Constraining the Large‐Scale Analysis of a Regional Rapid‐Update‐Cycle System for Short‐Term Convective Precipitation Forecasting

Abstract

This study examines the impact of a large‐scale constraint (LSC) on the large‐scale analysis and precipitation forecast of convective weather systems in a regional rapid‐update‐cycle system. The LSC is imposed by assimilating Global Forecast System forecast fields as bogus observations with a scale selection scheme. The scale selection is achieved by skipping data points of Global Forecast System forecast fields in the horizontal and vertical directions. It is shown that the LSC is able to modify the large‐scale component of the analysis fields while leaving the small‐scale component mostly intact compared with a control experiment without the constraint. The effects of the LSC on precipitation forecast are verified and analyzed using nine convective cases in the Rocky Mountain Front Range and its east plains. The results show that the LSC is effective in improving the precipitation forecast of different cases. However, the cases with weak large‐scale forcing show greater improvements than those with strong large‐scale forcing. Further analyses on the dynamic and thermodynamic variables indicate that the use of the LSC is able to construct a favorable environment for the initiation and development of convection in the case of weak large‐scale forcing, which leads to significant improvement of convective precipitation forecasting when radar observations are assimilated.

Meteorological OSSEs for New Zenith Total Delay Observations: Impact Assessment for the Hydroterra Geosynchronous Satellite on the October 2019 Genoa Event

“…where d c = y c − H(x b ) is the innovation vector that measures the departure of the LSC y c from its counterpart computed from the background x b ; v = U −1 (x − x b ) is the control variable vector, with U being the decomposition of the background error covariance B via B = UU T ; and H is the linearization of the nonlinear observation operator H. The y c variable includes the meridional and zonal wind components, the temperature, and the water vapour mixing ratio from the large-scale analysis that are being assimilated as bogus observations. The errors for wind, temperature, and water vapour mixing ratio are 2.5 m s −1 , 2°C, and 3 g kg −1 , respectively, and are determined by the diagnostics of the GFS product [82,83]. They form the R c matrix, which weights the importance of the LSC term in the cost function minimization.…”

“…Starting from the results obtained by Tang et al [83], some experiments are performed as sensitivity, to understand the effect of the LSC scheme to different scales of the analysis fields and the precipitation forecast (not shown). In [83] the sensitivity on the assimilation scheme is performed using LSC every 1, 5, 10 grid points of the outer WRF domain (d01) at 15 km resolution and starting from different vertical levels.…”

“…It has been shown that when high resolution radar observations are assimilated, if the cost function is not properly constrained, such a large number of inputs can dominate the analysis result by adding large unbalanced wind increments, especially when convective systems are present [82,83]. Furthermore, the high resolution ZTD Hydroterra-like observations can lead to unrealistic dynamics, by changing the atmospheric stability and producing very vigorous vertical motion throughout the domain (not shown).…”

“…This is defined in terms of the departure of a high resolution 3DVAR analysis from a coarser-resolution large-scale analysis, as explained more in detail in Appendix A [82]. In this work, the version of large-scale constraint (LSC) used in Tang et al [83] is adopted. Firstly, the GFS forecast fields (instead of analysis fields) are interpolated into the same regular grids as the outer domain via the WRF pre-processing system.…”

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

“…Nowadays, NWP models that run at 1 km grid spacing are considered high resolution for the forecast of heavy rainfall events since convection is explicitly represented in the equations of motion. However, the assimilation of observations at such high resolution is still the object of active research (Tang et al, 2019), because current data assimilation techniques cannot take into account the covariances in the observation error (Bouttier and Courtier, 1999;Lagasio et al, 2019).…”