RESEARCH · RESEARCH · #1978
Microsoft publishes open-source 'Probabilistic Bias Correction' to improve 2–6 week forecasts
Microsoft researchers published a Nature paper and released open-source software called Probabilistic Bias Correction (PBC) that uses machine learning to correct systematic biases in existing weather models; the team reports PBC improved ECMWF model performance for most precipitation and temperature targets and can run on CPUs. The paper lists coauthors from MIT, ECMWF and others and the method is being tested with partners such as Rhiza Research and the Kenya Meteorological Service.
KEY POINTS
- Microsoft researchers published a Nature paper and released open-source software called Probabilistic Bias Correction (PBC) that uses machine learning to correct systematic biases in existing weather models; the team reports PBC improved ECMWF model performance for most precipitation and temperature targets and can run on CPUs.
- The paper lists coauthors from MIT, ECMWF and others and the method is being tested with partners such as Rhiza Research and the Kenya Meteorological Service.
- An open-source, CPU-efficient ML method that measurably reduces bias in leading weather models could make more reliable medium-range forecasts accessible to resource-constrained agencies worldwide.
WHY IT MATTERS
An open-source, CPU-efficient ML method that measurably reduces bias in leading weather models could make more reliable medium-range forecasts accessible to resource-constrained agencies worldwide.