Kansas and Missouri are surprised by tornadoes after weather service cuts reduce early warnings 38%

By Frank Morris30%

6/22/2026, 9:00:00 AM

BS Summary: This article contains 33 faulty reasoning types, including Appeal to Authority, Confirmation Bias, and Appeal to Emotion, with Negativity Bias as the most egregious example at 17.9% saturation with 178 hits. Analysis detected 1,802 faulty-reasoning hits from 993 analyzed words, generating a BS Score of 43.9% and a BS Rank of 38% (13,317 of 21,198 articles). This article is better (less manipulative) than 62.80% of the article peer group.

Early on April 13, the National Weather Service forecast almost no chance of tornadoes in east central Kansas. 
But that evening, several twisters tore across the region. 
The next morning, people in Ottawa, Kansas picked up the pieces of homes and businesses. 
“We're just looking at the aftermath of a horrible tornado,” said Tila Davis, a student at Ottawa University helping with cleanup. 
“We're seeing windows broken, debris through windows. 
We're seeing trees bent over, just branches everywhere, debris just everywhere in the street.” 
Forecasters did issue a warning several minutes before the storm hit that likely saved lives. 
Despite all the property damage, the tornado didn’t seriously injure anyone. 
But weather service forecasters typically know hours in advance where tornadoes are likely, and this time, they didn’t. 
“I'll tell you, forecasting in general, it has been degraded,” said John Morales, a TV meteorologist with four decades of experience. 
“The amount of flip-flopping going on in forecasting is beyond anything I can recall in the modern era.” 
Morales means basically this decade. 
Forecasting has improved a lot since the 1980s, when he started, but Morales maintains that it took a big step backwards after the Trump Administration pressured and cajoled almost 600 National Weather Service employees to quit last year. 
Those cuts have consequences. 
In past years, the National Weather Service would have released weather balloons all across the country precisely at 7 a.m. 
Eastern Time. 
But that didn’t happen the morning of the Ottawa tornado. 
“That particular day, on the morning cycle of weather balloon releases, there were vast areas of the Midwest, Southwest, and Intermountain West that did not have a weather balloon release,” said Morales, who tracks them using National Weather Service data. 
No weather balloons went up across a big oval centered over the Southwest that spanned about 1,000 miles. 
They were launched early that afternoon, but by then, storms were already brewing over Kansas. 
Weather balloons are essentially enormous latex balloons dangling 100-dollar gizmos the size of cell phones that measure temperature, air pressure and humidity as they rise through the atmosphere. 
They start out at about five feet across, but can balloon to nearly 25 feet as they rise 100,000 feet and then pop. 
They can be hard to launch, especially on windy days, and typically require at least two people on duty at a given weather service office to release. 
But some NWS offices no longer have staff available for early morning launches, Morales said. 
“So you see them being released during office hours, as opposed to the hours that we really need them,” Morales said. 
The National Weather Service says that almost all of its “approximately 92” weather balloon launch sites are functioning normally, with only a few missed launches. 
“Model accuracy continues to improve year over year due to improvements in available compute power, model sophistication and more varied observation types,” said National Weather Service Spokesperson Erica Grow. 
“NOAA's Environmental Modeling Center regularly evaluates the performance of the Agency's weather models and publishes its findings on the EMC's website. 
NOAA's weather model performance shows no evidence of degradation.” 
Weather models are only as good as the information fed into them. 
Satellites and hundreds of ground-level weather stations generate reams of data, but the weather balloon piece is crucial. 
Take it out, and you’re running a “real-time experiment,” said retired National Weather Service meteorologist Alan Gerard. 
“Okay, what happens if you take a significant number of the balloons that we would normally release in the morning and delay them to midday,” asked Gerard hypothetically. 
“How is that going to impact our forecasts?” 
It’s tough to tell how weather balloon data that was never collected may have changed forecasts. 
Anecdotally, the results haven’t been great. 
Weeks before the surprise tornadoes in Kansas, a similar situation popped up in Michigan. 
Weather Service forecasters put the state at only marginal risk of tornadoes before deadly twisters struck the state. 
“I mean, this is literally life and death decisions that get made based on the warning system,” said Kansas congresswoman Sharice Davids, a Democrat. 
“Meteorologists are saying that data collection is starting to lag, that we aren't continuing to keep people the safest that we possibly can.” 
The National Weather Service is still short-staffed. 
The Service won’t comment on its current number of personnel but maintains that it’s working hard to reverse at least some of the cuts made by President Trump’s Department of Government Efficiency last year. 
“We have been and continue to hire since late 2025,” said Grow. 
“NWS has been hiring and onboarding a targeted number of meteorologists and other positions deemed necessary for operational continuity. 
We have filled over 200 positions since then.” 
But the new hires take time to train. 
