XPost: alt.ufo.reports

EXECUTIVE SUMMARY:
- We use daily radar data to estimate amounts of sea ice around the N
Pole and Arctic Ocean.
- The No Po "sea ice" and UFO sightings across N Am are surprisingly
highly correlated (R2 74%) with about a 2 month lag.
- If you thought that was large, the R2 with radar-determined sea ice
over the Kara Sea and UFO sightings is 90% and seems to be the
highest value to be found in Arctic.
- It's hard to escape a tentative conclusion that UFO's may be coming
to N Am from either N Russia or the polar seas immed to the N of
Siberia -- a long-suspected link. Of course there may be other
"suspect areas" that seemingly predict "almost all" daily UFO
sightings across N Am. N Russia may not be "ground zero" for activity.


It's long been appreciated that UFO's by and large have an affinity
with the poles.

With ghost rockets in the news lately we recall the post-war
investigations of 1000s of sightings, of "missiles" that flew over
Sweden and seemingly sometimes crashed into lakes and fjords without
leaving a trace, concluded they may have been Russian weapons tests
launched from a base in Siberia.

And with various satellites buzzing around the planet since the 1970s
we might now have some way to check whether such ideas hold any water.

NOAA maintains a library of daily radar images taken by satellite over
various regions of the world.
(See https://manati.star.nesdis.noaa.gov/ascat_images/ice_image/).
As far as we know they are fairly low-resolution and mostly used to
check sea ice.

While the images above appear to be "color" they only encode the
strength of a radar return signal as the sats move around their polar
orbits a dozen times a day. "White" areas have a strong
return. "Black" areas have a weak return. To complicate matters, black
areas can also mean "no data" because the satellite(s) in question
broke down that day or did not overfly the region in question within a
given 24 hr period. A "strong return" can indicate sea ice in that
area. Or it might be land. A "weak return" may indicate ocean waves
which scatter the radar pulse in all directions, not returning much
back up to the sat. But it also may indicate forest or a city full of skyscrapers.

You can write an AI to analyze each pixel and take into account its
history across years of data to decide whether it's land, ocean,
skyscrapers, forest, ice or something else. But we don't have time to
do that.

I'll take the "Northern Hemisphere" images (tagged with NHe at the URL
above) and split the return signal into 3 levels. Too low I will
consider to be "missing return" and ignore it. Too high I will
consider to be an anomalously large return and also ignore it. The
other signals I will count up to give an average signal strength over
a whole region on a given day. I'll assume these numbers represent an
approx area of sea ice on that day.

We'll also chop the NH image into (initially) 5 chunks. One centered
on the geographic North Pole (that appears as a dot on the images),
and 4 about equal size areas to the top left, top right, bottom left,
bottom right of the NoPo.

We can then cook up a time series for each region that roughly
computes the "amount of ice" in that region day by day. The idea is
then to compare these 5 time series, possibly lagged by a number of
days, with UFO sightings data (as usual I'll use the NUFORC data, this
time aggregated by day rather than month) and see which region is "the
most similar" and by what kind of margin it appears similar.

Just for fun I'll leave out any UFO sighting of "lights". While it
does not affect the result, why ask for the inclusion of a lot of
reports of Starlink satellites?

To give a flavor of the low-level processing here's a time-series
regression for the "North Pole" central region from the images in question:

MODEL:
(Lag is 82 days).
y = -2.27715*x + 11.89
beta in -2.27715 +- 0.543011 95% CI
alpha in 11.89 +- 0.659667 = [11.2303, 12.5497]
P(beta<0.000000) = 1.000000
calculated Spearman corr = -0.856048
Critical Spearman = 0.448000 2-sided at 1%; reject H0:not_connected
r2 = 0.74078828
Binned data:

