Government Weather and Climate Forecasts Are Failures

Guest essay by Dr. Tim Ball

In general, we look for a new law by the following process: First we guess it; then we compute the consequences of the guess to see what would be implied if this law that we guessed is right; then we compare the result of the computation to nature, with experiment or experience, compare it directly with observation, to see if it works. If it disagrees with experiment, it is wrong. In that simple statement is the key to science. It does not make any difference how beautiful your guess is, it does not make any difference how smart you are, who made the guess, or what his name is—if it disagrees with experiment, it is wrong. – Richard Feynman

Richard Feynman’s comment describes the scientific method but also applies to weather and climate forecasting. Many medium and long-term weather and climate forecasts are wrong and below any level of usability. (for example, the Met office forecast for a dry 2013-2014 winter that ended up with major flooding – Anthony) Most forecasting agencies swing between determining their own accuracy level or openly detailing the inadequacy of their work. No production of society is as wrong as government weather forecasts and yet continues to operate. Apparently people just lump it in with all government waste and failure. Their real view is reflected in the fact that few activities receive more ridicule and disdain than weather forecasts.

History of Forecasts

Around 300 BC Theophrastus, a student of Aristotle’s, wrote a book setting out the first rules for weather forecasting. In the Book of Signs, he recorded over 200 empirical indicators such as A halo around the moon portends rain. Many skeptics, including me, say we haven’t come very far since. Indeed, I would argue we have regressed. 

Various attempts to forecast weather and climate exist over the centuries. Benjamin Franklin’s Old Richards Almanac began a service in 1757, especially to farmers, of long-term forecasts. It expanded on Theophrastus’ ideas of weather folklore that actually were climatic observations reflecting seasonal events and their change. It was replaced in 1792 by The Farmers Almanac, now known as The Old Farmers Almanac and used by many people, especially farmers. Founder, Robert B. Thomas combined solar activity, weather patterns, and astronomy cycles to create his forecasts. We can translate those to mean sunspot activity, historical weather data and variations in magnetism to create a better chance of accuracy than the limited variables in most forecasts, but especially those of the Intergovernmental Panel on Climate Change (IPCC). They have a better record of accuracy than official long term forecasts, Consider the UKMO seasonal inaccuracies over the last many years, most recently the prediction of a dry winter in 2013 in one of the wettest on record.

In 1837, Patrick Murphy, an English gentlemen of science published a paper titled, The Weather Almanac (on Scientific principles, showing the State of the Weather for Every Day of the Year of 1838). It included one approximately accurate forecast for January 20, 1838; Fair, and probably the lowest degree of winter temperature. The actual temperature was a remarkable -20°C, the coldest anyone could remember. Heavy ice formed on the Thames, thick enough to allow a sheep to be roasted over a roaring fire at Hammersmith. The temperature seems remarkable today, but was consistent with an earth recovering from the nadir of the Little Ice Age (LIA) in the 1680s set back by the cooling associated with the Dalton Minimum.

The winter of 1838 became Murphy’s winter, however the rest of the year’s forecasts were mostly wrong. His poor results prompted a poem printed in The Times.

When Murphy says ‘frost’, then it will snow

The wind’s fast asleep when he tells us ’twill blow.

For his rain, we get sunshine; for high, we have low.

Yet he swears he’s infallible – weather or no!

This appears just as applicable to the UK Met Office (UKMO ) today.

A Dr. Merriweather from Whitby Yorkshire developed a technique for forecasting weather from watching the leeches he used for bleeding in his practice. He noticed the position of the leeches in their jar appeared to predict the weather. In calm conditions they were placid on the bottom, but if they began to rise up the side a weather change was half a day away. When rain was due the leeches climbed above the water line and if they stayed above the line and curled into a tight ball a storm was coming.

Merriweather wrote a paper titled An Essay Explanatory of the Tempest Prognostication to accompany a special jar he designed with a leech and a bell that rang when the leech left the water. He sold it at the 1851 Great Exhibition (World’s Fair). His failed prognostications are comparable to today’s claims of increased severe weather.

