Development of Moist Singular Vectors in GRAPES-GEPS and a Preliminary Evaluation

In this study, moist singular vector (MSV) was developed based on GRAPES-GEPS (Global/Regional Assimilation and Prediction System - Global Ensemble Prediction System), the adjoint model of large-scale condensation and cumulus deep convection in GRAPES-4DVar (Four-dimensional variational assimilation). Five consecutive days of numerical experiments were performed for a preliminary evaluation of MSV. The singular values, horizontal distribution structure, spread of MSVs perturbation and its influence on the ensemble prediction were compared for each group of tests. The results showed that in the middle and high latitudes of the northern and southern hemispheres, the addition of both linearized moist physical processes increased the spread of the mid- and low-level SVs, but the linearized large-scale condensation (LC) process plays a leading role in the structure of MSV. The analysis of ensemble forecast shows the inclusion of moist linearized physical processes led to a greater effect of MSV on the rainfall levels of 10 and 25 mm and a slight improvement in anomaly correlation coefficient (ACC) of the atmospheric circulation field, and more obvious improvement due to linearized large-scale condensation. In the future, continuous multi-year testing and tropical-specific analyses are required for operation.

Automated summary

In this study, a moist singular vector (MSV) was developed and evaluated through numerical experiments. The results showed that the linearized moist physical processes have an impact on the spread and structure of MSV, and they can improve the prediction performance of rainfall and atmospheric circulation field.