EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Adaptive Bias Correction for Improved Subseasonal Forecasting

Soukayna Mouatadid1, Paulo Orenstein2, Genevieve Flaspohler3,4, Miruna Oprescu5, Judah Cohen6,7, Franklyn Wang8, Sean Knight3,9, Maria Geogdzhayeva10, Sam Levang11, Ernest Fraenkel12, and Lester Mackey5
Soukayna Mouatadid et al.
  • 1Department of Computer Science, University of Toronto, Toronto, ON, Canada
  • 2Instituto de Matemática Pura e Aplicada, Rio de Janeiro, Brazil
  • 3Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 4Department of Applied Ocean Science and Engineering, Woods Hole Oceanographic Institution, Falmouth, MA, USA
  • 5Microsoft Research New England, Cambridge, MA, USA
  • 6Atmospheric and Environmental Research, Lexington, MA, USA
  • 7Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 8Department of Mathematics, Harvard University, Cambridge, MA, USA
  • 9Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 10Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
  • 11Salient Predictions Inc., Cambridge, MA, USA
  • 12Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Improving our ability to forecast the weather and climate is of interest to all sectors of the economy and government agencies from the local to the national level. In fact, weather forecasts 0-10 days ahead and climate forecasts seasons to decades ahead are currently used operationally in decision-making, and the accuracy and reliability of these forecasts has improved consistently in recent decades. However, many critical applications require subseasonal forecasts with lead times in between these two timescales. Subseasonal forecasting—predicting temperature and precipitation 2-6 weeks ahead—is indeed critical for effective water allocation, wildfire management, and drought and flood mitigation. Yet, accurate forecasts for the subseasonal regime are still lacking due to the chaotic nature of weather.

While short-term forecasting accuracy is largely sustained by physics-based dynamical models, these deterministic methods have limited subseasonal accuracy due to chaos. Indeed, subseasonal forecasting has long been considered a “predictability desert” due to its complex dependence on both local weather and global climate variables. Nevertheless, recent large-scale research efforts have advanced the subseasonal capabilities of operational physics-based models, while parallel efforts have demonstrated the value of machine learning and deep learning methods in improving subseasonal forecasting.

To counter the systematic errors of dynamical models at longer lead times, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. We evaluate our adaptive bias correction method in the contiguous U.S. over the years 2011-2020 and demonstrate consistent improvement over standard meteorological baselines, state-of-the-art learning models, and the leading subseasonal dynamical models, as measured by root mean squared error and uncentered anomaly correlation skill. When applied to the United States’ operational climate forecast system (CFSv2), ABC improves temperature forecasting skill by 20-47% and precipitation forecasting skill by 200-350%. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 8-38% and precipitation forecasting skill by 40-80%.

Overall, we find that de-biasing dynamical forecasts with our learned adaptive bias correction method yields an effective and computationally inexpensive strategy for generating improved subseasonal forecasts and building the next generation of subseasonal forecasting benchmarks. To facilitate future subseasonal benchmarking and development, we release our model code through the subseasonal_toolkit Python package and our routinely updated SubseasonalClimateUSA dataset through the subseasonal_data Python package.

How to cite: Mouatadid, S., Orenstein, P., Flaspohler, G., Oprescu, M., Cohen, J., Wang, F., Knight, S., Geogdzhayeva, M., Levang, S., Fraenkel, E., and Mackey, L.: Adaptive Bias Correction for Improved Subseasonal Forecasting, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10519,, 2022.