• ufos and seaice: alaska chapter

    From MrPostingRobot@kymhorsell.com@1:229/2 to All on Sunday, March 07, 2021 20:40:04
    XPost: alt.ufo.reports

    EXECUTIVE SUMMARY:
    - Seaice in key areas around Alaska seem to predict daily UFO
    sightings some time later.
    - The biggest association comes from the Bering Straight and some
    large areas along longitude 170W.
    - A small region appears in the Mackenzie Bay nr the AK/YT border.
    - Given other data associated with UFO sightings nr the Bering Sea and
    Aleutians we might suspect UFOs regularly travel via air or water
    along 170W perhaps on their way to visit the US or other points S.
    - Basic images and plots at <kymhorsell/UFO/Alaska>.

    We've seen in a previous post that certain key regions have an
    affinity or predictive power for UFO sightings.

    We might interpret a region where e.g. thickness of sea ice is
    negatively correlated with daily UFO sightings across the US 30 days
    later as somehow "encouraging" more UFO sightings when sea ice
    disappears.

    The distribution of these particular regions has a pattern we might
    expect for other "folkloric" information going back decades. It turns
    out not just any region that is subject to seasonal seaice can closely
    predict UFO activity.

    We've seen that seaice across the Arctic Ocean contains key regions
    where the correlation between seaice and UFO sightings seems to go
    very high (~90%). From the very rough map of the region posted
    previously we could almost see a path from N Russia across the N
    America where the data suggested a UFO track might be highly probable.

    Here we'll zoom in at least part of the "destination" region in
    question and look at the satellite radar data gathered around Alaska.

    As before the s/w has gathered together daily NOAA radar maps for the
    region from 2010 to the present. For each smaller 100x100 pixel region
    of each image a time series is created and correlated against daily
    NUFORC sightings data. The s/w finds an optimum lag (days) that
    maximize the R2 statistic -- the fraction of day-to-day variations in
    UFO sightings that seem to exactly correspond with similar day-to-day variations in radar returns (aka "sea ice proxy") from each area
    across the Alaska region.

    Big surprise, a lot of the region shows no association whatever.
    Seaice across most of the Pacific off Alaska has no predictive power
    for UFO sightings across N Am.

    But equally no surprise, some key regions "light up" as if they were
    key flight or submarine routes between (wherever) and N Am,
    particularly the USA.

    The biggest "lit up" area is along longitude 170W starting at the
    Bering Straight.

    It's as if UFO's normally travel along this route and are inhibited
    (maybe ever so slightly :) by the presence of icebergs or seasonal seaice.

    The best areas have about an 80% R2. E.g. the stat model for one of
    the "best" predictive areas along 170W looks like:

    (Log transform enabled).
    (58 day lag).
    (No serial corr detected).
    y = 12.0597*exp(-0.128055*x)
    Doubling Rate -5.41
    beta in -0.128055 +- 0.0179935 90% CI (42 df)
    alpha in 2.48987 +- 0.0203901
    P(beta<0.000000) = 1.000000
    r2 = 0.77331859

    Binned data:

