The worst solar storms could be far more powerful than we think, NASA study warns 62%

By Munis Raza0%

7/21/2026, 10:45:00 AM

BS Summary: This article contains 26 faulty reasoning types, including Appeal to Authority, Pessimism Bias, and Availability Heuristic, with Biased Writer Voice as the most egregious example at 23.8% saturation with 140 hits. Analysis detected 970 faulty-reasoning hits from 588 analyzed words, generating a BS Score of 57.7% and a BS Rank of 62% (8,038 of 21,161 articles). This article is worse (more manipulative) than 62.00% of the article peer group.

The Sun’s most extreme storms may be far more powerful than scientists currently plan for. 
New research led by physicists at NASA’s Goddard Space Flight Center, published in Nature on July 19, found that a persistent flaw in how space weather is measured has led scientists to assume there is a ceiling on how severe geomagnetic storms can get. 
There may not be one. 
The 1859 Carrington Event  which knocked out telegraph networks across Europe and North America and sent auroras as far south as Florida  remains the benchmark for worst-case scenarios. 
More recent events have also shown what is at stake. 
During the 2003 Halloween Storms, solar radiation disrupted an FAA navigation system for 26 hours, and the FAA issued its first advisory warning of excessive radiation doses on commercial flights. 
These events are rare. 
And that rarity, the researchers argue, is precisely the problem. 
Why the measurements have been wrong all along 
Spacecraft monitoring solar weather typically sit about one million miles from Earth at a gravitational sweet spot called Lagrange point 1, or L1. 
There, probes like NASA’s IMAP hover in a stable orbit where the gravitational pulls of Earth and the Sun cancel out. 
They measure the energy in the solar wind as it approaches Earth. 
The problem is where these measurements are taken. 
Solar particles traveling from L1 to Earth pass through a turbulent layer called the magnetosheath, where the solar wind interacts with Earth’s magnetic field and loses energy. 
L1 measurements do not capture that loss. 
They record solar wind as it was, not as it arrives. 
“We usually assume the truth may be around its measurement,” said Nithin Sivadas, the study’s lead author and a physicist at NASA Goddard. 
“But probability theory says it leans one way. 
That’s why space weather risks appear underestimated.” 
This consistent one-directional bias has fed into models that appear to show a natural upper limit on how much energy can be transferred from the solar wind into Earth’s polar ionosphere. 
But that ceiling may simply be an artifact of flawed data, not a real physical boundary. 
What satellites closer to Earth actually show 
To test this, the team turned to spacecraft positioned much closer to Earth: NASA’s THEMIS all-sky imager , the Magnetospheric Multiscale (MMS) mission, and the DoubleStar satellite. 
Together, these allowed the team to compare over one million solar wind measurements with readings taken directly in the magnetosheath and magnetosphere. 
The result was unambiguous. 
“There is currently no statistical evidence to suggest an upper limit to the energy transferred from the solar wind to the polar ionosphere,” the team concluded. 
What this means for the next big storm 
The implications are significant. 
If there is no ceiling, then the worst-case scenarios currently used in space weather planning may be systematically too mild. 
“Our planet’s magnetic field does a really great job of protecting us against many space weather effects,” said Maria Walach of Lancaster University, the study’s co-author. 
“There are, however, extreme cases where satellites unexpectedly fall back to Earth, or we lose communication and GPS signals .” 
A truly extreme storm  the kind that might occur once in a thousand years  could be more disruptive than current models predict. 
“If there is no upper limit to our planet’s response to the solar wind, modeling for extreme cases needs to take this into account,” Walach said, “and we should be vigilant of space weather effects.” 
