Application and Characteristic Analysis of the Moist Singular Vector in GRAPES-GEPS

Abstract

The singular vector (SV) initial perturbation method can capture the fastest-growing initial perturbation in a tangent linear model (TLM). Based on the global tangent linear and adjoint model of GRAPES-GEPS (Global/Regional Assimilation and Prediction System—Global Ensemble Prediction System), some experiments were carried out to analyze the structure of the moist SVs from the perspectives of the energy norm, energy spectrum, and vertical structure. The conclusions are as follows: The evolution of the SVs is synchronous with that of the atmospheric circulation, which is flow-dependent. The moist and dry SVs are located in unstable regions at mid-to-high latitudes, but the moist SVs are wider, can contain more small- and medium-scale information, and have more energy than the dry SVs. From the energy spectrum analysis, the energy growth caused by the moist SVs is reflected in the relatively small-scale weather system. In addition, moist SVs can generate perturbations associated with large-scale condensation and precipitation, which is not true for dry SVs. For the ensemble forecasts, the average anomaly correlation coefficient of large-scale circulation is better for the forecast based on moist SVs in the Northern Hemisphere, and the low-level variables forecasted by the moist SVs are also improved, especially in the first 72 h. In addition, the moist SVs respond better to short-term precipitation according to statistical precipitation scores based on 10 cases. The inclusion of the large-scale condensation process in the calculation of SVs can improve the short-term weather prediction effectively.