Data Assimilation ofWeather Radar and LIDAR for Convection Forecasting and Windshear Alerting in Aviation Applications - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 520

Fig. 22.2 Domain of 3D
wind retrieval computation
and locations of radars in
Shenzhen and Hong Kong

to quality control procedure to filter out those observations with large departure
from the model first-guess. Additionally, to better adjust the moisture content in the
model analysis, pseudo-observations of humidity near to the saturation values at that
height level are created in case the relative humidity in the first-guess is low at the
grid point corresponding to the observed radial velocity and non-zero reflectivity.
The radial wind data and the relative humidity observations are then assimilated
together into the 3DVAR together with other available observations.

22.3 Wind Retrieval Technique of Doppler Weather

Radar Data

22.3.1 Doppler Weather Radars and Wind Retrieval Algorithm

While the mesoscale features of convective systems could be delineated from the
radar radial velocity field, it would be better if the 3-dimensional (3D) wind compo-
nents can be estimated to facilitate real-time diagnosis, nowcasting applications as
well as for ingestion into mesoscale NWP models to capture the flow characteristics.
In this study, the weather radar data from Hong Kong and Shenzhen (Fig. 22.2 ).
They are both S-band radars and complete one volume scan in every 6 min.
Prior to the wind retrieval calculation, the weather radar data are pre-processed
by a couple of steps. In the first step, velocity de-aliasing is performed with the