Development of an ensemble data assimilation system with LMDZ5 AGCM for regional reanalysis

Abstract

The present study describes a newly developed LMDZ–DART data assimilation system based on the Ensemble Kalman Filter (EnKF) with a stretched grid atmospheric model and evaluates the potential of the system for generating high-resolution reanalysis products over the Indian region. This system is composed using the LMDZ5 (Laboratoire de Meteorologie Dynamique, Zoom, version 5) global atmospheric model, the Data Assimilation Research Testbed (DART), and a set of interface routines. The interface routines enable assimilation of observations with the model using the EnKF assimilation method. The stretched grid capability of the LMDZ5 model has been utilized to generate computationally efficient high-resolution experimental reanalysis data over the Indian region. The assimilation experiments have been conducted for 4 months during the Indian summer monsoon (ISM) period using LMDZ–DART system with two different configurations: REG (1° × 1° regular grid size over the globe) and ZOOM (~ 0.35° × 35° over the Indian region and coarser resolution outside). The results of the assimilation experiments are validated by comparing with observations as well as with an independent atmospheric reanalysis (ERA-Interim). It has been found that LMDZ–DART system is robust and reliable throughout the investigated ISM period for both REG and ZOOM configurations. In the REG experiment, spatial patterns and magnitude of the global fields portray a high degree of resemblance with ERA-Interim. The spatial patterns are also well reproduced in the ZOOM reanalysis. However, at some places outside the zoom area, the differences of the ZOOM reanalysis with ERA-Interim are slightly higher than the regular grid. Over the Indian region, the ZOOM configuration provides higher quality analysis and better regional features than REG. These results thus provide confidence in the stretched grid LMDZ–DART system to serve as a basis for generating computationally economical high-resolution regional reanalysis.