Initialization and simulation of a landfalling typhoon using a variational bogus mapped data assimilation (BMDA)
- First Online:
- 69 Downloads
Recently, a new data assimilation method called “3-dimensional variational data assimilation of mapped observation (3DVM)” has been developed by the authors. We have shown that the new method is very efficient and inexpensive compared with its counterpart 4-dimensional variational data assimilation (4DVar). The new method has been implemented into the Penn State/NCAR mesoscale model MM5V1 (MM5_3DVM). In this study, we apply the new method to the bogus data assimilation (BDA) available in the original MM5 with the 4DVar. By the new approach, a specified sea-level pressure (SLP) field (bogus data) is incorporated into MM5 through the 3DVM (for convenient, we call it variational bogus mapped data assimilation – BMDA) instead of the original 4DVar data assimilation. To demonstrate the effectiveness of the new 3DVM method, initialization and simulation of a landfalling typhoon – typhoon Dan (1999) over the western North Pacific with the new method are compared with that with its counterpart 4DVar in MM5. Results show that the initial structure and the simulated intensity and track are improved more significantly using 3DVM than 4DVar. Sensitivity experiments also show that the simulated typhoon track and intensity are more sensitive to the size of the assimilation window in the 4DVar than that in the 3DVM. Meanwhile, 3DVM takes much less computing cost than its counterpart 4DVar for a given time window.
Unable to display preview. Download preview PDF.
- Bouttier, F, Rabier, F 1997The operational implementation of 4D-VarECMWF Newsletter7825Google Scholar
- China Meteorological Administration1999Tropical cyclone annals observational reportWeather PressBeijingGoogle Scholar
- Daley, R 1991Atmospheric data analysisCambridge University PressCambridge457Google Scholar
- Fujita, T 1952Pressure distribution within a typhoonGrophys Mag23437451Google Scholar
- Grell GA, Dudhia J, Stauffer DR (1994) A description of the fifth generation Penn State/NCAR mesoscale model (MM5). NCAR Tech Note NCAR/TN-3981STR, 138 ppGoogle Scholar
- Le Dimet, F-X, Talagrand, O 1986Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspectsTellus38A97110Google Scholar
- Wang, B, Zhao, Y 2006A new data assimilation approachACTA Meteorol Sinica20275282Google Scholar
- Zou X, Vandenberghe F, Pondeca M, Kuo Y-H (1997) Introduction to adjoint techniques and the MM5 adjoint modeling system, NCAR Technical Note, NCAR/TN-435-STR, National Center for Atmospheric Research, Boulder, CO, 110 ppGoogle Scholar