Modern Forecast Failures

Over 200 years ago Lavoisier (1743-1794) said, It is almost possible to predict one or two days in advance, within a rather broad range of probability what the weather is going to be. I understand that because of persistency of weather systems and Markov probability the chances of tomorrow being the same as today are 63 percent. Currently the UK Met Office claims 93.8% accuracy for temperatures for the first day of forecast, but minimum temperatures for the first night are only 84.3%. The problem is both are with a ±2°C error range so the gain on probability is minimal. It appears little improved on Lavoisier’s “broad range of probabilities. Most achieve better results because thy practice what I call “gradual approximation”. They make a five-day forecast and then change it every six hours up to the actual time period. I am not aware of any research that compares the accuracy from the first five-day forecast to the reality. How much change was made to even come close to reality?

Weather forecasts had a practical use during the First World War when airplanes and their pilots were at the mercy of the weather. It is why most weather stations are at airports where they became compromised by heat from runways, jet engines, and in many cases the expanding urban heat island (UHI). Bjerknes created many of the terms used in forecasting such as Cold and Warm Fronts or advancing or retreating frontal systems from battle terminology. Now, as aviation advances the need for forecasts diminishes. Weather needs of aviation are now simply station data of current conditions and only for small aircraft. Larger or more sophisticated aircraft can land in virtually zero visibility so only a closed runway is a problem. The problem with most weather station data is it is not “real time”, so pilots rely on what the control tower is telling them.

Farmers need accurate forecasts either a week or months ahead so they can plan operations. Neither is available with any useable accuracy. Other agencies such as forestry, power producers create their own forecasts, many even collecting their data. The problem is insufficient weather stations to create weather maps with sufficient accuracy to produce useful results. The longer the forecast the more expansive the number of stations involved – looking out five days means weather developing a long way down wind. In most cases this means the gaps of information simply are too great.

Public images of weather forecasting come from television. It is the 2 or 3-minute segment at the end of the news that is forgotten shortly after it’s presented. Most stations try to hype the information with visuals and hyperbole. Some present “Extreme weather” or present it from the “Storm Center”. They distort reality presenting wind chill or heat indices as if it is actual temperature. Everything is exaggerated and that causes people to pay less attention. They lose more credibility because they frequently fail to forecast extreme events.

I began flying before computer generated weather maps were introduced. Weather forecasts were not very good, but certainly better than those that are made today. In those days the weather person took individual station data and plotted their own isobaric based maps. While preparing the map they developed a sense of the weather patterns that they then combined with experience of local conditions. Still there was little faith in the forecasts, especially for people who needed a more accurate product. Hubert Lamb as a forecaster for the UKMO took seriously complaints about poor forecasts from aircrew flying over Germany during WWII. He realized better forecasts required better knowledge of past weather and that was a driving force for establishing the Climatic Research Unit (CRU).

When Wigley took over from Lamb he took the CRU in a different direction effectively abandoning reconstruction of past climate. The work some were doing exposed the limitations of the data and the computer models ability to create accurate weather maps. Benjamin Santer a CRU graduate completed a thesis titled, Regional Validation of General Circulation Models. It used three top computer models to recreate North Atlantic conditions. Apparently the area was chosen because it was the largest area with the best data. Despite this the computer models created massive pressure systems that don’t exist.

Santer used regional models in 1987 but things haven’t improved in 21 years. In 2008 Tim Palmer, a leading climate modeller at the European Centre for Medium-Range Weather Forecasts in Reading England said in the New Scientist.

I don’t want to undermine the IPCC, but the forecasts, especially for regional climate change, are immensely uncertain.

How uncertain is reflected in the skill testing measures carried out by the National Oceanographic Atmospheric Administration (NOAA) in the US and Environment Canada.. Figures 1 and 2 are NOAA measures of 3 month forecasts.


Figures 1 and 2; Skill Test of 3 month forecasts

The following explains how the test works.

The term “skill” in reference to forecasts means a measure of the performance of a forecast relative to some standard. Often, the standard used is the long-term (30-year) average (called the the climatology) of the parameter being predicted. Thus, skill scores measure the improvement of the forecast over the standard.

CPC uses the Heidke skill score, which is a measure of how well a forecast did relative to a randomly selected forecast. A score of 0 means that the forecast did no better than what would be expected by chance. A score of 100 depicts a “perfect” forecast and a score of -50 depicts the “worst possible” forecast. The dashed lines in the skill graph indicates the average skill score for all forecasts and for “Non-CL” forecasts. “CL” refers to climatology or a forecast of equal chances of Above, Near Normal, and Below Normal temperature or precipitation. “Non-CL” refers to all forecasts where enhanced above normal or enhanced below normal temperatures or precipitation are predicted. “Percent Coverage” refers to the percent of the forecast region where enhanced above or below temperature or precipitation is predicted.

