Abstract
We develop an ensemble data assimilation system using the four-dimensional local ensemble transform kalman filter (LEKTF) for a global hydrostatic numerical weather prediction (NWP) model formulated on the cubed sphere. Forecast-analysis cycles run stably and thus provide newly updated initial states for the model to produce ensemble forecasts every 6 h. Performance of LETKF implemented to the global NWP model is verified using the ECMWF reanalysis data and conventional observations. Global mean values of bias and root mean square difference are significantly reduced by the data assimilation. Besides, statistics of forecast and analysis converge well as the forecast-analysis cycles are repeated. These results suggest that the combined system of LETKF and the global NWP formulated on the cubed sphere shows a promising performance for operational uses.
Highlights
Data assimilation is a key element in a numerical weather prediction system in that it provides an improved initial state for the forecast by obtaining an optimal analysis state from statistical treatments of available observations and current forecasts
MIYOSHI (2011) compared the local ensemble transform Kalman filter (LETKF) and the operational 4D-Var system implemented to the global model at the Japanese Meteorological Agency (JMA), and their results suggested that the LETKF has comparable performance to the 4D-Var
At the Korea Institute of Atmospheric Prediction Systems, we have applied the 4D-LETKF algorithm derived in HUNT et al (2007) for the development of data assimilation system coupled to a new global numerical weather prediction (NWP) model
Summary
Data assimilation is a key element in a numerical weather prediction system in that it provides an improved initial state for the forecast by obtaining an optimal analysis state from statistical treatments of available observations and current forecasts. MIYOSHI (2011) compared the LETKF and the operational 4D-Var system implemented to the global model at the Japanese Meteorological Agency (JMA), and their results suggested that the LETKF has comparable performance to the 4D-Var. At the Korea Institute of Atmospheric Prediction Systems (thereafter KIAPS), we have applied the 4D-LETKF algorithm derived in HUNT et al (2007) for the development of data assimilation system coupled to a new global NWP model. The forecast model that is being developed at the KIAPS (KIAPS Integrated Model: KIM) is using the spectral element method for discretization of governing equations and formulated on the cubed sphere (SADOURNY 1972) so that a singularity problem at poles can be avoided This leads to an unstructured grid system for the model (KIM) and we need to develop a tool for the observation operator in the 4D-LETKF framework. We provide a list of abbreviations in the Abbreviation group
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