wradlib jupyter notebooks

A series of Jupyter Notebooks on exploring Unidata technology with Python. See website for more information.

🌞 Jupyter notebooks for scraping Environment Canada weather data

A Jupyter Notebook that creates a modern way to visualize sounding data for the purpose of environmental analysis.

Notebook based verification of weather and climate models

My last self-written app.

Python notebooks designed for Google Colab that handle weather data, such as the National Blend of Models.

A simple Jupyter Notebook that create's a 24hr meteogram of user-specified buoy data

Notebooks for learning the Descartes Labs API

Foundational exploratory data analysis (EDA) of climate data of Ghana since 1973, in jupyter notebook environment.

Weather and Vacation API project using python, pandas, jupyter notebook, matplotlib, numpy, gmaps

A comparation along 20 years of weather forecasting data from Porto Alegre, RS, Brazil. Using the pandas library, jupyter notebook and seaborn.

Creates a five-day temperature forecast for any group of cities. Forecasts are generated by parameterised jupyter notebooks that run in parallel.

Analysis of the weather in the Netherlands during the 2020 corona crisis

Analyzing the relationship between latitude and weather with Python and Jupyter Notebook to produce map of the ideal vacation locations.

A python library that streamlines access to the NOAA dataset API with functions that utilize date and time for aggregation of data. Prototyped and tested in jupyter notebooks.

This repository provides tools for forecasting Great Britain’s power demand using Facebook’s Prophet model. It includes Python functions and Jupyter notebooks for daily and monthly forecasts, integrating weather and GDP as regressors, with hyperparameter tuning via HyperOpt.

My personal project consisting of my personally created notebooks to work with time series forecasting and analysis. In these projects, I've used deep learning using tensorflow, xgboost, statsmodels and scipy libraries of python. The series were of weather, energy consumption and that of stocks.