From the Cliff Mass Weather Blog
Cliff Mass
Numerical weather prediction has improved dramatically over the past decades, providing potent warnings for extreme weather, such as hurricanes.
There are few better examples than the prediction of Hurricane Lee, which will make landfall near the Maine/New Brunswick border late Saturday.
The U.S. global model, the GFS, has been spectacularly skillful in predicting this storm, well more than a week ahead.
The latest forecast run shows the storm making landfall near the international border around 5 PM PDT on Sunday. That is a 54-h hour prediction This is so close enough in time…and so consistent with other model forecasts… that you can be assured that this is close to what will happen.
But how did extended forecasts do?
The 72-h prediction is pretty much the same.
The 126 h prediction is nearly identical in position:
The 198 hr (8.25 day) forecast has a strong hurricane in pretty much the same location.
Folks, this is a stunningly good forecast for over a week ahead.
Professor Brian Tang of the University of Albany has a wonderful website that verifies the hurricane track (position) forecasts of major modeling/forecasting systems. The results for Hurricane Lee are shown below for forecasts of 120 hours (5 days) or less.
In general, the track accuracy gets better for shorter forecasts…which makes sense. But let’s compare the American model (blue color, AVNO), the European Center model (red color), and the UKMET office model (green color). The human (official) forecast is shown in black.
Wow. The American model is STUNNINGLY accurate at all projections in time.
It is FAR better than the nominally top two global modeling systems in the world: the European Center and UKMET. The forecast error is under 100 km (60 miles) for all projections shown.
Extraordinary.
The model forecasts are better than the official Hurricane Center forecasts….I suspect that humans are probably hedging their bets with the European Center model solution.😅
This was a truly excellent forecast and not the only success for the American model this season. Hopefully, this extraordinary performance will be persistent for future storms, perhaps reflecting recent improvements in the U.S. global modeling system.
Finally, I should note there are real policy implications of the rapidly advancing weather prediction skill now available to decision-makers. Excellent forecasts can help protect people and economic assets from extreme weather.
Better forecasts are the first line of defense against severe weather.
Better forecasts have great potential for reducing the negative impacts of global warming.
One of the reasons I have spent some time trying to calm down some who are panicking over global warming and extreme weather.
Climate-related deaths are down…and I mean WAY down. Better adaptation and a richer world have contributed, but so have better forecasts.
Importantly, we have only begun taking advantage of improved forecast skill.
The winds on Maui were nearly perfectly predicted on August 7-8 of this year, yet 115 people died and nearly 10 billion dollars in damage was done. We could have easily stopped the carnage, by shutting off the power and effectively evacuating the population.
Most major wildfires are related to strong winds and such winds are often forecast with great skill. Few should be a surprise.
In summary, coastal New England has had nearly a week to prepare for strong winds and heavy precipitation (over northern Maine)– and we can be proud of the technological advances and investments in NOAA and in other government agencies that made such forecasting prowess possible.
How satisfying to read of something going right. And from NOAA. But to avoid misinterpretation by others I would have liked to see these accurate limited-purpose weather models clearly distinguished from the hopelessly inaccurate climate models.
I suspect many here are going to be disappointed to find out that the GFS has been using the same radiative transfer scheme (RRTM) that many of the CMIP6 models use since 2003. In fact, many of the worlds’ leading weather prediction models use the RRTM. The ECMWF, for example, has been using it since 2000. The ERF of CO2 as computed by RRTM is 4.2 W/m2..
And yet, CMIP6 models run unphysically hot:
https://www.carbonbrief.org/guest-post-how-climate-scientists-should-handle-hot-models/
No, your link says a subset runs hot.
Making the average of all models run too hot, a problem admitted by Gavin, who is lead author of the linked article.
