At OpenWeather, we employ a proprietary forecasting framework that blends multiple data sources such as observational data from weather stations, satellite imagery, radar data, and sensor networks into a unified model. Some key elements include:
Data Assimilation. We continuously collect and ingest weather observations from global meteorological agencies, airports (METAR reports), buoys, and private data networks. This data is combined with satellite inputs and radar imagery, allowing us to track atmospheric changes in near-real time.
Global and Regional Modeling. Our system incorporates outputs from large-scale global models (e.g., ECMWF, GFS) to form the initial state and boundary conditions.We then run high-resolution, regional models to capture local phenomena such as microclimates, sea-land interactions, and urban heat islands.
Ensemble Forecasting. To account for uncertainty, we use ensemble techniques that run multiple forecast scenarios in parallel.This helps us quantify the range of possible outcomes and generate probabilistic forecasts, which are especially valuable for precipitation and severe weather events.
High-Performance Computing (HPC). The complex numerical calculations required to run these high-resolution models are processed on dedicated HPC infrastructure. This allows us to update forecasts more frequently and incorporate the latest observations efficiently.
Machine Learning Integration. We apply machine learning techniques (such as neural networks and gradient boosting) to post-process raw model outputs.These methods help correct for known biases, refine local precipitation predictions, and improve temperature/humidity forecasts in microclimates.