Article reasoning-pattern comparisonThis article: 2.0%Munis Raza: 7.6%Interesting Engineering: 3.9%Confirmation Bias2.0%This article: 5.1%Munis Raza: 3.1%Interesting Engineering: 1.2%Anchoring Bias5.1%This article: 11.6%Munis Raza: 5.1%Interesting Engineering: 2.5%Availability Heuristic11.6%This article: 0.7%Munis Raza: 2.6%Interesting Engineering: 1.1%Representativeness Heuristic0.7%This article: 1.4%Munis Raza: 0.2%Interesting Engineering: 0.3%Hindsight Bias1.4%This article: 4.4%Munis Raza: 7.3%Interesting Engineering: 5.2%Overconfidence Bias4.4%This article: 7.8%Munis Raza: 5.6%Interesting Engineering: 6.4%Framing Effect7.8%This article: 0.0%Munis Raza: 0.4%Interesting Engineering: 0.2%Loss Aversion0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.6%Status Quo Bias0.0%This article: 3.4%Munis Raza: 1.0%Interesting Engineering: 0.5%Sunk Cost Effect3.4%This article: 4.1%Munis Raza: 8.0%Interesting Engineering: 17.2%Optimism Bias4.1%This article: 12.6%Munis Raza: 2.5%Interesting Engineering: 0.5%Pessimism Bias12.6%This article: 6.1%Munis Raza: 2.0%Interesting Engineering: 0.9%Negativity Bias6.1%This article: 0.0%Munis Raza: 0.3%Interesting Engineering: 4.5%Self-Serving Bias0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%Fundamental Attribution Error0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Actor-Observer Bias0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.9%In-Group Bias0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%Out-Group Homogeneity Bias0.0%This article: 4.4%Munis Raza: 2.6%Interesting Engineering: 5.2%Halo Effect4.4%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Horn Effect0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Dunning-Kruger Effect0.0%This article: 1.7%Munis Raza: 1.4%Interesting Engineering: 1.1%Recency Bias1.7%This article: 0.0%Munis Raza: 0.4%Interesting Engineering: 0.2%Primacy Effect0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Blind-Spot Bias0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Ad Hominem0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Straw Man0.0%This article: 13.9%Munis Raza: 5.3%Interesting Engineering: 8.9%Appeal to Authority13.9%This article: 7.5%Munis Raza: 2.2%Interesting Engineering: 1.5%False Dilemma7.5%This article: 3.4%Munis Raza: 0.4%Interesting Engineering: 0.3%Slippery Slope3.4%This article: 0.0%Munis Raza: 0.4%Interesting Engineering: 0.1%Circular Reasoning0.0%This article: 7.8%Munis Raza: 5.9%Interesting Engineering: 5.1%Hasty Generalization7.8%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%Red Herring0.0%This article: 0.0%Munis Raza: 0.6%Interesting Engineering: 0.8%Bandwagon0.0%This article: 4.4%Munis Raza: 1.3%Interesting Engineering: 2.3%Appeal to Emotion4.4%This article: 3.9%Munis Raza: 0.9%Interesting Engineering: 1.2%Begging the Question3.9%This article: 1.2%Munis Raza: 2.9%Interesting Engineering: 2.2%Post Hoc (False Cause)1.2%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Tu Quoque0.0%This article: 10.4%Munis Raza: 1.7%Interesting Engineering: 0.6%Burden of Proof10.4%This article: 0.0%Munis Raza: 0.5%Interesting Engineering: 0.2%Appeal to Nature0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.3%Composition/Division0.0%This article: 6.8%Munis Raza: 1.3%Interesting Engineering: 0.7%Anecdotal6.8%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%No True Scotsman0.0%This article: 1.9%Munis Raza: 0.7%Interesting Engineering: 2.6%Ambiguity (Equivocation)1.9%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Middle Ground0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Personal Incredulity0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%Special Pleading0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Genetic Fallacy0.0%This article: 8.7%Munis Raza: 2.0%Interesting Engineering: 1.8%Unattributed Quote8.7%This article: 0.0%Munis Raza: 0.2%Interesting Engineering: 0.7%Quote-first Misdirection0.0%This article: 23.8%Munis Raza: 6.8%Interesting Engineering: 3.9%Biased Writer Voice23.8%This article: 6.0%Munis Raza: 3.1%Interesting Engineering: 0.7%Indoctrination6.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.0%Politically Left Leaning Bias0.0%This article: 0.0%Munis Raza: 0.0%Interesting Engineering: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Munis Raza: 1.5%Interesting Engineering: 10.7%Attempt to Sell a Product or S…0.0%

588 words analyzed.

Speakers

2speakers20%attributed speech469writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 15 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 44 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageNithin Sivadas • 23 words • 100.0% coverageNithin Sivadas • 8 words • 100.0% coverageNithin Sivadas • 7 words • 0.0% coverageWriter's voice • 31 words • 0.0% coverageWriter's voice • 16 words • 100.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 22 words • 100.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageMaria Walach • 26 words • 0.0% coverageMaria Walach • 20 words • 100.0% coverageWriter's voice • 24 words • 100.0% coverageMaria Walach • 35 words • 100.0% coverage
Selected voice

Maria Walach

100%flagged-word coverage
81 attributed words68% of attributed speech75% writer coverage
0%22.5%45.0%Indoctrination+43.2 ptsWriter: 0.0%Maria Walach: 43.2%43.2%Biased Writer Voice-29.9 ptsWriter: 29.9%Maria Walach: 0.0%0.0%Unattributed Quote+24.7 ptsWriter: 0.0%Maria Walach: 24.7%24.7%

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

Loading…
Loading…
Loading…

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.