Data Assimilation Research Team Events / Media

Atmospheric gravity waves (GWs) play an important role in driving the circulation of the middle and upper atmosphere by transferring momentum and energy from below. Although their effects have been parameterized in most general circulation models (GCMs), an explicit treatment of them may provide new insights into the prediction of atmospheric phenomena. Recently, we have realized such a trial by using a high-top (extended to an altitude of about 150 km) model named Japanese Atmospheric GCM for Upper Atmosphere Research (JAGUAR). However, the high computational cost of GW-resolving simulations and the shortage of observational data prevented us from reproducing real atmospheric fields (including GWs) directly. Therefore, the following two steps are adopted in practice. First, data assimilation is performed using a high-top but relatively low-resolution setting (T42L124 with GW parameterizations) to create grid data for the real atmosphere from the ground to the lower thermosphere. Then, GW-resolving predictions are conducted in high-resolution settings (e.g., T639L340) by using the data assimilation product as initial conditions. In this talk, results of two prediction experiments are presented as examples of the later step, which are targeting (1) a drastic change of GW behavior in the middle atmosphere during the largest stratospheric sudden warming event in January 2009 and (2) the upward propagation of concentric GWs from the super typhoon Hagibis in October 2019. In both experiments, we have adopted the spectral nudging technique to make small-scale motions evolving freely while constraining large-scale motions to the data assimilation product. Merits and current problems of such a taming approach to mesoscale motions during the initialization will be discussed.