the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Accelerating assimilation development for new observing systems using EFSO
Daisuke Hotta
Eugenia Kalnay
Takemasa Miyoshi
Tse-Chun Chen
Related authors
This work extends the Mapping Particle Filter to account for local dependencies. Two localization methods are tested: a global particle flow with local kernels, and iterative local mappings based on correlation radius. Using a two-scale Lorenz-96 truth and a one-scale forecast model, experiments with full/partial and linear/nonlinear observations show Root Mean Square Error (RMSE) reductions using localized Gaussian mixture priors, achieving competitive performance against Gaussian filters.
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.
assimilation windowand a long
observation window. The analysis is more accurate using the short assimilation window and is exposed to the future observations that accelerate the spin-up. In OSSE, the system reduces the analysis error significantly, suggesting that this method could be used for other data assimilation problems.
assimilation windowand a long
observation window. The analysis is more accurate with the short assimilation window and is exposed to the future observations accelerating the spin up. In OSSE, the system reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.
Related subject area
Using an operational numerical weather prediction framework, our numerical results show that TCI makes the system accurately generate new reflectivity cells and significantly improves the fractional skill score of forecasts over lead times of up to six hours by up to 10 %.
naturein a computational simulation. Idealized experiments with a low-order chaotic system show successful results by small control signals of only 3 % of the observation error. This is the first step toward realistic weather simulations.