著者

IKUTA Yasutaka FUJITA Tadashi OTA Yukinari HONDA Yuki

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2021-076, (Released:2021-09-14)

被引用文献数

11

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The regional data assimilation system at the Japan Meteorological Agency employs a variational data assimilation system on the basis of the non-hydrostatic model ASUCA (named ASUCA-Var). This paper reviews configurations and the current status of ASUCA-Var. To consider the consistency of analysis and prognostic variables, the control variables of ASUCA-Var include soil variables and basic atmospheric variables. The background-errors based on the control variables are calculated every three hours for land and sea grid points to better reflect the representative error covariance structure, taking into account daily variations and differences in structure on land and sea. Although the cost function is designed to be a perfect quadratic form, the basic field update method in the optimization process allows the nonlinearity of the observation operator and numerical weather prediction model to be incorporated into the solution of optimization problem in the incremental four-dimensional variational (4D-Var) method. The outer/inner models used in the incremental 4D-Var method are based on ASUCA, with suitable configurations according to each resolution and applied linearization. Observation operators are implemented for various kinds of observations used, with unified interfaces encapsulating external simulators. Variational quality control and variational bias correction are also introduced for advanced observation handling within the variational system. Parallelization is introduced to enhance computational efficiency, including adjoint calculations. To assess the impact of assimilated observations, degrees of freedom for signal are also available. In addition, as a system for operational use, ASUCA-Var is designed for sustainable development. The meso-scale analysis and local analysis workflows are presented as operational implementations of ASUCA-Var. ASUCA-Var improves forecasting in a wide range of validation indices. The major future improvements of ASUCA-Var include the introduction of the flow-dependent background-error and the extension of the control variable to hydrometeors, which are expected to enhance the prediction accuracy of the operational regional model.

著者

BARREYAT Marylis CHAMBON Philippe MAHFOUF Jean-François FAURE Ghislain IKUTA Yasutaka

出版者

Meteorological Society of Japan

雑誌

気象集誌. 第2輯 (ISSN:00261165)

巻号頁・発行日

pp.2021-050, (Released:2021-04-30)

被引用文献数

3

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The assimilation of cloudy and rainy microwave observations is under investigation at Météo-France with a method called ‘1D-Bay+3D/4D-Var’. This method consists of two steps: (i) a Bayesian inversion of microwave observations and (ii) the assimilation of the retrieved relative humidity profiles in a 3D/4D-Var framework. In this paper, two estimators for the Bayesian inversion are used: either a weighted average (WA) or the maximum likelihood (ML) of a kernel density function. Sensitivity studies over the first step of the method are conducted for different degrees of freedom: the observation error, the channel selection and the scattering properties of frozen hydrometeors in the observation operator. Observations over a two-month period of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) on-board the GPM-Core satellite and forecasts of the convective scale model Application of Research to Operations at Mesoscale (AROME) have been chosen to conduct these studies. Two different meteorological situations are analysed: those predicted cloudy in AROME but clear in the observations and, on the contrary, those predicted clear in AROME but cloudy in the observations.Main conclusions are as follows. First, low observational errors tend to be associated with the profiles with the highest consistency with the observations. Second, the validity of the retrieved profiles varies vertically with the set of channels used. Third, the radiative properties used in the radiative transfer simulations have a strong influence on the retrieved atmospheric profiles. Finally, the ML estimator has the advantage of being independent of the observation error but is less constrained than the WA estimator when few frequencies are considered. While the presented sensitivities have been conducted to incorporate the scheme in a data assimilation system, the results may be generalized for geophysical retrieval purposes.