iowaclimatescienceeducation--2020-04-20--Hornsdale Power Reserve: huffing and puffing old men coming to the aid of their colleague

Now it was established in previous two posts that the Hornsdale Power Reserve did surprisingly little to avert a frequency drop caused by a 560 MW capacity loss ( contrary to what was suggested in the RenewEconomy article ) , the focus of this post will be on how the message was brought .
Knowing how little the battery actually did , then how on earth could Giles Parkinson paint it as if something extraordinary had happened ?
This post will explain how this is done .
There are several elements of the story in this title .
First , @ @ @ @ @ @ @ involved in the story : a Tesla battery ( the Hornsdale Power Reserve ) , coal units and the Loy Yang unit ( this is a also a coal fired power unit in Victoria ) .
Then there is the tripping event of the Loy Yang unit that led to the Hornsdale battery “ outsmarting ” coal units .
It is interesting how two of the players are characterized .
The battery is described as “ big ” and the coal units as “ lumbering ” .
I didn ’ t encounter the @ @ @ @ @ @ @ before , it seems that “ lumbering ” is also used to describe moving in a slow , awkward way or with a rumbling sound .
Now it makes more sense .
On the one hand there is the “ big ” battery ( suggesting powerful ) and on the other side there are those “ lumbering ” coal units ( suggesting not very stable , maybe even having difficulties keeping up ) .
The first paragraph builds on this characterization : This confirms the “ big ” from the title .
The @ @ @ @ @ @ @ as in already ) , even reaching far beyond the state border .
The statement that one of the battery ’ s task is to be “ emergency back-up in case of a major problem ” follows the same line .
We also get the information that the big battery is underrated .
The expectation was that it would only have local impact , yet it is suggested that it could handle an event that happened far beyond the South Australia grid .
The next paragraph contains some factual information about the tripping @ @ @ @ @ @ @ the Loy Yang A3 unit that tripped , also when it happened ( 1:59 am ) and how much the capacity loss was ( 560 MW ) .
Then the story develops further : The Hornsdale battery surprised the insiders because of its response to an event happening 1,000 km away , confirming the “ big impact far beyond the state border ” statement from the first paragraph .
This is how it is described what the battery did : This paragraph praises the speed with which the battery responded ( before the @ @ @ @ @ @ @ also get to know that the battery injected 7.3 MW of its 100 MW capacity into the grid and , according to the author , this was enough to help arrest the frequency drop .
Then follow some informative paragraphs that were the subject of the first post on this RenewEconomy article , among which this paragraph : This is why the battery “ outsmarts ” the lumbering coal units .
The Gladstone generator is a coal unit that is contracted to help in the case a major problem arises , like a @ @ @ @ @ @ @ faster to responded than the Gladstone generator and that 7.3 MW didn ’ t just help stopping the frequency drop as we seen earlier , the frequency drop was already reversed before the Gladstone had the chance to step in… Then that “ lumbering ” Gladstone generator finally stepped in : By that time the battery already made the difference and retreated , letting the Gladstone finish the job that it started .
It also became clear that it was not the Hornsdale battery that was contracted to respond to such events .
@ @ @ @ @ @ @ : Looking at it that way , it seems logical that readers would come to the conclusion that the response of the battery was exceptional , maybe even crucial in the recovery process of the event that happened 1,000 km away .
• It is underrated : it was expected that it only would have a local impact , it however has a big impact , even over the state borders • It is capable of providing emergency back-up in case of a major problem • There was such a major event ( @ @ @ @ @ @ @ away ) and its response stunned the insiders • It “ outsmarted ” the “ lumbering ” coal units and initially took over the task that was in fact contracted to the Gladstone coal generator .
That is very impressive , but is it also true ?
Looking at the numbers and what actually happened , not in the slightest : • Although its capacity of 100 MW/129 MWh is big for a battery , it is small potatoes in a grid the size of South Australia • A battery with that capacity @ @ @ @ @ @ @ grid , it would come woefully short in case of a “ major problem ” , as the tripping event showed • The insiders were surprised about its speedy response , not the amount of power discharged • The battery may have responded insanely quick ( when it got the signal that the frequency dropped below the 49.8 Hz threshold ) , but its response came just seconds before the capacity of the Loy Yang A3 unit would have reached zero anyway • During the time that it would have made the difference @ @ @ @ @ @ @ 7.3 MW , that is an incredibly tiny capacity being used to respond to a frequency drop caused by the unexpected capacity loss of 560 MW in less than 30 seconds… No meaningful contribution is expected when seeing those numbers .
That should also be clear from the graph shown in previous post : The response of that battery ( blue ) was downright pathetic when it comes to the scale of that capacity loss ( brown ) .
There is no way that discharging at this capacity would have made a significant @ @ @ @ @ @ @ drop caused by this capacity loss .
But then , if the numbers tell a different story , why is Giles Parkinson so convinced that the Hornsdale battery made the difference ?
His reasoning became clear in the comment section below the article .
There he responded to a comment of a reader who ( rightfully ) stated that this 7.3 MW would have no measurable effect on the frequency , that it basically is a rounding error and as effective as “ a gnat peeing on a fire ” ( I wish @ @ @ @ @ @ @ and this is his revealing answer : Now it becomes clear .
The frequency drop was indeed arrested and reversed in the time frame after the Hornsdale battery discharged its ( underwhelming ) load to the grid and Giles Parkinson limited the necessary response to the Gladstone coal generator .
Meaning , if is the Gladstone generator that was supposed to respond to the frequency drop and the Hornsdale battery responded 4 seconds earlier , then that frequency uptick can easily be used as proof that the battery had a significant contribution to @ @ @ @ @ @ @ drop… My objection in the first post I wrote about this article is that the time frame of the uptick was also the time frame that the Gladstone coal generator stepped in .
I was also not convinced that only the Gladstone generator would be triggered by such a frequency drop .
In the meanwhile I found an article of the Australian Energy Council that made an analysis of the December 14 event , similar to what I did in previous two posts ( including the exercise of giving the secondary y-axis the @ @ @ @ @ @ @ .
It also had a graphic that confirmed that the Gladstone generator was not the only generator that was triggered to come to the aid : There were 16 ( ! )
generators other than the Gladstone generator responding to this event .
The Gladstone generator response became prominent after the frequency drop was halted , but the other generators were already filling in the gap at that moment .
The meager contribution of the Hornsdale battery would very likely made not much of a difference .
It would certainly have “ helped @ @ @ @ @ @ @ but in the same way as the proverbial gnat would reduce the temperature of the fire by peeing on it… Correlation is not necessarily causation .
It is not because the frequency reversed after the Hornsdale battery injected its ( underwhelming ) load to the grid , that it was the one that actually arrested and reversed the frequency drop .
A lot of other power generators worked in unison here .
The author surely told a compelling story that readers could relate to .
The story was widely shared uncritically and it @ @ @ @ @ @ @ two years later ) , as evidence that the Hornsdale battery prevented a potential blackout by tackling a problem that occurred in a neighboring state .
Giles Parkinson is certainly a gifted story teller .
He told the story of two characters in a Western setting .
One probably young and vital , the other old and lumbering .
Then an emergency happened and the young man responded immediately .
Before the old guy got out of bed , the young guy had already averted the emergency and then he left it to @ @ @ @ @ @ @ , putting its gun in its holster and wandering away into the bar for a glass of milk… That is surely a captivating story , but it is not exactly in line with what happened in reality .
I can tell a story too , so just let me try to correct this so it fits reality better .
An old man trips and falls to the ground .
A young boy noticed the fall , runs quickly to the man , but nevertheless arrives mere seconds before the old man would hit @ @ @ @ @ @ @ be much of avail , the young boy wouldn ’ t have the strength to to prevent the man from falling anyway .
Some other old men saw their colleague falling and came huffing and puffing to his aid .
They managed to catch him before he hit the ground and while they were busy getting the fallen old man back upright , the young boy went already home to his mammy for a glass of milk , hoping to do better when he grows older…

