Global warming, climate change, all these things are just a dream come true for politicians. I deal with evidence and not with frightening computer models because the seeker after truth does not put his faith in any consensus. The road to the truth is long and hard, but this is the road we must follow.
By Andy Singer -November 11, 2020
What are the most ridiculous things the media has blamed climate change on? Andy Singer breaks down the thirty funniest claims the media has made yet.
The post Funniest Things Blamed on Climate Change appeared first on Climate Realism.
via Climate Realism
How much have we lost in the last month?
Until a week ago there was something slightly sacred about post marks– it was a legal document, a historical record of the day. Something we could rely on. In a small way, USPS represents The United States: all decent nations have decent postage systems. All crooked banana republics, don’t.
Then we found out some US Postal Workers were told to backdate stamps. It may not seem like much compared to losing the right to free and fair elections. But on any normal day, this is a big deal.
Everyone understands what a backdated post mark means.
Project Veritas has been investigating whistleblower reports.
In Pennsylvania Richard Hopkins, a postal worker, says that he heard one worker getting in trouble for accidentally datestamping one ballot envelope with the correct date — Nov 4 — when it should have been Nov 3. They has postdated all the other ballots picked up that day for Nov 3rd, thus laundering illegal votes and ensuring they would be counted.
He said “I’m nervous. I am nervous because this is a big deal.” But he is willing to testify before Congress.
Hopkins did the original interview anonymously, but at work he was implicated as the one who had spoken up. They called him in to “bring up old problems” and ask questions. Then they told him to he was placed on unpaid leave. And now he’s been hauled into the U.S. Postal Service Office of Inspector General, quizzed and coerced without a lawyer, until he signed an affadavit that was so weak, the Washington Post generated a Fake News headline saying he had recanted.
Richard Hopkins says he did no such thing. And since he was wearing a wire, he has a record to back him up.
““I am right at this very moment looking at an article written by the Washington Post. It says that I fabricated the allegations of ballot tampering,” Mr. Hopkins said in a video posted on Twitter. “I am here to say that I did not recant my statements. That did not happen. That is not what happened.””
A fundraiser was set up for Richard Hopkins at GoFundMe, because he’s done the right thing and is being punished for it. But for no good reason GoFundMe shut down the account and issued refunds. Undeterred he has started a new fundraiser on GiveSendGo.
Hopkins deserves to be supported by every patriotic American.
The liars and the corrupt cannot win this battle. The truth must prevail.
A second post worker has also come forward. The postmaster told workers to keep ballots collected on Nov 4th separate from the other mail.
There are others in Pennsylvania, Michigan:
O’Keefe said, “The new postal whistleblower’s testimony to us further confirms that post offices in different parts of the country are systematically sorting off late-ballots, so they would be eligible for counting.”
The Erie-based postal worker’s account lines up with the testimony of a postal worker from Traverse City, Michigan.
The Michigan USPS whistleblower said Wednesday there was a process set up for the post office workers involved in the bogus postmark scheme.
Presumably those who did backdate stamps don’t want to risk coming forward. It is illegal.
Think about the ugly choice all these workers had — to keep their jobs they had to break the law. Now some of them must be worried they might get caught.Rating: 10.0/10 (8 votes cast)
US Post workers told to backdate Stamps, then stood down, interrogated, 10.0 out of 10 based on 8 ratings
via JoNova
November 11, 2020 at 12:57PM
https://www.windy.com/-New-snow-snowAccu?snowAccu,65.339,-14.985,6,m:fssafIq
Thanks to Duncan for this link
“Keep up the great work!” says Duncan. “I’m a frequent reader and trying to keep the local academics honest on their flawed sea level rise projections, plan on filing a complaint with the NH PE board as the state now requires designing for their sea level rise amounts that already exceed observations (FEMA 1% flood analysis requires a PE stamp)”
The post Three feet of new snow headed for Iceland appeared first on Ice Age Now.
via Ice Age Now
November 11, 2020 at 12:10PM
By Paul Homewood
Global renewable electricity installation will hit a record level in 2020, according to the International Energy Agency, in sharp contrast with the declines caused by the coronavirus pandemic in the fossil fuel sectors.
The IEA report published on Tuesday says almost 90% of new electricity generation in 2020 will be renewable, with just 10% powered by gas and coal. The trend puts green electricity on track to become the largest power source in 2025, displacing coal, which has dominated for the past 50 years.
