From: kymhorsell@gmail.com   
      
   It seems those guys talking to Congress were not jus wislin Dixie.   
      
      
   EXECUTIVE SUMMARY   
   - We look at sightings of "objects" over my part of SE Aus versus met   
     data from GHCN stations around the world.   
   - The corr map is here: .   
   - It turns out daily met data from key regions of Nevada explain 60%   
     or more of the objects seen. Not only does at least one military   
     base live in the area -- it has a documented interest in flying   
     military drones. Experimental military drones.   
   - Another location is found on the US east coast with a similar   
     provenance.   
   - And other locations in Russia seem to also have similar links.   
   - It seems some fraction of the objects seen may relate to on-going   
     military operations of major players.   
   - But these operations don't explain all the data. Interactions between   
     local military choppers and light aircraft and "lights on the sky"   
     suggest at least some of the objects observed are something else.   
   - We posit rumours of recovery and rev engineering of "unknown   
     technology" may hold water. Some objects are links to Antarctica,   
     outer planets and asteroids in our solar system are daily wandering   
     around the world.  And trailing them, waiting for any "accident"   
     that may befall the first bunch, is ready to swoop in and capture   
     the relevant tech.   
      
      
   We've looked in the past couple posts at sightings of unusual "lights   
   in the sky" during the past couple years over my property in   
   semi-rural SE Aus. I tried to keep a detailed log of them based on a   
   daily sky-watch for around 1 hr during at least 2022 and less   
   religiously in the years before and upto today.   
      
   We created correlation maps relating the ups and down of the daily   
   object count -- that varied between 0 (a rare occurrence in 2022) and   
   ~30 (pretty much the record for my observations and prob nothing that   
   can be replicated since 2022) -- against weather data for each "grid   
   square" around the world. In one case the grids were monthly weather   
   or related data of any kind incl cosmic rays, earthquakes, cloud   
   parameters (height, area, color), and anything else the various AI   
   programs could snaffle off the Internet and extract sense from over   
   the past 10-20 years. In the 2nd cut we looked at daily SST from   
   satellites, but broke the voluminous data up into regions for the AI's   
   to sort out.   
      
   In both cases we got patterns of correlations spread across a map of   
   the world that looked very familiar. Familiar with similar maps for   
   NUFORC data. Similar to many other phenomena incl lake monsters,   
   shadow people, certain types of ghosts, plane crashes, amnesia cases,   
   missing children, and a long list of other things the programs have   
   sorted through over the past few years. Interestingly certain other   
   phenomena that may at first seem very similar do not light up the same   
   regions of the world. E.g. generalised ghost sightings. Kinda a   
   surprise that poltergeists are similar, but people sighting things   
   moving around in cemeteries and old asylums at night are quite different.   
      
   But now we'll scan another kind of dataset because the AI's did it   
   some time back and, again, the results were surprising if not   
   shocking.  If any of you have been out in your yards at night having a   
   quiet coffee and turned around to spot SOMETHING hovering quietly   
   behind you, you'll know the kind of shocking I'm talking about. :)   
      
   This next dataset is the accumulated output from met stations. Weather   
   stations have been setup all around the world since the start of the   
   20th cent.  But it was only after WWII they started to become fully   
   automatic and made systematic and accurate measurements. One   
   collection of the data is the Global Historical Climatology Network (I   
   use v3.22 because it's a bit of an upload if you want it all and I'm   
   not prepared to do it again :).   
      
   The dataset consists of the daily data from about 100,000 stations   
   located around the world.  Each station records at least some minimum   
   number of met data e.g.  daily max temp, daily min time, daily precip,   
   but many record all kinds of other data as well incl wind-speed and   
   direction, sky color, snow depth, etc.   
      
   So with my UFO records timestamped by my own computers with approx the   
   right date and time over the past few years, we could line up those   
   ups and downs with met data anywhere in the world at specific lat and   
   long and see whether anything, anywhere over the past few years varies   
   in statistically exactly the same way and, if so, how close a match is   
   it. And plot the strength of the matches across a map.   
      
   If course there are some limitations this time. The data is daily.   
   It's detailed. These things bring their own problems with the size of   
   the data we're playing with. My old creaking computers can still   
   handle that.  But it takes a day. :) But the main thing is -- met   
   stations are almost always on land.  Some may be on islands, some are   
   even on ships or research stations in international waters -- but   
   these are few and far between.  So we will be mostly looking at land   
   areas in our map.   
      
   So now the shocking part. Which stations and type of met data most   
   consistently follow the daily ups and downs of UFO sightings over my   
   property over the last few years?   
      
   The top 10 lines of the  table looks like:   
      
   Type	Station		Lag	Filter	Trans	R2   
   prcp    USW00023237     7       2               0.61367791   
   snwd    USC00360140     14      2               0.40623455   
   snwd    USC00265191     7       2               0.34722080   
   snwd    RSM00030439     0       2               0.33888551   
   prcp    RSM00025745     1       2               0.30663091   
   prcp    ITW00033126     1       2               0.28108416   
   snwd    USC00307484     14      2               0.26970166   
   snwd    USC00471578     14      2               0.26257401   
   snwd    RSM00023803     7       2       -x      0.25647477   
   prcp    MA000067009     7       2               0.24731767   
      
      
   The columns should be more or less familiar from other posts.  The   
   "type" column shows which weather data we're looking at.  "prcp" is   
   the daily precipitation at some met station; "snwd" is snow depth   
   (sometimes manually entered; sometimes taken robotically). Other data   
   types like daily max and min temps, wind speed and dir, etc do not   
   appear in the top10.  And this itself may be some kind of clue.   
      
   If we are looking at "flying objects" and they seem to have links with   
   remote locations then maybe certain types of things at those remote   
      
   [continued in next message]   
      
   --- SoupGate-Win32 v1.05   
    * Origin: you cannot sedate... all the things you hate (1:229/2)