    Bin label av radar return av daily UFO predicted UFO
    sightings sightings
    2010209 -0.642513 12 13.0939* (+1sd over obs) 2015141 0.799166 10.6667 10.8866
    2018061 -0.121636 11.3333 12.249
    2018194 -2.03485 15 15.6495
    2018273 -1.08003 13.2381 13.8484
    2019056 0.477173 11.75 11.3449
    2019078 1.00353 10.4 10.6054
    2019101 2.25662 9.5 9.03311
    2019132 0.693845 13 11.0344**(-2sd)
    2019228 -1.40318 13.45 14.4335
    2019232 -1.2387 13 14.1327*(+1sd)
    2019288 -1.75659 17.4286 15.1017*(-1)
    2019318 1.31776 9 10.1871*(+1)
    2019358 1.64419 9.375 9.77005
    2020022 -0.266284 13.125 12.478
    2020033 1.3734 10.5 10.1148
    2020048 -0.710915 14.2 13.2091
    2020053 0.000560869 14.25 12.0588**(-2)
    2020112 0.928496 11.45 10.7078
    2020128 0.0698588 12.4 11.9523
    2020138 1.08781 10.7778 10.4915
    2020148 1.56027 9.16667 9.87561
    2020153 1.16237 10.1333 10.3918
    2020157 1.70302 9.5 9.69672
    2020161 0.750138 11.2593 10.9551
    2020188 1.27131 9.7619 10.2479
    2020198 0.624474 11 11.1329
    2020201 -0.361818 13.72 12.6316*(-1)
    2020218 -1.34287 13 14.3225*(+1)
    2020228 -1.85943 13.9 15.3019*(+1)
    2020237 -1.18838 12.0526 14.0419*(+1)
    2020250 -1.68671 17 14.9672*(-1)
    2020254 -0.815058 13.7391 13.3864
    2020274 0.425204 12.5667 11.4206*(-1)
    2020293 -0.618811 12.5 13.0542
    2020298 -1.29014 16 14.2261*(-1)
    2020305 -1.52538 13.8889 14.6612
    2020315 0.851102 9.80645 10.8144*(+1)
    2020325 -0.429844 13.25 12.7421
    2020335 0.150114 11.7 11.8301
    2020345 0.538486 10.186 11.2561*(+1)
    2020347 -0.504824 13.7368 12.865
    2020355 1.47428 9.9 9.98496
    2020365 -0.0701027 12.4286 12.1684

    This is a binned time-series regression. Datapoints are assigned to
    bins that contain "similar" points. The average X and Y is computed
    for each bin and those numbers passed to a time-series sensitive
    regression. The final statistics are from that regression. In this
    case the time-series regression found no problems with serial/auto
    correlation so the output is "the same" as an OLS for the same dataset
    (with a log transform on the Y data).

    The X data is a "normalized" version of the radar return data. Bright
    areas on the radar image represent points that have a large
    return. These might be flat areas of land or ocean, or sea ice. By
    tuning the model to ignore certain signal strengths some of the
    clutter (e.g. land or missing satellite data) can be ignored. The normalization involves translating the return strength into "Z scores"
    that have an average of 0 and standard deviation of 1. I.e. a value
    of -1 represents a signal around the 16% weakest level detected in the
    dataset and +1 represents a signal around the 16% strongest in the dataset.

    On that basis the model finds a reduction in signal of 5.4 is
    associated with a doubling of daily UFO sightings over average levels.

    All other models for each small 100x100 image pixel area across the
    Alaska region is treated similarly.

    A rough ASCII map of the region where the reader's imagination can
    fill in the outline of the Bering Straight and Alaska is as follows:

    180W 130W
    - - - - 45 47 55 40 40 35 60 73< - - - - - 73N
    - - - 61 48 74<59 - 44 36 56 58 36 - - - -
    - - - 30 56 72<75<19 15 68 57 43 - - - - -
    - - - 14 50 67 29 - - - 10 - - - - - -
    - - 66 27 29 69 6 - 6 14 - - - 14 - - -
    - - 73<69 36 71<11 - - 6 - 16 5 31 - - -
    - - 70<74<63 54 38 - - - - 19 - - - - -
    - 60 73<71<77<67 6 - - 9 5 - - - 32 - -
    - 54 63 64 71<56 - - 19 20 57 17 5 10 28 - -
    - 63 65 61 64 53 11 19 39 34 58 58 70<37 30 - -
    - 60 69 50 61 60 54 12 63 55 55 54 64 37 21 26 -
    61 71<76<73<62 58 49 61 61 62 51 42 50 62 42 21 -
    70<70<67 69 63 54 56 53 49 42 45 42 47 58 56 50 -
    73<75<70<61 61 57 48 42 57 48 59 64 41 69 41 40 -
    73<67 61 60 43 63 43 48 45 61 59 53 42 63 41 60 -
    - - - - - - - - - - - - - - - - -
    - - - - - - - - - - - - - - - - - 50N

    A sample radar image and the density plot of the R2's showing where
    the "most significant" association between the presence of seaice and
    later UFO daily sightings is at <kymhorsell.com/UFO/Alaska/>.

    "<" == R2 immed to right is >=70%

    NOTE: Some areas to the left and right of the original image of the
    radar data are black because of the relevant equal-area projection used.

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