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Daryl T. Kleist

Abstract

The assimilation of official advisory minimum sea level pressure observations has been developed and tested in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) to address forecaster concerns regarding some tropical systems being far too weak in operational Global Forecast System (GFS) analyses. The assimilation of these observations has been operational in the GFS since December 2009. Using the T574 version of the NCEP GFS model, it is demonstrated that the assimilation of these observations results in a substantial reduction in the initial intensity bias for tropical systems, resulting in improved track and intensity guidance for lead times out to 5 days.

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Daryl T. Kleist, David F. Parrish, John C. Derber, Russ Treadon, Wan-Shu Wu, and Stephen Lord

Abstract

At the National Centers for Environmental Prediction (NCEP), a new three-dimensional variational data assimilation (3DVAR) analysis system was implemented into the operational Global Data Assimilation System (GDAS) on 1 May 2007. The new analysis system, the Gridpoint Statistical Interpolation (GSI), replaced the Spectral Statistical Interpolation (SSI) 3DVAR system, which had been operational since 1991. The GSI was developed at the Environmental Modeling Center at NCEP as part of an effort to create a more unified, robust, and efficient analysis scheme. The key aspect of the GSI is that it formulates the analysis in model grid space, which allows for more flexibility in the application of the background error covariances and makes it straightforward for a single analysis system to be used across a broad range of applications, including both global and regional modeling systems and domains.

Due to the constraints of working with an operational system, the final GDAS package included many changes other than just a simple replacing of the SSI with the new GSI. The new GDAS package contained an upgrade to the Global Forecast System model, including a new vertical coordinate, as well as new features in the GSI that were never developed for the SSI. Some of these new features included changes to the observation selection, quality control, minimization algorithm, dynamic balance constraint, and assimilation of new observation types. The evaluation of the new system relative to the SSI-based system was performed for nearly an entire year of analyses and forecasts. The objective and subjective evaluations showed that the new package exhibited superior forecast performance relative to the old SSI-based system. The new system has been shown to improve forecast skill in the tropics and substantially reduce the short-term forecast error in the extratropics. This implementation has laid the groundwork for future scientific advancements in data assimilation at NCEP.

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Michael J. Brennan, Daryl T. Kleist, Kate Howard, and Sharanya J. Majumdar

Abstract

The impact of assimilating synoptic surveillance dropwindsonde data on the analysis and forecast of the structure and intensity of Tropical Storm Karen (2013) was examined. Data-denial experiments were conducted using the NCEP hybrid 3D ensemble–variational GSI and forecasts were made using the NCEP GFS model. The assimilation of dropwindsonde data resulted in a slightly more tilted tropical cyclone vortex, stronger vertical wind shear, and more upper-tropospheric dry air west of Karen in the initial conditions. These differences grew with time in the GFS forecasts, and resulted in a weaker and more sheared vortex by 24 h in the forecast that included the dropwindsonde data. After 24 h, the cyclone reintensified in the experiment where dropwindsonde data were excluded, likely because of moist processes in a favorable region for synoptic-scale ascent ahead of a baroclinic trough. In contrast, the forecast including the dropwindsonde data kept Karen weak and also did a better job forecasting the structure and track of Karen. These results suggest that differences in the analysis and short-term evolution of Karen and the environment due to the dropwindsonde data played a role in the longer-term structure and intensity of the cyclone, including the distribution and magnitude of associated diabatic heating. These results strongly suggest that a systematic study be undertaken to examine the impact of these data on tropical cyclone structure and intensity, since previous work has focused largely on the impact on track.

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