Implementation of statistical methods used to describe, analyze, test, and forecast atmospheric data using: Python, Apache Spark, Dask, Xarray, Pandas, GeoPandas.

This repository contains code, notes, exercises and solutions for statistical methods in the atmospheric sciences. These are meant to serve as a learning tool to complement the theoretical materials from:

• Daniel Wilks' Statistical Methods in the Atmospheric Sciences (3rd Edition)

Python 2.7 and Python 3:

• basemap==1.0.7
• bokeh==0.12.6
• Cartopy==0.14.2
• dask==0.15.1
• distributed==1.18.0
• fastparquet==0.1.0
• Fiona==1.7.8
• folium==0.3.0
• GDAL==1.11.5
• geopandas==0.2.1
• geoviews==1.2.0
• graphviz==0.8
• h5py==2.7.0
• ipython==5.4.1
• Iris==2.0
• Keras==2.0.6
• matplotlib==2.0.2
• matplotlib-venn==0.11.5
• netCDF4==1.2.9
• notebook==5.0.0
• numpy==1.13.1
• pandas==0.20.3
• pandas-datareader==0.4.0
• pyproj==1.9.5.1
• pyshp==1.2.11
• pyspark==2.2.0
• pyspark4climate==0.1.dev0
• rasterio==0.36.0
• scikit-learn==0.18.2
• scipy==0.19.1
• seaborn==0.8
• Shapely==1.5.17.post1
• -e [email protected]:andersy005/spark-xarray.git@1349b44c3c476ed8491ce241af956263eb1c62c9#egg=spark_xarray
• statsmodels==0.8.0
• tables==3.4.2
• xarray==0.9.6

Command Line Operators:

• netCDF Operator (NCO)
• Climate Data Operators (CDO)