New GOES-16 weather data could improve tornado-warning times

From the “not Mann” department.

A team of Penn State researchers are first to use satellite data from the newly online GOES-16 satellite in numerical models designed to forecast tornadic thunderstorms, which cause 17 percent of all severe weather deaths in the United States. IMAGE: Pixabay

UNIVERSITY PARK, Pa. — Penn State researchers are the first to use data obtained from recent next-generation satellites in a numerical weather-prediction model used to provide guidance for tornadic thunderstorm forecasting.

GOES-16, which was launched in 2016, recently became fully operational but methods for incorporating the data, until now, did not exist.

Researchers used a method for all-sky infrared radiance developed through Penn State’s Center for Advanced Data Assimilation and Predictability Techniques (ADAPT), to incorporate data into models for weather events in the Midwest. The experiments were hindcast, meaning the models were run after the weather event and compared with actual events. The model was able to forecast supercell thunderstorms with atmospheric conditions that are very conducive to tornadoes.

The results, reported in Monthly Weather Review published by the American Meteorological Society, suggest that we can greatly enhance our ability to predict thunderstorms capable of producing tornadoes.

“It’s not just the data that’s important,” Fuqing Zhang, professor of meteorology and director of ADAPT said. “It’s how we design very sophisticated numerical mathematical algorithms to ingest that satellite data into the model. This is really our expertise and our pride. Our team is the first to be able to effectively take in this high resolution satellite data and prove it can be useful in real-case scenarios.”

Forecasting tornadic thunderstorms is important because these events are especially quick to form, hard to predict and can cause catastrophic damage. Thunderstorms account for 40 percent of all severe weather events in the United States, causing 14 percent of damage and 17 percent of related deaths, according to the National Climate Data Center.

“For many storms in the United States, we have good radar data, however, it’s very hard using any of the existing technologies to capture the environmental and storm conditions before the storm totally develops,” Zhang said. “We’re able to extend the warning time for these events because the satellite can look at the field even before the clouds form and our models can ingest that information to improve and advance forecasts.”

In the past 40 years, tornado warning lead-time — meaning the time interval between when a warning is issued and the tornado occurs — has increased on average from 3 to 14 minutes. Zhang said this method could extend the lead time even further.

“Researchers have made huge improvements in tornado lead times but, for many people, 14 minutes isn’t enough,” said David Stensrud, head of the Department of Meteorology and Atmospheric Science at Penn State. “If you have a big sports stadium or a hospital it takes more than 14 minutes to prepare for the weather threat. There is certainly a need for more advanced warnings. Our research indicates that by combining data assimilation and high-resolution models we can get lead times beyond 30 minutes. Doubling the lead time would have huge potential societal impacts.”

Better models and better data supplied by GOES-16 could also reduce false alarm rates, he said.

Researchers are working with NOAA and the National Weather Service to ready the algorithms for ingesting these satellite data for widespread use.

Satellite data has proven tricky for use in weather models because satellites do not capture key variables such as wind speed, pressure, temperature and water vapor. But satellites capture data known as brightness temperature, which show how much radiation is emitted by objects on Earth and in the atmosphere at different infrared frequencies. Using all-sky radiance, researchers can use brightness temperature captured at different frequencies to paint a picture of cloud formations and water vapor fields.

In research that’s still under review and profiled in Nature, Zhang and his colleagues show this method forecasted that Hurricane Harvey would reach a category 4 while existing models forecast it as a category 1. Harvey became the first category 4 hurricane to make landfall along the Texas coast since 1961.

GOES-16 covers one-sixth of the Earth, including the Eastern portion of the United States and all of the Atlantic Ocean, and is geostationary. It replaces GOES-13, offering data resolution at a scale slightly larger than half a mile, much better than its predecessor at 2.5 miles, and with data available every 5 minutes or less.

The increased spatial and temporal resolution is important because it offers much more information about what is taking place within thunderstorms, hurricanes and other severe weather events. The satellite uses 16 bands of image data using visible and infrared light to reveal factors such as fog, winds, vegetation, snow and ice, fires, water vapor and lightning. It is one of three similar satellites in operation that collectively cover nearly all habitable land and surrounding oceans.

The National Oceanic and Atmospheric Administration operates GOES with contributions from NASA. Postdoctoral scholar Yunji Zhang contributed to this research that was funded by NASA.

Source: Penn State

Advertisements

29 thoughts on “New GOES-16 weather data could improve tornado-warning times

  1. But as cool as GOES-16 data is, it has been the NexRad doppler weather radar network that has improved the tornado warning times. Modeling will help with the 24-48 hr forecast accuracy to identify threatened areas, does little for real-time warning (minutes to half-hour) compared to radar.

    https://www.roc.noaa.gov/WSR88D/Maps.aspx

    Still too many holes in the coverage below 10,000 feet, especially in Texas-Oklahoma, Missouri, and Arkansas – prime tornado threat areas.

    • Between the weather radar and the storm chasers we pretty much know exactly where a tornado is located, in this neck of the woods.