I don’t know how large the subset might be. Maybe 37 out of 38 GCMs. Only the Russian model is close to observed reality.
and the Russian model might only have gotten lucky- if it’s close to reality, that doesn’t prove the model is correct and will always predict correctly- clearly, with so many models, probably all highly defective, at least one ought to hit the target
The Le Chatelier Principle (LCP) states that in an interactive system, any change in one component of the system (say temperature) is resisted by compensating reactions of the other components, such that the perturbing change is greatly reduced.
Let’s sssume a climate model
has ‘correctly’ accounted for the physics of the the main drivers of climate. The nature of such formulations are known as ceteris paribus (“all other things being equal”) calculations
https://www.investopedia.com/terms/c/ceterisparibus.asp
In an LCP interacting complex system, by definition, “all other things” not only do not remain the same, but they change in such a way as to markedly
diminish the “expected” perturbing change. The Climate Models forecast an anomalous warming change that turned out to be 300%. If properly calculated, they should have multiplied their result by an LCP coefficient of 0.33.
I was pleased to see Dr Will Happer reference the Le Chatelier Principle as the reason why the warming of the planet has been so modest.
300% to high
They nearly all run hot, Nick. Way, way too hot.
There are so many factors affecting temperatures that exact figures for climate sensitivity to changes in CO2 concentration are impossible to determine. But, based on comparing historical trends in temperature to concurrent changes in GHG levels, ECS is probably only about 1.5 °C / doubling, and TCR is only 2/3 to 3/4 of that.
The exact figures you find will depend on whose temperature estimates you use, and what time periods you choose. Here’s where I did that exercise a few years ago, using a period chosen to start and end in similar ENSO-neutral conditions:
https://sealevel.info/sensitivity.html
Alternately, you can just use the entire record of anthropogenic CO2 increases. Due to the increase of CO2 concentration from 280 ppmv to 420 ppmv, we’ve already had 58% of the “radiative forcing” which we would get from a doubling of CO2. At the same time, we’ve had perhaps 1.1 °C of averaged global warming, of which at most perhaps 2/3 could be attributed to the CO2 rise.
Even if you attribute all of that 1.1 °C of warming to anthropogenic GHGs (which I doubt is correct), you’ll still find a “practical sensitivity” (roughly the average of ECS & TCR) = (2/3) × 1.1°C / 0.58 = only 1.3 °C / doubling.
If TCR = 3/4 of ECS, we have two equations and two unknowns:
(ECS+TCR)/2 = 1.3
TCR = 0.75×ECS
So:
ECS = 1.486
TCR = 1.114
Or if TCR = 2/3 of ECS:
ECS = 1.560
TCR = 1.040
It is well-nigh impossible to approach the IPCC’s “central” estimate of 3°C per doubling with this sort of analysis.
That is because the Chimps used unphysical assumptions. and false data.
They also use the same chemical formula for water, H2O.
Why would anyone here be disappointed? Most of us are already aware that long-term weather forecasts and climate models are highly inaccurate.
NOAA started out the hurricane season predicting an “Above Normal” Atlantic hurricane season for 2023.
Now NOAA has revised its prediction to a “Near-normal” 2023 Atlantic hurricane season.
We haven’t had many hurricanes this season. I guess that’s why NOAA had to change their prediction from earlier in the year.
“has revised its prediction”
how convenient to revise a prediction after the fact
On May 10th NOAA forecasted 5-9 hurricanes. On August 10th the revised that number up to 6-11. We only about half way through the Atlantic hurricane season and there as already been 15 named storms, 5 hurricanes, and 3 major hurricanes. A normal season would see 14 named storms, 7 hurricanes, and 3 major hurricanes.
Oh dearie me.
We have a very time constrained forecast with known inputs and data which did well.
And you are trying to compare it to long-term assumption-driven models with corrupted data inputs.
That is really sad !!
Only a mindless unthinking fool would thing that atmospheric CO2 comes into a 14 day forecast. !
The fact that portions of the two models are the same, is not evidence that both are equally skillful.
That is like saying the same jockey on two different horses is bound to win.
