Reposted from the Cliff Mass Weather Blog
Weather prediction is an essential technology that both protects the economy and saves lives.
National Weather Service personnel are considered critical personnel and are still working, but they are dependent on numerical weather prediction models, which in turn are dependent on the quality and quantity of weather data going into them.
And it appears that one important data source is declining rapidly in volume, aircraft observations.
And such observations are particularly important for the West Coast of the U.S., which has a vast ocean to our west.
To produce a numerical weather prediction, a three-dimensional description of the atmosphere needs to be created, something called the initialization. Over land there are lots of surface observations and balloon-launched weather observations (radiosondes), but obviously there are far fewer of these over the ocean. In the old days of numerical weather prediction, forecast skill was less downstream of oceans because of the large oceanic data voids.
But this situation changed profoundly with the advent of weather satellites and the use of weather observations from commercial aircraft. The oceans now had substantial numbers of observations, driving a rapid increase of weather prediction skill. Weather satellites are now the dominant source of oceanic weather information, but aircraft observations (known as ACARS observations or AMDAR) are quite important.
The distribution of aircraft observations in January 2020 is shown below (courtesy of the the European Center –ECMWF). The number of observations is shown by the colors (red and orange are the most). Importantly, the are a large number of observations between the West Coast and Hawaii, most of which are at important jet stream elevations (30,000 to 40,000 ft). There are also considerable number of observations from flight going between North America and Asia.
As noted by this graphic from ECMWF, the number of aircraft observations has grown rapidly due to more flights and increased numbers of aircraft with the appropriate weather sensors.
A number of studies have examined the importance of aircraft observations for weather prediction. As illustrated below, automated aircraft observations (AIREP) are about fourth in importance overall (more important than surface observations!), and data denial experiments at ECMWF, in which they reran forecasts without using the aircraft observations) indicated a decline of forecast skill in the upper troposphere (again roughly 30,000 to 40,000 ft) by about 10% and some degradation near the surface (by roughly 3%).
Not the end of the world, but significant. But what about regions downstream of oceans? Could the impact be larger? That is an analysis I have not seen.
But there is a problem, particularly for us on the West Coast for short-term forecasts and for the entire nation in the longer term. There is a huge decline of air travel going on now. And the decline in air travel is about to plummet.
Hawaiian Airlines will soon cancel most of its flights to the mainland, and Alaska is planning on pausing on about 70% of its flights (some to Hawaii). Flights to Asia are down profoundly already. The latest statistics from the FlightAware website indicates nearly 16,000 flight cancellation today, with nearly half cancelled out of San Francisco and about a quarter at SeaTac and LA (red colors below). This is only the beginning.
So how much degradation in forecasts will occur as aircraft observations profoundly decline? To what degree will the impacts be greater for land areas downstream of oceans?
Considering the key role of satellite observations, one might expect the degradation to be modest, but perceptible.
The latest forecast skill statistics over the Pacific/North American area (called the PNA region) available from the National Weather Service for the five-day forecast of near jet stream level (see below) does not suggest anything significant at this point (particularly since there is a lot of natural variation in forecast skill). In this plot, 1 (top) is a perfect forecast and several forecast models are shown (black–US GFS, red-European Center, green-Canadian), orange-UKMET).
Numerical experiments to determine the impact could be done, but will probably be relatively low priority.
Aircraft in flight at 9:30 AM this morning (from the wonderful FightAware site). This is already well down from normal, but a lot more than will be flying in a week
In the longer term, the impacts on weather prediction will be substantial for other reasons. Weather research and communication is being profoundly degraded. Major meetings and conferences have been canceled (including the NW Weather Workshop) and research is made difficult and far less effective. We are all trying to work at home and use online communication, but the degradation is real and will increase with time.
The predictive track record of the USA’s Nation Weather Service is abysmal. It could not get much worse – you could do better with a dartboard.
https://wattsupwiththat.com/2018/05/climate-scientist-air-pollution-cleanup-may-be-major-driver-of-global-warming/#comment-2365792
One more successful prediction – this one by my friend Joe D’Aleo.
