meteorology

Crackpot Morons: “Weather Control”

October 7, 2024 by tornado Leave a Comment

Hurricane Helene, and now the ongoing Milton, have brought the tinfoil-hat crazies out of the woodwork on social media like the mentally deficient cockroaches they are. What’s mentally deficient? Combining a pathetic lack of scientific education (thanks a lot, American public schools) with still worse critical-thinking skills, and a level of gullibility that make con men the world over foam at the mouth. The result? Patently idiotic crap like this, which somehow gets likes, clicks and attention. I’ll not give these attention-starved crackpots the glory of linking to their page nor YouTube channel.

This keeps coming up, over and over for many years, like flies on turds, and no matter how many times truth swats away the flies, more keep returning.

Here is the truth, from an expert radar meteorologist (me) who has reviewed tens of thousands of radar images over my nearly 40 years in meteorology, as a student (the first four under the tutelage of world-renowned NSSL radar expert Don Burgess) then full-time operational meteorologist. I have published several formal and conference papers dealing with radar analysis and interpretation. Furthermore, I quite possibly have published more lead-authored formal peer-reviewed papers total (ten) than any full-time forecaster in U.S. history, with many more as a coauthor. It’s not bragging if it’s the truth. Now, with that out of the way…

These are rings of lower-atmospheric and ground returns around radar sites. You can see the centers of them at places like Springfield, Wichita, Tulsa (Inola), Kansas City (Pleasant Hill), Norman and many others. They are normal and common every night when radar waves are directed downward off temperature inversions to bounce off bugs, bats, birds, and all kinds of objects on the ground, and enough of the bounced signal returns to the antenna to be detected.

That’s it! That’s all! It really is that simple. No sinister agenda, no “geoengineering”. The radiation (10-cm microwaves weakening fast at distances of miles) cannot “change the weather”. They only can bounce off targets dense enough and return a tiny fraction of their original, already weakening power.

Elected officials can be morons about it too.

Who specifically is “they”? Where is your proof? You don’t have any. Busted liar! Margie, as a conservative, I see your behavior here as an embarrassment to conservatism and as desperate, opportunistic attention-seeking clickbait of the worst order. You know nothing of this topic; sit down and shut up. [Damn right I told a woman to sit down and shut up, no apologies. Get over it, wokes. That’s because it has nothing to do with her gender, and everything to do with her acting as dumb as a brick. She’d get the very same treatment if she were a dude, and her name was Marty. Now that’s real “equality”!]

Cloud seeding, despite how often and worldwide it has been tried (and still is), can work on very small, controlled scales in lab freezers. It is not proven to work versus unseeded clouds in the real, vast, chaotic atmosphere. Why? You can’t have a control group. Because of that, there’s no way to prove whether rain did or did not fall as a direct result of seeding. Extensively published research meteorologist Chuck Doswell has written an excellent summary of the seeding issue. Read that. Learn something.

As for Project Stormfury? That was an experimental attempt to seed hurricanes and change their rainfall patterns to reduce flooding. It failed. How? Results were inclusive to nil, for one, and for another (again), there were no “control hurricanes” of identical nature to compare against.

As for lasers, powerful, experimental ones have been shown to produce enough plasma to redirect a lightning flash down a path of lesser resistance, basically acting as a lightning rod. That isn’t “weather control”, anymore than lightning-current redirection through the walls of my house, by my rod-and-cable system, is “weather control”. What a load of crap. You’d need more power than mankind can muster to have even one hour’s chance of matching the energy in a hurricane.

Give these “weather control” sites, posts and videos no attention, and debunk them whenever possible with your gullible friends. They prey on ignorance and a terrible lack of scientific education in this nation.

Notice I am NOT for censorship of them. Instead, the way to react to bad ideas is with better ideas (truth). This is that, at least to the extent I feel like addressing the stupidity.

Yes, it doesn’t help that numerous real, damaging, cruel and/or otherwise evil governmental conspiracies have been done against citizens. That has set the plate for any cockamamie idea to seem legit. But that doesn’t mean they are!

