L
Leonard A. Smith
Researcher at London School of Economics and Political Science
Publications - 153
Citations - 10059
Leonard A. Smith is an academic researcher from London School of Economics and Political Science. The author has contributed to research in topics: Ensemble forecasting & Consensus forecast. The author has an hindex of 44, co-authored 151 publications receiving 9346 citations. Previous affiliations of Leonard A. Smith include Goddard Institute for Space Studies & University of Florida.
Papers
More filters
Journal ArticleDOI
Forecasting wave height probabilities with numerical weather prediction models
Mark S. Roulston,Mark S. Roulston,Jerome Ellepola,Jost von Hardenberg,Jost von Hardenberg,Leonard A. Smith,Leonard A. Smith +6 more
TL;DR: In this paper, a method for post-processing the ensemble forecasts of wave statistics is described and demonstrated using the significant wave height forecasts for four locations of interest to the offshore industry.
Journal ArticleDOI
Geophysical flows as dynamical systems: the influence of Hide's experiments
TL;DR: Ghil, Ghil, Peter L Read and Leonard A Smith recount the many and various ways that Raymond Hide has influenced their life and work in geophysical fluid dynamics, meteorology, climatology and planetary sciences as mentioned in this paper.
Journal ArticleDOI
Visualizing bifurcations in high dimensional systems: the spectral bifurcation diagram
TL;DR: Methods to visualize bifurcations in flows of nonlinear dynamical systems, using the Lorenz '96 systems as examples are presented, and a plot is produced which clearly shows the changes between periodic, quasi-periodic, and chaotic states, and reveals structure not shown by the other methods.
Journal ArticleDOI
Gradient free descent: shadowing, and state estimation using limited derivative information
TL;DR: This paper investigates gradient descent methods that use limited derivative information and demonstrates the methods with an application to a moderately high-dimensional system using no derivative information at all.
Journal ArticleDOI
Probabilistic noise reduction
TL;DR: Combining the results of the twoschemes produces a probabilistic estimate of the system state that is superior to either in isolation or isolation.