In the meantime, National Weather Service forecasters are working overtime. 
“Weather Service employees, overwhelmingly, are very dedicated, mission-focused, so they're going to try to minimize the impacts on their mission and their services as much as they possibly can,” said Gerard. 
Government forecasters face more dark clouds on the horizon. 
The White House budget for next fiscal year makes deep cuts to the National Oceanic and Atmospheric Administration’s ocean monitoring budget. 
Much like altering weather balloon launches, pulling up buoys that monitor changes in ocean temperatures at various depths would starve forecasters of useful information, like the formation of this year’s El Niño climate pattern, leaving them less informed about what’s ultimately going to happen in the atmosphere. 
“All of your weather, all of your risk, it all originates with the ocean, and your forecasts don't just depend on your local Doppler radar and your local forecasters; it depends on this entire array of global and national infrastructure,” said Jeff Waters with Ocean Conservancy. 
And there’s another thing. 
Gerard expects a big reorganization at the National Weather Service to be announced this summer, at about the start of hurricane season. 
Article reasoning-pattern comparisonThis article: 12.4%Frank Morris: 2.1%Columbia Missourian: 1.7%Confirmation Bias12.4%This article: 0.0%Frank Morris: 0.7%Columbia Missourian: 0.8%Anchoring Bias0.0%This article: 9.1%Frank Morris: 3.3%Columbia Missourian: 2.7%Availability Heuristic9.1%This article: 5.3%Frank Morris: 1.7%Columbia Missourian: 0.9%Representativeness Heuristic5.3%This article: 4.6%Frank Morris: 1.3%Columbia Missourian: 0.4%Hindsight Bias4.6%This article: 4.6%Frank Morris: 1.3%Columbia Missourian: 1.3%Overconfidence Bias4.6%This article: 3.9%Frank Morris: 3.6%Columbia Missourian: 5.5%Framing Effect3.9%This article: 0.0%Frank Morris: 0.5%Columbia Missourian: 1.0%Loss Aversion0.0%This article: 2.0%Frank Morris: 1.1%Columbia Missourian: 0.7%Status Quo Bias2.0%This article: 0.0%Frank Morris: 0.2%Columbia Missourian: 0.3%Sunk Cost Effect0.0%This article: 7.5%Frank Morris: 5.1%Columbia Missourian: 4.5%Optimism Bias7.5%This article: 5.7%Frank Morris: 0.5%Columbia Missourian: 1.6%Pessimism Bias5.7%This article: 17.9%Frank Morris: 2.8%Columbia Missourian: 5.4%Negativity Bias17.9%This article: 0.0%Frank Morris: 0.3%Columbia Missourian: 1.7%Self-Serving Bias0.0%This article: 1.2%Frank Morris: 0.3%Columbia Missourian: 0.6%Fundamental Attribution Error1.2%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.2%Actor-Observer Bias0.0%This article: 3.1%Frank Morris: 2.2%Columbia Missourian: 1.6%In-Group Bias3.1%This article: 0.0%Frank Morris: 0.6%Columbia Missourian: 0.4%Out-Group Homogeneity Bias0.0%This article: 3.1%Frank Morris: 1.1%Columbia Missourian: 2.4%Halo Effect3.1%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Horn Effect0.0%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Dunning-Kruger Effect0.0%This article: 5.4%Frank Morris: 1.8%Columbia Missourian: 0.9%Recency Bias5.4%This article: 0.0%Frank Morris: 0.1%Columbia Missourian: 0.3%Primacy Effect0.0%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Blind-Spot Bias0.0%This article: 0.0%Frank Morris: 0.3%Columbia Missourian: 0.5%Ad Hominem0.0%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.2%Straw Man0.0%This article: 13.7%Frank Morris: 3.5%Columbia Missourian: 2.9%Appeal to Authority13.7%This article: 0.0%Frank Morris: 0.6%Columbia Missourian: 1.1%False Dilemma0.0%This article: 4.7%Frank Morris: 1.1%Columbia Missourian: 1.0%Slippery Slope4.7%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.1%Circular Reasoning0.0%This article: 3.6%Frank Morris: 4.0%Columbia Missourian: 3.6%Hasty Generalization3.6%This article: 1.8%Frank Morris: 0.3%Columbia Missourian: 0.2%Red Herring1.8%This article: 0.0%Frank Morris: 1.2%Columbia Missourian: 0.7%Bandwagon0.0%This article: 11.6%Frank Morris: 2.6%Columbia Missourian: 5.3%Appeal to Emotion11.6%This article: 0.4%Frank Morris: 0.2%Columbia Missourian: 0.6%Begging the Question0.4%This article: 11.1%Frank Morris: 2.7%Columbia Missourian: 2.0%Post Hoc (False Cause)11.1%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Tu Quoque0.0%This article: 1.5%Frank Morris: 0.3%Columbia Missourian: 0.4%Burden of Proof1.5%This article: 4.6%Frank Morris: 0.3%Columbia Missourian: 0.2%Appeal to Nature4.6%This article: 4.7%Frank Morris: 0.4%Columbia Missourian: 0.2%Composition/Division4.7%This article: 6.1%Frank Morris: 3.2%Columbia Missourian: 2.6%Anecdotal6.1%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.1%No True Scotsman0.0%This article: 2.2%Frank Morris: 1.8%Columbia Missourian: 1.3%Ambiguity (Equivocation)2.2%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.1%Middle Ground0.0%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.0%Personal Incredulity0.0%This article: 3.4%Frank Morris: 0.2%Columbia Missourian: 0.1%Special Pleading3.4%This article: 0.0%Frank Morris: 0.0%Columbia Missourian: 0.1%Genetic Fallacy0.0%This article: 4.5%Frank Morris: 1.2%Columbia Missourian: 0.8%Unattributed Quote4.5%This article: 2.1%Frank Morris: 0.7%Columbia Missourian: 0.7%Quote-first Misdirection2.1%This article: 3.5%Frank Morris: 2.2%Columbia Missourian: 2.9%Biased Writer Voice3.5%This article: 4.6%Frank Morris: 1.3%Columbia Missourian: 1.3%Indoctrination4.6%This article: 9.7%Frank Morris: 2.0%Columbia Missourian: 0.7%Politically Left Leaning Bias9.7%This article: 0.0%Frank Morris: 0.2%Columbia Missourian: 0.2%Politically Right Leaning Bias0.0%This article: 1.4%Frank Morris: 0.6%Columbia Missourian: 1.6%Attempt to Sell a Product or S…1.4%

