对温度/降水时序执行 Mann-Kendall 趋势检验与 Sen 斜率（Sen slope）估计，输出趋势斜率栅格、显著性（p 值）栅格与时序统计 JSON。Mann-Kendall trend test and Sen slope estimator for temperature/precipitation time series, outputting slope raster, significance (p-value) raster, and time-series JSON.
编程
geoskill-temperature-anomaly-mapping
试用计算温度距平（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.
它能做什么
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.
技能文档
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
pip install numpy rasterio scipy
Usage / 使用方法
Basic Usage (synthetic data, offline)
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)
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
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)
python geoskill-temperature-anomaly-mapping.py --input temp_monthly_stack.tif --n-dates 12 --output-dir ./real_anom
Example 4: Seasonal phase (4 seasons)
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
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.
--syntheticmode 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
pip install numpy rasterio scipy
使用方法
基本用法（合成数据，离线）
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）
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
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）
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）
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
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
相关技能
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.
基于月均温与月降水栅格的气候区划，支持柯本-盖格（Köppen-Geiger）规则树与简化 Strahler 发生分类，输出气候类别码 GeoTIFF + 面积统计 JSON，可选双期变化检测。Climate zone classification from monthly temperature/precipitation rasters (Köppen-Geiger rule tree & simplified Strahler), with area statistics and optional change detection.
霜冻风险制图：由日最低温时序与 DEM 做高程递减率、冷空气湖（TPI 洼地）与坡向地形修正，逐像元统计霜冻频率/霜冻日数、初霜日、终霜日与无霜期，并分级霜冻风险。Frost risk mapping: terrain-corrected min-temperature (lapse rate, cold-air pooling via TPI, aspect) with frost frequency, first/last frost date, frost-free period and risk classification. 输出霜冻风险/无霜期/频率 GeoTIFF + 统计 JSON。
热浪影响评估：逐像元 P90 分位数阈值 + 连续≥3天连通域热浪检测，Stull/简化湿球温度健康风险分级，人口暴露与脆弱性制图。Heatwave impact assessment: per-pixel P90 threshold with connected-run detection, wet-bulb temperature health risk, population exposure and vulnerability mapping. 输出热浪日数/暴露人口/脆弱性/湿球风险 GeoTIFF + 事件清单 JSON。
LSTM 时序预测网络（torch+CUDA，默认） + 经典线性/多项式/AR(p) 解释基线：多步外推 + 留出 MAE/RMSE 验证 + 逐像元栅格输出
1 次安装