Operational MesoScale Weather Prediction with Japan Spectral Model

Segami, Akihide; Kurihara, Kazuo; Nakamura, Hajime; Ueno, Mitsuru; Takano, Isao; Tatsumi, Yasuo

doi:10.2151/jmsj1965.67.5_907

Cited by 133 publications

(77 citation statements)

References 21 publications

(19 reference statements)

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“…The dynamic TCSM framework is the same as the Japan Spectral Model (JSM) -the operational NWP model at the Japan Meteorological Agency (JMA) (Segami et al, 1989). Instead of original JSMM precipitation parameterization schemes, the following are used in TCSM: 1) Layer Cloud Precipitation Scheme (LCPS), which explicitly includes grid-scale cloud water variables and calculates stratiform precipitation rates from these variables (Smith, 1990); and, 2) Economical Prognostic Arakawa-Schubert Scheme (EPAS), which parameterizes convective precipitation rates at low computational cost (Kuma, 1993).…”

Section: Rain Flagmentioning

confidence: 99%

Direct Assimilation of Multichannel Microwave Brightness Temperatures and Impact on Mesoscale Numerical Weather Prediction over the TOGA COARE Domain

Aonashi¹,

Liu

1999

Journal of the Meteorological Society of Japan

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Direct assimilation using 1-dimensional variational method in the vertical (1D-VAR) was developed to incorporate vertically polarized brightness temperatures (TB's), and rain flag data (index of existence of precipitation) from the special sensor microwave imager (SSM/I), into a mesoscale numerical weather prediction (NWP) model. We used a radiative transfer model (RTM) developed by Liu (1998) to calculate TB's from NWP model variables. Observational residuals of TB's are assumed to be nonlinear functions of precipitation rates in rainy areas, and of other thermodynamic variables in rain-free areas. Quasiequilibrium assumptions on humidity and convective instability in model precipitation areas are used to assimilate TB's in rainy areas and rain flag data, which are functions of precipitation. TB's in rain-free areas are assimilated into total water content (cloud water content + humidity). Smith's method (1990) is used to calculate cloud water content and humidity from total water content. To simplify 1D-VAR, the following approximations are introduced: 1) In the tropics, variations in temperature are much smaller than variations in humidity.2) Variations in divergence dominates the rainy areas, compared to vorticity and mass variables. Based on the above approximation, we assumed that the background error covariance of relative vorticity and the unbalanced component of mass variables is negligible, so we divided 1D-VAR into that for total water content and that for divergence. Newtonian iteration is used to solve these 1D-VAR problems.To study the assimilated variables, assimilation was applied to cases during 19-20 Dec. 1992 over the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) domain. Results indicate: 1) Precipitable water content (PWC) obtained by assimilation agreed well with PWC calculated from radiosonde data and PWC retrieved from TB's using Shibata's algorithm (1994).2) Consistency between humidity and cloud water content profiles was attained by Smith's method (1990), although no significant improvement was seen in humidity profile accuracy by assimilation. 3) Assimilation of rain flag and TB's in rainy areas successfully produced model precipitation areas agreeing with radar observation data by Short et al. (1997).To test the impact of assimilation on NWP forecasts, experiments were conducted assimilating TB data for 19 Dec. 1992. Results indicate: 1) Assimilation modified large-scale model humidity distribution, improving large-scale humidity and precipitation forecasts for 48 hours.

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Section: Rain Flagmentioning

confidence: 99%

Direct Assimilation of Multichannel Microwave Brightness Temperatures and Impact on Mesoscale Numerical Weather Prediction over the TOGA COARE Domain

Aonashi¹,

Liu

1999

Journal of the Meteorological Society of Japan

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“…Here we discuss the performance of the NNMI applied to the Japan Spectral Model (JSM) (Segami et al, 1989).…”

Section: Resultsmentioning

confidence: 99%

“…A boundary relaxation method modified from the one proposed by Davies (1976) is employed to reduce the amplitude of spurious waves near the boundaries. The model includes the following physical processes: * Convective adjustment * Large-scale condensation * Planetary boundary layer process by Mellor and Yamada's (1974) level-2 closure model * Surface flux computed by the similarity theory The initial state is obtained by a 2-dimensional optimum interpolation method with the grid interval of 80 km from analysis of geopotential height, winds, temperature and T -T d at 15 pressure levels (Segami et al, 1989). No other initialization procedure is applied prior to the NNMI.…”

Section: Resultsmentioning

confidence: 99%

Non-linear Normal Mode Initialization for a Spectral Limited-Area Model

Takano

Nakamura

Tatsumi

1990

Journal of the Meteorological Society of Japan

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Non-linear normal mode initialization (NNMI) proposed by Machenhauer (1977) is applied to the JMA spectral limited area models. The normal modes of the models are constructed using a constant map factor and Coriolis parameter, which was first performed by Briere (1982) for a grid model. The initialization procedure is carried out in spectral space. The NNMI is designed to preserve the prescribed boundary conditions of the model. By introducing a cut-off period, gravity waves with very low frequency are excluded from the procedure.The initialization using the adiabatic time tendencies successfully reduced the large time tendencies of the gravity wave modes of the first five vertical modes. The changes of the initial states by the NNMI with a short cut-off period of 6 hours were very small, while the NNMI with a longer cut-off period produced larger initial changes in the baroclinic zone. The NNMI eliminated high frequency oscillations in the subsequent forecast.

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“…Its equivalent grid interval is 40 km and it has 19 vertical layers. Details of the model are described in Segami et al (1989). The vertical profile of the predicted equivalent potential temperature (a dotted line in Fig.…”

Section: Discussionmentioning

confidence: 99%