Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

Abstract

This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model.Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as fourdimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

Assimilation impact of high‐temporal‐resolution volume scans on quantitative precipitation forecasts in a severe storm: Evidence from nudging data assimilation experiments with a thermodynamic retrieval method

“…Our TRM was based on Liou et al 's () method with the pseudo‐water vapour method of Wang et al (). Further details about our thermodynamic retrieval procedure and pseudo‐water‐vapour estimation are described in appendix .…”

“…Absolute was recovered from = ′ -⟨ ′ ⟩ + ⟨ ⟩ by adding the absolute pressure horizontally averaged at each level ⟨ ⟩, which is usually given independently (e.g. from sounding data or a numerical model), to the retrieved perturbation ′ -⟨ ′ ⟩ (Hane et al, 1981;Liou et al, 2014). Similarly, horizontal differentials of ′ were deduced from Equations B1-B3 as follows ……”

“…S and T were specified by F, G and H. Absolute was similarly recovered from = ′ -⟨ ′ ⟩ + ⟨ ⟩. Based on Liou et al (2014), ⟨ ⟩ and ⟨ ⟩ were obtained from simulation results (see section 2.2) instead of sounding data. Absolute was assimilated using a simple Newtonian relaxation nudging scheme (see section 2.3).…”

“…They confirmed the validity of their algorithm using a complete wind field (simultaneous input without any contamination) in OSSEs, finding high predictability that was comparable to 4D‐Var in the real case‐study. The Liou et al () study showed that their TRM is a computationally efficient tool for model initialisation if the complete wind field is provided. However, such a complete wind field cannot be obtained from actual non‐instantaneous volume‐scanning operations.…”

“…Navier-Stokes equations are employed to identify the field of thermodynamic variables that are consistent with the observed velocity fields. Three sequential procedures are commonly used (Weygandt et al, 2002a): (a) determination of the three-dimensional wind field by single- (Shapiro et al, 1995) or multiple-Doppler radar analysis (Gao et al, 1999;Protat and Zawadzki, 1999); (b) blending of the wind field with the background wind from model forecasts or large-scale analysis to fill data-void regions (Lin et al, 1993); and (c) Gal-Chen-type TRM (Gal-Chen, 1978;Hane et al, 1981;Hane and Ray, 1985;Weygandt et al, 2002b;Liou et al, 2014), which applies a two-dimensional Poisson equation derived from the Navier-Stokes equations at each height level to determine the perturbation fields of potential temperature and pressure, with an adjustment for water vapour mixing ratio saturated in convective regions (Lin et al, 1993;Weygandt et al, 2002b). The main advantage of this method is the computational efficiency for obtaining unobservable thermodynamic variables.…”