整合巴西气象站25年数据,统一格式并标注质量,支持多领域研究。
METBRA25Y: Brazil Surface Meteorology Archive with Harmonized Variables and Quality Control

- 将巴西各地气象站原始数据统一为标准字段和时间戳
- 覆盖2000-2025年,含605个有效站点,涵盖10类气象变量
- 提供质量标志与缺失审计,适合气候、农业、城市风险等研究
本文介绍METBRA25Y,一个基于巴西国家气象研究所(INMET)公开历史记录构建的小时级地表气象观测数据集。该数据集旨在支持需站点级气象时间序列的可复现研究,包括环境、气候、水文、农业、城市风险及机器学习等领域。处理流程包含每年度INMET档案的读取、从原始文件头解析站点元数据、将异构葡萄牙语列名标准化为统一模式、构建小时时间戳、按城市和站点合并观测,并导出压缩CSV文件,附带站点清单、每站质量标记、日降水汇总、变量级故障摘要及缺失数据审计。质量控制采用双阶段策略:首先将物理上不合理值转为缺失并标记;其次进行时序与跨变量一致性检查,生成诊断标志但不覆盖原始数据。当前版本覆盖2000至2025年,含616个唯一站点代码,其中605个坐标位于巴西合理范围。本文详述数据来源、文件结构、标准化模式、质控规则、技术验证结果、局限性及推荐使用方式。
原文摘要 · Abstract (English)
This data paper describes METBRA25Y, a harmonized archive of hourly surface meteorological observations from Brazil derived from public historical records of the Instituto Nacional de Meteorologia (INMET). The dataset was designed to support reproducible environmental, climatological, hydrological, agricultural, urban-risk, and machine-learning studies that require station-level meteorological time series with standardized variable names and explicit quality-control metadata. The processing workflow ingests annual INMET archives, parses station metadata from raw file headers, normalizes heterogeneous Portuguese column names into a canonical schema, constructs hourly timestamps, consolidates observations by city and station, and exports compressed CSV files together with station manifests, per-station quality flags, daily precipitation aggregates, variable-level failure summaries, and missing-data audits. The quality-control protocol follows a two-stage strategy: first, physically implausible values are converted to missing values and flagged; second, temporal and cross-variable consistency checks generate diagnostic flags without necessarily overwriting the original measurements. The resulting package covers observations between 2000 and 2025, with stationspecific temporal coverage, and includes key meteorological variables such as precipitation, air temperature, dew point, relative humidity, atmospheric pressure, wind speed, wind gust, wind direction, and global solar radiation. Based on the summary files included in the current release snapshot, the archive contains 616 unique station codes across variable summaries, of which 605 have coordinates within a broad Brazil plausibility envelope. This paper documents the dataset provenance, file organization, harmonized schema, quality-control rules, technical validation outputs, limitations, and recommended usage practices.
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