Geoscience Reference
In-Depth Information
Chapter 22
Data Assimilation of Weather Radar and LIDAR
for Convection Forecasting and Windshear
Alerting in Aviation Applications
Wai Kin Wong and Pak Wai Chan
Abstract
In this paper, variational data assimilation techniques to retrieve
3-dimensional wind fields from weather radars and LIDAR are discussed. The
retrieved wind field from the 3-dimensional variational (3DVAR) technique applied
to the weather radar data are found useful to delineate the mesoscale features
leading to the convective development in a rainstorm event that brought significant
lightning and thunderstorms near the Hong Kong airport and heavy precipitation
over the territory. Impacts in improving analysis and forecast of a non-hydrostatic
NWP model are also obtained through the data assimilation of wind retrieval
data as additional observations in the model analysis. To capture the low-level
windshear due to complex wind flow around the Hong Kong airport, 3DVAR
and 4DVAR techniques are applied to LIDAR data. The performance of the wind
retrieval algorithms and results of case studies will be illustrated. It is found that the
wind fields obtained are useful to depict salient features of terrain-induced airflow
disturbances at HKIA, such as mountain waves and vortices in a gustnado event.
22.1
Introduction
Ground-based remote sensing platforms including radars, LIDARs (Light Detection
and Ranging), wind profilers and GPS (Global Positioning System) provide valuable
observations for monitoring and forecasting of the development of mesoscale
weather systems, significant convection, thunderstorm, windshear and turbulence.
In this paper, a brief summary on the use of radar data in the data assimilation system
of the non-hydrostatic NWP modeling system of Hong Kong Observatory will first
be given. Using data from multiple weather radars, 3-dimensional variational data
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