Atmospheric Phenomena

Preparation begins weeks before a trip. EEG training intensifies in the lead-up, working through the NSE protocol so the baseline is already stable by the time the field session starts. Data from prior sessions, including flight paths, timing windows, and scale estimates, is archived and reviewed, and informs where to post up and when to watch most closely on return trips.

Site selection is deliberate. Vantage points are chosen for sightline geometry, ideally a location with visibility across an entire canyon system and multiple ridgelines, paired with an eye for skyline composition developed through years of photography. Camp is set up within a short hike of the site, close enough for quick access, far enough to stay quiet.

A field day typically runs nine to twelve hours, across two to four consecutive days per trip. Time is budgeted with a buffer so pressure is never a factor. Gear stays active throughout the day, even during quiet stretches, with the camera, drone, and EEG headset run periodically as ongoing tests. EEG data doubles as NSE training regardless of what the sky does, so no day is wasted. Pack-out happens with an hour of daylight remaining.

Typical equipment: a dedicated video camera, a DJI Mini 3 drone, a Canon EOS camera, an iPhone, a Muse S EEG headband, an Apple Watch, a solar-powered battery pack, field notebooks, and a laptop for on-site review. A green laser is also carried as a contingency tool, though it has not yet been used.

Recording a sighting is only the first half of the process. Once footage comes back from the field, it goes through a structured breakdown to pin down what was actually recorded: size, speed, orientation, and distance.

The first pass draws on camera metadata alongside direct recall of the scene, where the camera was pointed, what else was visible in frame at the time. Distance and position get triangulated against fixed reference points in the shot itself: tree lines, ridgelines, and even atmospheric haze layers, cross-checked against the angle between the camera and the object. The full length of the clip gets tracked frame by frame, where the object enters and exits, how its apparent size changes across that span, and any spinning, morphing, or reorientation along the way.

Speed gets calculated by identifying a start and end point in the frame, mapping those points to real-world distance, then dividing by the elapsed time in the clip. That speed estimate then has to hold up against the size and clarity data from the same clip. If every measure agrees with the others, that's the signal the estimate is narrowing in on something real rather than compounding error from any single step.

From there, the case moves into Blender. The same real-world terrain gets modeled in 3D, and the estimated flight path gets built and tested against it directly, using animation and side-by-side comparison to further narrow size, speed, and trajectory. Flight paths and timing also get checked against the surrounding terrain and geography, looking for any correlation between where an object moved and the landscape it moved through.

The central case in this track is a series of observations recorded at the same site across a three-week interval, including roughly five minutes of continuous footage of a metallic, spinning object. Timestamps, GPS-referenced flight paths, velocity calculations, and the 3D terrain reconstruction for this case are being prepared for release here alongside the footage.