DART/WRF sensitivity plot Forecast sensitivity analysis provides an objective means of evaluating how initial condition errors affect a forecast and where to gather additional observations to reduce forecast errors. Most sensitivity studies use the adjoint of a linearized forecast model to determine the gradient of a forecast metric with respect to the initial conditions. Adjoints suffer from a number of difficulties including coding, linearity assumptions, and moist processes. Ensemble-based sensitivity analysis provides an attractive alternative to adjoint-based methods because it combines data assimilation and sensitivity analysis in a consistent manner. This image illustrates the effect of zonal winds aloft on the position of Hurricane Katrina. [link to more information]

Ryan Torn, torn@atmos.albany.edu



Sensitivity Analysis with NCAR's WRF

WRF is the Weather and Research Forecasting model and is a fully-configurable atmospheric ...

Changes in Katrina longitude from U wind variations My research focuses on applying ensemble-based data assimilation techniques, such as the ensemble Kalman filter (EnKF), to understand the predictability and dynamics of mesoscale weather systems. Ensemble-based techniques have some advantages over operationally-used variational methods because it uses an ensemble of short-term forecasts to compute flow-dependent background error statistics, which determine the weight given to observations and how to spread observation information to different locations and variables. Variational methods use quasi-fixed background error statistics based on long-term averages. Furthermore, the EnKF provides a set of equally-likely analyses, which can be used for ensemble forecasting.


Ryan Torn torn@atmos.albany.edu

Torn, R. D., and G. J. Hakim, 2008: Ensemble Data Assimilation applied to RAINEX observations of Hurricane Katrina (2005). Mon. Wea. Rev., In Review.