The Impact of MJO on Extreme Heatwave Events in South China
收藏资源简介：
# MJO-Heatwave Connection Analysis Code and data for reproducing figures in the manuscript:**"The Impact of MJO on Extreme Heatwave Events in South China"** ## Repository Structure ```submission/├── code/│ ├── preprocessing/ # Data preprocessing scripts│ │ ├── compute_olr_composites.py│ │ ├── compute_wind_composites.py│ │ ├── compute_geopotential_composites.py│ │ ├── compute_era5_hovmoller_composites.py│ │ ├── compute_variance_contribution.py│ │ └── compute_temperature_composites.py│ ├── analysis/ # Statistical analysis scripts│ │ ├── composite_analysis.py # Main composite analysis│ │ ├── regional_analysis.py # Regional heatwave analysis│ │ ├── mjo_phase_composites.py # MJO phase composite analysis│ │ ├── heatwave_statistics.py # Heatwave event statistics│ │ ├── identify_heatwaves.py # Heatwave identification│ │ ├── probability_bootstrap.py # Bootstrap significance testing│ │ ├── spectral_analysis.py # Spectral/wavelet analysis│ │ ├── power_spectrum.py # Power spectrum analysis│ │ ├── statistical_tests.py # Statistical test utilities│ │ └── ...│ └── visualization/ # Figure generation scripts│ ├── figure1_spatial_distribution.py│ ├── figure2_duration_analysis.py│ ├── figure3_temporal_evolution.py│ ├── figure4_mjo_phase_distribution.py│ ├── figure5_era5_hovmoller.py│ ├── figure6_spatial_evolution.py│ ├── figure7_circulation_patterns.py│ ├── figure8a_olr_preevent_composite.py│ ├── figure8b_mjo_phase_fraction.py│ ├── figure9_phase_lag_probability.py│ └── figure10_phase_evolution.py├── data/│ ├── processed/ # Processed heatwave and station data│ ├── mjo/ # MJO RMM index data│ └── plotting/ # Pre-computed composites for figures├── original_notebooks/ # Original Jupyter notebooks (research code)├── figures/ # Output directory for generated figures├── environment.yml # Conda environment specification├── requirements.txt # Python dependencies└── LICENSE # MIT License``` ## Quick Start ### 1. Setup Environment ```bash# Using conda (recommended)conda env create -f environment.ymlconda activate mjo_heatwave # Or using pippip install -r requirements.txt``` ### 2. Generate Figures All figures can be generated using the pre-computed data included in `data/plotting/`: ```bashcd code/visualization # Generate individual figurespython figure1_spatial_distribution.pypython figure2_duration_analysis.pypython figure3_temporal_evolution.pypython figure4_mjo_phase_distribution.pypython figure5_era5_hovmoller.pypython figure6_spatial_evolution.pypython figure7_circulation_patterns.pypython figure8a_olr_preevent_composite.pypython figure8b_mjo_phase_fraction.pypython figure9_phase_lag_probability.pypython figure10_phase_evolution.py``` Figures will be saved to `figures/`. ## Data Description ### Pre-computed Composites (`data/plotting/`) | File | Description | Used by ||------|-------------|---------|| `olr_mjo_phase_composites.nc` | OLR anomaly by MJO phase (1-8) | Figure 7 || `wind_mjo_phase_composites.nc` | 850 hPa wind by MJO phase | Figure 7 || `geopotential_mjo_phase_composites.nc` | 500 hPa geopotential height by phase | Figure 10 || `era5_hovmoller_composites.nc` | ERA5 Hovmöller diagrams | Figure 5 || `olr_event_composites_mean_5d.nc` | OLR composite around events (≥5 days) | Figure 6, 8a || `wind_event_composites_mean_5d.nc` | Wind composite around events (≥5 days) | Figure 6, 8a || `*.csv` | Temperature composites and statistics | Figures 1-4, 8-9 | ### Processed Data (`data/processed/`) | File | Description ||------|-------------|| `persistent_events.csv` | Identified persistent heatwave events || `heatwave_events_all.csv` | All heatwave events || `station_locations.csv` | Weather station coordinates || `tmax_anomaly.csv` | Daily temperature anomalies | ### MJO Data (`data/mjo/`) | File | Description ||------|-------------|| `rmm_index_1979_2020.csv` | RMM index (amplitude ≥ 1.0) || `rmm_index_all_amplitudes.csv` | Full RMM index data | ## Original Research Notebooks The `original_notebooks/` directory contains the original Jupyter notebooks used during research: | Notebook | Description ||----------|-------------|| `01_synthesis_tmax_mjo.ipynb` | Main synthesis analysis || `02_mjo_phase_temperature_composite.ipynb` | MJO phase composites || `03_olr_anomaly_analysis.ipynb` | OLR anomaly analysis || `04_preprocess_temperature.ipynb` | Temperature preprocessing || `05_preprocess_mjo.ipynb` | MJO data preprocessing || `06_heatwave_event_statistics.ipynb` | Event statistics || `07_regional_heatwave_analysis.ipynb` | Regional analysis || `08_south_china_heatwave_spatial.ipynb` | South China spatial analysis || `09_heatwave_probability_bootstrap.ipynb` | Bootstrap probability analysis || `10_power_spectrum_analysis.ipynb` | Power spectrum analysis || `11_lanczos_filter.ipynb` | Lanczos bandpass filter || `12_spectral_analysis.ipynb` | Spectral analysis | ## Methods ### Filtering- **Lanczos bandpass filter**: 201 weights, 30-60 day period- Applied to raw anomaly data before compositing ("filter then composite") ### Event Definition- Persistent heatwave events: ≥2 consecutive days above 90th percentile- Default analysis uses events with duration ≥5 days (25 events)- Option to use all events ≥2 days (146 events) via `--min-duration 2` ### MJO Phase Composites- Based on RMM index with amplitude ≥1.0- Persistent MJO: ≥3 consecutive days in same phase- 8 phases representing eastward propagation ## Requirements - Python ≥3.8- numpy, pandas, xarray- matplotlib, cartopy- scipy- meteva (for map plotting utilities) ## Citation If you use this code or data, please cite:[Paper citation to be added upon publication] ## License MIT License - see LICENSE file ## Contact [zhenhua.li@gmail.com]