Now it was established in previous two posts that the Hornsdale Power Reserve did surprisingly little to avert a frequency drop caused by a 560 MW capacity loss (contrary to what was suggested in the RenewEconomy article), the focus of this post will be on how the message was brought. Knowing how little the battery actually did, then how on earth could Giles Parkinson paint it as if something extraordinary had happened? This post will explain how this is done.
Let’s start with the title:
Tesla big battery outsmarts lumbering coal units after Loy Yang trips
There are several elements of the story in this title. First, there are the three players that are involved in the story: a Tesla battery (the Hornsdale Power Reserve), coal units and the Loy Yang unit (this is a also a coal fired power unit in Victoria). Then there is the tripping event of the Loy Yang unit that led to the Hornsdale battery “outsmarting” coal units.
It is interesting how two of the players are characterized. The battery is described as “big” and the coal units as “lumbering”. I didn’t encounter the word “lumbering” in such context before, it seems that “lumbering” is also used to describe moving in a slow, awkward way or with a rumbling sound.
Now it makes more sense. On the one hand there is the “big” battery (suggesting powerful) and on the other side there are those “lumbering” coal units (suggesting not very stable, maybe even having difficulties keeping up).
The first paragraph builds on this characterization (my emphasis) :
The Tesla big battery is having a big impact on Australia’s electricity market, far beyond the South Australia grid where it was expected to time shift a small amount of wind energy and provide network services and emergency back-up in case of a major problem.
This confirms the “big” from the title. The battery is having a big impact (as in already), even reaching far beyond the state border. The statement that one of the battery’s task is to be “emergency back-up in case of a major problem” follows the same line.
We also get the information that the big battery is underrated. The expectation was that it would only have local impact, yet it is suggested that it could handle an event that happened far beyond the South Australia grid.
The next paragraph contains some factual information about the tripping event. It specifies that it was the Loy Yang A3 unit that tripped, also when it happened (1:59 am) and how much the capacity loss was (560 MW).
Then the story develops further (my emphasis) :
What happened next has stunned electricity industry insiders and given food for thought over the near to medium term future of the grid, such was the rapid response of the Tesla big battery to an event that happened nearly 1,000km away.
The Hornsdale battery surprised the insiders because of its response to an event happening 1,000 km away, confirming the “big impact far beyond the state border” statement from the first paragraph.
This is how it is described what the battery did (my emphasis) :
Even before the Loy Yang A unit had finished tripping, the 100MW/129MWh had responded, injecting 7.3MW into the network to help arrest a slump in frequency that had fallen below 49.80Hertz.
This paragraph praises the speed with which the battery responded (before the tripping event was finished). We also get to know that the battery injected 7.3 MW of its 100 MW capacity into the grid and, according to the author, this was enough to help arrest the frequency drop.
Then follow some informative paragraphs that were the subject of the first post on this RenewEconomy article, among which this paragraph (my emphasis) :
Importantly, by the time that the contracted Gladstone coal unit had gotten out of bed and put its socks on so it can inject more into the grid – it is paid to respond in six seconds – the fall in frequency had already been arrested and was being reversed.
This is why the battery “outsmarts” the lumbering coal units. The Gladstone generator is a coal unit that is contracted to help in the case a major problem arises, like a tripping coal unit.
The battery was faster to responded than the Gladstone generator and that 7.3 MW didn’t just help stopping the frequency drop as we seen earlier, the frequency drop was already reversed before the Gladstone had the chance to step in…
Then that “lumbering” Gladstone generator finally stepped in (my emphasis) :
Gladstone injected more than Tesla did back into the grid, and took the frequency back up to its normal levels of 50Hz, but by then Tesla had already put its gun back in its holster and had wandered into the bar for a glass of milk.
By that time the battery already made the difference and retreated, letting the Gladstone finish the job that it started.
It also became clear that it was not the Hornsdale battery that was contracted to respond to such events. Then why did the battery respond anyway (my emphasis) :
So why did the Tesla big battery respond when not contracted? One reason is because it can, and so it did.
Looking at it that way, it seems logical that readers would come to the conclusion that the response of the battery was exceptional, maybe even crucial in the recovery process of the event that happened 1,000 km away.
A quick summary:
The Hornsdale Power Reserve is a big battery
It is underrated: it was expected that it only would have a local impact, it however has a big impact, even over the state borders
It is capable of providing emergency back-up in case of a major problem
There was such a major event (a tripping coal unit, 1000 km away) and its response stunned the insiders
It “outsmarted” the “lumbering” coal units and initially took over the task that was in fact contracted to the Gladstone coal generator.
That is very impressive, but is it also true?
Looking at the numbers and what actually happened, not in the slightest:
Although its capacity of 100 MW/129 MWh is big for a battery, it is small potatoes in a grid the size of South Australia
A battery with that capacity can only handle small fluctuations on the grid, it would come woefully short in case of a “major problem”, as the tripping event showed
The insiders were surprised about its speedy response, not the amount of power discharged
The battery may have responded insanely quick (when it got the signal that the frequency dropped below the 49.8 Hz threshold), but its response came just seconds before the capacity of the Loy Yang A3 unit would have reached zero anyway
During the time that it would have made the difference, it injected at a capacity of 7.3 MW, that is an incredibly tiny capacity being used to respond to a frequency drop caused by the unexpected capacity loss of 560 MW in less than 30 seconds…
No meaningful contribution is expected when seeing those numbers. That should also be clear from the graph shown in previous post:
The response of that battery (blue) was downright pathetic when it comes to the scale of that capacity loss (brown). There is no way that discharging at this capacity would have made a significant contribution in arresting and reversing the frequency drop caused by this capacity loss.
But then, if the numbers tell a different story, why is Giles Parkinson so convinced that the Hornsdale battery made the difference? His reasoning became clear in the comment section below the article. There he responded to a comment of a reader who (rightfully) stated that this 7.3 MW would have no measurable effect on the frequency, that it basically is a rounding error and as effective as “a gnat peeing on a fire” (I wish I came up with that expression) and this is his revealing answer (my emphasis) :
You may wish to think that, but the AEMO data is unequivocal. The frequency drop hit its nadir after Tesla response and before Gladstone got out of bed.
Now it becomes clear. The frequency drop was indeed arrested and reversed in the time frame after the Hornsdale battery discharged its (underwhelming) load to the grid and Giles Parkinson limited the necessary response to the Gladstone coal generator. Meaning, if is the Gladstone generator that was supposed to respond to the frequency drop and the Hornsdale battery responded 4 seconds earlier, then that frequency uptick can easily be used as proof that the battery had a significant contribution to the stopping and the reversing of that drop…
My objection in the first post I wrote about this article is that the time frame of the uptick was also the time frame that the Gladstone coal generator stepped in. I was also not convinced that only the Gladstone generator would be triggered by such a frequency drop.
In the meanwhile I found an article of the Australian Energy Council that made an analysis of the December 14 event, similar to what I did in previous two posts (including the exercise of giving the secondary y-axis the same scale as the primary y-axis). It also had a graphic that confirmed that the Gladstone generator was not the only generator that was triggered to come to the aid:
There were 16 (!) generators other than the Gladstone generator responding to this event. The Gladstone generator response became prominent after the frequency drop was halted, but the other generators were already filling in the gap at that moment. The meager contribution of the Hornsdale battery would very likely made not much of a difference. It would certainly have “helped” arresting and reversing the drop, but in the same way as the proverbial gnat would reduce the temperature of the fire by peeing on it…
Correlation is not necessarily causation. It is not because the frequency reversed after the Hornsdale battery injected its (underwhelming) load to the grid, that it was the one that actually arrested and reversed the frequency drop. A lot of other power generators worked in unison here.
The author surely told a compelling story that readers could relate to. The story was widely shared uncritically and it is used, until this day (two years later), as evidence that the Hornsdale battery prevented a potential blackout by tackling a problem that occurred in a neighboring state.
Giles Parkinson is certainly a gifted story teller. He told the story of two characters in a Western setting. One probably young and vital, the other old and lumbering. Then an emergency happened and the young man responded immediately. Before the old guy got out of bed, the young guy had already averted the emergency and then he left it to the old guy to finish the job, putting its gun in its holster and wandering away into the bar for a glass of milk…
That is surely a captivating story, but it is not exactly in line with what happened in reality.
I can tell a story too, so just let me try to correct this so it fits reality better.
An old man trips and falls to the ground. A young boy noticed the fall, runs quickly to the man, but nevertheless arrives mere seconds before the old man would hit the ground. Not that it would be much of avail, the young boy wouldn’t have the strength to to prevent the man from falling anyway. Some other old men saw their colleague falling and came huffing and puffing to his aid. They managed to catch him before he hit the ground and while they were busy getting the fallen old man back upright, the young boy went already home to his mammy for a glass of milk, hoping to do better when he grows older…
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April 19, 2020 at 06:57PM


iowaclimatescienceeducation--2020-04-19--I have had ENOUGH

I have had ENOUGH

Not about to give up all I have worked for because liberals want to overact .
I have had ENOUGH What happens when you have more cars on the road ?
More accidents .
( In response to Experts shocked by the rise in non-covid-19 deaths . )
How do so many of us avoid accidents ?
Defensive driving , obeying traffic laws and a strange thing called Common Courtesy .
Here are our options .
Continue the lockdown and lose everything we have worked for , or open the economy and take precautions .
This virus is the same @ @ @ @ @ @ @ life .
Many of us could meet our demise through the common flu .
We take precautions we do our best .
How many here want to lose that 401K , that house , that savings account that you have worked to build for years ?
Not me .
I ’ m a 66-year-old male .
I didn ’ t get this old indulging in risky behavior .
Not about to give up all I have worked for because liberals want to overact .
I have had ENOUGH .
The post I have @ @ @ @ @ @ @ Now .

