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Tadashi Tsuyuki

Abstract

A global primitive equation model is used to examine the performance of four-dimensional variational data assimilation (4D-VAR) with moist processes and to assess the impact of assimilating precipitation data in the Tropics. Several types of discontinuity in the parameterization schemes of moist processes are removed. In the assimilation experiments, truth and observations are provided by the full-physics model, while the assimilation model and the corresponding adjoint model include moist processes, horizontal diffusion, and simplified surface friction only. An idealized observation network that is sparse in the Tropics and the Southern Hemisphere is used.

It is demonstrated that the addition of a penalty term for suppressing gravity wave noise increases the efficiency of 4D-VAR with moist processes by avoiding locally large gradients in the cost function during the minimization process. It is found that 4D-VAR with moist processes included yields a much better analysis in the Tropics despite a slower convergence rate than 4D-VAR without the moist processes. 4D-VAR assimilates the simulated precipitation data quite well. Inclusion of the moist processes and assimilation of precipitation data improve the analyses of divergence, moisture, and lower-tropospheric vorticity. In particular, the wind field in the tropical planetary boundary layer is better analyzed, and the structure of a tropical cyclone is well retrieved. A 72-h forecast experiment shows that assimilation of precipitation data improves the precipitation forecast.

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Tadashi Tsuyuki

Abstract

The performance of four-dimensional variational data assimilation (4D-VAR) in the Tropics is examined by assimilating radiosonde and pibal data over the globe and Special Sensor Microwave/Imager (SSM/I) precipitation rates over the tropical oceans for the period 0000–1200 UTC 22 August 1992. The cost function consists of a discrepancy term between model and observations and a penalty term for suppressing gravity wave noise. The assimilation model (forward model) is a full-physics global spectral model, while physics of the adjoint model only includes moist processes, horizontal diffusion, and simplified surface friction. Several types of discontinuity are removed from the parameterizations of the moist processes.

It is found that the following three procedures improve the convergence performance of 4D-VAR in which the adjoint model includes moist processes: appropriate control of gravity wave level, removal of discontinuities from the parameterization schemes of the moist processes, and use of a higher-order horizontal interpolation operator for precipitation when assimilating precipitation data. 4D-VAR, using the adjoint model that lacks the moist processes, produces a poor analysis in the Tropics despite the fact that the full-physics model is used as the forward model. Inclusion of the moist processes in the adjoint model leads to a better precipitation analysis even without assimilating the SSM/I precipitation rates, especially in areas where several radiosonde and pibal observations are available. However, the convergence rate is slightly decelerated by including the moist processes. The impact of assimilating SSM/I precipitation rates on the precipitation analysis is not confined to near SSM/I observation times but spreads over the whole assimilation window. Its impact on the precipitable water analysis over the tropical oceans is positive but very small, suggesting the necessity of assimilating satellite precipitable water data. Assimilation of the SSM/I precipitation rates slightly improves the precipitation forecast over the tropical oceans.

An implication of the results for the Tropical Rainfall Measuring Mission (TRMM) project is discussed.

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Jun-ichi Furumoto, Shingo Imura, Toshitaka Tsuda, Hiromu Seko, Tadashi Tsuyuki, and Kazuo Saito

Abstract

Recently, a humidity estimation technique was developed by using the turbulence echo characteristics detected with a wind-profiling radar. This study is concerned with improvement of the retrieval algorithm for delineating a humidity profile from the refractive index gradient (M) inferred from the echo power. To achieve a more precise estimate of humidity, a one-dimensional variational method is adopted. Because the radar data provide only the absolute value of M, its sign must be determined in the retrieval. A statistical probability for the sign of M [Pr(z)] is introduced to the cost function of the variational method to determine the optimum result with reduced calculation cost. GPS-derived integrated water vapor (IWV) was assimilated together with the radar-derived |M| for constraining the signs of |M| to agree with the radar-derived IWV and the GPS-derived IWV. Humidity profiles were retrieved from the Middle and Upper Atmosphere (MU) radar–Radio Acoustic Sounding System (RASS) data for July–August 1999 using the first guess calculated from the time interpolation of radiosonde results. The |M| profiles from the MU radar–RASS were assimilated at 21 height layers between 1.5 and 7.5 km. A genetic algorithm is employed to find the global optimum. The humidity profiles are retrieved with the same vertical resolution as that of the observation values. The precision of the retrieval result using the new method is superior to that of the conventional method. The difference between the analysis and simultaneous radiosonde results was related to a large error in the first guess. The sensitivity of the analysis result to the shape of the Pr(z) profile was investigated, and the result appears to be insensitive to the profile of Pr(z). The improvement over the conventional method is especially evident for the case of a large error in the first guess.

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Mio Matsueda, Masayuki Kyouda, Zoltan Toth, H. L. Tanaka, and Tadashi Tsuyuki

Abstract

Atmospheric blocking occurred over the Rocky Mountains at 1200 UTC 15 December 2005. The operational medium-range ensemble forecasts of the Canadian Meteorological Center (CMC), the Japan Meteorological Agency (JMA), and the National Centers for Environmental Prediction (NCEP), as initialized at 1200 UTC 10 December 2005, showed remarkable differences regarding this event. All of the NCEP members failed to predict the correct location of the blocking, whereas almost all of the JMA members and most of the CMC members were successful in predicting the correct location. The present study investigated the factors that caused NCEP to incorrectly predict the blocking location, based on an ensemble-based sensitivity analysis and the JMA global spectral model (GSM) multianalysis ensemble forecasts with NCEP, regionally amplified NCEP, and globally amplified NCEP analyses.

A sensitive area for the blocking formation was detected over the central North Pacific. In this area, the NCEP control analysis experienced problems in the handling of a cutoff cyclone, and the NCEP initial perturbations were ineffective in reducing uncertainties in the NCEP control analysis. The JMA GSM multianalysis ensemble forecasts revealed that regional amplification of initial perturbations over the sensitive area could lead to further improvements in forecasts over the blocking region without degradation of forecasts over the Northern Hemisphere (NH), whereas the global amplification of initial perturbations could lead to improved forecasts over the blocking region and degraded forecasts over the NH. This finding may suggest that excessive amplification of initial perturbations over nonsensitive areas is undesirable, and that case-dependent rescaling of initial perturbations may be of value compared with climatology-based rescaling, which is widely used in current operational ensemble prediction systems.

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