All Outputs (4)

Probabilistic skill in ensemble seasonal forecasts (2014)
Journal Article
Smith, L. A., Du, H., Suckling, E. B., & Niehörster, F. (2015). Probabilistic skill in ensemble seasonal forecasts. Quarterly Journal of the Royal Meteorological Society, 141(689), 1085-1100. https://doi.org/10.1002/qj.2403

Simulation models are widely employed to make probability forecasts of future conditions on seasonal to annual lead times. Added value in such forecasts is reflected in the information they add, either to purely empirical statistical models or to sim... Read More about Probabilistic skill in ensemble seasonal forecasts.

Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario (2014)
Journal Article
Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario. Journal of the Atmospheric Sciences, 71(2), 469-482. https://doi.org/10.1175/jas-d-13-032.1

State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scena... Read More about Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario.

Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models (2014)
Journal Article
Du, H., & Smith, L. A. (2014). Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models. Journal of the Atmospheric Sciences, 71(2), 483-495. https://doi.org/10.1175/jas-d-13-033.1

Data assimilation and state estimation for nonlinear models is a challenging task mathematically. Performing this task in real time, as in operational weather forecasting, is even more challenging as the models are imperfect: the mathematical system... Read More about Pseudo-Orbit Data Assimilation. Part II: Assimilation with Imperfect Models.

Laplace’s Demon and the Adventures of His Apprentices (2014)
Journal Article
Frigg, R., Bradley, S., Du, H., & Smith, L. A. (2014). Laplace’s Demon and the Adventures of His Apprentices. Philosophy of Science, 81(1), 31-59. https://doi.org/10.1086/674416

The sensitive dependence on initial conditions (SDIC) associated with nonlinear models imposes limitations on the models’ predictive power. We draw attention to an additional limitation than has been underappreciated, namely, structural model error (... Read More about Laplace’s Demon and the Adventures of His Apprentices.