“Renewable power is defying the difficulties caused by the pandemic, showing robust growth while others fuels struggle,” said Fatih Birol, the IEA’s executive director. “The resilience and positive prospects of the sector are clearly reflected by continued strong appetite from investors.” Fossil fuels have had a turbulent time in 2020 as Covid-related measures caused demand from transport and other sectors to plunge.
“In 2025, renewables are set to become the largest source of electricity generation worldwide, ending coal’s five decades as the top power provider,” Birol said. “By that time, renewables are expected to supply one-third of the world’s electricity.”
https://www.theguardian.com/environment/2020/nov/10/renewable-energy-covid-19-record-growth-2020
Fatih Birol long ago became a shill for renewables, and consequently lost any trust. And, as you might have guessed, renewable energy is not about to take over the world, as Birol would like you to believe.
For a start, he sneakily includes hydro in with the other renewables, even though it is traditional to keep it separate. In essence, any further increases in hydro capacity will be extremely limited, so it is only wind and solar that are relevant. He also includes biomass, which is unlikely to increase much more.
Including hydro and bio, renewables already account for 26%, so to increase this share to a third by 2030 is no great deal. However, wind and solar only account for 8%.
BP Energy Review 2019
The Guardian also misleadingly claim that almost 90% of new electricity generation in 2020 will be renewable. This is a pretty meaningless statement, as very little new conventional generating capacity is being built in Europe and N America.
The simple fact is that new solar and wind capacity cannot even keep up with increasing demand:
BP Energy Review 2019
Currently about 60 GW of wind and 100 GW of solar power is being added each year globally. This is enough to produce about 200 TWh annually. Yet demand increased by more than 500 TWh a year since 2010.
The IEA report reckons that wind and solar will add 187 GW a year up to 2025, so little will actually change, despite the propaganda from Birol.
As the article points out, electricity only accounts for about a fifth of total energy. I(f attempts are made to electrify cars and heating, this extra demand cannot be met from renewable energy, and will have to be supplied from fossil fuels.
In short, the world will be as dependent on fossil fuels in 2030 as it is now.
via NOT A LOT OF PEOPLE KNOW THAT
November 11, 2020 at 12:03PM
November 9th, 2020 by Roy W. Spencer, Ph. D.
You might have seen reports in the last several days regarding evidence of fraud in ballot totals reported in the presidential election. There is a statistical relationship known as “Benford’s Law” which states that for many real-world distributions of numbers, the frequency distribution of the first digit of those numbers follows a regular pattern. It has been used by the IRS and financial institutions to detect fraud.
It should be emphasized that such statistical analysis cannot prove fraud. But given careful analysis including the probability of getting results substantially different from what is theoretically-expected, I think it is a useful tool. Its utility is especially increased if there is little or no evidence of fraud for one candidate, but strong evidence of fraud from another candidate, across multiple cities or multiple states.
From Wikipedia:
“Benford’s law, also called the Newcomb-Benford law, the law of anomalous numbers, or the first-digit law, is an observation about the frequency distribution of leading digits in many real-life sets of numerical data. The law states that in many naturally occurring collections of numbers, the leading digit is likely to be small. For example, in sets that obey the law, the number 1 appears as the leading significant digit about 30% of the time, while 9 appears as the leading significant digit less than 5% of the time. If the digits were distributed uniformly, they would each occur about 11.1% of the time. Benford’s law also makes predictions about the distribution of second digits, third digits, digit combinations, and so on.”
For example, here’s one widely circulating plot (from Github) of results from Milwaukee’s precincts, showing the Benford-type plots for Trump versus Biden vote totals.
Fig. 1. Benford-type analysis of Milwaukee precinct voting data, showing a large departure of the voting data (blue bars) from the expected relationship (red line) for Biden votes, but agreement for the Trump votes. This is for 475 voting precincts. (This is not my analysis, and I do not have access to the underlying data to check it).
The departure from statistical expectations in the Biden vote counts is what is expected when some semi-arbitrary numbers, presumably small enough to not be easily noticed, are added to some of the precinct totals. (I verified this with simulations using 100,000 random but log-normally distributed numbers, where I then added 1,2,3, etc. votes to individual precinct totals). The frequency of low digit values are reduced, while the frequency of the higher digit values are raised.