Bin label: av radar return av #UFO's model-est av #UFO's
2020097 0.527615 11.3333 10.6885
2020084 0.922511 9.47619 9.7893
2020349 0.230587 11.2333 11.3649
2020101 0.688298 12.32 10.3226*
2020142 0.37394 15.2778 11.0385**
2020181 -0.195785 12.8065 12.3358
2020339 -0.858156 15.4211 13.8441
2019310 -1.89593 14 16.2073*
2020309 -1.58308 13.5652 15.4949*
2019314 -1.72341 15.2 15.8145
2020278 -1.25416 13.1818 14.7459
2020319 -1.40026 16.7 15.0786
2019332 -0.539361 10.9333 13.1182*
2020359 0.0429902 10.4314 11.7921
2020067 0.800084 10.4706 10.0681
2019053 1.66385 7.11765 8.10116
2020050 1.10421 10.3 9.37554
2019018 1.90756 5.66667 7.54619*
2020274 -1.0348 14.8095 14.2464
2019279 -2.14654 17.4444 16.778
2019044 1.45298 9.6 8.58134
2020257 -0.690517 16.125 13.4624*
2012215 -0.284567 14 12.538
2020329 -0.0671441 12.5882 12.0429
2014002 1.42013 9 8.65614
2020267 -1.14177 12.5 14.49*
2017039 1.87069 6 7.63015
2018059 1.55445 6 8.35028*

As usual with these things the stats shows the time series related to
the radar data is highly highly correlated to the UFO sightings
data. Both a T-test and the Rank test show less than 1% each that this
link is just down to chance. IOW we're at 1 chance in 10,000 this is a
spurious result. It's 99.99% likely to be "something".

The radar signals have been normalised into so-called "Z scores" we
learned in high-school stats. The average is 0 and the standard
deviation is 1. So a "radar return" of 0 means "average return from
this region" and -1 means "1 standard deviation below average" IOW in
the bottom 85% of return strengths (from our old 1950s school tables,
back pages :).

The model says for each reduction in signal -- because there is
presumably less ice -- of around 1 std deviation there are an
additional 2.3 +- .5 UFO sightings 82 days later.

The R2 for the model shows more than 70% of the day-to-day variations
in UFO sightings in the NUFORC database are "explained" by variations
in radar returns over this part of the Arctic Ocean. AKA the
"majority". A healthy amount.

A possibly-naive way to interpret this is -- less ice means easier
travel for Our Friends (TM; not necessarily our friends). If they are
living under the polar ocean then when the ice melts in summer they
are a bit more likely to come out for a visit down south e.g. N Am.
But there are many other possible explanations. E.g. the No Po is an
important origin for N Am weather. Maybe UFO's are all bits of paper
being blown by the wind. When the ice melts winds pick up across N Am
(aka tornado season) and more pieces of paper get blown around and
"sightings" increase.

But now the important bit. How do the 5 regions compare using this
simple test. Which region is "most similar" to the UFO sightings and
by how much?

Region R2
zTR97 0.90018433
znopo82 0.74078828 <-- example region, above
zTL91 0.61592489
zBR52 0.49590443
zBL22 0.41031360

The "Region" is a code for which of the 5 regions we're talking about.
The "nopo" region is the central region (above). TL, TR, BL, BR
indicate a corner of the NHe images relative to the pole. The number
indicates a lag (in days) that was found to maximize the R2
("explanation power") of each model.

So it seems the "top right" corner of the images explain 90% of the
day-to-day variation in UFO sightings across N Am while the radar
signal bouncing off the central Arctic Ocean predicts "only" around 74%.
The other regions explain less downto 41% for the "bottom left" quadrant.

Punchline. The "top right" region in the images corresponds with
northern Russia, Kara Sea. Just like some of the folklore and some of
those old Swedish govt reports speculated.

Some people may have physically seen funny objects flying into NW
Europe from the general direction of Russia, but the data shows it's
actually "a thing" at least as far as this data is concerned.
Amusingly, the satellite data we are using is for the last 10 years,
not the 1940s.

If the origin of UFO's had *really* been Russia all the time since
WWII I'm sure someone would have told us.

--
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--- SoupGate-Win32 v1.05
* Origin: www.darkrealms.ca (1:229/2)