One of my friends and co-authors is Joe D’Aleo, an American Weather Forecaster who was the Founding Chief Meteorologist for the Weather Channel.
The National Weather Service (NWS) of the USA forecast a warm winter for 2014-15 and Joe told me in October 2014 that the NWS forecast was seriously incorrect, and that the next winter would be particularly cold and snowy, especially in the populous Northeast. This was the second consecutive year that the NWS has made a highly incorrect (excessively warm) Winter forecast, in Joe’s opinion – and he and his colleagues at WeatherBell have an outstanding track record of accurate forecasts.
Joe and I had been working together on a paper on Excess Winter Mortality, and I suggested to Joe that this false “warm winter” NWS forecast was dangerous, especially if the country and its people were unprepared. Joe agreed, but did not know how to tackle the problem.
I proposed an approach, and we prepared a presentation for my friend at the US Energy Information Administration (EIA). At the EIA’s request, Joe then prepared his own Winter Forecast by month and by region, and the EIA re-ran their Winter Energy Demand calculations. Using Joe’s forecast, the EIA projected 11% more total winter energy required for the entire USA than the “warm” NWS forecast had projected. That is an awful lot of energy – mostly oil, natural gas and coal.
After that brutally cold and snowy winter, a back-analysis showed that the actual winter energy used was 10% more than the projection using the NWS weather forecast, and just 1% less than that using Joe’s forecast.
I’m not sure if we saved any lives, but I still think we did a good deed.
Regards, Allan
I love dartboard analogy, although that does imply you can actually aim at getting it right. Maybe “pin a tail on a donkey” with a blindfold you be more representative.
What a desperate attempt to remain relevant. As convincing as Greta’s “extremely likely”.
with the entire friggin country shut down, it’s going to need more than a good weather forcast to keep it from imploding entirely. People suffocating with water filled lungs need ventilators not a weather outlook.
This cynical attempt at self promotion just points out how worthless most weather services are.
Yeah! There is a chance of approximately 100% that some inaccuracies might (will) seep into the temperature and humidity and rainfall and drought statistics during this time. It will, however, be impossible to separate these inaccuracies from the background inaccuracies which dwarf all reason and random chance since the beginning of weather observance (God of Thunder days).
For the least accurate results, best to core a tree on a random mountain in Russia and apply unknown mathematical gobbeldygook formulas to it. Then put a plus sign in front and publish before getting in line for your Nobel prize.
For reasonable results feel free to use my personal approach. The weather prediction for tomorrow is 75% likely to be correct. The weather report 3-4 days out is 50% likely to be correct. For weather one week out, assume the forecast is 100% incorrect and plan for the opposite of what it says. Weather farther out is tricky unless you have a prediction from a Global Warming “enthusiast”, in which case it is safe to assume they have no idea whatsoever.
The predictive track record of the USA’s National Weather Service is abysmal. It could not get much worse – you could do better with a dartboard.
https://wattsupwiththat.com/2018/05/climate-scientist-air-pollution-cleanup-may-be-major-driver-of-global-warming/#comment-2365792
One more successful prediction – this one by my friend Joe D’Aleo.
One of my friends and co-authors is Joe D’Aleo, an American Weather Forecaster who was the Founding Chief Meteorologist for the Weather Channel.
The National Weather Service (NWS) of the USA forecast a warm winter for 2014-15 and Joe told me in October 2014 that the NWS forecast was seriously incorrect, and that the next winter would be particularly cold and snowy, especially in the populous Northeast. This was the second consecutive year that the NWS has made a highly incorrect (excessively warm) Winter forecast, in Joe’s opinion – and he and his colleagues at WeatherBell have an outstanding track record of accurate forecasts.
Joe and I had been working together on a paper on Excess Winter Mortality, and I suggested to Joe that this false “warm winter” NWS forecast was dangerous, especially if the country and its people were unprepared. Joe agreed, but did not know how to tackle the problem.
