Abstract
The recently developed MM5 three-dimensional variational (3DVAR) assimilation system at NCAR has been used to investigate influences of ingested sounding and ship observation data on numerical simulations of severe weather in the vicinity of Taiwan. Three weather events were simulated in this study, which include a Mei-Yu front in June 1998, Supertyphoon Bilis in August 2000 and Typhoon Nari in September 2001. For the Mei-Yu front, the simulated low-pressure system northeast of Taiwan is stronger. when the 3DVAR is performed during initialization. The simulated patterns of heavy rainfall just off the southern tip of Taiwan are also closer to observations for the run with 3DV AR. For the second case of Typhoon Bilis, both runs with and without 3DVAR show a northward bias in track upstream of eastern Taiwan. However, due to less northward track deflection, the simulated heavy rainfall in Taiwan for the 3DV AR run is in better agreement with observations. For the no-3DVAR run, the simulated Typhoon Nari consistently moves southwestward toward Taiwan but then makes an incorrect landfall at northwestern Taiwan. With 3DVAR, the track simulation is improved with a landfall position at northeastern Taiwan. During landfall, the associated could convection is enhanced as the intense vortex core is in confrontation with the leading edge of the Central Mountain Range (CMR) and its movement is slowed down along the northwestern coast. The combination of both compression and stagnation of the embedded convective system may explain the extremely intense rainfall in northern Taiwan. The feature of observed intense rainfall over the southwestern slope base of the C／MR is also captured, but the intensity is considerably underpredicted due to lagging and weakening of the vortex core at later -times for both runs with and without 30V AR. The track simulation at later times has also been improved in the 3DVAR run, but the associated geometric distributions of 72h accumulated rainfall amounts in general are similar to those without 3DVAR. In the typhoon experiment with a bogus vortex, the 3DVAR rum preserves the initial vortex, quite well. However, the rainfall and track simulations are not improved by the ingestion of the bogus vortex for both runs with and without 3DVAR.
Highlights
Data assimilation (DA) has recently been recognized as a useful way of providing better "consistent" initial conditions for numerical weather prediction (NWP) in the meteorological community
Despite its complexities and heavy computational loads, 4DVAR by using the adjoint MMS to assimilate rainfall and precipitable water (PW) data has been shown to be capable of improv ing quantitative prediction forecast (QPF) due to improved initial divergence and moisture conditions (Zou and Kuo 1996)
The frontal structure of the near-surface wind in the vicinity of Taiwan is well captured in the model initial condition as shown in Fig. 1 for the run without
Summary
Data assimilation (DA) has recently been recognized as a useful way of providing better "consistent" initial conditions for numerical weather prediction (NWP) in the meteorological community. A full set of data in an all assimilation time window has beneficial impacts resulting from strong constraints upon the model integrated state with both physics and dynamics involved This method using variational minimization for data given within a definite time window is termed 4DVAR. With more and more unconventional data (including measurements from satellite, radar and other remote sensors), ingestion of these data into NWP models has been believed to be one of top priorities for improving on weather forecasts Whenever these data become available, they should be assimilated dur ing their ingestion time window provided that their error covariance matrices have been well known.
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