While I realize critical, scientific thinking is in short supply, again, thanks to generally woeful standards to which far too many public-school students have been held, it is needed more than ever. In an age when kooks of all political spectra think “they” can control the weather, that essential oils can resolve cancer or any other disease in particular, that mineral crystals or planetary alignments somehow impart a power in one’s life, that GMO is deadly (when it in fact has helped agricultural productivity and saved millions from starvation), when a genetic and anthropological male with XY chromosomes can ridiculously claim to be female simply via self-declaration (yeah, and when I was a kid, I was Superman too)…we have, as a society, lost our grip on reason, logic, statistical numeracy (outliers don’t disprove the rule!), and rationalism. We need to get them back. We live in a sea of gullible, hapless suckers for any cockamamie idea.

Human Weather Forecasting in an Automation Era, Part 3: Garbage In, Garbage Out

August 28, 2022 by tornado Leave a Comment

This short series (go to Part 1 or Part 2) arises from the recently published paper, “The Evolving Role of Humans in Weather Prediction and Communication“. Please read the paper first.

Objective verification of forecasts will remain hugely important, and the authors duly note that. But one factor not discussed (perhaps due to space limitations?) is the quality of the verification data. That matters…perhaps not to bureaucrats, who tend to overlook components of the verification sausage that provide context. But flawed verification datasets give you flawed verification numbers, even if the calculations are completely mathematically correct!

As someone who has analyzed and examined U.S. tornado, wind and hail data for most of my career, and published some research rooted in it, I can say two things with confidence:
1. It’s the most complete, precise and detailed data in the world, but
2. Precision is not necessarily accuracy. The data remain suffused with blobs of rottenness and grossly estimated or even completely fudged magnitudes, potentially giving misleading impressions on how “good” a forecast is.

How? Take the convective wind data, for example. More details can be found in my formally published paper on the subject, but suffice to say, it’s actually rather deeply contaminated, questionably accurate and surprisingly imprecise, and I’m amazed that it has generated as much useful research as it has. For example: trees and limbs can fall down in severe (50 kt, 58 mph by NWS definition) wind, subsevere wind, light wind, or no wind at all. Yet reports of downed trees and tree damage, when used to verify warnings, are bogused to severe numeric wind values by policy (as noted and cited in the paper). A patently unscientific and intellectually dishonest policy!

For another example, estimated winds tend to be overestimates, by a factor of about 1.25 in bulk, based on human wind-tunnel exposure (same paper). Yet four years after that research published, estimated gusts continue to be treated exactly like measured ones for verification (and now ML-informing) purposes. Why? Either estimated winds should be thrown out, or a pre-verification reduction factor applied to account for human overestimation. The secular increase in wind reports over the last few decades since the WSR-88D came online also should be normalized. That’s the far more scientifically justifiable approach than using the reports as-is, with no quality control nor temporal detrending.

For one more example, which we discussed just a little in the paper, all measured winds are treated the same, even though an increasing proportion come from non-AWOS, non-ASOS, non-mesonet instruments such as school and home weather stations. These are of questionable scientific validity in terms of proper exposure and calibration. The same can be said for storm-chaser and -spotter instrumentation, which may not be well-calibrated at a base level, and which may be either handheld at unknown height and exposure, or recording the slipstream if mounted on a vehicle.

Yet all those collectively populate the “severe” gust verification datasets also are used for training machine-learning algorithms — to the extent that actual, measured winds with scientific-grade, calibrated, verifiably properly sited instruments are a tiny minority of reports. With regard to wind reports, national outlooks, local warnings, and machine-learning training data use excess, non-severe wind data for verification, but because they all do, comparisons between them still may be useful, even if misleading.

