Complete Köppen-Geiger filter review with Mixed climate class

Classify each county by area-weighted Köppen class shares: a county is
predominantly its top class when that class covers at least 50% of its
land and leads the runner-up by at least 5 percentage points; otherwise
it is Mixed (133 of 3,143 counties in the 50 states and DC).

- Add build_county_koppen_metric.py (writes data/metrics/koppen.csv) and
  apply_koppen_metric_to_climate_data.py (writes koppenZone plus
  koppenPrimaryClass/koppenSecondaryClass for Mixed counties).
- Move shared helpers into scripts/common/ (county loading, Köppen
  legend, area-weighted raster shares); fix the 180th-meridian raster
  window for Aleutians West.
- Add check_climate_data.py to validate the app CSV.
- Draw Mixed counties in app.js as diagonal stripes of their top two
  classes, fixed to the ground and following the map at every zoom, with
  a crossfade only when the stripe size changes. Filtering a class also
  matches Mixed counties where it is primary or secondary.
- Document the rule, display, and pipeline plan in docs/ and update the
  README and data-source notes.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

scripts/county_data_sources.md

@@ -12,13 +12,24 @@ The browser blocks `fetch("data/climate-data.csv")` when `index.html` is opened
 
 Then open [http://localhost:8000/](http://localhost:8000/). This keeps the app CSV-only while allowing the map and filters to load normally.
 
-## Source 1: Koppen-Geiger classes (`koppenZone`)
+## Source 1: Koppen-Geiger classes (`koppenZone`, `koppenPrimaryClass`, `koppenSecondaryClass`)
 
 - Dataset: Beck et al. updated 1-km Koppen-Geiger climate classes (historical + future windows)
 - Landing page: [https://www.gloh2o.org/koppen/](https://www.gloh2o.org/koppen/)
 - Primary paper for updated release: [https://www.nature.com/articles/s41597-023-02549-6](https://www.nature.com/articles/s41597-023-02549-6)
 - Coverage: 1901-2099 (use historical 1991-2020 layer for this project to align with NOAA baselines)
 - License shown on dataset page: CC BY 4.0
+- Local files: `data/koppen_geiger_tif/1991_2020/koppen_geiger_0p00833333.tif` and `data/koppen_geiger_tif/legend.txt`
+
+Build the county metric and apply it to the app CSV:
+
+```powershell
+.venv\Scripts\python.exe scripts\build_county_koppen_metric.py
+.venv\Scripts\python.exe scripts\apply_koppen_metric_to_climate_data.py --dry-run
+.venv\Scripts\python.exe scripts\apply_koppen_metric_to_climate_data.py
+```
+
+The builder writes `data/metrics/koppen.csv` with each county's class, top and runner-up classes, and their area-weighted shares. The apply step writes `koppenZone`, `koppenPrimaryClass`, and `koppenSecondaryClass`; `--dry-run` reports the changes without writing. Run it after `build_county_climate_data.py`, which still writes an older largest-share `koppenZone`. The classification rule is documented in [`docs/filter-calculations.md`](../docs/filter-calculations.md) §1.
 
 ## Source 2: NOAA 1991-2020 gridded normals (`avgTempF`, `annualPrecipIn`, `seasonalityIndex`, previous `extremeDays`)
 
@@ -255,7 +266,8 @@ Then update the app CSV. Polygon archive GHI is used first; representative-point
 
 ## Metric definitions in generated output
 
-- `koppenZone`: majority class within county polygon from Koppen raster.
+- `koppenZone`: the county's predominant Koppen-Geiger class, meaning the class covering at least 50% of the county's land area and leading the runner-up by at least 5 percentage points; otherwise `Mixed`. Shares are area-weighted, with ocean and no-data cells excluded.
+- `koppenPrimaryClass` / `koppenSecondaryClass`: for Mixed counties only, the top and runner-up classes, drawn as stripes on the map; blank for predominant counties.
 - `avgTempF`: mean of 12 monthly county mean temperatures, converted C -> F.
 - `annualPrecipIn`: sum of 12 monthly county mean precipitation totals, converted mm -> inches.
 - `seasonalityIndex`: coefficient of variation of monthly precipitation totals, scaled to 0-100.
@@ -295,6 +307,7 @@ python scripts/build_county_climate_data.py `
 
 Notes:
 
+- This writes only the base columns and an older largest-share `koppenZone`. Do not run it over the live `data/climate-data.csv`: it would drop the columns added by later stages. After a full rebuild, run the Köppen build and apply steps (Source 1) and the enrichment stages.
 - This computes all counties in your geometry file, not just the sample records.
 - For counties outside CONUS coverage in NOAA gridded files, fallback values are applied by the script when no valid grid values intersect.
 - For physically-based daily `extremeDays`, provide true daily grids and set `--extreme-days-mode require-daily`.