      A tornado can form quickly, but once it forms the weather radar spots it, and has probably been flagging its circulation pattern for many minutes before the tornado actually touches down, and the storm chasers then surround it and give us real-time updates. No weather satellite data can make this forcast.

      I live in Tornado Alley (Oklahoma) and feel very confident that with current technology no tornado is going to sneak up on me if I am paying attention. But, or course, we welcome all technical advances.

  2. “Researchers used a method for all-sky infrared radiance developed through Penn State’s Center for Advanced Data Assimilation and Predictability Techniques (ADAPT), to incorporate data into models for weather events in the Midwest. The experiments were hindcast, meaning the models were run after the weather event and compared with actual events. The model was able to forecast supercell thunderstorms with atmospheric conditions that are very conducive to tornadoes.”

    Translation:
    A) They wrote a model.
    B) They hindcast the model
    C) They claim the model forecast supercell thunderstorms

    “Our team is the first to be able to effectively take in this high resolution satellite data and prove it can be useful in real-case scenarios.”

    D) Claim their model achieves wonders.

    Without proof.
    Instead they use another claim about allegedly forecasting Harvey’s hurricane strength as their proof. A forecast made by others, without State Penn super duper hindcast models.

    One, gets the impression they are trying to sell their models before real world evidence and proof.

  3. They claim nothing about models, in fact the team in question doesn’t do models.
    Beyond that the quote before your point ‘D’ says nothing about models.
    What they claim to have done is find a way to make more data available to already existing models, in real time.

    They never claimed the ability to forecast super cell thunderstorms, they claim the ability to forecast which super cell storms are likely to produce tornadoes. This is not a new ability, existing models have been able to do this for years. Their claim is that with more and better data, the EXISTING models are able to forecast tornadoes 30 minutes ahead of time instead of 14.

    It really is sad how the corruption of climate science has caused people to reflexively and without thinking, reject all models and science associated with them.

    • “….to incorporate data into models for weather events in the Midwest. The experiments were hindcast, meaning the models were run after the weather event and compared with actual events….” What part of that don’t you understand?

      • ”All models are wrong but some are useful.” – George Box

        The models extending tornado warnings out to 14 minutes I would count among the useful models. This paper appears to deal with incorporating more data to make the model more useful. Let’s give them a chance to work on this (even with some taxpayers’ money) and see what they come up with.

      • Incorporating data into models does not claim that they are making models.
        Running models does not say that you made the models.
        Comparing model output to what happened in the real world is how you test models, it’s the way model development is supposed to be done.

        I understand all of it. Perhaps you should read it again, this time for understanding.

    • “all models and science associated with them”

      models & science associated with –

      – evry one’s waiting for!

  4. The traditional forecasting using groundbased data is far superior over the sophisticated computer based – satellite data based forecasting. In fact the groundbased traditional forecasting takes in to several ground realities based on the forecasters experience. The governments/WMO must give more importance to such work by meteorological departments. The model based studies are a wasteful expenditure of public money and waste of energy.

    Dr. S. Jeevananda Reddy

    • Agreed. Models predict real weather and climate about as well as a Disney CGI animated princess simulates a real human.
      In contrast, first hand living on a tropical Pacific island for a couple years revealed a fairly predicable weather pattern. ie. February to December, daily warm sun with trade winds. A couple degrees cooler at night. And January, slightly less warm and a little cloudier and bigger waves on the windward side.

    • “The traditional forecasting using groundbased data is far superior…”

      Ahh, yes – back in the good ‘ol days of the 1800’s when forecasting was so accurate because they thought weather was only at the surface & didn’t understand (or know) that the meteorological active atmosphere (IOW, the Tropopause) was around 10-15km deep & what happens ‘up there’ affects what happens on the ground.

      “In contrast, first hand living on a tropical Pacific island for a couple years revealed a fairly predicable weather pattern.”

      And, again, here we are with ‘anecdotal climatology’. Yeah, sure all that is fine until… [each of these features enhance or suppress convection] – an El Nino/La Nina comes along or a MJO (Madden-Julien Oscillation) or a CCKW (Convectively-Coupled atmospheric Kelvin Wave) or a CCEW (Convectively Coupled Equatorial Wave), or an ER (Equatorial Rossby wave), or a MRG (Mixed Rossby-Gravity) wave, a TUTT (Tropical Upper Tropospheric Trough) or, in the Atlantic basin, an AEW (African Easterly Wave) a Tropical Cyclone (TC) come along & ‘upset the norm’

      Oh yeah, almost forgot – IOD (Indian Ocean Dipole) oscillation…

      https://www.esrl.noaa.gov/psd/map/clim/olr_modes/
      http://www.bom.gov.au/climate/iod/

  5. “Harvey became the first category 4 hurricane to make landfall along the Texas coast since 1961.”

    However . . . from the weather channel web site “Wind gusts from Harvey near its landfall point topped 100 mph in many locations, leading to widespread destruction of homes and buildings.”

    Was the Cat 4 designation over hype?

