| description 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. | |