Since I like the analysis of large amounts of data, I thought I would look into this issue with some voting data. Unfortunately, I cannot find any precinct-level data for the general election. So, I instead looked at some 2020 presidential primary data, since those are posted at state government websites. So far I have only looked at the data from Philadelphia, which has a LOT (6,812) of precincts (actually, “wards” and “divisions” within those wards). I did not follow the primary election results from Philadelphia, and I have no preconceived notions of what the results might look like; these were just the first data I found on the web.
Results for the Presidential Primary in Philadelphia
I analyzed the results for 4 candidates with the most primary votes in Philadelphia: Biden, Sanders, Trump, and Gabbard (data available here).
Benford’s Law only applies well to data that that covers at least 2-3 orders of magnitude (say, from 0 to in the hundreds or thousands). In the case of a candidate who received very few votes, an adjustment to Benford’s relationship is needed.
The most logical way to do this (for me) was to generate a synthetic set of 100,000 random, but log-normally distributed numbers ranging from zero and up, but adjusted until the mean and standard deviation of the data matched the voting data for each candidate separately. (The importance of using a log-normal distribution was suggested to me by a statistician, Mathew Crawford, who works in this area). Then, you can do the Benford analysis (frequency of the 1st digits of those numbers) to see what is theoretically-expected, and then compare to the actual voting data.
Donald Trump Results
First, let’s look at the analysis for Donald Trump during the 2020 presidential primary in Philadelphia (Fig. 2). Note that the Trump votes agree very well with the theoretically-expected frequencies (purple line). The classical Benford Law values (green line) are quite different because the range of votes for Trump only went up to 124 votes, with an average of only 3.1 votes for Trump per precinct.
So, in the case of Donald Trump primary votes in Philadelphia, the results are extremely close to what is expected for log-normally distributed vote totals.
Fig. 2. Benford-type analysis of the number of Trump votes across 6,812 Philadelphia precincts. The classical Benford Law expected distribution of the 1st digits in the vote total is in green. The adjusted Benford Law results based upon 100,000 random but log-normally distributed vote values having the same mean and standard deviation as the vote data in in purple. The actual results from the vote data are in black.
Tulsi Gabbard Results
Next, let’s look at what happens when even fewer votes are cast for a candidate, in this case Tulsi Gabbard (Fig. 3). In this case the number of votes was so small that I could not even get the synthetic log-normal distribution to match the observed precinct mean (0.65 votes) and standard deviation (1.29 votes). So, I do not have high confidence that the purple line is a good expectation of the Gabbard results. (This, of course, will not be a problem with major candidates).
Fig. 3. As in Fig. 2, but for Tulsi Gabbard.
Joe Biden Results
The results for Joe Biden in the Philadelphia primary vote show some evidence for a departure of the reported votes (black line) from theory (purple line) in the direction of inflated votes, but I would need to launch into an analysis of the confidence limits; it could be the observed departure is within what is expected given random variations in this number of data (N=6,812).
Fig. 4. As in Fig. 2, but for Joe Biden.
Bernie Sanders Results
The most interesting results are for Bernie Sanders (Fig. 5.), where we see the largest departure of the voting data (black line) from theoretical expectations (purple line). But instead of reduced frequency of low digits, and increased frequency of higher digits, we see just the opposite.
Is this evidence of fraud in the form of votes subtracted from Sanders’ totals? I don’t know… I’m just presenting the results.
Fig. 5. As in Fig 2, but for Bernie Sanders.
Conclusions
It appears that a Benford’s Law- type of analysis would be useful for finding evidence of fraudulently inflated (or maybe reduced?) voter totals. Careful confidence level calculations would need to be performed, however, so one could say whether the departures from what is theoretically expected are larger than, say, 95% or 99% of what would be expected from just random variations in the reported totals.
I must emphasize that my conclusions are based upon analysis of these data over only a single weekend. There are people who do this stuff for a living. I’d be glad to be corrected on any points I have made. Part of my reason for this post is to introduce people to what is involved in these calculations, after understanding it myself, since it is now part of the public debate over the 2020 presidential election results.
[CR note here is the actual title of Dr Spencer’s article. I modified it to reduce social media censorship.]
