|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
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Ryan Torn, firstname.lastname@example.org
WRF is the Weather and Research Forecasting model and is a fully-configurable atmospheric ...
|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.|