Pinned Repositories
awesome-atmos
A curated list of awesome Python libraries, software and resources in Atmosphere, Environment and Machine Learning
cdat
Community Data Analysis Tools
CMAQ
Code base for the U.S. EPA’s Community Multiscale Air Quality Model (CMAQ). For additional background on CMAQ please visit: www.epa.gov/CMAQ
kpp
Kinetic Pre-Processor (from http://people.cs.vt.edu/~asandu/Software/Kpp/) with updates to allow working with MCM.
nmc_met_map
国家气象中心天气诊断分析工具包(NMDT)
permm
Python Environment for Reaction Mechanisms/Mathematics provides dynamic analysis tools for evaluating chemical networks easily.
proplot
🎨 A comprehensive matplotlib wrapper for making beautiful, publication-quality graphics
pseudonetcdf
PseudoNetCDF like NetCDF except for many scientific format backends
pykpp
pykpp is a KPP-like chemical mechanism parser that produces a box model solvable by SciPy's odeint solver
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 (Niño 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.
xlilium's Repositories
xlilium/awesome-atmos
A curated list of awesome Python libraries, software and resources in Atmosphere, Environment and Machine Learning
xlilium/cdat
Community Data Analysis Tools
xlilium/CMAQ
Code base for the U.S. EPA’s Community Multiscale Air Quality Model (CMAQ). For additional background on CMAQ please visit: www.epa.gov/CMAQ
xlilium/kpp
Kinetic Pre-Processor (from http://people.cs.vt.edu/~asandu/Software/Kpp/) with updates to allow working with MCM.
xlilium/nmc_met_map
国家气象中心天气诊断分析工具包(NMDT)
xlilium/permm
Python Environment for Reaction Mechanisms/Mathematics provides dynamic analysis tools for evaluating chemical networks easily.
xlilium/proplot
🎨 A comprehensive matplotlib wrapper for making beautiful, publication-quality graphics
xlilium/pseudonetcdf
PseudoNetCDF like NetCDF except for many scientific format backends
xlilium/pykpp
pykpp is a KPP-like chemical mechanism parser that produces a box model solvable by SciPy's odeint solver
xlilium/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 (Niño 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.
xlilium/wrf_python_tutorial
Student Workbook Repository for the wrf-python Tutorial