This study conducted data assimilation experiments using the operational mesoscale four-dimensional variational data assimilation (4D-Var DA) system for Mesoscale Model (MSM) and three-dimensional variational data assimilation (3D-Var DA) system for Non-hydrostatic Model (NHM) of the Japan Meteorological Agency (JMA). Experiments investigated the impacts of GPS-derived water vapor and Doppler radar-derived radial wind (RW) on precipitation prediction for a heavy rain event on 21 July 1999. Mesoscale model (MSM) is a hydrostatic model with the horizontal grid interval of 10 km. If the only conventional meteorological data was assimilated into MSM, precipitation regions were generated over a mountainous area far from Tokyo. If GPS-derived water vapor data, RW data, and conventional data were all simultaneously assimilated, the precipitation position was modeled correctly, and precipitation onset occurred as observed. However, the intensity of the precipitation was much weaker than observed one. The fields obtained by MSM-4DVar DA system were used as the initial condition of NHM, which was expected to improve intensity of precipitation. However, the convections over the southern Kanto were not reproduced. To strengthen updraft, RW data was further assimilated by NHM-3DVar DA system. The convective cells were also considered by saturating water vapor at intense updraft grids within the precipitation region. Evolution of the precipitation system was considered by introducing rain water, snow estimated from observed reflectivity fields, and relative humidity (RH) at the grids of downdraft within the precipitation region. From this modified condition, intense convective system was well reproduced by NHM.
A cloud resolving 4-dimensional variational data assimilation system (4DVAR) based on the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) is under development. One of the targets of this system is the analysis of mesoscale convective systems. Features of background error statistics for the model with a horizontal resolution of 2km (hereinafter abbreviated as 2km model) are much different from those with a 5km resolution (5km model). Thus, forecast error estimated by the scale-down method from that forecast error obtained from the 5km model was not applicable. To develop the cloud resolving system, background error statistics for the system with 2km horizontal intervals were calculated and a suitable set of control variables was designed. Using the new background error statistics and the new control variable set, a preliminary data assimilation experiment of the Global Positioning System (GPS)-derived precipitable water vapor (PWV) and radial wind observed by Doppler radars (RW) was performed. By assimilating GPS-PWV and RW, the convergence of horizontal wind was strengthened, and observed features of horizontal winds and PWV were reproduced in the analyzed field.
A profile of temperature and relative humidity retrieved from Mt. Fuji observed GPS “Downward Looking (DL)” data was assimilated into mesoscale weather prediction model by using four-dimensional variational data assimilation (4D-var) procedure for typhoon case of September 9, 2001. The DL observation offered the profile of the atmosphere over the ocean where typhoon approached. Because the retrieved case was few, the observation error was expediently decided as 1 centigrade for temperature and 4 percent for relative humidity without a statistical investigation. The assimilation results show a small but positive impact for precipitation forecast. But the position of the typhoon center in the initial field slightly shifted to the opposite direction from the best track analysis by the Japan Meteorological Agency (JMA). To decide observation error of DL retrieved refractive index profile, error estimation using a three-dimensional (3D) ray-tracing model which uses mesoscale weather model outputs was executed. The 3D ray-tracing model simulated propagation of GPS signal in the model atmosphere every one-second. Then, Doppler shift, bending angle, partial bending angle (PBA), and finally refractive index profile were retrieved. It was proven that PBAs are able to reproduce from Doppler shift in high accuracy. Error of retrieved refractive index showed high correlation with horizontal variation of refractive index. The results suggest that we should assimilate bending angle or excess phase delay rather than profile of retrieved refractive index, temperature and humidity.
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