Download Heavy rainfall prediction over East Asia using the high resolution

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Global Energy and Water Cycle Experiment wikipedia , lookup

Weather Prediction Center wikipedia , lookup

Earth rainfall climatology wikipedia , lookup

Transcript
Lee et al.
HEAVY RAINFALL PREDICTION OVER EAST ASIA USING THE HIGH
RESOLUTION WRF MODEL
Lee Dong-Kyou, Hyun-Ha Lee, Jai-Won Lee, and Jung-Hoon Cho
Atmospheric Science Program, School of Earth and Environmental Sciences, Seoul National University,
KOREA, [email protected]
Multi-nested domains of 1 to 30 km horizontal resolution in the WRF (Weather Research and
Forecasting) model are used to investigate the potential predictability of heavy rainfall over East Asia in
which mesoscale convective systems are highly interacted with synoptic-scale environment. In the 1 to 5
km horizontal resolution domain, the spin-up time of precipitation tends to be reduced as the model initial
time is closer to the initiation of precipitation and the intensity of rainfall also increases. In the
experiments of the rapid update cycle (RUC) and nudging techniques for radar rain water and horizontal
wind assimilation, individual convective storms are well developed and the spin-up time of precipitation
is much reduced in the 1 to 5 km horizontal model domain. The 2-hour assimilation window with radar
data of 6-minute intervals is the most effective, which remains about 6 hours. In particular, horizontal
wind radar data contribute to not only enhanced rainfall amount and intensity but also the spin-up of
precipitation, in which the threat score of 6-hour accumulated rainfall amount has over 0.5 at the 25 mm
threshold. The radar data provide a well organized convergence line to trigger the storms in MCSs at the
model initial time.
WWRP International Symposium on Nowcasting and Very Short range Forecasting