The results are very poor; barely better than chance in most cases.

Environment Canada does a similar skill test. Figures 3 and 4 are examples of 4 to 6 month (left) and 12 month (right) temperature forecasts. Precipitation forecasts are even worse.

Figure 3: 4 to 6 month forecast and % correct Figure 4: 10 to 12 month forecast and % correct


Canadian results are worse than the US with the average in Figure 3 a mere 44.6% confidence and 41.5% in Figure 4. These examples were randomly selected and in most cases the results are worse as you can see for yourself. Again precipitation forecasts are worse.

A 2005 news release said, NASA/NOAA Announce Major Weather Forecasting Advancement. In light of the results identified above, JSCDA Director Dr. John LeMarshall made an odd statement.

“A four-percent increase in forecast accuracy at five or six days normally takes several years to achieve,” “This is a major advancement, and it is only the start of what we may see as much more data from this instrument is incorporated into operational forecast models at the NOAA’s Environmental Modeling Center.”

What does it mean that forecast accuracy has improved in 19 years (2014-2005)? If we assume that half that time (~9 years) is several years then accuracy presumably improved by 8 percent for 5 to 6 day forecasts. The trouble is you are starting from virtually zero accuracy at 5 to 6 days. It will take centuries to reach a useful level. The important question is at what point do you acknowledge that the science is wrong?

A recent WUWT article reporting on medium term forecasts detailed a study that concluded, “there is still a long way to go before reliable regional predictions can be made on seasonal to decadal time scales. ”That is understating the problems and potentials. Data is inadequate in all dimensions. Numbers of variables included are insufficient. Knowledge and modeling of mechanisms are inadequate. Computer capacity is insufficient. Nobody uses the output because it is so unreliable. It only continues because the government is funding it and bureaucrats who produce it are telling politicians it is valuable. It isn’t.

The IPCC never acknowledge their science is wrong. Every single IPCC forecast from the 1990 Report on was wrong. Instead of re-examining their science they began what became their standard practice of moving the goalposts. The full politicizing of climate science took hold between the 1990 and 1995 Reports. Instead of acknowledging the science was wrong they changed from forecasts to projections and all those were incorrect since. Figure 6 shows Clive Best’s three IPCC levels of projection from 1990 against actual surface (blue) and satellite (green) temperature.


Figure 6

Short (hours and days) medium (weeks and months) and long term (decades) forecasts are all wrong, being close to or less than chance in almost every case. Despite billions of dollars and concentrated efforts at improvement there is little or no improvement. If such a level of forecast failure persisted in any other endeavor logic would demand, at the very least, an acknowledgement that something is fundamentally wrong with the science.

Maybe the February 20, 2014 story in the Daily Caller will stir some response as the government forecasters are insulted in the worst way for their profession. The headline reads, Report: Farmers Almanac More Accurate Than Government Climate Scientists. I know most people are not surprised, they’ve known for a long time government forecasts are mostly wrong, very expensive and of very little value. As Thomas Sowell so pointedly asked,

Would you bet your paycheck on the weather forecast for tomorrow? If not, then why should this country (US) bet billions on global warming predictions that have even less foundation?

It is worse than that. The government science is wrong and therefore their forecasts are wrong. From this base they push policies that are the opposite of the evolving and future conditions.


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Great essay! One correction, though: I believe Benjamin Franklin’s original attempt at forecasting was called “Poor Richard’s Almanac” not “Old Richard’s Almanac.


Here is what the Met Office comes up with from their multi-million $ supercomputers:
“The probability that UK precipitation for December-January-February will fall into the driest of our five categories is around 25% and the probability that it will fall into the wettest category is around 15% (the 1981-2010 probability for each of these categories is 20%).”
I think in laymanese that means: “There’s a small chance it’s going to be very dry, a slightly smaller chance it’s going to be very wet and a very large chance it’s going to somewhere in between very dry and very wet.
So I disagree with Dr. Ball. With forecasts like that, they simply cannot be wrong.