I proposed an approach, and we prepared a presentation for my friend at the US Energy Information Administration (EIA). At the EIA’s request, Joe then prepared his own Winter Forecast by month and by region, and the EIA re-ran their Winter Energy Demand calculations. Using Joe’s forecast, the EIA projected 11% more total winter energy required for the entire USA than the “warm” NWS forecast had projected. That is an awful lot of energy – mostly oil, natural gas and coal.
After that brutally cold and snowy winter, a back-analysis showed that the actual winter energy used was 10% more than the projection using the NWS weather forecast, and just 1% less than that using Joe’s forecast.
I’m not sure if we saved any lives, but I still think we did a good deed.
Regards, Allan
“The predictive track record of the USA’s National Weather Service is abysmal. It could not get much worse – you could do better with a dartboard.”
Don’t you mean he predictive track record of the USA’s National Weather Service for the winter of 2014-15 was abysmal?
Even if we accept your 6 year old “Joe” anecdote as evidence for the winter of 2014-15, you’ve provided nothing else.
The NWS routinely makes too-hot winter forecasts – Winter 2014-15 was one of many.
Because they are convinced that snow is a thing of the past. Those biases are build into the climate models they also use for weather prediction.
Great story about the d’Aleo projection and the use you were able to make of it. Outstanding and effectively done.
Greg – you are correct – I had a long talk with Joe D’Aleo about this subject within the last 6 months.
NWS climate and weather models are apparently programmed to run hot, and then the results of the weather models are cooled to approach actual temperatures as the actual date gets close.
I am no expert in this field, but I understand that in addition to weather models, Weatherbell use historic analogues to tune their long-term forecasts. That is one of the keys to their continued success.
As you may know, the other senior forecasting expert at Weatherbell is Joe Bastardi – these guys are good!
“routinely”, “many”, such as?
Your point is?
Allan, I refer you to this link;
https://worldofwarcraft.com/en-gb/character/eu/ysondre/Loydo
Might explain a few things.
A very interesting discussion of accuracy vs precision should ensue…
Precisely, but its not accurate what you say!
Propaganda works. All people hear is warmer, warmer…but they never hear…oops we were wrong. And so the average TV viewer gets the warmer message embedded so deeply in their brain that it cannot be removed, even by reality. It’s not bias driving the predictions warmer it’s engineered mind control of the TV viewing masses. It works, and it works well.
One comment on previous post here by a construction related professional (works outdoors). His observation was 10 day forecasts were never correct and that even 3 days out were problematic. What I have not stumbled across is a a plot of TWC predictions vs reality…”we could see near record highs…this week…” for example…plot reality against 3 days, 7 days and 10 days out predictions by TWC (or NWS) for a given (high population) place in the country and see how they do.
Surely the correct question is:
“Did Climate Change create the coronaviTus epidemic?”
To which the answer is, of course, YES! Warmer weather must speed up chemical action like genetic mutations, and would encourage bats and pangolins to wander in teh woods where they will be caught and eaten. And, of course, increased warmth will make humans travel around the world a lot more…
Climate Change makes EVERYTHING worse! ™
Here is the real cause:
https://www.cellphonetaskforce.org/wp-content/uploads/2020/03/Is-the-Sky-Really-Falling.pdf
As noted by this graphic from ECMWF, the number of aircraft observations has grown rapidly due to more flights and increased numbers of aircraft with the appropriate weather sensors.
Aren’t aircraft using fossil-fuels? Don’t the enviro-wackos want us to shut down air-travel?
Early 2000’s our local weather forecaster discussed why predicting the weather in Oregon was so hard with little to no observations out in the pacific which made perfect sense to me. Here’s the rub, I moved to NM for 3 years (work related) and forecasting there was just as bad in a place where the sun shines 300 days out of the year. Don’t ask me how in the world you can have that bad a track record in an area with that much sunshine.
I moved back to Oregon and over the intervening years forecasting hasn’t gotten any better, I would say over my 50+ year lifespan they are worse at predicting the weather now then when I was a kid. To much reliance on models if you ask me. The old forecasters used to look at historical data coupled with life experiencing the weather in the area they are reporting on.
Rapid changes in temperature and weather will cause an increase in deaths in the UK.