  6. As a former operational weather forecaster who issued warnings, please allow me to provide a flavor for the reality of the situation. A (good) warner will meld all the tools available, first and foremost before the crap hits the fan, to establish situational awareness for what is likely to occur during his or her shift. Nowadays, it’s pretty well known where the hot spots for convection are likely to form on a mesoscale basis (based on NCEP model and manually-analyzed guidance), but it’s still useful to perform one’s own local mesoscale analysis. I can definitely see where this kind of model-based satellite analysis of real-time pre-storm environmental conditions would enhance the warner’s ability to focus attention. When storms begin forming, it is the time-saving value that is critical, because one runs out of time to analyze, decide what and where to warn, and compose and issue a warning. The more tools that help focus awareness, generally the better; however, I can also see where this kind of thing will lead to more false alarms, just as Doppler radars have led to more false alarms because of the display of velocity signatures which fail to become tornadoes. If a tool shows a propensity for a thunderstorm cell to produce a tornado, and a couplet signature is subsequently observed, a warner will be under even more pressure to issue a warning to cover his or her behind.

    Overall, though, the more (good and valid) tools that are available, the better the overall performance will be, especially if the tool paves the way or prepares one’s understanding of the evolution of the storms over the next few hours. This could also help in pre-storm staffing and sectorization decision-making. Time is often the limiting factor in successfully navigating severe weather situations. When one is trying to combine VIL, velocity, reflectivity, movement, satellite trends, computed LIs, etc etc, it quickly becomes an overly complex task. If one could have a jump on the formative stages of a severe weather event, one could more readily stay ahead of the power curve in a rapidly changing, complex, and demanding situation.

    One thing I would like to ask the researchers is if this data could be compared to, paired with, or amalgamated with ground-based mesoscale observations, to verify the satellite estimates (which all observations that are not in situ really are).

    Lastly, those not in the field of operational weather forecasting and warning probably don’t realize the tremendous advances that have been made in this field within the last 60 years. I was fortunate to start with teletypes and “facsimile” machines (not the ones from the 1980s) and see the revolution that has taken place with observation platforms, remote sensing tools, and especially numerical weather prediction and the integration of all of them. IMHO, climate “science” needs to undergo this kind of progress. But first, there needs to be an underlying realization that GHGs are not the driver and “forcing” that is now believed and claimed.

    • Nice to hear from a forecasting colleague. You summed up the situation very well. I have been an operational forecaster since the teletype and facsimile days as well. I came into forecasting shortly after the first baroclinic models were operational. I still do contract forecasting. I also did my PhD research on tornado prediction methods. As you say, huge advances have been made in the past 60 years. However, there have also been major set backs, especially with ground observations that have been automated and severely depleted. And I would say that there were major advancements in the numerical models until about the mid to late 90s and then the improvements really flattened out and became quite minor. But the models are still the major tool of forecasting, you just have to know what the limitations may be.

  7. Lead time is important. I am unsure how this plays out in the real world. I have never lived in a tornado prone area.

    If they get a warning that tornado producing weather features may develop in such-and-such a county in the next hour — what is expected to happen that will improve the damage and loss of life stats? Every hospital in the county will go into tornado drill? (Knowing that the chances of the tornado, if it does develop, will only strike an area three blocks wide, somewhere in the county?)

    Anyone out there who has lived there life in Tornado Alley?

    • “If they get a warning that tornado producing weather features may develop in such-and-such a county in the next hour — what is expected to happen that will improve the damage and loss of life stats? Every hospital in the county will go into tornado drill?

      To begin with, tornado-prone weather fronts will be indentified as heading our way a day or two in advance, so anyone with any sense is watching out for them. When the weather front arrives in our area people don’t take any action other than paying attention to the radar. There is no need for every facility to go to a tornado drill because very few if any of them will be hit by the tornado. So they watch the tv radar and when it looks like it is getting close to their location, then you take the necessary action and get in a shelter.

      I remember one day back in the 1970’s, we had a local radio DJ who interrupted a song and came on the radio and started yelling for people to “get out of your car and get in a ditch”! because a tornado had arrived. Me and the friend I was riding with looked out the windows of the car and saw that the sky was just as clear as a bell and there was no storm front in sight, although there was a storm approaching from the northwest. We were wondering what the radio DJ was smoking.

      What had happened was they had a wind velocity instrument at the radio station, and it somehow got broken and was showing the winds to be blowing 100 mph and the DJ thought a tornado was upon us! So he put out the warning over the radio. A few minutes later he came back on the air and said in effect “never mind” and explained what had taken place. We had a good laugh over that one. 🙂

  8. Max Dupilka, I agree completely. Those of us who railed against losing human observers at the expense of less able but somewhat more numerous automated equipment, as well as poor positioning of some radars and offices, lost out significantly in tangible and intangible ways. Sadly, newer forecasters and warners are not educated as observers were, and thus have lost out on the ability to use that knowledge in their duties. Almost on one knows what BINOVC or W0X0F means any more!

  9. For my next career, I’d like to become the person who comes up with acronyms for NASA spacecraft and launch vehicles. I would rename the (never-to-fly) Space Launch System the “Flexible, Advanced Rocket Transportation System.”

Comments are closed.