geoskill-extreme-weather-detection

基于百分位阈值检测热浪、寒潮与暴雨等极端天气事件，统计强度、持续时间与空间范围，输出事件清单 JSON 与空间分布栅格。Percentile-threshold detection of heatwaves, cold spells, and heavy rainfall, reporting intensity, duration, and spatial extent with an event-list JSON and spatial raster.

极端天气事件检测 | Extreme Weather Detection

Detects extreme weather events from temperature / precipitation time series using the percentile-threshold method, reporting each event's intensity, duration, and spatial extent. Suitable for heatwave / cold-spell / heavy-rainfall event cataloging, extreme-climate risk screening, and rapid disaster assessment.

Detection rules:

• Heatwave: temperature above a high percentile threshold (e.g., P90) for ≥ 3 consecutive days.
• Cold spell: temperature below a low percentile threshold (e.g., P10).
• Heavy rainfall: precipitation above P95 / P99.

By default the threshold is determined per pixel from the quantiles of the series itself (percentile ≥ 50 = upper-tail extreme, < 50 = lower-tail extreme). Events are extracted by connected-component labeling on the (time, y, x) 3-D exceedance volume (scipy.ndimage.label, with temporal adjacency plus 4-connected spatial connectivity); for each event the start/end time, duration in days, peak/mean intensity, number of spatial pixels, and centroid are reported.

A built-in --synthetic mode generates simulated series with embedded known extreme events (persistent heatwaves / heavy rainfall) for offline validation of detection correctness.

Dependencies / 依赖

bash

pip install numpy rasterio scipy

Usage / 使用方法

Basic usage (synthetic data, offline)

bash

python geoskill-extreme-weather-detection.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./output

Example 1: heatwave detection (temperature P90, ≥3 consecutive days)

bash

python geoskill-extreme-weather-detection.py --bbox 116 39 117 40 --variable temperature --threshold p90 --n-dates 30 --output-dir ./heatwave

Example 2: heavy rainfall detection (precipitation P99)

bash

python geoskill-extreme-weather-detection.py --bbox 121 31 122 32 --variable precipitation --threshold p99 --output-dir ./heavy_rain

Example 3: cold spell detection (temperature P10 lower tail)

bash

python geoskill-extreme-weather-detection.py --bbox 116 39 117 40 --variable temperature --threshold p10 --output-dir ./cold_spell

Example 4: real multi-temporal raster + custom minimum duration

bash

python geoskill-extreme-weather-detection.py --input temp_daily.tif --variable temperature --threshold p95 --min-duration 5 --output-dir ./real_events

Example 5: bbox-only auto-synthesis + silent mode

bash

python geoskill-extreme-weather-detection.py --bbox 110 30 111 31 --variable precipitation --threshold p95 --output-dir ./auto --quiet

Output / 输出

File	Format	Description
extreme_events.tif	GeoTIFF (float32, 2 band)	band1 = exceedance days per pixel, band2 = maximum anomaly intensity, EPSG:4326
event_list.json	JSON	Event list (start/end/duration/intensity/spatial extent) + summary
output-manifest.json	JSON	Run manifest (inputs/outputs/QA/software versions)

Data Source / 数据源 / Source

• Input mode: local multi-temporal GeoTIFF (each band = one time step).
• Synthetic mode: generated locally with embedded known extreme events; no external data sources.

Privacy / 隐私声明 / Privacy

• Runs fully offline by default and makes no network requests.
• --synthetic mode reads no external data.
• All computation is done locally; user data is never uploaded.

License / License

MIT

---

---

name: geoskill-extreme-weather-detection description: '基于百分位阈值检测热浪、寒潮与暴雨等极端天气事件，统计强度、持续时间与空间范围，输出事件清单 JSON 与空间分布栅格。Percentile-threshold detection of heatwaves, cold spells, and heavy rainfall, reporting intensity, duration, and spatial extent with an event-list JSON and spatial raster.'

极端天气事件检测 | Extreme Weather Detection

基于百分位阈值法从温度 / 降水时间序列中检测极端天气事件，并逐个统计 其强度、持续时间与空间范围。适用于热浪 / 寒潮 / 暴雨事件编目、极端气候 风险筛查与灾害快速评估。

检测规则：

• 热浪（heatwave）：温度高于高分位阈值（如 P90）且连续 ≥ 3 天。
• 寒潮（cold spell）：温度低于低分位阈值（如 P10）。
• 暴雨（heavy rainfall）：降水高于 P95 / P99。

阈值默认逐像元由序列自身的分位数确定（百分位 ≥ 50 为上尾极端，< 50 为 下尾极端）。事件通过在 (时间, y, x) 三维 exceedance 体上做连通分量标记 （scipy.ndimage.label，时间相邻 + 四邻域空间连通）提取，逐个统计起止时间、 持续天数、峰值 / 平均强度、空间像元数与质心。

内置 --synthetic 模式生成内嵌已知极端事件（持续热浪 / 强降水）的模拟 序列，用于离线验证检测正确性。

依赖

bash

pip install numpy rasterio scipy

使用方法

基本用法（合成数据，离线）

bash

python geoskill-extreme-weather-detection.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./output

示例 1：热浪检测（温度 P90，连续 ≥3 天）

bash

python geoskill-extreme-weather-detection.py --bbox 116 39 117 40 --variable temperature --threshold p90 --n-dates 30 --output-dir ./heatwave

示例 2：暴雨检测（降水 P99）

bash

python geoskill-extreme-weather-detection.py --bbox 121 31 122 32 --variable precipitation --threshold p99 --output-dir ./heavy_rain

示例 3：寒潮检测（温度 P10 下尾）

bash

python geoskill-extreme-weather-detection.py --bbox 116 39 117 40 --variable temperature --threshold p10 --output-dir ./cold_spell

示例 4：真实多期栅格 + 自定义最短持续天数

bash

python geoskill-extreme-weather-detection.py --input temp_daily.tif --variable temperature --threshold p95 --min-duration 5 --output-dir ./real_events

示例 5：仅 bbox 自动合成 + 静默

bash

python geoskill-extreme-weather-detection.py --bbox 110 30 111 31 --variable precipitation --threshold p95 --output-dir ./auto --quiet

输出

文件	格式	说明
extreme_events.tif	GeoTIFF (float32, 2 band)	band1=每像元 exceedance 天数，band2=最大异常强度，EPSG:4326
event_list.json	JSON	事件清单（起止/持续/强度/空间范围）+ 汇总
output-manifest.json	JSON	运行清单（输入/输出/QA/软件版本）

数据源 / Source

• 输入模式：本地多期 GeoTIFF（每波段 = 一个时间步）。
• 合成模式：本地生成，内嵌已知极端事件，无外部数据源。

隐私声明 / Privacy

• 默认完全离线运行，不发起任何网络请求。
• --synthetic 模式不读取任何外部数据。
• 所有计算在本地完成，不上传用户数据。

License

MIT