Not about to give up all I have worked for because liberals want to overact.
I have had ENOUGH
Adoni
What happens when you have more cars on the road? More accidents. (In response to Experts shocked by the rise in non-covid-19 deaths.)
How do so many of us avoid accidents? Defensive driving, obeying traffic laws and a strange thing called Common Courtesy.
Here are our options. Continue the lockdown and lose everything we have worked for, or open the economy and take precautions. This virus is the same as many of our diseases that consume life. Many of us could meet our demise through the common flu. We take precautions we do our best.
How many here want to lose that 401K, that house, that savings account that you have worked to build for years? Not me. I’m a 66-year-old male. I didn’t get this old indulging in risky behavior. Not about to give up all I have worked for because liberals want to overact.
I have had ENOUGH.
The post I have had ENOUGH appeared first on Ice Age Now.
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April 19, 2020 at 06:36PM


iowaclimatescienceeducation--2020-04-19--Guardian: Covid-19 is President Trump’s Fault for Failing to Protect Endangered Species in China

h/t Nick ; Orange man bad , even when the problems being discussed happen in another country outside the USA ’ s sphere of influence .
Wildlife conservation efforts are essential to preventing outbreaks , scientists and advocates say For more than five years , wildlife conservationists in the US have been clamoring for the government to provide Endangered Species Act protections to pangolins , a group of imperiled ant-eating mammals that are widely , and often illicitly , trafficked for their scales and meat .
The Trump administration , however , has refused to act and that refusal has @ @ @ @ @ @ @ Earlier this year , scientists in China identified pangolins , along with bats , as one of the possible animal hosts involved in the transmission of the deadly coronavirus from wildlife to humans .
Although there is still much uncertainty about the nature of the disease ’ s emergence , the unwillingness of the Trump administration , and the Obama administration before it , to provide legal protections to pangolins , and other species , has intensified scrutiny of America ’ s faltering role in international wildlife conservation efforts .
Scientists and advocates @ @ @ @ @ @ @ kind of pandemic currently sweeping the globe .
Though no pangolins live in the US , an endangered species designation could make additional funding available to preserve the species , bolster efforts to crack down on illegal trade and send a powerful signal to the international community that the animals ought to be protected .
“ It is not like any one thing would have stopped this pandemic , so you can ’ t make that generalization , ” said Brett Hartl , the government affairs director at the Center for Biological Diversity @ @ @ @ @ @ @ actually a leader in international wildlife conservation like it used to be back in the day and actually took its obligations seriously and was putting real pressure on trafficking and countries like China to stop decimation of pangolins and other species , who knows where we would be today .
”

Guest essay by Eric Worrall
h/t Nick; Orange man bad, even when the problems being discussed happen in another country outside the USA’s sphere of influence.
Pandemic shines harsh light on Trump’s failure to protect pangolins
Jimmy Tobias
Wed 15 Apr 2020 20.00 AESTLast modified on Wed 15 Apr 2020 20.02 AEST
Wildlife conservation efforts are essential to preventing outbreaks, scientists and advocates say
For more than five years, wildlife conservationists in the US have been clamoring for the government to provide Endangered Species Act protections to pangolins, a group of imperiled ant-eating mammals that are widely, and often illicitly, trafficked for their scales and meat. The Trump administration, however, has refused to act and that refusal has suddenly taken on grave new implications.
Earlier this year, scientists in China identified pangolins, along with bats, as one of the possible animal hosts involved in the transmission of the deadly coronavirus from wildlife to humans.
Although there is still much uncertainty about the nature of the disease’s emergence, the unwillingness of the Trump administration, and the Obama administration before it, to provide legal protections to pangolins, and other species, has intensified scrutiny of America’s faltering role in international wildlife conservation efforts. Scientists and advocates say these are essential to preventing the kind of pandemic currently sweeping the globe.
Though no pangolins live in the US, an endangered species designation could make additional funding available to preserve the species, bolster efforts to crack down on illegal trade and send a powerful signal to the international community that the animals ought to be protected.
…
“It is not like any one thing would have stopped this pandemic, so you can’t make that generalization,” said Brett Hartl, the government affairs director at the Center for Biological Diversity. “But if the US were actually a leader in international wildlife conservation like it used to be back in the day and actually took its obligations seriously and was putting real pressure on trafficking and countries like China to stop decimation of pangolins and other species, who knows where we would be today.”
…


iowaclimatescienceeducation--2020-04-19--Bad citizen China said Covid-19 was “preventable and curable” while profiteering medical gear

The price for the coverup will be huge Donald Trump crossed the Rubicon by pulling funding from the WHO and asking China to ‘ fess up .
Now no respectable politician wants to be left out in the race to demand answers and the WHO is an open target too .
The UN agency helped China hide the severity of this disease and advised nations not to stop flights with China .
The World Health Organisation failed in the one job they were designed for .
Australia ’ s Foreign Minister will demand a global inquiry into the spread of @ @ @ @ @ @ @ Organisation should play no part in it .
China told her the disease was both ‘ preventable and curable ’ during a conversation in late January .
As diplomatic faux pas go , it doesn ’ t get much worse than bringing a deadly disease to a party and telling everyone “ it ’ s just the flu ” .
But China was scalping the medical gear too .
Not only did China hide the true nature of the virus even as it shared the virus around .
It harvested all the protective @ @ @ @ @ @ @ , then hold the world hostage .
It even took donations from Italy , [ … ]

The price for the coverup will be huge
Donald Trump crossed the Rubicon by pulling funding from the WHO and asking China to ‘fess up. Now no respectable politician wants to be left out in the race to demand answers and the WHO is an open target too. The UN agency helped China hide the severity of this disease and advised nations not to stop flights with China. The World Health Organisation failed in the one job they were designed for.
DailyMail.co.uk
Australia’s Foreign Minister will demand a global inquiry into the spread of COVID-19 – and says the World Health Organisation should play no part in it.
China told her the disease was both ‘preventable and curable’ during a conversation in late January.
As diplomatic faux pas go, it doesn’t get much worse than bringing a deadly disease to a party and telling everyone “it’s just the flu”. But China was scalping the medical gear too. Not only did China hide the true nature of the virus even as it shared the virus around. It harvested all the protective gear it knew the world would need, then hold the world hostage. It even took donations from Italy, […]
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April 19, 2020 at 02:11PM


