geoskill-temperature-anomaly-mapping

计算温度距平（当期温度减多年同期气候态）与标准化距平，划分暖/冷异常等级，输出距平栅格、异常等级 GeoTIFF 与时序 JSON。Temperature anomaly mapping: current minus multi-year climatology, standardized anomalies, and warm/cold anomaly classes, outputting anomaly rasters, a class GeoTIFF, and a time-series JSON.

温度异常制图 | Temperature Anomaly Mapping

Computes and maps temperature anomalies to identify warm/cold anomaly regions relative to the climatological baseline and their intensity levels. Suitable for monthly/annual temperature anomaly monitoring, mapping of extreme warm/cold events, and regional diagnostics against the climatic background.

Core algorithm:

• Climatology: group the time series by seasonal phase (e.g., month, via --n-dates); the multi-year mean within each group is the multi-year same-phase climatology.
• Anomaly = current temperature − climatology.
• Standardized anomaly = anomaly / same-phase multi-year standard deviation, which removes the unit of measurement and allows cross-seasonal / cross-regional comparison of anomaly intensity.
• Anomaly class: thresholds of ±1σ and ±2σ partition the values into severe warm anomaly / warm anomaly / normal / cold anomaly / severe cold anomaly, output as an integer-encoded class raster.

The built-in --synthetic mode generates a simulated monthly temperature series with a climatological component (spatial baseline + seasonal cycle) plus inter-annual noise, and injects a regional warm anomaly at the end of the series for offline validation.

Dependencies / 依赖

bash

pip install numpy rasterio scipy

Usage / 使用方法

Basic Usage (synthetic data, offline)

bash

python geoskill-temperature-anomaly-mapping.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./output

Example 1: Monthly temperature anomaly (12-month phase × 5 years)

bash

python geoskill-temperature-anomaly-mapping.py --bbox 116 39 117 40 --n-dates 12 --n-years 5 --output-dir ./monthly_anom

Example 2: Longer climatology reference period

bash

python geoskill-temperature-anomaly-mapping.py --bbox 121 31 122 32 --n-dates 12 --n-years 10 --output-dir ./long_clim

Example 3: Real multi-epoch rasters (monthly stacked, with specified phase cycle)

bash

python geoskill-temperature-anomaly-mapping.py --input temp_monthly_stack.tif --n-dates 12 --output-dir ./real_anom

Example 4: Seasonal phase (4 seasons)

bash

python geoskill-temperature-anomaly-mapping.py --bbox 116 39 117 40 --n-dates 4 --n-years 8 --output-dir ./seasonal

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

bash

python geoskill-temperature-anomaly-mapping.py --bbox 110 30 111 31 --output-dir ./auto --quiet

Output / 输出

File	Format	Description
anomaly.tif	GeoTIFF (float32, 2 band)	Latest epoch; band1 = temperature anomaly, band2 = standardized anomaly, EPSG:4326
anomaly_class.tif	GeoTIFF (float32, 1 band)	Anomaly class encoding (2 = severe warm … 0 = normal … -2 = severe cold)
timeseries.json	JSON	Per-epoch spatially averaged anomaly / standardized anomaly + share of the latest class
output-manifest.json	JSON	Run manifest (inputs/outputs/QA/software versions)

Data Source / 数据源 / Source

• Input mode: local multi-epoch GeoTIFF (each band = one time step, arranged by year × month).
• Synthetic mode: generated locally with an injected warm anomaly; no external data source.

Privacy / 隐私声明 / Privacy

• Fully offline by default; no network requests are made.
• --synthetic mode reads no external data.
• All computation is performed locally; no user data is uploaded.

License / License

MIT

---

---

name: geoskill-temperature-anomaly-mapping description: '计算温度距平（当期温度减多年同期气候态）与标准化距平，划分暖/冷异常等级，输出距平栅格、异常等级 GeoTIFF 与时序 JSON。Temperature anomaly mapping: current minus multi-year climatology, standardized anomalies, and warm/cold anomaly classes, outputting anomaly rasters, a class GeoTIFF, and a time-series JSON.'

温度异常制图 | Temperature Anomaly Mapping

计算温度距平（anomaly）并制图，识别相对气候态的暖 / 冷异常区域及其 强度等级。适用于月度 / 年度温度异常监测、极端冷暖事件制图、与气候背景 对比的区域诊断。

核心算法：

• 气候态（climatology）：按季节相位（如月份，--n-dates）把时间序列 分组，组内多年平均即该相位的多年同期气候态。
• 距平 = 当期温度 − 气候态。
• 标准化距平 = 距平 / 同期多年标准差，消除量纲，可跨季节 / 区域比较 异常强度。
• 异常等级：按 ±1σ、±2σ 阈值划分 严重暖异常 / 暖异常 / 正常 / 冷异常 / 严重冷异常，输出整数编码等级栅格。

内置 --synthetic 模式生成含气候态（空间基线 + 季节循环）+ 年际噪声、 并在末期注入区域性暖异常的模拟月温度序列，用于离线验证。

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-temperature-anomaly-mapping.py --bbox 116.0 39.0 117.0 40.0 --output-dir ./output

示例 1：月度温度异常（12 个月相位 × 5 年）

bash

python geoskill-temperature-anomaly-mapping.py --bbox 116 39 117 40 --n-dates 12 --n-years 5 --output-dir ./monthly_anom

示例 2：更长的气候态参考期

bash

python geoskill-temperature-anomaly-mapping.py --bbox 121 31 122 32 --n-dates 12 --n-years 10 --output-dir ./long_clim

示例 3：真实多期栅格（按月排列，指定相位周期）

bash

python geoskill-temperature-anomaly-mapping.py --input temp_monthly_stack.tif --n-dates 12 --output-dir ./real_anom

示例 4：季节相位（4 季）

bash

python geoskill-temperature-anomaly-mapping.py --bbox 116 39 117 40 --n-dates 4 --n-years 8 --output-dir ./seasonal

示例 5：仅 bbox 自动合成 + 静默

bash

python geoskill-temperature-anomaly-mapping.py --bbox 110 30 111 31 --output-dir ./auto --quiet

输出

文件	格式	说明
anomaly.tif	GeoTIFF (float32, 2 band)	最新一期 band1=温度距平，band2=标准化距平，EPSG:4326
anomaly_class.tif	GeoTIFF (float32, 1 band)	异常等级编码（2=严重暖…0=正常…-2=严重冷）
timeseries.json	JSON	逐期空间平均距平/标准化距平 + 最新等级占比
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，按年×月排列）。
• 合成模式：本地生成，含注入暖异常，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