Recent Progress of Data Assimilation Methods in Meteorology

Abstract

Data assimilation is a methodology for estimating accurately the state of a time-evolving complex system like the atmosphere from observational data and a numerical model of the system. It has become an indispensable tool for meteorological researches as well as for numerical weather prediction, as represented by extensive use of reanalysis datasets for research purposes. New advances of data assimilation methods emerged from the 1980s. This review paper presents the theoretical background and implementation of two advanced data assimilation methods: four-dimensional variational assimilation (4D-Var) and ensemble Kalman filtering (EnKF), which currently draw much attention in the meteorological community. Recent research results in Japan on those methods are reviewed, especially on mesoscale applications of 4D-Var and tests of the local ensemble transform Kalman filter (LETKF). Comparison of 4D-Var and EnKF is also briefly discussed. An outline of the mesoscale 4D-Var system of the Japan Meteorological Agency, which is the first operational 4D-Var for a mesoscale model, is given in Appendix.