The Virus Wars

The proverb is “ Generals are always fighting the last war , ” and its origin is uncertain .
One possibility is a quote from Winston Churchill : “ It is a joke in Britain to say that the War Office is always preparing for the last war.
” 1948 Winston S. Churchill _The Second World War_ I ( Boston : Houghton Mifflin , 1985 ) 426 : Konrad Lorenz demonstrated how imprinting works upon animal behavior , while military historians have reported how powerfully human social animals are influenced by the past and instilled lessons from others .
Which @ @ @ @ @ @ @ current WuHanFlu outbreak .
The chart at the top summarizes our received epidemiological wisdom about the danger of viruses according to the dimensions of deadliness and contagiousness .
As the diagram shows , extremely deadly viruses tend to kill their hosts too quickly to be transmitted widely .
Conversely , a virus that spreads easily accomplishes that by not killing its hosts , perhaps even leaving them alive .
The biggest threats are the germs that are lethal , but spread widely because the symptoms are slow to develop ( longer incubation period @ @ @ @ @ @ @ , consider these four ( Source : Big Think .
Excerpts in italics with my bolds ) SARS ( started in Hong Kong in March 2003 ) , Swine flu ( started in Mexico in March 2009 ) , Ebola ( started in Western Africa in March 2014 ) , and MERS ( started in South Korea in May 2015 ) .
The video below explains the last two impactful wars were against SARS and Swine Flu ( HINI ) .
For the sake of comparison , the graphs for each epidemic are @ @ @ @ @ @ @ Day One of each outbreak .
At first , Ebola is the scary one .
Not only had it infected the most people after just one day , it had killed two thirds of those .
By comparison , SARS killed its first victim only after three days ( out of 38 people infected ) .
By Day 10 , SARS had overtaken Ebola as the most infectious of the outbreaks ( 264 vs. 145 patients ) , but the latter was ten times more lethal ( 91 dead from Ebola vs. 9 @ @ @ @ @ @ @ , the coronavirus had infected 39 people , killed none , and was still playing in the same minor league as the swine flu and MERS .
Day 20 , and SARS cases are skyrocketing : 1,550 people are ill , 55 have died .
That ’ s a death rate of 3.5 % .
Ebola has affected only 203 people by now , but killed 61.6 % of them , a total of 125 .
Meanwhile , the coronavirus has taken Ebola ’ s second place , but is still far behind @ @ @ @ @ @ @ this time , the coronavirus has claimed the lives of just five people .
But now the coronavirus cases are exploding ; by Day 30 , the new virus has infected 7,816 people , killing 204 .
That ’ s far more infected than any other virus ( SARS comes a distant second with 2,710 patients ) , and significantly more killed ( Ebola , though still just 242 people ill , has killed 147 , due to its high fatality rate ) .
Meanwhile , MERS is stuck in triple digits , @ @ @ @ @ @ @ .
The swine flu numbers keep growing exponentially : by Day 80 , they ’ ve passed 362,000 cases ( and 1,770 deaths ) , far surpassing any of the other diseases .
Day 100 : swine flu cases are approaching 1 million , deaths have surpassed 5,000 .
That ’ s far more than all the other diseases combined—they have merged into a single line at the bottom of the graph .
By Day 150 , swine flu hit 5.2 million patients , with 25,400 people killed .
By the time it @ @ @ @ @ @ @ , the outbreak would eventually have infected more than 60 million people and claimed the lives of almost 300,000 .
Swine flu was caused by the H1N1 virus , which also caused the Spanish flu .
That outbreak , in 1918/19 , infected about 500 million people , or 1 in 3 people alive at that time .
It killed at least 50 million people .
It was the combination of extreme infectiousness and high fatality that made the Spanish flu such a global , lethal pandemic .
None of the other infectious @ @ @ @ @ @ @ The swine flu , although more infectious than other diseases , was less infectious than the Spanish flu , and also less deadly ( 0.5 % ) .
Unlike COVID-19 or its fellow coronaviruses SARS and MERS , Ebola is not spread via airborne particles , but via contact with infected blood .
That makes it hard to spread .
Ironically , it may also be too lethal ( 39.6 % ) to spread very far .
And COVID-19 itself , while relatively lethal ( 2.4 % ) , is well below the @ @ @ @ @ @ @ does not seem to spread with the same ease .
As that history lesson shows , our pandemic generals have been likely preoccupied with three previous enemies : Spanish Flu , Swine Flu , and SARS .
The first one served as the castastrophic defeat to be avoided , H1N1 as the victory achieved through vaccine , and SARS as the coronavirus prototype .
Naming the Wuhan virus SARS-CoV-2 ( Severe acute respiratory syndrome coronavirus 2 ) predisposed tacticians and soldiers to fight against a viral pneumonia , and to expect air borne @ @ @ @ @ @ @ The battle plan was drawn up to protect the health care system against the deluge of victims coming to hospitals and ICUs .
Flattening the curve of such cases was the strategy and social distancing and personal immobility was imposed to that end .
What has been the effect ?
For that there is an analysis from John Nolte What Terrible Coronavirus Models Tell Us About Global Warming Models H/T Joe D ’ Aleo Excerpts in italics with my bolds .
Let ’ s face it , the coronavirus models are terrible .
@ @ @ @ @ @ @ way , way off in their predictions of a doomsday scenario that never arrived .
That ’ s not to say that over 20,000 dead Americans is not a heartbreaking reality .
That ’ s not even to say that parts of the country should not have been shut down .
But come on… We shut the entire country down using the Institute for Health Metrics and Evaluation ( IHME ) models , and in doing so put 17 million ( and counting ) Americans out of work , shattered 17 million ( @ @ @ @ @ @ @ , take a look for yourself below .
That gigantic hump is the IHME ’ s April 1 prediction of coronavirus hospitalizations .
The smaller humps way , way , waaaay below that are the IMHE ’ s predictions of coronavirus hospitalizations after they were revised just a few days later on April 5 , 7 , and 9 .
The green line is the true number of hospitalizations , starting with the whole U.S. , and into the states .
So why does this matter ?
And why are we looking at @ @ @ @ @ @ @ whole reason for shutting down the economy was to ensure our healthcare system was not overloaded .
And it should be noted that these expert models are based on full mitigation , based on what did indeed happen , which was basically a full shutdown of the economy by way of a lockdown .
And these models are still horribly , terribly wrong .
Even if you believe the correct decision was made , that does not change how wildly wrong the coronavirus models were , are , and will almost certainly continue @ @ @ @ @ @ @ the fact we shut down our entire economy based on incredibly flawed models .
Now I realize that the people who did the terribly flawed coronavirus models are not the same people who do the modeling for global cooling global warming climate change or whatever the hell these proven frauds are calling it today .
But hear me out… We ’ re still talking about “ experts ” our media and government grovel down to without question .
We ’ re still talking about models with the goal of destroying our way of @ @ @ @ @ @ @ of living , and our individual freedoms to live our lives in whatever way we choose We ’ re still talking about models with the goal of handing a tremendously scary amount of authority and power to a centralized government .
The coronavirus modeling was based on something real , on something happening at the time .
The experts doing the coronavirus models had all kinds of data on which to make their assumptions .
Not just reams and reams of scientific data based on previous pandemics , viruses , and human behavior @ @ @ @ @ @ @ coronavirus itself from China , Italy , and other countries… And they still blew it .
They still got it horribly wrong .
What Went Wrong ?
California Provides a Clue As the diagram at the top shows , WuHanFlu looked like an especially dangerous mix of deadly contagion .
Thus California with its large population and extensive contact with China should be the US viral hot spot , and yet it isn ’ t .
Maybe the contagion is real but the effects are milder than imagained .
Victor Davis Hanson writes @ @ @ @ @ @ @ Mysterious — Despite the Weaponization of the Debate .
Excerpts in italics with my bolds .
How Many People Already Have COVID-19 ?
California is touchy , and yet still remains confused , about incomplete data showing that the 40-million-person state , as of Sunday , April 12 , reportedly had 23,777 cases of residents who have tested posted for the COVID-19 illness .
The number of infected by the 12th includes 674 deaths , resulting in a fatality rate of about 17 deaths per million of population .
That is among the @ @ @ @ @ @ @ ( Texas has 10 deaths per million ) , and lower than almost all major European countries , ( about half of Germany ’ s 36 deaths per million ) .
No doubt there are lots of questionable data in all such metrics .
As a large state California has not been especially impressive in a per capita sense in testing its population ( about 200,000 tests so far ) .
Few of course believe that the denominator of cases based on test results represent the real number of those who have been @ @ @ @ @ @ @ now another old debate over exactly how the U.S. defines death by the virus versus death because of the contributing factors of the virus to existing medical issues .
Certainly , the methodology of coronavirus modeling is quite different from that of , say , the flu .
The denominator of flu cases is almost always a modeled approximation , not a misleadingly precise number taken from only those who go to their doctors or emergency rooms and test positive for an influenza strain .
And the numerator of deaths from the flu @ @ @ @ @ @ @ those currently listed as deaths from the coronavirus .
Nonetheless , the state ’ s population is fairly certain .
And for now , the number of deaths by the virus is the least controversial of many of these data , suggesting that deaths per million of population might be a useful comparative number .
As I wrote in a recent NRO piece , the state on the eve of the epidemic seemed especially vulnerable given the large influx of visitors from China on direct flights to its major airports all fall and @ @ @ @ @ @ @ ( and sometime after ) .
It ranks rather low in state comparisons of hospital beds , physicians , and nurses per capita .
It suffers high rates of poverty , wide prevalence of state assistance , and medical challenges such as widespread diabetes .
Certainly , both then and more recently , there have been a number of anecdotal accounts , media stories , and small isolated studies suggesting that more people than once thought , both here and abroad , have been infected with the virus and developed immunity , that @ @ @ @ @ @ @ and the U.S. earlier than once or currently admitted by Chinese researchers — so , inter alia , California in theory could weather the epidemic with much less death and illness than earlier models of an eventual 25.5 million infected had suggested .
Since then , a number of models , including Governor Newsom ’ s projection of 25.5 million infected Californians over an eight-week period , have been questioned .
Controversy exists over exactly why models are being recalibrated downward .
One explanation is that the shelter-in-space orders have been more successful @ @ @ @ @ @ @ flawed modeling assumptions .
Front-line physicians who see sick patients do not necessarily agree with researchers in the lab .
For example , a Los Angeles Times story was widely picked up by other news outlets that quoted Dr. Jeff Smith , the chief executive of Santa Clara County .
Smith reportedly now believes that the virus arrived in California much earlier than often cited , at least in early 2020 : The severity of flu season made health care professionals think that patients were suffering from influenza given the similarity of some @ @ @ @ @ @ @ however , a handful of sick Californians that were going to the doctor earlier this year may have been among the first to be carrying the coronavirus .
“ The virus was freewheeling in our community and probably has been here for quite some time , ” Smith , a physician , told county leaders in a recent briefing .
The failure of authorities to detect the virus earlier has allowed it to spread unchecked in California and across the nation .
“ This wasn ’ t recognized because we were having a @ @ @ @ @ @ @ Symptoms are very much like the flu .
If you got a mild case of COVID , you didn ’ t really notice .
You didn ’ t even go to the doctor .
.
.
.
The doctor maybe didn ’ t even do it because they presumed it was the flu.
” Footnote : See also Good Virus News from the Promised Land