“two different horses”
One a horse… one an ass !!
Maybe that says something about why I don’t trust the rain forecasts here in Ohio. The precipitation forecasts are frequently wrong 24-hours before, and seem to be at their worst when they are predicting rain — which often doesn’t fall. On the other hand, it is rare to have rain when it isn’t forecast; so, unpleasant surprises are unusual, but sometimes happen. When I lived in New England decades ago, I wrote off the poor forecasts to turbulence in the mountains. However, considering that I’m now living in relatively flat terrain, and we now have Doppler radar and geostationary satellites, I’m not impressed.
Living in Ohio, I remember the storms three weeks ago. It was crystal clear what was happening with that front hours away. It crossed Lake Erie even stronger and tornado warnings lit up the northern coast from Sandusky to the border. We had 12 ef0-2 tornadoes confirmed, including one in downtown Cleveland.
No injuries, fortunately. But seeing that radar so clearly showing a problem in Michigan, we should have been warned. I told my wife before she went to bed that I was probably getting her up around 1 am to head to the basement. Which is what I ended up doing. The tv weather people did a nice job late, even showing the rotation at the edge of the front and telling people to take cover. But I think we were lucky that no one along the coast was hurt.
We have the capability to provide decent warning now. It saves lives. But we can still do better. I’m sure most of my neighbors had no idea how close we were to a tornado forming in our town.
Of course, the danger is that the media starts taking advantage, and every time there’s a hint of some event like that, it’s “OMG, the end is near – it’s all Big Oil’s fault – it’s all Trump’s fault – take cover, take cover!” 20 times every storm season.
Aren’t they already doing that, Joe? All last week the entire northeast was treated to scaremongering stories about a category 5 hurricane that COULD hit anywhere from New Jersey to Maine. It was cat 2 but it COULD re-intensify, the jet stream (according to the millennial writer at USA Today, that’s the place where jets fly) COULD shift.
On Wednesday we had some heavy rain. Three times we had emergency alerts on our cell phones about flash flooding. What irks most is that people will die from ignoring the alert to a true hazard because so many false alarms preceded it.
In the end, yesterday was a lovely day in most of New England. By 5pm, we had large patches of blue sky and bright sunshine in north central Connecticut.
The core principle of the media was “if it bleeds it leads”. Now it’s “if it COULD or MIGHT bleed it leads”.
Forecasting any parameter is hard. Precipitation is no different. However, the GFS is more skillful then most people realize.
https://www.emc.ncep.noaa.gov/users/verification/
Speaking of geostationary satellites…the GOES-R satellites were designed and developed using the RRTM.
Same in St. Louis. Is it the forecasts themselves, or the CYA that you allude to? Broadly, I think if I arbitrarily reduced their probabilities by X (say 25%) I would end up with a better record than both out local TV weather personalities and TWC.
bdgwx, you are iboth n Steve Templeton’s forecasting area and viewing area, and keep up on this. How well does he forecast?
My edit button is completely gone. I must have used up all I was allotted when signing up again.
I don’t pay much attention to Steve Templeton specifically. I do participate frequently on Chris Higgin’s locally focused weather forum and so I’m more familiar and engaged with his forecasts.
I can tell you that all meteorologists in the area rely heavily on global circulation models including the GFS because their skill is superior to anything they can present on their own. In fact, studies have shown that computer-only forecasts are actually more skillful than computer+human forecasts over the long run. That’s right…when humans try to augment the compute forecast it generally results in a worse forecast. There may be niche scenarios where a human can add value but they are usually the result of known bugs in the computer models that are already being worked on.
The best forecasts for our area come from the National Blend of Models (NBM) which is an blend of 44 individual models.
BTW…I don’t have the edit button anymore either. I think WUWT may have taken that away from everyone.
I don’t have an edit button either. I would be very helpful to have one. I often noticed errors after I hit the Post Comment button and then have to add a reply message to correct the error.
