The influence of erroneous background, beam-blocking and microphysical non-linearity on the application of a four-dimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts

Abstract

A series of observation system simulation experiments (OSSEs) and a real case study are conducted to investigate the application of the Doppler radar data assimilation technique for numerical model quantitative precipitation forecasts (QPFs). A four-dimensional variational Doppler radar analysis system (VDRAS) is adopted for all experiments. The first set of OSSEs demonstrates that when the background field contains the imperfect information predicted from a mesoscale model, the incorrect convective-scale perturbations in the background can result in spurious scattered precipitation. However, a smoothing procedure can be used to remove the fine structures from the primitive model output in order to avoid this over-prediction. Results from the second set of OSSEs indicate that the lack of low-elevation data owing to radar scan and/or beam blockage could significantly alter the retrieved low-level thermal and dynamical structures when a different number of data assimilation cycles is applied. These impacts could lower the rainfall forecast capability of the model. The third set of OSSEs shows that, when the rainwater is assimilated over a long assimilation window, the non-linearity embedded in the microphysical process could lead the minimization algorithm in a wrong direction, causing a further degradation of the rainfall prediction. However, using multiple short assimilation cycles produces better minimization and forecast results than those obtained with a single long cycle. A real case experiment based on data collected during Intensive Operation Period (IOP) #8 of the 2008 Southwest Monsoon Experiment (SoWMEX) is conducted to provide a verification of the conclusions obtained from OSSEs under a realistic framework.

Comparison of Environmental and Mesoscale Characteristics of Two Types of Mountain‐to‐Plain Precipitation Systems in the Beijing Region, China

“…The VDRAS system is a rapid update four‐dimensional variational (4DVAR) radar data assimilation system constrained by a cloud model (Sun & Crook, ). The microphysics scheme in VDRAS combines a Kessler warm rain scheme and a simple ice scheme (Chang et al, ). In addition to its use as a research tool for convective‐scale analyses (Friedrich et al, ; Gochis et al, ; Sun, ; Sun & Zhang, ; Tai et al, ), VDRAS has been used for operational nowcasting in several forecasting offices since 2001 (Crook & Sun, ; Mueller et al, ; Sun et al, ; Sun & Crook, ).…”

Initiation and Development of a Squall Line Crossing Hangzhou Bay

“…VDRAS can retrieve mesoscale dynamical and thermodynamic fields with frequent updating of less than 20 min by assimilating radial velocity and reflectivity data from radar networks and surface observations from dense mesonets. It has been used for convective‐scale data assimilation research (Chang et al., 2016; Sun & Crook, 1994; Tai et al., 2017) and for severe convective events studies (Chang et al., 2014; Friedrich et al., 2016; Gochis et al., 2014; Sun & Zhang, 2008). The 3‐dimensional gridded high‐resolution and frequency updated analyses of meteorological fields created by VDRAS can provide valuable information for this study in which meso and convective‐scale dynamical processes play crucial roles.…”

The CLARIS LPB database: constructing a long‐term daily hydro‐meteorological dataset for La Plata Basin, Southern South America

“…Several other works used daily surface minimum and maximum temperature and rainfall data observed at governmental and non-governmental stations from Argentina, Brazil, Paraguay, and Uruguay, also compiled in CLARIS LPB database (Bettolli et al, 2010a,b;D'Orgeval et al, 2010;Detzel & Mine, 2011;Renom et al, 2011;Urcola et al, 2011;Bettolli & Penalba, 2012;among others).…”