see1m™ Skywater — city weather time tissue
Research preview (not live)

see1m™ Skywater — see decades of weather as a moving system

Skywater compresses hourly weather-station history into a compact, browser-native “time tissue”. Scrub years, replay extreme events, and compare cities — without dashboards, APIs, or map servers.

see1m.com is a research preview from Hold2X. No login. No install.

Hourly resolution
Station truth (no gridding)
Event replay
Portable city format

How to use (30 seconds)

1) Pick a yearUse the year dropdown (or click a row in the year overview list).
2) Hit PlayWatch the system evolve hour-by-hour. Pause when something “pops”.
3) Scrub timeDrag the slider to move through storms, drought periods, and recovery.
4) Jump to major eventsSelect an event to replay the highest-severity rain episodes.

Tip: Auckland is larger because it includes more stations plus a small coastal inset (Browns Bay). NYC is lighter but still spans 20+ years of hourly history.

Cities currently available

Auckland, NZ

~12 MB dataset dense station network includes coastal inset

Maritime climate with sharp coastal gradients. Great for demonstrating dryness ↔ rain transitions and flood “pop”.

▶ Open Auckland viewer

New York City, USA

~4 MB dataset extreme rain events inland flood propagation

Shows many “unnamed but huge” hydrologic events that rarely have a public narrative — and that’s the point.

▶ Open NYC viewer

London, UK (preview)

1 month demo rapid deployment proof

A short-range prototype demonstrating that the same pipeline and viewer can be stood up quickly for new regions.

▶ Open London demo

Built for direct visualisation

Skywater is designed to render long time-series data directly in the browser, without spatial preprocessing workflows, map servers, or heavyweight toolchains. Alignment is intrinsic to the encoding — allowing fast deployment across cities, including environments where traditional pipelines are impractical.

Skywater is a lens for exploring historical station behaviour. It does not forecast, interpolate, or claim ownership of source data.

Related preview: rain → stream → coast

Browns Bay — rainfall-driven stream response (timelapse)

Dynamic coastal layers (intertidal study)

Why this matters

Two quick visual studies using the same spatial frame: rainfall drives stream response (with lag), and coastal layers provide context for how water behaves at the edge.

  • Rain → catchment lag
  • Persistence after rain stops
  • Coastal context alongside national datasets