The forecast model was effective in predicting the physics of how multiple factors such as high pressure systems and the jet stream would interact with the hurricane to influence its track. There seems to be little or nothing to do with predicting temperatures in that.
Just as a student can be brilliant in engineering but abysmal in English, a model may do extremely well predicting kinetics but still be horrendous at predicting temperature, which indeed the evidence seems to bear out.
Of course a model, like a student, can be bad at science and worse at English. Take mosh for example.
I think managing fuels in Maui would have helped much more than any forecasting. Some goats or cattle eating the grass before it got so high would have been better.
The thousands of flood deaths in Derna, Libya will be blamed on “climate change”.
They have …
BBC today program was waxing lyrical this morning.
If you put ‘flood deaths in Libya blamed on climate change.’
into a search engine, you get pages of links like this …
https://www.theguardian.com/world/2023/sep/13/libya-floods-the-outcome-of-the-climate-crisis-meeting-a-failed-state
Little mention of poor/no maintenance.
Nor of lack of functioning weather service, which could have ordered water let out of the reservoirs and evacuations downstream.
But then even Germany failed on the weather warning front, with a massive rain storm still over Britain.
I looked at the satellite images of the dams, which are highly visible because water levels were very low when the images were made. Here’s the upstream one:
https://maps.app.goo.gl/sCA7HZhdYYuLJ9Fu5
and here’s the one on the outskirts of Derna itself
https://maps.app.goo.gl/JVEjtyrJGnm7MXaH9
Both show vegetation and erosion/cracking of concrete on the faces of the dam. Neither has a formal spillway – the dam is simply overtopped and the roadway across submerged. Water getting into the dam core would see it collapse. There is only a small horn shaped opening to a bypass tunnel that would be quite inadequate when the wadi fills – which can happen at shocking speed, as my own experience in Fujairah many years ago attests – we only made it out by being in a bus with a good ground clearance, sufficient to get across the submerged bridge over a wadi.
If only they had modern science, the Damn Busters could have dropped CO2 on those damns, rather than bombs.
Paul Homewood latched on to Sky’s version of the story.
https://notalotofpeopleknowthat.wordpress.com/2023/09/16/sky-blame-libyan-floods-on-climate-change/
He managed to find an excellent account from a Turkish news source (in English) that points the fingers very clearly in the right directions.
There have been major storms in that area of the Mediterranean since before St Paul was shipwrecked on Malta after a 14 day storm, which continues after the shipwreck.
Acts 27:27-28:527 On the fourteenth night we were still being driven across the Adriatic Sea, when about midnight the sailors sensed they were approaching land. 28 They took soundings and found that the water was a hundred and twenty feet deep. A short time later they took soundings again and found it was ninety feet deep. 29 Fearing that we would be dashed against the rocks, they dropped four anchors from the stern and prayed for daylight. 30 In an attempt to escape from the ship, the sailors let the lifeboat down into the sea, pretending they were going to lower some anchors from the bow. 31 Then Paul said to the centurion and the soldiers, “Unless these men stay with the ship, you cannot be saved.” 32 So the soldiers cut the ropes that held the lifeboat and let it drift away.
33 Just before dawn Paul urged them all to eat. “For the last fourteen days,” he said, “you have been in constant suspense and have gone without food—you haven’t eaten anything. 34 Now I urge you to take some food. You need it to survive. Not one of you will lose a single hair from his head.” 35 After he said this, he took some bread and gave thanks to God in front of them all. Then he broke it and began to eat. 36 They were all encouraged and ate some food themselves. 37 Altogether there were 276 of us on board. 38 When they had eaten as much as they wanted, they lightened the ship by throwing the grain into the sea.
39 When daylight came, they did not recognize the land, but they saw a bay with a sandy beach, where they decided to run the ship aground if they could. 40 Cutting loose the anchors, they left them in the sea and at the same time untied the ropes that held the rudders. Then they hoisted the foresail to the wind and made for the beach. 41 But the ship struck a sandbar and ran aground. The bow stuck fast and would not move, and the stern was broken to pieces by the pounding of the surf.
