- 1Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Ciencias de la Atmósfera y los Océanos. Buenos Aires, Argentina.
- 2CONICET – Universidad de Buenos Aires. Centro de Investigaciones del Mar y la Atmósfera (CIMA). Buenos Aires, Argentina.
- 3CNRS – IRD – CONICET – UBA. Instituto Franco-Argentino para el Estudio del Clima y sus Impactos (IRL 3351 IFAECI). Buenos Aires, Argentina.
- 4Instituto de Modelado e Innovación Tecnológica, CONICET-UNNE, Corrientes, Argentina.
- 5Servicio Meteorológico Nacional, Buenos Aires, Argentina.
- 6Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina.
- 7RIKEN Cluster for Pioneering Research, Kobe, Japan.
- 8University of Zürich, Zurich, Switzerland.
Extreme weather events associated with deep moist convection pose significant social risks, requiring advanced technologies for anticipatory measures. Numerical forecasting, particularly at convection-resolving scales, relies heavily on high-quality initial conditions obtained through the assimilation of complex remote-sensing-based observations. Integrating these observations into assimilation systems presents challenges due to the nonlinear relationships between observed quantities and model variables. This research explores an iterative implementation of the Local Ensemble Transform Kalman Filter based on the tempering of the observation likelihood (tempered LETKF), which can partially handle these non-linearities.
In this work, we use an N-variable Lorenz model for its simplicity and low computational cost to evaluate the performance of the method against the traditional implementation of the LETKF. We conducted comparisons under various levels of uncertainty concerning both the model and the observations. Additionally, we tested the behavior of the system for different ensemble sizes and for varying degrees of tempering. The initial findings show notable enhancements in the estimation of initial conditions and the stability of the data assimilation cycle, indicating potential benefits in more realistic model applications. The encouraging results motivate further research on tempering methods in mesoscale modeling systems, especially for predicting severe weather events linked to deep moist convection.
How to cite: Gacitua Gutierrez, J., Ruiz, J. J., Pulido, M., Dillon, M. E., García Skabar, Y., Maldonado, P., Otsuka, S., Amemiya, A., Miyoshi, T., and Pajarola, R.: Tempered local ensemble transform kalmann filter: simple model experiments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12910, https://doi.org/10.5194/egusphere-egu25-12910, 2025.