Several of us severe-storms forecasters have noticed operationally that some ML-informed algorithms for generating calibrated wind probabilities put bull’s-eyes over CWAs and small parts of the country (mainly east) known to heavily use “trees down” to verify warnings, and that have much less actual severe thunderstorm wind (based on peer-reviewed studies of measured gusts, such as mine and this one by Bryan Smith) than the central and west. This has little to do with meteorology, and much to do with inconsistent and unscientific verification practices.

To improve the training data, the report-gathering and verification practices that inform it must improve, and/or the employers of the training data must apply objective filters. Will they?

This concludes the three-part series stimulated by Neil’s excellent paper. Great gratitude goes to Neil and his coauthors, and the handful who ever will read this far.

Human Weather Forecasting in an Automation Era, Part 2: Lessons of Air France 447

August 26, 2022 by tornado Leave a Comment

This short series (go to Part 1) arises from the recently published paper, “The Evolving Role of Humans in Weather Prediction and Communication“. Please read the paper first.

The authors briefly mention the need for forecasters to avoid the temptation to get lazy and regurgitate increasingly accurate and complex postprocessed output. I’m so glad they did, agree fully, and would have hammered the point even harder. That temptation only will grow stronger as guidance gets better (but never perfect). To use an example from my workplace, perhaps in 2022 we’re arriving at the point that an outlook forecaster can draw probabilities around ensemble-based (2005 essay), ML-informed, calibrated, probabilistic severe guidance most of the time and be “good enough for government work.”

Yet we strive higher: excellence. That necessarily means understanding both the algorithms behind such output, and the meteorology of situations enough to know when and why it can go wrong, and adapting both forecast and communication thereof accordingly. How much of the improvement curve of models and output vs. human forecasters is due to human complacency, even if unconscious? By that, I mean flattening or even ramping down of effort put into situational understanding, through inattention and detachment (see Part 1).

It’s not only an effect of model improvement, but of degradation of human forecast thinking by a combination of procedurally forced distraction, lack of focused training on meteorological attentiveness, and also, to be brutally honest, culturally deteriorating work ethic. I don’t know how we resolve the latter, except to set positive examples for how, and why, effort matters.

As with all guidance, from the early primitive-equation barotropic models to ML-based output of today and tomorrow: they are tools, not crutches. Overdependence on them by forecasters, being lulled into a false sense of security by their marginally superior performance much of the time, that complacency causing atrophy of deep situational understanding, invites both job automation and something perhaps worse: missing an extreme and deadly outlier event of the sort most poorly sampled by ML training data.

Tools, not crutches! Air France 447 offers a frightening, real-world, mass-casualty example of this lesson, in another field. Were I reviewing the Stuart et al. AMS paper, I would have insisted on that example being included, to drive a subtly made point much more forcefully.

The human-effort plateau is hidden in the objective verification because the models are improving, so net “forecast verification” appears to improve even if forecasters generally just regurgitate guidance and move on ASAP to the next social-media blast-up. Misses of rare events get averaged out or smoothed away in bulk, so we still look misleadingly good in metrics that matter to bureaucrats. That’s masking a very important problem.

Skill isn’t where it should or could be, still, if human forecasters were as fully plugged into physical reasoning as their brain capacity allows. The human/model skill gap has shrunk, and continues to, only in part because of model improvements, but also, because of human complacency. Again, this won’t manifest in publicly advertised verification metrics, which will smooth out the problem and appear fine, since the model-human combination appears to be getting better. Appearances deceive!

The problem of excess human comfort with, and overreliance on, automation will manifest as one or more specific, deadly, “outlier” event forecasts, botched by adherence to and ignorance of suddenly flawed automated guidance: the meteorological equivalent of Air France 447. This will blow up on us as professionals when forecasters draw around calibrated-guidance lines 875 times with no problem, then on the 876th, mis-forecast some notorious, deadly, economically disastrous, rare event because “the guidance didn’t show it.”

That disaster will be masked in bulk forecast verification statistics, which shall be of little consolation to the grieving survivors.

Consider yourself warned, and learn and prepare accordingly as a forecaster!