Ryan Torn

Introduction

Understanding the sources and evolution of errors in numerical weather prediction (NWP) models is critical to improving forecasts of various atmospheric phenomenon. Errors can originate from two primary sources: the model initial conditions (i.e., the analysis), or errors in how the model simulates the various processes in the atmosphere (i.e., model error). Initial conditions for NWP models are generated via data assimilation, whereby new observation information is incorporated into a model's short-term forecast to produce a best estimate of the atmospheric state. Improvements to the initial conditions can be achieved by either adjusting how observations impact the model state, or taking observations in regions where forecast errors quickly grow. Furthermore, observations can be used to evaluate whether models are simulating these atmospheric processes correctly.

 My research focuses on trying to understand atmospheric predictability by determining the source and growth of errors within numerical models across a number of timescales using ensemble forecasts. Having knowledge about error growth processes within numerical models also provides insight into the governing dynamics. In addition, I use data assimilation to evaluate our numerical models and develop strategies for improving forecasts. At the present time, I am working on understanding the predictability of tropical cyclones, atmospheric rivers, the jet stream, and Arctic cyclones. This work involves collaborations with the National Center for Atmospheric Research, National Hurricane Center, and the Center for Western Weather and Water Extremes (CW3E), and the European Centre for Medium Range Weather Forecasts.

Research Interests

Predictability, data assimilation, synoptic and mesoscale meteorology.

Additional Information

Torn Research Group

CV