The proverb is “Generals are always fighting the last war,” and its origin is uncertain. One possibility is a quote from Winston Churchill: “It is a joke in Britain to say that the War Office is always preparing for the last war.” 1948 Winston S. Churchill _The Second World War_ I (Boston: Houghton Mifflin, 1985) 426:
Konrad Lorenz demonstrated how imprinting works upon animal behavior, while military historians have reported how powerfully human social animals are influenced by the past and instilled lessons from others.
Which brings me to these reflections about the current WuHanFlu outbreak. The chart at the top summarizes our received epidemiological wisdom about the danger of viruses according to the dimensions of deadliness and contagiousness. As the diagram shows, extremely deadly viruses tend to kill their hosts too quickly to be transmitted widely. Conversely, a virus that spreads easily accomplishes that by not killing its hosts, perhaps even leaving them alive. The biggest threats are the germs that are lethal, but spread widely because the symptoms are slow to develop (longer incubation period).
Regarding the recent virus wars, consider these four (Source: Big Think. Excerpts in italics with my bolds)
SARS (started in Hong Kong in March 2003),
Swine flu (started in Mexico in March 2009),
Ebola (started in Western Africa in March 2014), and
MERS (started in South Korea in May 2015).
The video below explains the last two impactful wars were against SARS and Swine Flu (HINI).
For the sake of comparison, the graphs for each epidemic are aligned so they all start together on Day One of each outbreak.
At first, Ebola is the scary one. Not only had it infected the most people after just one day, it had killed two thirds of those.
By comparison, SARS killed its first victim only after three days (out of 38 people infected).
By Day 10, SARS had overtaken Ebola as the most infectious of the outbreaks (264 vs. 145 patients), but the latter was ten times more lethal (91 dead from Ebola vs. 9 from SARS). At this time, the coronavirus had infected 39 people, killed none, and was still playing in the same minor league as the swine flu and MERS.
Day 20, and SARS cases are skyrocketing: 1,550 people are ill, 55 have died. That’s a death rate of 3.5%. Ebola has affected only 203 people by now, but killed 61.6% of them, a total of 125. Meanwhile, the coronavirus has taken Ebola’s second place, but is still far behind SARS (284 infected). At this time, the coronavirus has claimed the lives of just five people.
But now the coronavirus cases are exploding; by Day 30, the new virus has infected 7,816 people, killing 204. That’s far more infected than any other virus (SARS comes a distant second with 2,710 patients), and significantly more killed (Ebola, though still just 242 people ill, has killed 147, due to its high fatality rate). Meanwhile, MERS is stuck in triple digits, and the swine flu in double digits.
The swine flu numbers keep growing exponentially: by Day 80, they’ve passed 362,000 cases (and 1,770 deaths), far surpassing any of the other diseases.
Day 100: swine flu cases are approaching 1 million, deaths have surpassed 5,000. That’s far more than all the other diseases combined—they have merged into a single line at the bottom of the graph.
By Day 150, swine flu hit 5.2 million patients, with 25,400 people killed. By the time it was declared over, a year later, the outbreak would eventually have infected more than 60 million people and claimed the lives of almost 300,000.
Swine flu was caused by the H1N1 virus, which also caused the Spanish flu. That outbreak, in 1918/19, infected about 500 million people, or 1 in 3 people alive at that time. It killed at least 50 million people. It was the combination of extreme infectiousness and high fatality that made the Spanish flu such a global, lethal pandemic.
None of the other infectious diseases comes close to that combination. The swine flu, although more infectious than other diseases, was less infectious than the Spanish flu, and also less deadly (0.5%). Unlike COVID-19 or its fellow coronaviruses SARS and MERS, Ebola is not spread via airborne particles, but via contact with infected blood. That makes it hard to spread. Ironically, it may also be too lethal (39.6%) to spread very far. And COVID-19 itself, while relatively lethal (2.4%), is well below the deadliness of the Spanish flu, and does not seem to spread with the same ease.
As that history lesson shows, our pandemic generals have been likely preoccupied with three previous enemies: Spanish Flu, Swine Flu, and SARS. The first one served as the castastrophic defeat to be avoided, H1N1 as the victory achieved through vaccine, and SARS as the coronavirus prototype. Naming the Wuhan virus SARS-CoV-2 (Severe acute respiratory syndrome coronavirus 2) predisposed tacticians and soldiers to fight against a viral pneumonia, and to expect air borne transmission as happened with SARS 1.
The battle plan was drawn up to protect the health care system against the deluge of victims coming to hospitals and ICUs. Flattening the curve of such cases was the strategy and social distancing and personal immobility was imposed to that end. What has been the effect? For that there is an analysis from John Nolte What Terrible Coronavirus Models Tell Us About Global Warming Models H/T Joe D’Aleo Excerpts in italics with my bolds.
Let’s face it, the coronavirus models are terrible. Not just off, but way, way, way off in their predictions of a doomsday scenario that never arrived.
That’s not to say that over 20,000 dead Americans is not a heartbreaking reality. That’s not even to say that parts of the country should not have been shut down. But come on…
We shut the entire country down using the Institute for Health Metrics and Evaluation (IHME) models, and in doing so put 17 million (and counting) Americans out of work, shattered 17 million (and counting) lives, and… Well, take a look for yourself below.
That gigantic hump is the IHME’s April 1 prediction of coronavirus hospitalizations. The smaller humps way, way, waaaay below that are the IMHE’s predictions of coronavirus hospitalizations after they were revised just a few days later on April 5, 7, and 9.
The green line is the true number of hospitalizations, starting with the whole U.S., and into the states.
So why does this matter? And why are we looking at hospitalizations?
Well, remember, the whole reason for shutting down the economy was to ensure our healthcare system was not overloaded. And it should be noted that these expert models are based on full mitigation, based on what did indeed happen, which was basically a full shutdown of the economy by way of a lockdown. And these models are still horribly, terribly wrong.
Even if you believe the correct decision was made, that does not change how wildly wrong the coronavirus models were, are, and will almost certainly continue to be. That does not change the fact we shut down our entire economy based on incredibly flawed models.
Now I realize that the people who did the terribly flawed coronavirus models are not the same people who do the modeling for global cooling global warming climate change or whatever the hell these proven frauds are calling it today. But hear me out…
We’re still talking about “experts” our media and government grovel down to without question.
We’re still talking about models with the goal of destroying our way of life, our prosperity, our standard of living, and our individual freedoms to live our lives in whatever way we choose
We’re still talking about models with the goal of handing a tremendously scary amount of authority and power to a centralized government.
The coronavirus modeling was based on something real, on something happening at the time. The experts doing the coronavirus models had all kinds of data on which to make their assumptions. Not just reams and reams of scientific data based on previous pandemics, viruses, and human behavior; but also real-time data on the coronavirus itself from China, Italy, and other countries… And they still blew it. They still got it horribly wrong.
What Went Wrong? California Provides a Clue
As the diagram at the top shows, WuHanFlu looked like an especially dangerous mix of deadly contagion. Thus California with its large population and extensive contact with China should be the US viral hot spot, and yet it isn’t. Maybe the contagion is real but the effects are milder than imagained.
Victor Davis Hanson writes at National Review Yes, California Remains Mysterious — Despite the Weaponization of the Debate. Excerpts in italics with my bolds.
How Many People Already Have COVID-19?
California is touchy, and yet still remains confused, about incomplete data showing that the 40-million-person state, as of Sunday, April 12, reportedly had 23,777 cases of residents who have tested posted for the COVID-19 illness. The number of infected by the 12th includes 674 deaths, resulting in a fatality rate of about 17 deaths per million of population. That is among the lowest rates of the larger American states (Texas has 10 deaths per million), and lower than almost all major European countries, (about half of Germany’s 36 deaths per million).
No doubt there are lots of questionable data in all such metrics. As a large state California has not been especially impressive in a per capita sense in testing its population (about 200,000 tests so far). Few of course believe that the denominator of cases based on test results represent the real number of those who have been or are infected.
There is the now another old debate over exactly how the U.S. defines death by the virus versus death because of the contributing factors of the virus to existing medical issues. Certainly, the methodology of coronavirus modeling is quite different from that of, say, the flu. The denominator of flu cases is almost always a modeled approximation, not a misleadingly precise number taken from only those who go to their doctors or emergency rooms and test positive for an influenza strain. And the numerator of deaths from the flu may be calibrated somewhat more conservatively than those currently listed as deaths from the coronavirus.
Nonetheless, the state’s population is fairly certain. And for now, the number of deaths by the virus is the least controversial of many of these data, suggesting that deaths per million of population might be a useful comparative number.
As I wrote in a recent NRO piece, the state on the eve of the epidemic seemed especially vulnerable given the large influx of visitors from China on direct flights to its major airports all fall and early winter until the January 31 ban (and sometime after). It ranks rather low in state comparisons of hospital beds, physicians, and nurses per capita. It suffers high rates of poverty, wide prevalence of state assistance, and medical challenges such as widespread diabetes.
Certainly, both then and more recently, there have been a number of anecdotal accounts, media stories, and small isolated studies suggesting that more people than once thought, both here and abroad, have been infected with the virus and developed immunity, that the virus may have reached the West and the U.S. earlier than once or currently admitted by Chinese researchers — so, inter alia, California in theory could weather the epidemic with much less death and illness than earlier models of an eventual 25.5 million infected had suggested. Since then, a number of models, including Governor Newsom’s projection of 25.5 million infected Californians over an eight-week period, have been questioned. Controversy exists over exactly why models are being recalibrated downward. One explanation is that the shelter-in-space orders have been more successful than expected; others point to various flawed modeling assumptions.
Front-line physicians who see sick patients do not necessarily agree with researchers in the lab. For example, a Los Angeles Times story was widely picked up by other news outlets that quoted Dr. Jeff Smith, the chief executive of Santa Clara County. Smith reportedly now believes that the virus arrived in California much earlier than often cited, at least in early 2020:
The severity of flu season made health care professionals think that patients were suffering from influenza given the similarity of some of the symptoms. In reality, however, a handful of sick Californians that were going to the doctor earlier this year may have been among the first to be carrying the coronavirus. “The virus was freewheeling in our community and probably has been here for quite some time,” Smith, a physician, told county leaders in a recent briefing. The failure of authorities to detect the virus earlier has allowed it to spread unchecked in California and across the nation. “This wasn’t recognized because we were having a severe flu season. . . . Symptoms are very much like the flu. If you got a mild case of COVID, you didn’t really notice. You didn’t even go to the doctor. . . . The doctor maybe didn’t even do it because they presumed it was the flu.”
Footnote: See also Good Virus News from the Promised Land
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April 19, 2020 at 12:50PM


