Global Atmospheric Ensemble Data Assimilation using NICAM global icosahedral model and Maximum Likelihood Ensemble Filter with State Space Localization: Real Observation Experiment

The Maximum Likelihood Ensemble Filter (MLEF) with State Space Localization (MLEF-SSL) was recently developed as a new ensemble data assimilation method that incorporates state space covariance localization in model space. The main motivation for developing this method was to enable global numerical optimization and assimilation of vertically integrated observations in an ensemble data assimilation system with covariance localization. MLEF-SSL uses random projection to compute the localized forecast error covariance and reduce the analysis dimensions to a manageable space. MLEF-SSL is being applied to a global NWP system named Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with the assimilation of atmospheric observations, i.e., the NICAM-MLEF-SSL system, to explore its capability under a realistic high-dimensional dynamical application.

To apply MLEF-SSL in a high-dimensional system such as NICAM, a substantially large number of ensembles of the order of O(10⁴) - O(10⁵) is necessary to represent the localized forecast error covariance, thus, requiring special attention on the algorithmic development for the NICAM-MLEF-SSL system. This presentation will discuss the practical implementation of the NICAM-MLEF-SSL system on the RIKEN supercomputer Fugaku with an emphasis on the use of advanced math libraries for parallel computing.

In addition, the performance of NICAM-MLEF-SSL in assimilation of real atmospheric observations will be evaluated in detail and compared to that of NICAM-LETKF, which is the global data assimilation system that is currently used at RIKEN. 

Future plans related to the use of strong dynamical balance constraints and Artificial Intelligence (AI) techniques in NICAM-MLEF-SSL will also be discussed.