42 The soldiers planned to kill the prisoners to prevent any of them from swimming away and escaping.43 But the centurion wanted to spare Paul’s life and kept them from carrying out their plan. He ordered those who could swim to jump overboard first and get to land.44 The rest were to get there on planks or on other pieces of the ship. In this way everyone reached land safely.
Instead of DECADES on not investing in proper water management infrastructure, largely due to West-imposed sanctions, which made it difficult to sell Libyan gas and oil.
The West was aiming to do the same to Russia, but the Russians were prepared; its economy is growing in 2023, while the EU economy is shrinking in 2023.
The Dutch had an historic flood that killed about 200 people and destroyed whole viullages and may houses.
Immediately, the Dutch started to implement a 50-year DELTA Plan, which protects 50% of the population in below-sealevel areas from a TEN THOUSAND-y flood
Correction, instead of edit button
historic flood in 1953. I was there at that time. killed about two thousand people
Consistently getting the forecast track error at 5 days to under 100km would have an enormous economic benefit as it allow for a much more focused preparation for the storm. Kudos to the group working on the GFS model for the stunningly accurate forecast.
Wait a minute, wasn’t it predicted to hit Southern Florida for a while? One day it was gone from the headlines as it stayed at sea.
Getting a landfall right 4 days in advance, two or three times a decade, can’t really be claimed as accuracy…..but they are getting better….
The hype (including FoxWeather) might have talking about it but I don’t think the National Hurricane Forecast Center ever did.
(I might be wrong.)
I am glad GFS was so accurate. But this case was fairly simple: the macro steering elements were both well defined and fairly long lived. The big question is what did ECMWF do to be worse when it is usually better?
Put differently, sometimes you get lucky.
Nice.
Yes, another informative post from Prof Cliff Mass.
What a clear observer and analyst of all things weather and climate.
His grasp of the Pacific Northwest behaviors is unsurpassed.
California is doing it again.
https://www.foxbusiness.com/politics/california-goes-war-oil-gas-giants-climate-change-deception
story tip
It really is time that the oil and gas industry struck back…
.. and totally stopped supplying oil and gas to California.
They could give all their gas station workers a week long vacation at the same time. California Gas and Oil celebration week they could call it.
So here’s the puzzle for us all – where does Meteoblue (MB) get their data from?
Because what’s happened here is exactly what MB have been saying I first checked on Lee when it was way way south and (seemed to be) headed for Florida.
MB said ‘no’ – its’ gonna stay out at sea and run aground at the day/time on the Canada/US border = exactly where/when it has done now
Meteoblue also very correctly predicted what Hilary would do, at the day/time that Hilary did what she did and I copied it into here nearly 4 or 5 days in advance
BUT, when I did that, The News, The Forecasts, MSM and youtube pundits were in Total Panic Mode – talking of a hideous hurricane shaped disaster looming for LA and places north of there
(If you just enter meteoblue-dot-com – this is where you land)
It all came soooo true – we all saw what 0.75mm (over 3 days) of torrential rain made of Black Rock Desert in Nevada. Thanks Hils
(Girls are sometimes like that – making total fools out of boys)
Meanwhile. here’s an MB screenshot for now (midnight BST Saturday 16th)
The colours are not = Temperature, the colours are indicating wind speed where Red = strong wind and Blue = weak wind
Isn’t it so lovely how it shows where all the bare ground is after the combine harvesters are nearly all finished this year’s work
See how North America and all of Europe are under heat domes = high barometric pressure and low winds.
Temps now (midnight BST) across the SE tip of England and all of Europe are about the same at ~ 20°C
On that map it’s hard to tell them apart from The Sahara. (desert)
Don’t you wonder why that might be…
You’re making a fool of yourself – anyone looking at the map can see even the Congo is the same colour as Ageria, which is basically also the colour of almost all of North America from Mexico to NWT. Are they all desert or bare dirt?
