Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC)
Chan, M. Y.. (2024). Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC). Nonlinear Processes in Geophysics, doi:https://doi.org/10.5194/npg-31-287-2024
| Title | Improving ensemble data assimilation through Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC) |
|---|---|
| Genre | Article |
| Author(s) | Man Yau Chan |
| Abstract | Small forecast ensemble sizes (< 100) are common in the ensemble data assimilation (EnsDA) component of geophysical forecast systems, thus limiting the error-constraining power of EnsDA. This study proposes an efficient and embarrassingly parallel method to generate additional ensemble members: the Probit-space Ensemble Size Expansion for Gaussian Copulas (PESE-GC; "peace gee see"). Such members are called "virtual members". PESE-GC utilizes the users' knowledge of the marginal distributions of forecast model variables. Virtual members can be generated from any (potentially non-Gaussian) multivariate forecast distribution that has a Gaussian copula. PESE-GC's impact on EnsDA is evaluated using the 40-variable Lorenz 1996 model, several EnsDA algorithms, several observation operators, a range of EnsDA cycling intervals, and a range of forecast ensemble sizes. Significant improvements to EnsDA (p |
| Publication Title | Nonlinear Processes in Geophysics |
| Publication Date | Jul 1, 2024 |
| Publisher's Version of Record | https://doi.org/10.5194/npg-31-287-2024 |
| OpenSky Citable URL | https://n2t.org/ark:/85065/d70r9tm4 |
| OpenSky Listing | View on OpenSky |
| EDEC Affiliations |