Beyond the Historical Record: How I Simulated Seattle’s Future Climate
This case study demonstrates the end-to-end application of PrecipGen for stochastic weather simulation. We examine the workflow for generating synthetic daily precipitation data for Seattle-Tacoma International Airport, covering automated data acquisition, statistical parameter estimation, trend analysis, and model validation against historical records. Raw weather data is often incomplete or inconsistent. For this project, rather than manually retrieving data, we established an automated pipeline to identify, download, and validate the necessary records. Using PrecipGen , we automated the station search. We targeted Sea-Tac Airport and filtered for stations within a 25km radius containing at least 15 years of historical data. Code Example: Automated Station Discovery import precipgen as pg # Sea-Tac coordinates LAT = 47.4502 LON = -122.3088 # Find nearby stations with sufficient data stations = pg.find_nearby_stations( latitude=LAT, longitude=LON, radius_km=...