I think they all have the same wind speed.
Cliff Mass (and some commenters) wax lyrical about the accuracy of US predicted hurricane landings.
I’m bemused that little information is given about the accuracy of the predicted STRENGTH of the hurricanes.
I do hope that the US predictions of strength are just as impressive as those of location.
Certainly, accurate prediction of both would be a huge life saver.
Much to the disappointment of our shroud waving climate apocalypse chums, of course.
In recent experience, the UK MET prophecies of storm strength or heat waves are pretty much a bad joke.
UKMET is the one I rely on for track predictions. Strength really doesn’t matter as my prep doesn’t change whether it is a TS or a hurricane. My main concern is long term power outage and inability to get resupplied.
I noted that the track forecasts for Idalia were remarkably accurate 5 days out – before it even started moving from near Cozumel and intensifying. However, the intensity forecast for landfall was a long way out. The highest gust I could find from local weather stations was 85mph – and that was gust, not sustained wind. Perhaps others have better evidence, but it was a long way off Cat 3 on landfall AFAICS. The storm surge was also way lower than forecast.
Tracks do matter for shipping and aircraft as well as those on land. I checked out the flight patterns as Lee approached Canada. Many flights seem to be headed well north over Greenland (there is also a big storm in the Fram Strait). The most extreme I spotted was a flight from Dubai to Houston, which started out crossing Iran, flying up the Caspian, reaching the Russian Arctic coast a bit East of Archangel, crossing northern Spitzbergen and hitting the Greenland coast only a few miles south of Station Nord, then flying across Baffin Bay and Hudson Bay and on south to Houston. Quite the great circle.
These model forecast about the weather are NOT climate model forecast.
Short term. Fewer variables. If a value for something is off a couple of decimal points? Short term no great effect in the forecast. Observations are “quick”. Success for the forecast is quickly evident.
Climate models have been around for a few decades now. They have yet to be even close.
I will agree from start to finish the GFS did pretty well with this one. But before we get carried away, this time of year in the Northern Hemisphere weather systems, fronts and winds are fairly predictable. As the seasons moves on the weather gets more chaotic and so do the forecasts.
Story Tip — William Happer live now on Outsiders – Sky News Australia. 10:30HRS est
Darn, missed it, I hope they recorded it! 🙂
program is replayed tonight. 18:00 EST
I disagree. The UKMET model at Weather Underground is a better model. GFS tends to vary too much before finally setting onto the track show by UKMET. The UKMET had Ian hitting Ft Myers a full week in advance. It also had Idalia well off shore near me the entire time. It had Lee making the turn well before the GFS model setttled in. Because of my location, on the central west Coast of Florida, the decisions concerning my life and property depends on me trusting the forecast. For that I prefer the UKMET model.
NOAA has had significant upgrades to their computing power over the last few years.
https://www.noaa.gov/news-release/us-supercomputers-for-weather-and-climate-forecasts-get-major-bump
As if the will help, GIGO only faster.
The U.S. global model, the GFS, has been spectacularly skillful in predicting this storm, well more than a week ahead.
Even a blind hog finds an acorn every once in a while. From what I have observed the European wins the majority of the time.
But projecting tracks has generally gotten better with time. Wish I could say the same for climate models.
My real issue with NOAA is that they will not adopt a better way of classifying storms in order to have a real reference of the power of a given storm tto better forewarn those in the potential path and to be able to do a far more accurate reanalysis of storms of the past.
The Saffir Simpson is only about sustained windspeed at 10 meters. In tropical cyclones the size of the wind field matters a great deal when it comes to the actual impact at landfall. It’s not just the area of the wind fields, it is also about storm surge. All other variables being equal but size, the larger the storm the more severe the surge.
