Tutorials

This section provides step-by-step tutorials for using bsrn for various tasks. These tutorials are provided as interactive Jupyter notebooks.

1.data_downloading — BSRN FTP credentials, browse station file inventory with get_bsrn_file_inventory(), and download station-to-archive .dat.gz files with download_bsrn_stn().

2.quality_control — QIQ March LR0100 month: Part A runs qc_test() and tallies tier row counts from flag* columns; Part B exercises individual *_test helpers (PPL, ERL, closure, diffuse k, k-indices, tracker-off), and §7 checks that Part A and Part B agree per tier (using run_qc()). §8 is the daily QC audit table (ds.plot.table); §9 is a faceted day plot (ds.plot.daily) before masking; §10 applies qc_mask() and replots the audit table on the masked minute series.

3.time_averaging — explicit time aggregation with pretty_average() (floor / ceiling / center, match_ceiling_labels, samples per interval).

4.clear_sky_detection — McClear-based clear-sky columns, QC, Reno clear-sky detection via detect_clearsky(), and CSD-point visualization (QIQ September example).

5.cloud_enhancement_event — REST2 clear-sky (MERRA-2 via Hugging Face), QC (closure, diffuse ratio, tracker-off), and Killinger/Yang/Gueymard cloud enhancement detection with detect_cee().

Core Workflows

• BSRN Data Retrieval / BSRN 数据获取
• Quality control, step by step (QIQ March)
• 质量控制逐步演示（QIQ 3 月）
• Time Averaging of 1-min Data (floor / ceiling / center)
• 分钟级数据时间平均（floor / ceiling / center）
• Clear Sky Detection Demo With McClear
• 基于 McClear 的晴空检测演示
• Cloud Enhancement Event Detection with REST2
• 基于 REST2 的云增强事件检测