton.

Polling firms try to take age and other factors into account when estimating support, but they can’t account for everything. Trump’s ground game of getting voters out the polls by all accounts is lacking. Meanwhile, Clinton has been offering free bus rides from Universities to polling stations among other measures to ensure she gets her supports out to vote. Obama’s support was underestimated because African-Americans showed up at the polls in much higher numbers than they expected. It could be that some Trump supporters will show up in higher numbers than expected, but by all accounts Hispanic voters who overwhelming support Clinton are going to vote in higher numbers than forecast on November 8th.

As of the latest polling data, Clinton has a 2/3 chance of winning against Trump’s 1/3 chance. Trump probably needs to win New Hampshire, which is a tall order. The polls have to be 2 points wrong in his favor there. He also needs to win Nevada, North Carolina, and Florida, where the polls show a dead heat. If Clinton is underestimated by the polls as I suspect, then she will win all three of those states. If I’m wrong (which I doubt since I’ve never been wrong in my entire life), Trump will win all three. If I’m really wrong, he’ll win New Hampshire too, and the presidency to boot.

I’m putting my money on Clinton.

probability

Update: The polls missed Trump’s support in the Mid-West, but still, the shy voter theory is still not the reason.

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