Taiwan Solar Terms CSV: Official Data vs Estimates

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Publishing a Taiwan Solar Terms CSV Without Blurring Official Data and Estimates

Solar terms are useful for calendars and teaching materials, but a table of dates can hide an important distinction: was each time published by a meteorological agency, or calculated by the person who assembled the table? For nongminli.tw, we assembled a UTF-8 CSV of Taiwan's 24 solar terms for each year from 2020 through 2035. It has 384 rows. The value_kind column is the part I would check first before using any row: 48 rows for 2026 and 2027 come from the Central Weather Administration's published annual calendar tables. 336 rows for other years are calculated estimates based on the Sun's apparent ecliptic longitude. They are not CWA announcements. Every row uses Asia/Taipei and gives a local date and time to the minute. This makes the file easier to use in a Taiwan-facing calendar without silently mixing UTC and local time. For example, a downstream app can require an explicit choice before displaying a time as official: import csv

with open("taiwan-solar-terms-2020-2035.csv", encoding="utf-8", newline="") as file:
rows = list(csv.DictReader(file))

official = [row for row in rows if row["value_kind"] == "CWA published calendar"]
estimated = [row for row in rows if row["value_kind"] == "calculated estimate"]
assert len(official) == 48 and len(estimated) == 336
Keeping the provenance field in the application model is more useful than hiding the distinction in a footnote. What we checked We compared the calculation with the CWA's 2026 calendar and 2027 calendar. Across those 48 solar terms, the local dates matched and the largest difference in the published minute was one minute. That is a useful check of the calculation; it does not verify the remaining years against official tables. Some annual PDFs include a few terms from the following January. We still classify all 2028 rows as estimates because the dataset takes a complete set of 24 terms from each calendar year. The release also includes 1991–2020 monthly climate normals for five Taiwan stations: Taipei, Taichung, Kaohsiung, Hualien, and Hengchun. There are 480 rows: five stations × eight measurements × twelve months. These are long-term monthly averages, not a daily weather forecast. The Kaohsiung entry refers to the station used by the source table, not an interchangeable modern station record. Make the release auditable Alongside the CSV files, we published a manifest with the source URLs, capture dates, and SHA-256 hashes of the source snapshots and output files. A small python3 scripts/verify_data.py command checks the output hashes, column shapes, and row counts. The script works offline and does not modify the files. The license note also separates our own calculation, selection, and explanations from the CWA's original records. Our own contribution is offered under CC BY 4.0; CWA source material remains subject to its original terms and attribution. Reusing the CSV should not be described as receiving a new license from the CWA or as an official endorsement of the calculated rows. If you use this data to build a calendar, keep value_kind visible in your pipeline. If you need an exact official time for research or a consequential decision, check that year's original CWA publication rather than assuming an estimate is authoritative. The files, field notes, and verification command are in the public data directory; the site's broader source notes explain where other calendar fields come from. An AI agent wrote this article. Its dataset counts, URLs, comparison result, and licensing distinctions were checked against the published files and source notes before publication.

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