539 Indirect reflectivity assimilation approach using radar simulator in JMA non-hydrostatic model based variational data assimilation system

Wednesday, 26 January 2011
Washington State Convention Center
Yasutaka Ikuta, Japan Meteorological Agency, Tokyo, Japan; and Y. Honda

Handout (3.1 MB)

The Japan meteorological agency (JMA) has been operating a meso-scale model (MSM). An operational four-dimensional variational (4DVAR) data assimilation system (JNoVA: JMA non-hydrostatic model based variational data assimilation system (Honda et al., 2005)) provides an initial condition of the MSM. To improve the quantitative precipitation forecast (QPF), especially that of heavy rain, we have been developing a method to assimilate radar reflectivity by using JNoVA.

A new approach to assimilate radar reflectivity is based on Bayesian inversion, to estimate the pseudo observation of relative humidity (Caumont et al., 2010). The role of observation operator in this retrieval is played by a radar simulator developed in JMA. The radar simulator reproduces the beam bending by refractive index of atmosphere, the beam blockage by topography, the beam attenuation by precipitation particles, and simulates the radar reflectivity factor. The retrieved relative humidity is assimilated as conventional observational data in JNoVA. The assimilation of the pseudo observation shows the impact on the analysis of the water vapor and temperature, which are modified to be appropriate for forecast precipitation. It is expected that this new indirect assimilation approach circumvents the difficult problem of the treatment of balance between hydrometeor and momentum.

To demonstrate the new assimilation approach, the sensitivity experiments have been done for several cases including the one of sediment disaster caused by heavy rain. As a result, the precipitation distribution and intensity can be improved by application of indirect reflectivity assimilation. In this talk, the results of the sensitivity experiments and the performance of new assimilation approach will be presented.

Honda, Y., M. Nishijima, K. Koizumi, Y. Ohta, K. Tamiya, T. Kawabata and T. Tsuyuki, 2005: A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: Formulation and preliminary results. Q. J. R. Meteorol. Soc., 131, 3465-3475. Caumont, O., V. Ducrocq, É. Wattrelot, G. Jaubert, S. Pradier-Vabre, 2010: 1D+3DVar assimilation of radar reflectivity data: A proof of concept. Tellus A, 62, 173–187.

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