Vince Causey

Richard Feynman’s way is the old way.
The modern method is take a hypothesis and formulate a prediction. Then build a computer model that also makes some kind of prediction from the hypothesis. Check the computer output against the original prediction. If the two disagree, modify the computer parameters until they do agree. If they agreed first time, skip this step. Finally announce to the world that your hypothesis is very probably correct.
You can also use the computer model to calibrate real world measuring devices that some still feel is necessary in checking the prediction. If the observed data disagrees with the models that have been validated against the hypothesis, then make adjustments to the observed data until they agree. This ensures accuracy of the measured data which were obviously wrong.


Excellent historical perspective. Thank you, Dr. Ball. Many of the academic professions are afflicted with what is called “historical presentism,” which is essentially generational intellectual chauvinism. Climate Science may be one of the worst fields in that respect. Granted the information provided by satellite and other modern methods should give them a leg up on their predecessors. But they are apparently misusing the new technology and the data it creates or completely oblivious as to how best to use it.


Great programs you have with Sheila Zilinsky . I think they are even better then the work you did with Kim Greenhouse.
If people think science is boring they must have had bad teachers.

The problems with both seasonal/yearly forecasts and long term models is the most important reason for my rejection of the mainstream science. When a modeler can start producing forecasts on a global scale (i.e. total annual precipitation, cloud and ice cover, surface and high level temperatures, etc.) for 2-3 years ahead that are as accurate as current 2-3 day forecasts (i.e. usually enough to forecast the general positions and intensities of low and high pressure systems) then I will accept that they have a pretty good general handle on what is going on.
It will happen one day, but probably not in my lifetime.

“So I disagree with Dr. Ball. With forecasts like that, they simply cannot be wrong.”
they will FIND A WAY!!!
(I think they were far more honest when they “cast the bones” openly)

The “science” that is the base of meteorology is wrong. Surface heating or cooling is not the cause of high or low pressure areas but the result! Back to the drawing board boys. You would think that, after 100 years of little improvement in forecast, someone in the “science” would get a better understanding of cause and effect in this field. I would suggest the examination of magnetic & earth electrical fields might be the key. These are not as stable as most would assume. pg

Tom Anderson

Offering a trivial nitpick: “Poor” Richard’s Almanac. I have a copy on the shelf and double checked.
But it’s a first rate discussion!


“The probability that UK precipitation for December-January-February will fall into the driest of our five categories is around 25%”
I.E. the MO gave a 75% probablilty that it wouldn’t fall into the driest category, and they were right.


Unfortunately, no matter how good data you would have, no matter how fast computers you would have, no matter how much memory you would have, there are inherent errors in measuring and more, perturbations that cannot be forecasted do occur. This makes the attempt to forecast weather (or climate, for that matter) to be futile for much more than 10 days. The Lyapunov exponents take over. Discretization errors, measurement errors (which currently are quite big to create troubles even for short time) do not matter after a long enough period of time.


Right from the start one thing that stood out for me , was the claims of great accuracy over great periods of time were being made from an area notorious for its inability, because of its complexity and many poorly understood factors, to predict thing to a good level of accuracy more than 72 hours head.
In realty before the AGW scam we all knew how forecasts in this area where problematic and we all accepted that was the nature of things. It was only once a toxic combination of claims of great accuracy , massive spending requirements and demand for wide spread social changes came about that we started to demand ‘prove it ‘ Once that occurred the old ‘problems ‘ of uncertainty came back to haunt them for they never actual go away. And so as the stakes got high and this new found religion came about , the claims became sillier and sillier with some very unscientific approaches being taken , such as the dehumanized the sceptics .

Mindert Eiting

Someone who always lies, is very reliable. You only have to take the negation of what he says in order to know the truth.


I long for the good old days when temperature was measured in sheep roasting on the Thames units. /sarc.
Seriously, I’ve noticed that this winter the 5 day predictions of storminess have been quite accurate for the northeast US in terms of timing. Amounts of snow much less so, but still not bad compared to the past.


