data/README.md

Data card: data/

The curated data our code reads. Together with code/, it reproduces submission.csv and the Policy Idea ranking.

Files

File	What it holds	Source	License	Years covered	How we prepared it	Used for
annual_valid.csv	One row per city and year: all annual_indices columns, including the four forecast metrics (Avg Temp, ToT Prcp, Max Tmax, cdd) and the Miss * missing-day counts	Challenge dataset, annual_indices	As on the challenge dataset card	Each city's record, ending 2018	Replaced -inf (empty years in Max Tmax) with missing; dropped city-years where any Miss * column exceeds 10 days (the scorer's rule). Written by code/baseline.py.	Forecast input
city_features.csv	One row per city: Lat, Lon, elevation_m, distances to the coast / Gulf Stream / California Current / Labrador Current (km), 0–1 ocean-influence weights, tree_canopy_frac (0–1) and tree_canopy_valid_frac (QC)	Elevation: Open-Meteo Elevation API (Copernicus DEM). Coastline: Natural Earth 1:50m (public domain). Current paths: hand-drawn approximations (see code/build_features.py). Canopy: NLCD/USFS Tree Canopy Cover CONUS 2018 v2021-4, via the MRLC WCS service	Natural Earth and NLCD: public domain; Open-Meteo: CC BY 4.0	Static (canopy: 2018)	code/build_features.py, then code/add_tree_canopy.py (mean canopy over a ~10 km box around each station)	Geography trend: tested, not chosen. Canopy: analysis and policy only
raw/cities_elev.csv	City ID, name, lat/lon, elevation	Open-Meteo Elevation API	CC BY 4.0	Static	One API call per station	Input to build_features.py
raw/coast50.geojson	Natural Earth 1:50m coastline	Natural Earth	Public domain	Static	Unchanged	Input to build_features.py
policy_ranking.csv	Per city: canopy %, mean CDD 1989–2018, mean forecast CDD 2019–2025, increase, priority rank	Our forecasts + the files above	CC BY 4.0 (ours)	1989–2025	code/policy_ranking.py	Analysis and policy only

How the files fit together

• Join key: ID = the station ID in the challenge city_info table = city_id in submission.csv.
• Units: °F, inches, °F-days; elevation in m; distances in km; canopy as a 0–1 fraction (×100 = %).
• Known limitations: many stations are at airports, so canopy around the station is lower than in the city's neighborhoods. Ocean-current paths are approximate. Rows with too little history fall back to the 30-year average.

Data rule check

• Every forecast input is weather data up to 2018-12-31 (code/baseline.py stops if any year is after 2018) or static geography that doesn't respond to the weather.
• No 2019–2025 data is used anywhere.