MeteoSwiss' continuosly updated Open Data documentation

Large Scale Precipitation Tracking (LPT). Kerns and Chen (2020, JGR Atmospheres).

In this project, I analyzed weather data from the NCEI Global Surface Summary of Day dataset using PySpark in Jupyter Notebook. Tasks included data cleaning, statistical analysis, and forecasting for temperature, wind speed, precipitation, and extreme weather events. The project also predicts future weather patterns for Cincinnati and Florida.

PPTSA (Precipitation Probabilistic Time Series Analysis) examines the statistical structure and predictability of precipitation, emphasizing the impulse-like nature of rainfall and its implications for forecasting. The study uses ~140 years of daily precipitation data from Central Park, New York City.

Python implementation for calculating the Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI)

Code from locus-usace without the input files

SQLAlchemy Homework - Surfs Up! vacation planning.

An atmospheric science fieldwork project analyzing precipitation trends over Ghana (2000-2009) using climate data.

This project aims to create a predictive model that forecasts the likelihood of a patient being readmitted to the hospital within 30 days of discharge.

This entails a workflow to build Rainfall Monthly Data from Daily datasets and also the 30 minutes data from the NASA GPM (Global Precipitation Mission) satellite Mission.

Analysis of Hawaii weather using SQLAlchemy, Python, and Matplotlib to create a weather API to perform climate analysis based on the data stored in SQLite database.