Being pilot, weather forecasts are important. One learns early on to be prepared for weather that is not the forecast, else the consequence be a pine box. There has been more than one anxious moments of “WTF is this.” Having been around awhile and being astutely observant of weather, it can quickly morph to something unexpected. Relying on a forecast is only a rough guide. NOAA has a web site, as to many others at our disposal that has improved the picture. Plus there are web cams in many airports, towns and cities providing a real time “look see.” Life is better in that regard.
BUT…It seems that some, not all, weather forecasters are geared at sensationalizing their weather forecasting. I imagine the billions spent on forecasting weather keeps some people’s liquor cabinet well stocked. People in general like all the graphics, pretty colors, and video snips of sensational weather. The tornado chasers, Weather Channel personalities bravely (stupidly) doing their show in the middle of a blizzard or hurricane, brings it all to the living room. It teaches to some that it is ok to be stupid. Natural selection I suppose. 🙂


As a professional meteorologist, I would disagree that the short-term forecasts are “wrong”. With faster computers and more sophisticated models, there has been a vast improvement in short-term accuracy over the past thirty years. Accurately forecasting the arrival of significant weather events as much as five days in advance is pretty routine, now.
However, for long term forecasts, like anything over a week, there’s no question we still have limited skill. It surprises me that some people have so much faith in year, decade or century-long predictions when skill drops off so rapidly after about a week.

Colin Porter

The problem with the Met Office is that they are still employing leeches to predict the weather and climate.

Stephen Kelly

But Piers Corbyn got the forecast of the extream cold in the USA right on the button, and he had the winter in the UK correct MIld and Wet.

Paul Westhaver

I think it should be a mandatory requirement that forecasters preface their daily predictions with their historical failure rate. That is what I do by second nature. I hear the weather and then doubt enters my mind….”I wonder if it will be wrong again”.
I use animated satellite and radar now and make my own judgements…All I need is 6- 12 hour advance warning most time.


“I would argue we have regressed. ”
Indeed, because real meteorologists are being retired and replaced by computer programs. Weather forecasters no longer to much forecasting they read the computer print out and present it.
Weather forecasting requires specific local knowledge, ie. on the ground EXPERIENCE. And such knowledge will not be incorporated into some computer algorithm.

Paul Westhaver

Stephen Kelly,
🙂 even a broken clock is correct twice a day.
I don’t know who Piers Corbyn is and I am not flaming you or him. Just couldn’t hep myself.


Colin Porter says: “The problem with the Met Office is that they are still employing leeches to predict the weather and climate.”
That’s no way to talk about climatologists !
Well, OK I can think of a few ….

Ed Zuiderwijk

You can say what you want about forecasts done by leeches, but one thing is certain: it’s soooo much cheaper than getting the same result with a supercomputer.


@Jack- Perhaps in certain areas weather forecasting is accurate, but where I live in the foothills of Colorado, I would say the accuracy rate is less than 50%. Maybe it’s the topography, but the only way I can truly know what the weather conditions will be is to look at the current surface conditions and radar. NOAA and the online weather sites have incorrectly predicted heavy snow at least five times this winter. The snow was either nonexistent or only a flurry. Today, all weather sites predicted the high to be about 38 degrees here in Evergreen. When I awoke just before six this morning, it was already 41 degrees. It is now 51 degrees and climbing. They are also predicting snow this afternoon, and I don’t see that happening either. That’s not what I would call accurate.


Anyone living in a country like Canada with the second largest land and sea mass on earth isn’t surprised by Canada’s 44% accuracy record. Moreover we do not have anywhere near enough data stations for such a vast land and sea mass to improve much in the foreseeable future. Much of this nation is uninhabited or scatter so thinly it makes little sense to spend money we don’t have to improve much in the future. Which is why I have often argued on here we have no idea of what global temperatures are with any degree of accuracy when both Russia and Canada, the two largest land and sea masses on earth, are so poorly served. Moreover, forecasters are only as good as the information they are given like any modern method of information gathering systems, and when most of the northern hemisphere are the very worst two gathering systems how anyone can pretend they can make two hundred year “climate” forecasts on the above are insane.


Even short-term forecasts will never be perfect. We are dealing with non-linear processes and are starting out with what can best be described as poorly sampled initial conditions. But I don’t think it’s fair to say there have been no improvements in short term weather forecasting.
Where we really miss the mark is anything past about a week to ten days, which is what I think was the main focus of the article. Why is there so much faith in climate models when we can’t even get weather forecasts correct a week in advance? It’s a very good question and one that advocates of CAGW seem to miss.

