Python-Practical-Application-on-Climate-Variability-Studies
This tutorial is a companion volume of Matlab versionm but add more. Main objective is the transference of know-how in practical applications and management of statistical tools commonly used to explore meteorological time series, focusing on applications to study issues related with the climate variability and climate change. This tutorial starts with some basic statistic for time series analysis as estimation of means, anomalies, standard deviation, correlations, arriving the estimation of particular climate indexes (Niรฑo 3), detrending single time series and decomposition of time series, filtering, interpolation of climate variables on regular or irregular grids, leading modes of climate variability (EOF or HHT), signal processing in the climate system (spectral and wavelet analysis). In addition, this tutorial also deals with different data formats such as CSV, NetCDF, Binary, and matlab'mat, etc. It is assumed that you have basic knowledge and understanding of statistics and Python.
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Download Latest Version (.zip)- co2_mm_mlo.txt
- ctrl_h_shear_ts_pdi.mat
- gta_1958_2018.csv
- h_scatter.mat
- lsmask.19294.nc
- Orange.csv
- sample_wind_poitiers.csv
- SST_19821999.T42.ctl
- SST_19821999.T42.grd
- sst_nino3.dat
- sst_WA.csv
- ssta.nino3.30y.npz
- monthly_global_precip.png
- outlayer.png
- spi_lom_1.png
- ssta_series_30y.png
- dim_spi_n.py
- ex00-Introduction Life is short, use Python.ipynb
- ex01-Read SST NetCDF data, subsample and save.ipynb
- ex02-Read SST data, create and save nino3 time series.ipynb
- ex03-Read SST, mask and calculate global mean.ipynb
- ex04-Read nino3 SSTA series in npz format, plot and save the image.ipynb
- ex05-Process uWind (Mean and Std).ipynb
- ex06-Process uWind (Zonal Mean and Interpolation).ipynb
- ex07-Interpolate 2D field on regular and irregular grids.ipynb
- ex08-Read SST, calculate and subplot monthly climatology.ipynb
- ex09-Read SST and visualize in different projections.ipynb
- ex10.Compute and plot ACW - Hovmoeller.ipynb
- ex11-Primary analysis of GPCC precipitation data.ipynb
- ex12-Analysis of Monthly GPCP precipitation.ipynb
- ex13-Apply Hovmoller Diagram-The seasonal migration of rainfall in Africa.ipynb
- ex14-Standardized Precipitation Index (SPI).ipynb
- ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb
- ex16-Create anomaly correlation maps of nino3 SSTA-Sea Level Pressure.ipynb
- ex17-EOF analysis global hgt500.ipynb
- ex18-EOF analysis global SST.ipynb
- ex19-EOF analysis in central pacific ocean.ipynb
- ex20-Power spectral density.ipynb
- ex21-Continuous Wavelet Spectrum-Example for NINO3 SST.ipynb
- ex22-Visualize GAR Global Flood Hazard Map with Python.ipynb
- ex23-CO2 and Global Temperature Anomaly.ipynb
- ex24-Visualize CO2 Time Series with Python.ipynb
- ex25-Heatmap of Global Temperature Anomaly.ipynb
- ex26-Identify Marine Heatwaves from High-resolution Daily SST Data.ipynb
- ex27-Wind Rose.ipynb
- ex28-Identify Coupled Patterns between SLP and SST through Maximum Covariance Analysis.ipynb
- ex29-Identify Patterns in the Coupled Fields of SLP and SST through Canonical Correlation Analysis.ipynb
- ex30-Identify_North_Atlantic_winter_weather_regimes by KMeans.ipynb
- ex31-Harmonic Analysis - Monthly Mean Temperature at Orange, Australia.ipynb
- ex32-North Atlantic Winter Weather Regimes from a Self-Organizing Map Perspective.ipynb
- ex33-View Northeast Pacifc sea surface temperature based on an ensemble empirical mode decomposition.ipynb
- ex34-Correlations between SOI and SLP, Temperature and Precipitation.ipynb
- HA_helpers.py
- LICENSE
- mySSA.py
- README.md
- SeaIce.ipynb
- Singular Spectrum Analysis for NH Monthly Land Temperature.ipynb
- wave_bases.py
- wave_signif.py
- wavelet.py
- wavelet_inverse.py