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Mohan K. Ramamurthy and Tai-Yi Xu

Abstract

The current major expansion in observational capability of line National Weather Service is principally in the volume of asynchronous data rather than synchronous observations at the standard synoptic times. Hence, the National Meteorological Center is considering a continuous data assimilation system to replace at some time the intermittent system now used by its regional and global operational models.

We describe this system, based on the Newtonian relaxation technique, as developed for the eta model. Experiments are performed for the first intensive observing period of the Genesis of Atlantic Lows Experiment (GALE) in January 1986, when strong upper-level cyclogenesis occurred, with a pronounced tropopause fold but only modest surface development. The GALE level IIIb dataset was used for initializing and updating the model.

Issues addressed in the experiments include choice of update variable, number, and length of update segments; need for updating moisture and surface pressure information; nudging along boundaries; and noise control. Assimilation of data from a single level was also studied.

Use of a preforecast assimilation cycle was found to eliminate the spinup problem almost entirely. Multiple, shorter assimilation segments produced better forecasts than a single, longer cycle. Updating the mass field was less effective than nudging the wind field but assimilating both was best. Assimilation of moisture data, surprisingly, affected the spinup adversely, but nudging the surface pressure information reduced the spurious pillow effect. Assimilation of single-level information was ineffective unless accompanied by increased vertical coupling, obtained from a control integration.

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Mohan K. Ramamurthy and Frederick H. Carr

Abstract

A limited primitive equation model has been used to study the feasibility of four-dimensional data assimilation in the monsoon region and, further, to study the applicability of several assimilation techniques currently being employed in global models. The two fundamental objectives of this research are

(i) to understand how the model atmosphere responds to the insertion of asynchronous data and its impact on the assimilation-prediction cycle, and

(ii) to determine what assimilation strategies work best for limited-area models in the tropics.

A sequence of ten assimilation experiments are performed using different update procedures; all insertions are carried out with only the wind observations. The model is initialized with the ECMWF FGGE level III-b data for the onset vortex case of 17 June 1979, and assimilations are carried out using the summer MONEX level II-b data during the first 12 hours. From these assimilated states, 24-h forecasts are then made.

The results lend support to the premise that the required initial conditions can be obtained by the process of four-dimensional updating of the prognostic variables. The results also clearly demonstrate the superiority of the continuous assimilation approach via Newtonian nudging over that by indirect insertion. Furthermore, the insertion shocks are significantly minimized by assimilating only the rotational component of the winds. On the other hand, the application of noise control measures only marginally alleviate the insertion shocks accompanying continuous indirect insertion.

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Mohan K. Ramamurthy and Frederick H. Carr

Abstract

The problem of four-dimensional data assimilation in the tropics has been studied using a limited-area primitive equation model. Of prime concern is the relative importance of different update variables and their impact on data assimilation. Five new experiments complement a set of ten previously reported experiments that investigate the feasibility of four-dimensional data assimilation in the monsoon region using only the wind observations. In addition to assessing the relative importance of update variables, the present study investigates the role of model physics in data assimilation.

The assimilation experiments are carried out for the onset vortex case of the 1979 Indian summer monsoon for which many special FOGE/MONEX datasets are available. The assimilation-forecast system for all of the experiments comprises a 12-h assimilation phase followed by a 24-h forecast period. In all experiments, updating is done via the Newtonian nudging approach which, in our previous study, was found to be more effective than other methods of updating.

It is found that at least for this dataset, the wind data were wore valuable than the temperatures. Although temperature assimilation alone had some unexpected positive results, it did not offer appreciable improvement over wind-only assimilations when the two variables were inserted together. On the other hand, a combination of wind and moisture data produced the most positive results. This confirms the importance of wind and moisture data in the tropics. Finally, it has been found that the incorporation of physical parameterizations during the assimilation period is important for a proper spinup of the model and its smooth transition into the forecast stage.

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Mohan K. Ramamurthy and I. M. Navon

Abstract

A conjugate-gradient variational blending technique, based on the method of direct minimization, has been developed and applied to the problem of initialization in a limited-area model in the summer monsoon region. The aim is to blend gridded winds from a high-resolution limited-area analysis with a lower-resolution global analysis for use in a limited-area model that uses the, global analyst for boundary conditions. The ability of the variational matching approach in successfully blending meteorological analyses of varying resolutions is demonstrated. Reasonable agreement is found between the blended analyses and the imposed weak constraints, together with an adequate rate of convergence in the unconstrained minimization procedure. The technique is tested on the 1979 onset vortex vortex case using data from the FGGE Summer MONEX campaign. The results indicate that the forecasts made from the variationally matched analyses show positive impact and perform better than those from the unblended analyses.

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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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Matthew S. Mayernik, Mohan K. Ramamurthy, and Robert M. Rauber
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