Tropical Cyclone Data Assimilation: Experiments with a Coupled Global-Limited-Area Analysis System
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Date
2014-04-22
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Abstract
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.
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Keywords
Tropical cyclone, data assimilation, quality control, Sinlaku, numerical weather prediction, ensemble