izpinto

MSO_analogs

AR6_regions_country_-names

ATLAS

Datasets, code and virtual workspace for the Climate Change ATLAS

Auckland_Python_Workshop

Python for Climate and Meteorological Data Analysis and Visualisation

ClimateModeling_courseware

A collection of interactive lecture notes and assignments in Jupyter notebook format.

climpact2

Combining climdex.pcic and climpact @ UNSW

colormaps

IPCC AR6 Colormaps

cpdn_extract_scripts

Scripts for extraction of climateprediction.net data

home

portfolio website

ncar-python-tutorial

Numerical & Scientific Computing with Python Tutorial

izpinto/AR6_regions_country_-names

izpinto/MSO_analogs

izpinto/home

portfolio website

izpinto/xskillscore

Metrics for verifying forecasts

izpinto/sompy

Implementation of a Self Organizing Map algorithm in Python

izpinto/scrape_global_temps

Quick python code to scrape the latest data from all the global surface temperature datasets and return it on a common baseline period.

izpinto/ncar-python-tutorial

Numerical & Scientific Computing with Python Tutorial

izpinto/climpact2

Combining climdex.pcic and climpact @ UNSW

izpinto/ClimateModeling_courseware

A collection of interactive lecture notes and assignments in Jupyter notebook format.

izpinto/ATLAS

Datasets, code and virtual workspace for the Climate Change ATLAS

izpinto/cpdn_extract_scripts

Scripts for extraction of climateprediction.net data

izpinto/OldModels

izpinto/colormaps

IPCC AR6 Colormaps

izpinto/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.

izpinto/pyvis

izpinto/wrf_python_tutorial

Student Workbook Repository for the wrf-python Tutorial

izpinto/Auckland_Python_Workshop

Python for Climate and Meteorological Data Analysis and Visualisation

izpinto/soms_howto

izpinto/SOMs.vis.files

izpinto/sompak

SOMPAK related code