Stephen Kelly

Paul Westhaver, What has a brocken clock got to do with weather forecasting or anything else for that matter. Corbyn is a scientist not a clock mender.


I wasn’t speaking of long-range forecasts. I was speaking of next-day forecasts, which seem to have worsened over the past few years. There is too much reliance on models, which have proven time and time again to be inaccurate. I don’t believe I have seen a modern weather forecast on television recently in which there wasn’t some mention of the all-important computer models.


The reason why weather forecasts rely so heavily on models, today, is that it’s very difficult to beat them. In my office, each forecaster’s performance is compared with model consensus. At the end of each year, maybe three out of ten forecaster’s performance was better than the model performance. Now, you might argue that the team of forecasters at my office wasn’t very good, but this was something common to a vast majority of National Weather Service offices in the U.S.
Over time, a lot of forecasters simply gave up trying to beat the model consensus and just went along with it.


I think you have a point, but I also think it is a liability issue. When I lived in New Orleans, one of the local forecasters made a forecast different from the NWS. His forecast turned out to be incorrect, and some boaters got into trouble. Since them, I’ve heard him say what the “official” forecast is and then tweak it to fit what he believed. Do you really believe that models that achieve less than fifty percent accuracy, can’t be beaten by humans?

re Piers Corbyn
In 4,000 Weather Test Bets over 12 years with William Hill, Weather Action forecasts made a profit of some 40% (£20,000). The Odds were statistically fair and set by the Met Office before being shortened by William Hill by a standard 20%; the results were then provided by the Met Office for William Hill to settle each bet. Piers Corbyn was excluded by the bookies from such account betting in 2000.
any model that makes money at the bookies will have truth in it which is why they stopped taking his bets. If the co2 deathstar climate models could predict they would be making a fortune in bets. But they don’t yet politicians place billion dollar bets of taxpayer money on their predictions.
next time you want some tootsie froostie ask a co2 deathstar believer

Bloke down the pub

Jack says:
February 25, 2014 at 9:03 am
As a professional meteorologist, I would disagree that the short-term forecasts are “wrong”. With faster computers and more sophisticated models, there has been a vast improvement in short-term accuracy over the past thirty years. Accurately forecasting the arrival of significant weather events as much as five days in advance is pretty routine, now.
When trying decide whether it’s going to be good weather to go camping for the weekend, I’ll consult a number of different forecasts. The Met Office forecasts available through the BBC can be accessed on a number of different formats, eg as part of a news programme, on teletext or online. There have been occasions when I’ve viewed half a dozen forecasts in a short space of time, sometimes from the same presenter, where the forecasts were barely recognisable as being for the same day. If the short-term forecasts are not ”wrong” then perhaps they are not even wrong.


Whether it is an argument for “global” warming?
“Winter Has Not Been Cold Everywhere
According to Senior Meteorologist Brett Anderson, “Despite the cold winter over eastern North America and much of Russia, it was business as usual for much of the globe over the past few months.”

The local weather forecasts, not knowing where they get their forecasts, have been doing a reasonably useful, as opposed to accurate, forecasts for a number of years. If they predict an 80% chance of rain for tomorrow, tomorrow virtually always has many visble storm clouds. Most of those drop rain. Like eastern Colorado, the weather in southcentral PA is highly variable. Many forecast will be something like “snow, progressively heavier north of the turnpike, mostly rain to the south” with a proviso “that’s the most accurate prediction, but as usual, if the storm shifts a few miles so will the snow, it just depends on where the freezing line ends up”. We can have heavy snow 20 miles away from heavy rain, or a snow forecast than can vary by multiple inches over the same distance. Not absolutely accurate, but useful. The weather will be bad tomorrow. Stay home if you don’t really need to go out, and then be very careful.
Much of the weather forecasting now seems to combine previous pattern forecasts with computer modelling, seasoned with a bit of experience. “we saw this pattern in 1993, but the low pressure looks like it will be a little more agressive and drift a bit further north and the winds are likely to push the high pressure further out to sea.” The final result is about as good as we used to get when my mom listened to Krick Weather Central on the radio. It was usually much better than the local weather reports.