Lee was a monster of a storm in size relative to the majority of the Atlantic basin Hurricanes we have been seeing the last few years.
The skillfulness of computer models (of all types) is dependent on
🗹 1(a). how well the processes which they model are understood, AND
🗹 1(b). how faithfully those processes are simulated in the computer code, AND
🗹 2. whether the results can be repeatedly tested so that the models can be refined.
If you can check ALL those boxes then you can certainly build a useful computer model.
If you can check EITHER boxes 1(a) & (b) or box 2, but not both, then you still have a fighting chance of building something useful, but it will be very challenging
There are an average of about 86 named storms per year, so the models for predicting their development and tracks get tested over and over, every year. That’s why they’re improving.
But the current crop of GCMs (climate models) are all untested, and they are effectively untestable, because the predictions which they make are for the distant future.
The predictions of some very early GCMs can now be compared against reality, but (contrary to claims by some climate alarmists) they failed miserably. I wrote about the most famous of them, here:
https://www.sealevel.info/hansen1988_retrospective.html
Weather models and climate models have one problem in common: we don’t really understand what we’re trying to model very well. But climate models also have a second problem: their predictions are for so far into the future that they cannot be checked.
Climate modelers sometimes cite “hindcasting” as evidence that their models are accurate. That’s wrong, because that’s the same data the models are tuned to. It’s not evidence that they’re any good at forecasting the future, because, in the words of the late Larry Niven, “Any damn fool can predict the past.”
For instance, a model predicated on the assumption that rates of cheese consumption govern deaths by bedsheet entanglement can do a good job of hindcasting actual deaths by bedsheet entanglement. But it’s still 100% worthless for forecasting future deaths by bedsheet entanglement.
Short-term weather forecasts are often wrong, yet they’re noticeably better than they used to be. They’ve been gradually improving for many years. but those improvements are only possible because 10-day weather forecasts are continually tested against reality. How good do you think weather models would be if their forecasts were untestable, like climate models are?
By the time a GCM’s predictions can be compared to reality, it is long obsolete (and the source code has probably been lost).
Most climate modelers at NASA and elsewhere think that that their sophisticated (but untestable) computer models of the Earth’s climate make reliable predictions. That belief is based on nothing but wishful thinking.
Do you remember NASA’s “sophisticated computer model of the sun’s inner dynamo,” and its prediction for Solar Cycle 24? Those modelers were confident in that model, too.
https://science.nasa.gov/science-news/science-at-nasa/2006/21dec_cycle24/#:~:text=a%20sophisticated%20computer%20model%20of%20the%20sun%E2%80%99s%20inner%20dynamo%20to%20produce%20a%20physics%2Dbased%20prediction
Excerpt:
It turned out to be the weakest in a century.
(The only thing consistent from one solar cycle to the next is that NASA’s “sophisticated computer model of the sun’s inner dynamo” is apparently always wrong. They predicted that Solar Cycle 25 would be another very weak one, but it is shaping up to be much stronger than SC24 was.)
Ask anyone in the weather biz how good they think weather models would be if they’d NEVER been tested at all, and NEVER corrected when they made inaccurate forecasts. That’s the fundamental problem with climate models: they’re basically untestable.
The fact that CMIP6 models vary wildly in their baked-in assumptions about even the most basic & heavily studied climate parameters proves that the Earth’s climate is not well-enough understood to be modellable. If anyone ever says that GCMs do a good job of modeling reality, point out to them that the CMIP6 models differ from one another by a factor of three (!!!) in their baked-in estimates of ECS climate sensitivity. If the modelers knew what they were doing, that’d obviously be impossible.
I doubt if any of them have been trained on the Grand Solar Minimum the Sun has entered. The last time that happened was around 400 years ago.
