The Local Ensemble Transform Kalman Filter with the Weather Research and Forecasting Model: Experiments with Real Observations

Abstract

The local ensemble transform Kalman filter (LET-KF) is implemented with the Weather Research and Forecasting (WRF) model, and real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies. The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble members improves the analyses consistently.

Assessment of a Nonlinear Ensemble Transform Filter for High-Dimensional Data Assimilation

“…The most pragmatic solution consists of simply using an EnKF for the spinup phase and switching to the nonlinear filter afterward. Alternatively, more advanced adaptive inflation methods (e.g., Anderson 2009;Miyoshi 2011) could be adopted for the NETF or the spinup could be reduced by smoothing the estimates with future observations (Cosme et al 2010). Such a nonlinear smoother based on the NETF can be derived in analogy to the ensemble transform Kalman smoother (Kalnay and Yang 2010;Nerger et al 2014) and will be presented in an upcoming paper.…”

“…Over the past two decades, the EnKF has evolved to a robust scheme that is applicable to largescale systems with small ensemble sizes, such as in numerical weather prediction (e.g., Reich et al 2011;Miyoshi and Kunii 2012) or oceanography (e.g., Nerger et al 2007;Losa et al 2012). Deterministic variants such as the (local) ensemble transform Kalman filter [(L)ETKF, Bishop et al 2001;Hunt et al 2007] avoid sampling noise in the analysis step by applying a matrix square root transform.…”

The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

“…Indeed, this is why we have incorporated the adaptive multiplicative inflation introduced by MIYOSHI (2011) to better represent background uncertainties when the difference between the background and observation is large. MIYOSHI and KUNII (2012) also used the adaptive multiplicative inflation in real data assimilation using the LETKF implemented to the Weather Research and Forecast (WRF) model (SKAMAROCK et al 2005). The adaptive inflation is originally designed to balance between the departure of background from observation and ensemble spread.…”

Using Observations near the Poles in the AFES-LETKF Data Assimilation System

“…The LETKF can be implemented independently of the model, is suitable for ensemble forecasting and is efficient for parallel computing. Recently, the LETKF has been implemented with various models such as the global and regional atmosphere (e.g., Miyoshi and Aranami 2006;Miyoshi et al 2010;Miyoshi and Kunii 2012;Terasaki et al 2015), global and coastal ocean (Hoffman et al 2008;Penny et al 2013) and Martian atmosphere (Hoffman et al 2010;Greybush et al 2012).…”