KnouandClaude Opus 5
867a07cecb
Close gaps found in a review of the documentation: - Track data/metrics/ and data/metric_sources.json in git so the data checker passes on a fresh clone (finding 23; decision logged) - State the Alaska and Hawaii coverage gap in the README limitations and extend finding 12 - File findings 24-26: the data-sources doc lacks gridMET and several pipeline commands; wettest/driest month are computed twice; solar GHI is written by the extreme-temperature apply step - Add the stale "fallback values" note to filter 2's tasks Tidy the document system: - Add docs/reviews/README.md with the numbering rules and a finding index - Rename koppen-mixed-display-plan.md to koppen-mixed-display.md and fix its stale Puerto Rico and "stage 5" text - Add the precipitation-month step to the README enrichment list - Describe the Current method / Previous method pattern in plan section 5 - Add CLAUDE.md with the project guardrails and doc layout Format filter-calculations.md so it renders on GitHub and in VS Code: inline math uses $...$, ranges use en dashes, and implementation references name functions instead of line numbers. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
1.6 KiB
Filter 4: Extreme temperature days
Status: Not started.
Data key: absoluteExtremeDays. Calculation:
filter-calculations.md §4.
Findings
5. Extreme-day counts are not completeness-normalized. A partially observed year contributes a raw count and receives the same weight as a complete year. Consider requiring a minimum number of valid days or annualizing partial counts explicitly.
6. The absolute-extreme metric depends on unrelated percentile thresholds.
build_annual_counts skips a county when its retired local p95/p05 thresholds
are missing, even though the active 95 °F / 0 °F calculation does not require
those percentiles. The absolute calculation should be separated from that
prerequisite.
18. The data-sources doc describes the retired locally extreme metric.
Source 5 in scripts/county_data_sources.md is headed locallyExtremeDays
and says apply_locally_extreme_metric_to_climate_data.py writes the locally
extreme average into extremeDays, keeps oldExtremeDays, and adds eight
detail and audit columns. None of those columns is in data/climate-data.csv,
and the script's docstring says locally percentile-based metrics are not
included in the app CSV.
Cross-filter findings that affect this filter, in 00-cross-filter.md: 12 (Alaska and Hawaii not covered), 13 (Lexington, VA blank), 17 (missing from the data-sources metric list), and 26 (this filter's apply step also writes solar GHI).
Decisions
None yet.
Tasks
Not started; follow the per-filter review checklist in pipeline-plan.md §5.