Better forecast are a good thing but not the only good thing. Experience has show that adequately constructed structures survive hurricanes with minimal damage, yet the $ cost of hurricane damage continues to grow in many instances, along with the value of the human infrastructure in hurricane prone areas. What would be very interesting is a comparison between the repair and replacement costs (always computed after a storm) with what it would have cost to have built adequately in the first place. I know that many people cannot afford to build adequately, it is a societal level problem, but reasonable numbers would be interesting and might be useful.
Let’s just hope it doesn’t cause the death and destruction of the Newfoundland Hurricane of 1775 which was Canada’s all time worst natural disaster, killing over 4,000 people back in the later part of the Little Ice Age.
The tropical storm will again bypass Hawaii from the south, which means an easterly circulation continues in the central Pacific.
I am just guessing that the storm predictions have zero sensitivity to CO2 in the simulation.
Weather forecasts from GCMs are incredibly sensitive to radiative transfer. In fact, they are so sensitive that if, for example, you removed the RRTM from the GFS it is doubtful that even a 6 hour forecast would have the same skill as a 7-day forecast with the RRTM. Radiative transfer schemes were among the first physics modules incorporated into GCMs back in the 70s.
So, in essence, zero sensitivity to CO2.
I’m not sure what you mean. RRTM shows a radiative forcing for 2x CO2 at 4.2 W/m2.
What do they have for water vapor?
A reduction of RH from 80% to 70% results in a radiative force of -3.0 W/m2.
Great to highlight but then again, even a blind squirrel catches a nut every now and then.
From the article: “Better forecasts have great potential for reducing the negative impacts of global warming.”
Well, I have to ask the question: What negative impacts?
I don’t see any negative impacts coming from CO2. I’m assuming “global warming” means human-caused/CO2-caused global warming.
There is no evidence that CO2 is causing global warming, at least not enough warming to be detectable, therefore, no evidence, and certainly not enough to have negative impacts, or change how the Earth’s weather unfolds. No evidence.
Never mind whether CO2 is or is not enhancing warming. The observed warming is not having net negative effects, regardless of the root causes.
Sure, every change potentially benefits some and harms others, but the benefits of more arable land with longer growing season and fewer deaths from cold far outweigh any harm.
The increased green area in the Sahara is the size of Germany and France combined.
From the above article, referring to the second-to-last graph (the bar graph) of positional error versus forecast lead time:
“Wow. The American model is STUNNINGLY accurate at all projections in time.
“It is FAR better than the nominally top two global modeling systems in the world: the European Center and UKMET. The forecast error is under 100 km (60 miles) for all projections shown.
“Extraordinary.”
My take is a bit more reserved. I don’t view a 120 hour-in-advance predicted landfall positional error of about 80 km, nor a 24 hour-in-advance predicted landfall positional error of about 40 km, as anything exceptional.
And this is for the single example of Hurricane Lee . . . what are the similar positional errors vs. forecast lead time for other hurricanes having more erratic tracks over, say, the last three years? . . . much worse I daresay. Without consideration of such, this article can be considered to be nothing more than “cherry picking” to front an unfounded assertion.
After all:
“The first hurricane forecast models (dynamical and statistical) were developed during the 1950’s in response to two major technological advancements: aircraft reconnaissance of hurricanes starting in the mid-1940’s, which provided accurate estimates of a hurricane’s current position and intensity, and the development of computer technology in the mid-1950’s.”
— http://www.hurricanescience.org/science/forecast/models/modelshistory/
This is the best we can do after some 70 years of modeling hurricane tracks, especially given the additional tracking and weather forcing inputs obtained from orbiting weather satellites starting in the mid-1960’s?
Even a blind pig finds a acorn once a while. When they get the tracks right over 50% or the time then we may have something.
Three cheers for a really great forecast. Well done. But I am not clear after reading this if the graph on errors for various models is a statistical measure of performance across many different storms or just about this latest event. Clearly the most meaningful data would be for cumulative accuracy over many storms and seasons. Still I am impressed at how well those who predict weather events are doing and how much more reliable it is than when I was much younger.