iowaclimatescienceeducation--2020-04-19--Robert Bryce: Responding To The Terawatt Challenge

In chapter 16 of his seminal book , A Question of Power : Electricity and the Wealth of Nations , @ @ @ @ @ of “ the Terawatt Challenge ” – a term coined by the late Nobel laureate @ @ @ @ @ that if we can provide sufficient electricity to all the peoples of the world , @ @ @ @ @ problems of food security , water quality , poverty , and a clean environment .
@ @ @ @ @ that as a world , we are far from that goal .
But we can @ @ @ @ @ whose first book , Pipe Dreams : Greed , Ego , and the Death of @ @ @ @ @ of the best nonfiction books of 2002 , traces the history of harnessed electricity from @ @ @ @ @ Edison , and Westinghouse – and the much less well known but equally…

PA Pundits – International
By Duggan Flanakin ~
“Electricity has become a human right.” – Robert Bryce
In chapter 16 of his seminal book, A Question of Power: Electricity and the Wealth of Nations, Austin-based futurist Robert Bryce speaks of “the Terawatt Challenge” – a term coined by the late Nobel laureate Richard Smalley.
Smalley posited that if we can provide sufficient electricity to all the peoples of the world, we can eliminate the massive problems of food security, water quality, poverty, and a clean environment. And Bryce solemnly points out that as a world, we are far from that goal.
But we can get there.
Bryce, whose first book, Pipe Dreams: Greed, Ego, and the Death of Enron, was named one of the best nonfiction books of 2002, traces the history of harnessed electricity from Benjamin Franklin through Tesla, Edison, and Westinghouse – and the much less well known but equally…


iowaclimatescienceeducation--2020-04-19--Lock Down Exposes Diesel Ban Folly As Air Quality Fails To Improve …Other Factors In Play

Cars are not as dirty as claimed .
Once again we find in Germany another article reporting how pollution from diesel cars has been massively over-hyped .
Despite a massive reduction in car traffic due to the COVID-19 lock down , nitrogen oxide levels still remain high , reports German weekly FOCUS here .
The air is no cleaner than before the lock down .
This virtually disproves the basis of the diesel bans that environmentalists have been hotly pursuing across over the recent months .
The diesel engine , they told us , was the primary source of fine @ @ @ @ @ @ @ it was high time to ban them .
This prompted activist city administrations to plan action against cars in cities .
Diesel emissions , they said , were causing millions to die prematurely .
The science , they claimed , told us there was no choice .
COVID-19 has since exposed that claim to be fake .
That ’ s no surprise because for months skeptics accused officials of relying on dubious data and studies on which to base the bans .
But they were ignored .
Now the skeptics feel vindicated .
@ @ @ @ @ @ @ random evaluation of air quality data from the Federal Environment Agency ( UBA ) over the past ten weeks by the news magazine FOCUS , nitrogen oxide levels at Stuttgart ’ s Neckartor and Landshuter Allee in Munich , for example , initially decreased gradually after the corona shutdown on 23 March , but then rose again.
” “ Ban is off the table ” Steffen Bilger ( CDU ) , Parliamentary State Secretary to the Federal Minister of Transport , told FOCUS : “ In my view , the subject of diesel @ @ @ @ @ @ @ .
Why the nitrogen oxide levels are not falling despite the rapid decline in traffic raises questions that the responsible environmental authorities must clarify.
” Yet the UBA Environment Office continues to back diesel vehicle bans , pointing out that wind , temperatures , and precipitation also have an effect on air quality and that the period of time is still too short to assess the effects of the shutdown .
Nice to see that for once the Ministry realizes that there are more factors at play than other than just CO2 and @ @

Cars are not as dirty as claimed.
Once again we find in Germany another article reporting how pollution from diesel cars has been massively over-hyped. Despite a massive reduction in car traffic due to the COVID-19 lock down, nitrogen oxide levels still remain high, reports German weekly FOCUS here. The air is no cleaner than before the lock down.
This virtually disproves the basis of the diesel bans that environmentalists have been hotly pursuing across over the recent months. The diesel engine, they told us, was the primary source of fine particle pollution in German cities and that it was high time to ban them. This prompted activist city administrations to plan action against cars in cities. Diesel emissions, they said, were causing millions to die prematurely.
The science, they claimed, told us there was no choice. COVID-19 has since exposed that claim to be fake.
That’s no surprise because for months skeptics accused officials of relying on dubious data and studies on which to base the bans. But they were ignored. Now the skeptics feel vindicated.
Air quality has not improved
FOCUS reports: “According to a random evaluation of air quality data from the Federal Environment Agency (UBA) over the past ten weeks by the news magazine FOCUS, nitrogen oxide levels at Stuttgart’s Neckartor and Landshuter Allee in Munich, for example, initially decreased gradually after the corona shutdown on 23 March, but then rose again.”
“Ban is off the table”
Steffen Bilger (CDU), Parliamentary State Secretary to the Federal Minister of Transport, told FOCUS: “In my view, the subject of diesel driving bans is finally off the table. Why the nitrogen oxide levels are not falling despite the rapid decline in traffic raises questions that the responsible environmental authorities must clarify.”
Environment Ministry (unwittingly) concedes other factors
Yet the UBA Environment Office continues to back diesel vehicle bans, pointing out that wind, temperatures, and precipitation also have an effect on air quality and that the period of time is still too short to assess the effects of the shutdown.
Nice to see that for once the Ministry realizes that there are more factors at play than other than just CO2 and cars.
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April 19, 2020 at 12:42PM


iowaclimatescienceeducation--2020-04-19--Wuhan #Coronavirus and Covid-19 Rumination #5

