Data Assimilation ofWeather Radar and LIDAR for Convection Forecasting and Windshear Alerting in Aviation Applications - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 518

assimilation algorithm is applied to retrieve the horizontal and vertical wind fields
that are found to provide useful source of data in showing the mesoscale circulation
characteristics in a heavy rain event. Furthermore, the retrieved wind data show
impacts on improving the NWP model analysis and forecast of thunderstorms and
convective systems.
In monitoring the occurrence of low-level windshear and turbulence around the
Hong Kong airport due to complex wind flow over the Lantau Island, LIDAR
data have been put into operational and as a key component in the windshear
and turbulence alerting system to capture the wind and their changes at high
spatial and temporal resolution. To further investigate the use of LIDAR to depict
a complete view of three dimensional wind flow over the complex terrain around
the airport area, the variational data assimilation techniques are utilized and found
to be very useful to analyze the wind flow and capture the small-scale features.
The formulation of the variational methods will be discussed in this paper and their
performance will be illustrated through some case studies.

22.2 An Overview of Data Assimilation of Weather Radar

Data in Operational Non-Hydrostatic Mesoscale NWP
Model in HKO

In June 2010, Hong Kong Observatory (HKO) started to operate a new generation of
mesoscale NWP model suite called the Atmospheric Integrated Rapid-cycle (AIR)
Forecast Model System ( Wong 2010 ) based on the Non-Hydrostatic Model (NHM)
of the Japan Meteorological Agency (JMA) ( Saito et al. 2006 ). In brief, the new
NWP system contains two domains called Meso-NHM and RAPIDS-NHM with
horizontal resolution of 10 and 2 km respectively to provide forecast up to 72 and
15 h ahead respectively. With increased in model resolution, use of 3-dimensional
variational data assimilation (3DVAR) system and better representation of physical
processes like cloud microphysics and convective parameterization, benefits in fore-
casting of severe weather phenomena are obtained to support aviation applications
of AIR/NHM ( Wong et al. 2011 ).
In particular, to capture the fast evolving convective systems its associated
mesoscale circulation features, RAPIDS-NHM is executed every hour to pro-
vide storm-scale prediction over Hong Kong and its nearby Guangdong region
(Fig. 22.1 ). 3DVAR analysis at full model resolutions and vertical levels, and the
boundary conditions from Meso-NHM forecast in one-way nesting configuration
are used. Due to short observation cut-off time (35 min) in RAPIDS-NHM
3DVAR, the numbers of conventional observations from synoptic surface and upper-
air stations, ships, buoys and aircrafts (AMDAR—Aircraft Meteorological Data
Relay) are usually limited depending on availability in real-time. The observations
ingested in 3DVAR of RAPIDS-NHM are mostly from mesoscale observation
networks in Hong Kong (HK) and the Guangdong Province, including data from