Tropical Cyclone Data Assimilation: Experiments with a Coupled Global-Limited-Area Analysis System
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This study investigates the benefits of employing a limited-area data assimilation (DA) system to enhance lower-resolution global analyses in the Northwest Pacific tropical cyclone (TC) basin. Numerical experiments are carried out with a global analysis system at horizontal resolution T62 and a limited-area analysis system at resolutions from 200 km to 36 km. The global and limited-area DA systems, which are both based on the Local Ensemble Transform Kalman Filter algorithm, are implemented using a unique configuration, in which the global DA system provides information about the large-scale analysis and background uncertainty to the limited-area DA system. In experiments that address the global-to-limited-area resolution ratio, the limited-area analyses of the storm locations for experiments in which the ratio is 1:2 are, on average, more accurate than those from the global analyses. Increasing the resolution of the limited-area system beyond 100 km adds little direct benefit to the analysis of position or intensity, although 48 km analyses reduce boundary effects of coupling the models and may benefit analyses in which observations with larger representativeness error are assimilated. Two factors contribute to the higher accuracy of the limited-area analyses. First, the limited-area system improves the accuracy of the location estimates for strong storms, which is introduced when the background is updated by the global assimilation. Second, it improves the accuracy of the background estimate of the storm locations for moderate and weak storms. Improvements in the steering flow analysis due to increased resolution are modest and short-lived in the forecasts. Limited-area track forecasts are more accurate, on average, than global forecasts, independently of the strength of the storms up to five days. This forecast improvement is due to the more accurate analysis of the initial position of storms and the better representation of the interactions between the storms and their immediate environment. Experiments that test the treatment and quality control (QC) methods of TC observations show that significant gainful improvements can be achieved in the analyses and forecasts of TCs when observations with large representativeness error are not discarded in the online QC procedure. These experiments examine the impact of assimilating TCVitals SLP, QuikSCAT 10 m wind components, and reconnaissance dropsondes alongside the conventional observations assimilated by NCEP in real time. Implementing a Combined method that clips the special TC observations via Huberization when multiple observation types are unavailable, and keeping the TCVital observation when other special observations are present, showed significant systematic improvements for strong and moderate storm analyses and forecasts.