New Perspectives on Ensemble Sensitivity Analysis with Applications to a Climatology of Severe Convection

Brian C. Ancell, Austin A. Coleman

Research output: Contribution to journalArticlepeer-review


Ensemble sensitivity analysis (ESA) is a statistical technique applied within an ensemble to reveal the atmospheric flow features that relate to a chosen aspect of the flow. Given its ease of use (it is simply a linear regression between a chosen function of the forecast variables and the entire atmospheric state earlier or simultaneously in time), ensemble sensitivity has been the focus of several studies over roughly the last 10 years. Such studies have primarily tried to understand the relevant dynamics and/or key precursors of high-impact weather events. Other applications of ESA have been more operationally oriented, including observation targeting within data assimilation systems and real-time adjustment techniques that attempt to utilize both sensitivity information and observations to improve forecasts. While ESA has gained popularity, its fundamental properties remain a substantially underutilized basis for realizing the technique's full scientific potential. For example, the relationship between ensemble sensitivity and the pure dynamics of the system can teach us how to appropriately apply various sensitivity-based applications, and combining sensitivity with other ensemble properties such as spread can distinguish between a fluid dynamics problem and a predictability one. This work aims to present new perspectives on ensemble sensitivity, and clarify its fundamentals, with the hopes of making it a more accessible, attractive, and useful tool in the atmospheric sciences. These new perspectives are applied in part to a short climatology of severe convection forecasts to demonstrate the unique knowledge that can gained through broadened use of ESA.

Original languageEnglish
Pages (from-to)E511-E530
JournalBulletin of the American Meteorological Society
Issue number2
StatePublished - Feb 2022


  • Ensembles
  • Forecasting techniques
  • Numerical analysis/modeling
  • Probability forecasts/models/distribution
  • Regression analysis
  • Statistical techniques


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