“It is worse than that. The government science is wrong and therefore their forecasts are wrong. From this base they push policies that are the opposite of the evolving and future conditions.”
I agree with that assessment totally. Great article by the way, I really enjoyed it. I have your new book in hand from Amazon as it just arrived. Now if I can just get some spare time to read it.


Agenda 21 Be afraid be very afraid. Humanity is in big problems but not because of the weather. Climate never was a problem.
If only the weather would be our biggest problem the world would be a better place to live.
They know who we are and they know where we live while we talk about the weather.


The re are failures and there are protected classes. We are dealing with the latter in the cases of the MET office and NOAA.


Jack says:
The reason why weather forecasts rely so heavily on models, today, is that it’s very difficult to beat them. In my office, each forecaster’s performance is compared with model consensus. At the end of each year, maybe three out of ten forecaster’s performance was better than the model performance. Now, you might argue that the team of forecasters at my office wasn’t very good, but this was something common to a vast majority of National Weather Service offices in the U.S.
Over time, a lot of forecasters simply gave up trying to beat the model consensus and just went along with it.

The same scenario happens in the Canadian gov’t forecast system. Forecasters are so heavily verified against the models that they eventually just go along with the models. That way the forecaster at least won’t be beat by them. Some have little tricks to beat the verification systems, but not to try to produce better forecasts. In Canada, forecasts beyond two days are now strictly computer generated. The forecaster is not even allowed to touch them, even if there is something obviously wrong. Overall, I would say the forecast skill has decreased in the last decade. In large part due to a lot of very experienced people retiring and the new people not being allowed to apply the science lest they may be beaten at times by the models.
Possibly it will open up opportunities for private forecast companies. However, who is going to pay for a forecast when it is now free and, for the most part, “good enough”? I do private forecasting for a very specific niche. However, that could change if the clients decide to go with a free forecast that they deem is “good enough”.

Here are some of my cartoons dealing with bad forecasts:-
The Met off:-
A satirical sequence about BOM:
Climate supercomputer:-


By design? I think so.

Lil Fella from OZ

They can’t predict the weather. Therefore they cannot be accurate on climate. Simple!


The weather was the inspiration for Chaos Theory.
The point about chaotic systems like this is that if you know everything about every molecule on the planet, and you have infinite computing power and the best programmers, you still can not predict the future state of the system.
More commonly known as the butterfly effect the question is whether methods used by the Farmers Almanack are identifying regular butterflies! That is, knowable influences which can give a general idea of what kind of weather to expect.


Quick nitpick:
This does not really apply to the current issue of weather-forecasting, but disagreement with experiment does not necessarily imply that a theory is wrong. There are two other possibilities:
First, the experiment could be wrong. Machinery and procedures must be checked. More often than not, I suspect that causes of disagreement between good-looking theories and experiment lie in the metadata of the experiment. For example, if the scale flooding had been measured by property-damage and just happened to be concentrated in coastal cities, then there could be a relatively dry winter with severe flooding.
Second, the theory may be correct, but incomplete. This is a central issue in current climate-science. Many of the scientific theories which have been produced are, strictly speaking, correct. However, there were other factors not included in calculation of effects which would alter the results of experiments. We see this in, for example, the omission of cloud-based feedback from climate-models.

Jeff L

Nice Summary.
Personally, I don’t feel the situation is quite as dire. My local forecast office (Boulder CO) provides the best local forecasts, IMHO – better than the local media based forecasts.
If you have the technical skill & time to analyze the current data & model outputs, you certainly can understand the big picture enough that you can make plans based on it & understand the potential uncertainties of what might happen. That’s not a forecast per se, but it is an understanding of the weather that can be used for decision making.
I make the above point because all the data & model outputs are generated by the government – and I do find them to be very useful in making forecasts & planning, so for me I wouldn’t say the money spent on the weather service is a waste – on the contrary , I would have a hard time making reasonable forecasts & planning with out it.
Now the challenge is to be able to convey these uncertainties & range of outcomes to the general public. It has to be concise, quick & easy to understand, even for the non-educated. That’s a big challenge. Until that can be achieved, the general public’s opinion of forecast accuracy will not improve.
In summary, I think our understanding of the weather & what is going on weather-wise is probably better than our “forecasting”, as measured by skill scores so we need to do a better job communicating our understanding of the current & upcoming weather.

