Improving model QPN skill using multiple-Doppler radar observed and retrieved meteorological state variables

A newly designed technique named WInd Synthesis System using DOppler Measurements (WISSDOM) is combined with a modified thermodynamic retrieval scheme and a moisture/temperature adjustment scheme to formulate a sequential procedure. This procedure is applied for the purpose of improving the model quantitative precipitation nowcasting (QPN) skill in convective-scale. A series of idealized tests and a real case study from 2008 SoWMEX IOP#8 are conducted. The experimental results show that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to re-initialize a fine-resolution numerical model, its QPN skill can be significantly improved. During the thermodynamic retrieval calculation, utilization of in-situ radiosonde(s) to obtain the horizontal average properties of the weather system at each altitude is tested. It is also demonstrated that using the model forecasts to replace the role played by observing devices is a feasible choice. The moisture field is found to be beneficial to the rainfall forecast within the first hour after the re-initialization of the model. Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements, and directly uses them to re-initialize the model, fewer (2~3 volume scans) radar data and shorter model spin-up time are needed to correct the rainfall forecasts.