Extended representation of wind–mass correlation by ensemble forecasting for data assimilation

Abstract

Initialization for numerical weather prediction models is of utmost importance especially in the short‐range forecast of the weather accompanying extreme events such as heavy rainfall, heatwaves, and so on. The balance relationship between wind and mass in the initialized field has been an important issue since Richardson's first attempt at numerical prediction. In a climatological framework, regressed‐linear and analytic‐nonlinear balancing methods are used to impose the wind–mass correlation onto the analysis increment, which results from data assimilation procedures. The advection of horizontal wind destroys the isotropy assumption by the regressed‐linear balance approach. The nonlinear balance equation approach including the advection process reduces this disadvantage more or less. However, it is shown that forecast ensemble correlation helps even the regressed balance approach to address the anisotropy in the hybridization framework. The regressed balance approach, in common with the nonlinear balance, experiences (a) considerable residual of the horizontal momentum conservation in the Antarctic stratosphere, and (b) lack of thermodynamic energy relationship between divergent wind and temperature in the Tropics. This study demonstrates with phenomenological examples that these weaknesses of the traditional (especially, regressed) methods are effectively resolved by forecast ensemble covariance.