Richards in Vancouver

Ed Zuiderwijk says:
February 25, 2014 at 9:30 am
“You can say what you want about forecasts done by leeches, but one thing is certain: it’s soooo much cheaper than getting the same result with a supercomputer.”
Ed, you don’t understand. Nowadays the leeches are running the supercomputers.

Jeff L

Patrick says:
February 25, 2014 at 9:42 am
“Today, all weather sites predicted the high to be about 38 degrees here in Evergreen. When I awoke just before six this morning, it was already 41 degrees. It is now 51 degrees and climbing. They are also predicting snow this afternoon, and I don’t see that happening either. That’s not what I would call accurate.”
This is a classic example of what I was talking about in my last comment – a major bust in specifics but reasonably accurate for the regional weather – the regional weather was understood well but that didnt translate into accurate specifics locally.
Today, in northern Colorado, we have a shallow arctic air mass pushing in east of the mountains. Weather is 3 dimensional & Evergreen , at least at the time of your post was above the front – the cold air had not pushed up that high into the hills. Now, for your sensible weather , the forecast was wildly off, however, had it been better communicated what was going on,you wouldn’t be surprised. It needed to be communicated that the cold air was very shallow & that the air above was very warm & that the timing of the front will greatly impact the high temp for the day, especially in the foothills. By forecasting 1 number (38), it really didn’t communicate to you the information you needed to understand the weather of the day – that is the communication issue I mentioned in my previous comment. I am guessing that by now the temperature has dropped back into the teens & you are seeing a few flurries.
All of this was known but not properly communicated. The regional weather is happening pretty much as expected, although the pinpoint details have varied from the “official forecast”.
Patrick, if you are reading this, my base case forecast for Evergreen would be less than 1 inch of snow this afternoon & evening. Most of the lift will stay north & the upslope is shallow. So although I gave props to the NWS in my last post, I do disagree with them on this forecast , where they were forecasting 2-5″ – the basis for this was the idea that the lift would drop further south. Latest radar trends & high res NAM model say otherwise.
Also , FWIW, I know the storms you are referring to that missed us this year. The problem – NW flow aloft – very hard for us to get significant / deep upslope into the southern foothills with NW flow aloft – which we have had pretty much had all winter, including today.

Margaret Smith

Have I missed something or did the 24hr forecasts get better after the weather satellites became available?

Jeff L

Final comment on forecasting :
Forecasting is as much an art as it is a science. You can be a trained artist but that doesn’t mean you will create a timeless masterpiece. The same is true with forecasters. They may all be degreed scientists but that doesn’t mean they all can create forecasts of equal accuracy. They all start with the same data & models but those who are great can see what others can’t . They can see what the computers can’t. They can create great forecasts long before their peers & before the computers can.
I know there are a fair number of geoscientists on this blog (myself being one). By an analog you will understand, it is much like exploring for oil & gas. There are those can find it & those who can’t despite everyone being degreed scientists. In fact , the majority can’t find it. 20% of the explorers make 80% of the discoveries. The ones who can find it will see what others can’t in the data & they will find new deposits. By analog, they are essentially “oil & gas forecasters”.
And the analog goes further in that there is no substitute for experience. Experienced forecasters have seen so much weather & all of that goes into their thinking when generating a forecast, just as experienced explorers have seen so much data & that all goes into their thought process.
What’s this all say – the weather forecast you get is only as good as the forecaster that generates it & all forecasters are not created equal.
No reason to believe this isn’t also true for climate forecasting.
As a side comment, much of what passes as a forecast these days is actually just grid extracted model output – and the public is fed a lot of that info via the web , mobile apps , etc. This will always be your worst source of forecast information as it has zero human touch in generating it. It is also not helping the negative perception of weather forecasting.


Hurricane forecasts; I’ve always had this feeling, and never able to prove, that I want to be near the center of the 5 to 7 day track. The storms always deviate. The closer to edge of the track, well, is the storm going to deviate left or right? 3 days generally dictate what preparations are prudent. If you’re in the 2 days generally your going to get wet to some extent or another. Any forecast beyond 7 days I use just to note when I need to start paying attention to the forecast.
Despite my aversion to weatherunderground CAGW meme I do use the website for tropical weather analysis. Masters seems to have a really good grasp on which models are handling hurricanes in this year and this part of the season. Global warming models, my opinion, not so good