The world continues to learn about this sometimes deadly new zoonotic virus .
We know now from NYC that it is disproportionately serious in males ( 61 % of tested symptomatics ) , more fatal in people over 65 ( 63 % ) , and is disproportionately lethal ( 84 % ) in people with especially the comorbidities hypertension ( 55 % ) , diabetes ( 35 % ) and obesity ( 50 % of serious/critical [ s/c ] hospital admissions in NOLA , under 60 years old 2x s/c in NYC ) .
More on the significance of these @ @ @ @ @ @ @ from two inadvertent extreme ‘ lack of social distancing ’ “ experiments ” , Diamond Princess ( DP ) and CVN71 ( Theodore Roosevelt , TR ) , that only about 15 % ( TR ) to 19 % ( DP , with several more weeks of exposure ) ever test positive for the virus .
That means that something like 80-85 % of people have an immune system that seems to handle the virus effortlessly even in highly infectious , high viral titer circumstances–so that not even a positive viral shedding test @ @ @ @ @ @ @ very good innate immune system ; it could also be a primed active immune system ( i.e .
the population was NOT naïve ) .
A very plausible explanation hypothesis is developed below with important ramifications for opening countries from extreme lockdown measures .
We know from those same two ‘ experiments ’ that between 45 % ( DP ) and 55 % ( TR ) test positive but are asymptomatic .
Now , some of that is just the now known incubation period with a mean ~5 days from infection and 97.5 @ @ @ @ @ @ @ But we know also from South Korea that of the ~10600 now tested positive but asymptomatic and quarantined 14 days , that ~20 % NEVER developed even mild symptoms .
This is likely also explained by the naiveté hypothesis developed below .
In what follows , the primary technical sources are the Journal of Virology on coronaviruses , ncbi.nlm.NIH.gov for science publications , www.CDC.gov , and for clinical morbidity a very new ‘ anecdotal ’ survey article at Sciencemag.org .
The CDC says that there are four common cold coronavirus serotypes causing something @ @ @ @ @ @ @ common colds , ( about 5 % more are DNA adenoviruses that do not mutate much so immunity to them is long lasting ) , with the remainder caused by about 100 different rhinovirus serotypes .
For all three common cold virus types , the route of transmission is known to be cough , touching ( handshake after cough ) , or hand face contact ( cough droplets to a surface , touch surface , then touch mouth , nose , eyes . )
This is why common colds are only weakly seasonal @ @ @ @ @ @ @ is why for COVID-19 , social distancing , frequent hand washing , and consciously NOT face touching ‘ work ’ .
From a transmission perspective , Wuhan is ‘ just ’ another coronavirus .
An aside argued in rumination # 4 and in other previous comments to others .
Observational fact : flu is strongly seasonal , common colds are not .
The reason lies in route of transmission .
Inhaling infected aspirate principally spreads flu ( aspirate particles are less than 5 microns ) .
These aspirate particles dry out rapidly in @ @ @ @ @ @ @ to volume ratio ) and remain circulating for many hours .
In summer humidity , they don ’ t dry out and sink ‘ rapidly ’ to where they can not be inhaled .
Winter contact route of flu transmission exists but is distinctly secondary according to my personal communications with Dr. Fauci summer of 2009 .
Anything less than an N95 respirator will not prevent you from catching flu .
Quarantine is ineffective .
Annual flu shot is advised .
Now , there are two Covid-19 possibilities .
Dr. Fauci might be @ @ @ @ @ @ @ flu , implying primary infection route would be inhaled aspirate .
In which case , all the public mask nonsense is pointless .
Or , it is ‘ just ’ another corona virus , the three recommended mitigation measures work , and public masks are still nonsense–unless you have active mild symptoms : dry cough plus fever > 100.4F .
In which case you should quarantine yourself and not be in public even with a mask .
Lack of transmission under extreme circumstances in 80-85 % of cases The four common cold coronaviruses @ @ @ @ @ @ @ ‘ alpha ’ serotype group , and OC43 and HKU1 in the ‘ beta ’ serotype group .
Wuhan is also in the beta serotype group .
All four common cold coronavirus spike proteins have two binding sites .
In all four , the S1-CTD site binds the ACE2 receptor on epithelial cells such as line the nose , mouth , throat , and lungs .
So their spike proteins , just like Wuhan , ‘ key ’ to that cellular receptor lock as well ( as after being fully humanized ) to @ @ @ @ @ @ @ natural target for antibodies .
My naiveté hypothesis is that those exposed but not ‘ infected ’ on DP and TR may actually have been , but had a coronavirus common cold in the sufficiently recent past that their active immune system is NOT naïve thanks to S1-CDT .
The antibodies simply clear the Wuhan virus before it can sufficiently replicate to even be detected .
A lesser degree of immunity ( older exposure ) might allow the virus to sufficiently replicate to be detected , but not ever sufficiently to cause symptoms @ @ @ @ @ @ @ to finally clear it .
If this naiveté hypothesis is correct , then the country should be opened immediately using the steps outlined by President Trump on Thursday .
THAT is a HUGE deal economically .
My hypothesis came about as I thought more about my vicious coronavirus cold referenced in guest post # 1—without fever , so not Wuhan , but at 9 day duration plus more cough and less runny nose , also not Rhino .
Hence a personal recent experience motivation for rumination # 5 .
Age dependency is easy @ @ @ @ @ @ @ immune systems and more co-morbidities .
Male dependency , dunno , just is .
Hypertension , diabetes , and obesity ( in the under age 60 less impacted cohort ) as the main co-morbidities take more thought to make causal connections , but can be explained with more scientific background knowledge .
We know from all the ventilator brouhaha that the primary Covid-19 clinical cause of death is viral pneumonia .
The ‘ ground glass ’ in lower lung Xray is determinative .
But in ‘ many ’ cases , there are heart @ @ @ @ @ @ @ or cardiac arrest , or renal failure , BEFORE blood oxygenation failure , and without evidence for cytokine storms that would also damage other organs leading to a multiple organ failure diagnosis like in sepsis .
And prior to death , there is in a very significant number of cases clinical evidence of kidney damage ( e.g .
bloody urine ) and/or cardiac disfunction ( e.g .
arrhythmia , tachycardia ) .
How can a respiratory virus cause those ?
The answer derives from the hypertension/diabetes/obesity clues .
Overweight ( BMI > 25 @ @ @ @ @ @ @ ) is VERY strongly associated with both hypertension and type 2 diabetes according to the CDC .
Hypertension by itself mechanically damages small blood vessels and capillaries .
Diabetes by itself damages blood vessels and capillaries via several biological mechanisms .
The leading cause of death from diabetes ( itself underlying cause # 7 in the US ) is cardiovascular in some form according to the CDC .
Now consider lower lung viral pneumonia .
It starts when epithelial cell alveoli ‘ air sacs ’ are infected and eventually burst from virus .
@ @ @ @ @ @ @ dead and infected alveoli cells .
Each single cell thick alveoli sac is intimately surrounded by capillaries ; this is structurally how the lungs exchange oxygen into blood and CO out .
That immune attack can not avoid damaging these capillaries , already weakened/damaged by hypertension and diabetes .
Now the Wuhan virus is in the bloodstream , not just in the lungs .
And it turns out ( from the biological role of the ACE2 receptor itself ) that the heart and kidneys are the two other organs in the body with @ @ @ @ @ @ @ receptor : in cardiac and renal cells .
So it is clinically unsurprising that a lot of critical patients exhibit these other mortality modes before respiratory failure despite ventilators .
This also explains why a virus that can not possibly be racist ( despite DeBlasio ’ s politically inspired intimations this past week ) disproportionately kills African Americans and Hispanics in NYC .
Those racial groups have a disproportionate amount of obesity .
See ncbi article PMC4265895 for NYC only details , or for all New York counties health.ny.gov .