993 words analyzed.

Speakers

7speakers45%attributed speech545writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 14 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageTila Davis • 21 words • 100.0% coverageTila Davis • 7 words • 0.0% coverageTila Davis • 14 words • 100.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageJohn Morales • 21 words • 100.0% coverageJohn Morales • 18 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 38 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageJohn Morales • 40 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageJohn Morales • 15 words • 0.0% coverageJohn Morales • 21 words • 0.0% coverageNational Weather Service • 25 words • 0.0% coverageErica Grow • 29 words • 0.0% coverageErica Grow • 21 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageAlan Gerard • 17 words • 0.0% coverageAlan Gerard • 28 words • 0.0% coverageAlan Gerard • 8 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageSharice Davids • 24 words • 100.0% coverageSharice Davids • 23 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 34 words • 100.0% coverageErica Grow • 12 words • 0.0% coverageErica Grow • 19 words • 0.0% coverageErica Grow • 8 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageAlan Gerard • 31 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 47 words • 0.0% coverageJeff Waters • 46 words • 100.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverage
Selected voice

Sharice Davids

100%flagged-word coverage
47 attributed words10% of attributed speech77% writer coverage
0%27.5%55.0%Politically Left Leaning B+37.9 ptsWriter: 13.2%Sharice Davids: 51.1%51.1%Unattributed Quote+44.9 ptsWriter: 4.0%Sharice Davids: 48.9%48.9%Biased Writer Voice-2.6 ptsWriter: 2.6%Sharice Davids: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

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