YEA Atmosphere Panel: GPM-Driven Precipitation Layer Specification

@manual{cnl2026yea, author = {}, title = {YEA Atmosphere Panel: GPM-Driven Precipitation Layer Specification}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-SP-2026-028}, month = {march}, url = {https://canemah.org/archive/document.php?id=CNL-SP-2026-028}, abstract = {This technical note specifies the design of an Atmosphere panel for the Your Ecological Address (YEA) system, with particular emphasis on integrating NASA’s Global Precipitation Measurement (GPM) / IMERG satellite precipitation data as a Mapbox GL overlay. The Atmosphere panel is conceived as a point-centric view of conditions in the atmospheric column above a selected ground location, complementing existing terrain and ecological information already implemented in YEA. It synthesizes four main classes of variables: satellite-derived precipitation, modeled cloud state, vegetation greenness (NDVI), and modeled air quality. For precipitation, the design uses IMERG’s half-hourly, 0.1° global precipitation fields to provide both a global raster visualization and a point-level estimate of current precipitation rate, with optional short-term accumulation. Cloud variables and air quality are obtained from the existing Open-Meteo APIs, while NDVI values are sourced from external EO datasets specified in separate documents. The Atmosphere panel combines these sources into a unified user interface element composed of: (1) a toggleable precipitation overlay rendered as a semi-transparent raster in Mapbox GL, and (2) a compact information card summarizing current atmospheric conditions at the selected point. This specification defines the conceptual model, panel layout, required variables, and Mapbox GL layer configuration, while deliberately abstracting over low-level ETL details for IMERG ingestion and tiling. The resulting design aims to preserve scientific transparency, exploit open and public datasets, and align with the broader YEA philosophy of representing ecological context at human-relevant spatial and temporal scales.} }