By Rud Istvan
The world continues to learn about this sometimes deadly new zoonotic virus. We know now from NYC that it is disproportionately serious in males (61% of tested symptomatics), more fatal in people over 65 (63%), and is disproportionately lethal (84%) in people with especially the comorbidities hypertension (55%), diabetes (35%) and obesity (50% of serious/critical [s/c] hospital admissions in NOLA, under 60 years old 2x s/c in NYC). More on the significance of these facts is developed below.
We know from two inadvertent extreme ‘lack of social distancing’ “experiments”, Diamond Princess (DP) and CVN71 (Theodore Roosevelt, TR), that only about 15% (TR) to 19% (DP, with several more weeks of exposure) ever test positive for the virus. That means that something like 80-85% of people have an immune system that seems to handle the virus effortlessly even in highly infectious, high viral titer circumstances–so that not even a positive viral shedding test develops. This could just be a very good innate immune system; it could also be a primed active immune system (i.e. the population was NOT naïve). A very plausible explanation hypothesis is developed below with important ramifications for opening countries from extreme lockdown measures.
We know from those same two ‘experiments’ that between 45% (DP) and 55% (TR) test positive but are asymptomatic. Now, some of that is just the now known incubation period with a mean ~5 days from infection and 97.5% displaying symptoms within 11.5 days. But we know also from South Korea that of the ~10600 now tested positive but asymptomatic and quarantined 14 days, that ~20% NEVER developed even mild symptoms. This is likely also explained by the naiveté hypothesis developed below.
In what follows, the primary technical sources are the Journal of Virology on coronaviruses, ncbi.nlm.NIH.gov for science publications, www.CDC.gov, and for clinical morbidity a very new ‘anecdotal’ survey article at Sciencemag.org.
Mitigation
The CDC says that there are four common cold coronavirus serotypes causing something between 15 and 35% of all common colds, (about 5% more are DNA adenoviruses that do not mutate much so immunity to them is long lasting), with the remainder caused by about 100 different rhinovirus serotypes.
For all three common cold virus types, the route of transmission is known to be cough, touching (handshake after cough), or hand face contact (cough droplets to a surface, touch surface, then touch mouth, nose, eyes.) This is why common colds are only weakly seasonal while flu is strongly seasonal. That is why for COVID-19, social distancing, frequent hand washing, and consciously NOT face touching ‘work’. From a transmission perspective, Wuhan is ‘just’ another coronavirus.
An aside argued in rumination #4 and in other previous comments to others. Observational fact: flu is strongly seasonal, common colds are not. The reason lies in route of transmission. Inhaling infected aspirate principally spreads flu (aspirate particles are less than 5 microns). These aspirate particles dry out rapidly in dry indoor winter air (high surface to volume ratio) and remain circulating for many hours. In summer humidity, they don’t dry out and sink ‘rapidly’ to where they cannot be inhaled. Winter contact route of flu transmission exists but is distinctly secondary according to my personal communications with Dr. Fauci summer of 2009. Anything less than an N95 respirator will not prevent you from catching flu. Quarantine is ineffective. Annual flu shot is advised.
Now, there are two Covid-19 possibilities. Dr. Fauci might be right that it could be seasonal like flu, implying primary infection route would be inhaled aspirate. In which case, all the public mask nonsense is pointless. Or, it is ‘just’ another corona virus, the three recommended mitigation measures work, and public masks are still nonsense–unless you have active mild symptoms: dry cough plus fever >100.4F. In which case you should quarantine yourself and not be in public even with a mask.
Lack of transmission under extreme circumstances in 80-85% of cases
The four common cold coronaviruses are: 229E and NL63 in the ‘alpha’ serotype group, and OC43 and HKU1 in the ‘beta’ serotype group. Wuhan is also in the beta serotype group. All four common cold coronavirus spike proteins have two binding sites. In all four, the S1-CTD site binds the ACE2 receptor on epithelial cells such as line the nose, mouth, throat, and lungs. So their spike proteins, just like Wuhan, ‘key’ to that cellular receptor lock as well (as after being fully humanized) to another that varies. S1-CTD is a natural target for antibodies.
My naiveté hypothesis is that those exposed but not ‘infected’ on DP and TR may actually have been, but had a coronavirus common cold in the sufficiently recent past that their active immune system is NOT naïve thanks to S1-CDT. The antibodies simply clear the Wuhan virus before it can sufficiently replicate to even be detected. A lesser degree of immunity (older exposure) might allow the virus to sufficiently replicate to be detected, but not ever sufficiently to cause symptoms before the active immune system spools up to finally clear it.
If this naiveté hypothesis is correct, then the country should be opened immediately using the steps outlined by President Trump on Thursday. THAT is a HUGE deal economically. My hypothesis came about as I thought more about my vicious coronavirus cold referenced in guest post #1—without fever, so not Wuhan, but at 9 day duration plus more cough and less runny nose, also not Rhino. Hence a personal recent experience motivation for rumination #5.
Clinical Morbidity
Age dependency is easy to understand. Older people have weaker immune systems and more co-morbidities. Male dependency, dunno, just is.
Hypertension, diabetes, and obesity (in the under age 60 less impacted cohort) as the main co-morbidities take more thought to make causal connections, but can be explained with more scientific background knowledge.
We know from all the ventilator brouhaha that the primary Covid-19 clinical cause of death is viral pneumonia. The ‘ground glass’ in lower lung Xray is determinative.
But in ‘many’ cases, there are heart attack symptoms without coronary artery blockage, or cardiac arrest, or renal failure, BEFORE blood oxygenation failure, and without evidence for cytokine storms that would also damage other organs leading to a multiple organ failure diagnosis like in sepsis. And prior to death, there is in a very significant number of cases clinical evidence of kidney damage (e.g. bloody urine) and/or cardiac disfunction (e.g. arrhythmia, tachycardia). How can a respiratory virus cause those? The answer derives from the hypertension/diabetes/obesity clues.
Overweight (BMI>25) and Obese (BMI>30) is VERY strongly associated with both hypertension and type 2 diabetes according to the CDC. Hypertension by itself mechanically damages small blood vessels and capillaries. Diabetes by itself damages blood vessels and capillaries via several biological mechanisms. The leading cause of death from diabetes (itself underlying cause #7 in the US) is cardiovascular in some form according to the CDC.
Now consider lower lung viral pneumonia. It starts when epithelial cell alveoli ‘air sacs’ are infected and eventually burst from virus. The active immune system attacks, scavenging dead and infected alveoli cells. Each single cell thick alveoli sac is intimately surrounded by capillaries; this is structurally how the lungs exchange oxygen into blood and CO 2 out. That immune attack cannot avoid damaging these capillaries, already weakened/damaged by hypertension and diabetes. Now the Wuhan virus is in the bloodstream, not just in the lungs. And it turns out (from the biological role of the ACE2 receptor itself) that the heart and kidneys are the two other organs in the body with an unusually high concentration of expressed ACE2 receptor: in cardiac and renal cells. So it is clinically unsurprising that a lot of critical patients exhibit these other mortality modes before respiratory failure despite ventilators.
This also explains why a virus that cannot possibly be racist (despite DeBlasio’s politically inspired intimations this past week) disproportionately kills African Americans and Hispanics in NYC. Those racial groups have a disproportionate amount of obesity. See ncbi article PMC4265895 for NYC only details, or for all New York counties health.ny.gov.


iowaclimatescienceeducation--2020-04-19--Record snowfall in Michigan

Record snowfall in Michigan

Video – Easter snowstorm dumps more than 18 inches ( 45 cm ) of heavy , wet snow on Marquette @ @ @ @ @ hours between Sunday and Monday .
Some reports to the National Weather Service indicate closer @ @ @ @ @ landed in places to the East .
The snow on Sunday alone measured 9.5 inches @ @ @ @ @ snowfall record of 9.4 inches set on April 12 , 2013 .
Thanks top Clay @ @ @ @ @ post Record snowfall in Michigan appeared first on Ice Age Now .

Video – Easter snowstorm dumps more than 18 inches (45 cm) of heavy, wet snow on Marquette County in less than 24 hours between Sunday and Monday.
Some reports to the National Weather Service indicate closer to 2 feet of snow landed in places to the East.
The snow on Sunday alone measured 9.5 inches, breaking the previous daily snowfall record of 9.4 inches set on April 12, 2013.
https://www.upmatters.com/weather/easter-snowstorm-breaks-records-and-brings-beauty-to-the -upper-peninsula
Thanks top Clay Olson for this link
The post Record snowfall in Michigan appeared first on Ice Age Now.
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April 19, 2020 at 10:55AM