Synthetic Weather Generation

Introduction

There are a series of excellent blog posts that step through many different aspects of synthetic weather generation, including parametric weather generation techniques, non-parametric and semi-parametric weather generators, techniques for correlating weather generated at multiple sites, and conditioning weather generation on climate change projections and seasonal climate forecasts

Learning Objectives

This series of blog posts doesn’t have any interactive components like the synthetic streamflow generation sereis, but rather can be viewed as more filtered background reading on synthetic weather generation.

Prerequisites

None

Training activities

Table 8 Synthetic Weather Generation Reading

Topic	Commitment	Tasks	Readings	Outcomes
Parametric Generators	S	Read this blog post link	N/A	You will learn about parametric generators, whose distributions are fit based on parameters from a historical record.
Non-parametric Generators	S	Read this blog post link	N/A	You will learn about non-parametric generators, in which the underlying distribution is not known, and future realizations are simulated by resampling from the historical record.
Multi-Site Weather Generators	S	Read this blog post link	N/A	You will learn about generating correlated weather data.
Conditioning Synthetic Weather Generation on Climate Change Projections	S	Read this blog post link	N/A	You will learn about modifications to parametric and non-parametric weather generators to simulate weather that is consistent with climate change projections.
Conditioning Synthetic Weather Generation on Seasonal Climate Forecasts	S	Read this blog post link	N/A	You will learn about modifications to parametric and non-parametric weather generators to simulate weather that is consistent with shorter seasonal forecasts.

• Commitment: S = Short ( < 1 day), M = Medium (1-5 days), L = Long (>5 days)