Adaptive Observation Strategies with the Local Ensemble Transform Kalman Filter

Adaptive observation strategies (AOS) aim to improve forecasts by adding additional observations at a few locations that have no standard observations. Lorenz and Emanuel (1998) designed experiments to evaluate different adaptive strategies with Lorenz 40-variable model. Routine observations are observed over “land” (grid points from 21 to 40) every 6 hours. One adaptive point is chosen from one of the points over “ocean” (grid points from 1 to 20) every 6 hours. They found that the performance of adaptive methods (multiple breeding, multiple replication, singular vector) is better than random choice. The best result was obtained from multiple replication (a variation of multiple breeding with perturbed observations). With 1024 ensemble members Ensemble Kalman Filter (EnKF) assimilation scheme, Hansen and Smith (2000) got comparable results from singular vector adaptive observation strategy (SVAOS) as with the other methods investigated by Lorenz and Emanuel (1998), who had concluded that SVAOS is inferior to the other methods. Trevisan and Uboldi (2004) used the most unstable vector of the observation-analysis-forecast (OAF) system (obtained by breeding) to choose the adaptive point and to define the analysis increment. Their average analysis errors over the ocean were reduced by 50% compared with Lorenz and Emanuel (1998).

Here we explore the use of an optimal adaptive method that could be directly derived from the output of the Local Ensemble Kalman Filter (LEKF) (Ott et al, 2004). LEKF is a square root EnKF that does data assimilation over small local patches around each grid point and is thus very parallel and efficient. In these experiments we use only 15 ensemble members, and use different measures of ensemble uncertainty to choose the point of maximum uncertainty where the next adaptive observation should be made. The measures that we use are ensemble forecast spread, largest eigenvalue of the forecast error covariance, and the trace of the forecast error covariance. The simplest strategy, choosing the point at which the ensemble forecast spread is maximum gives the best results, which are significantly better than those obtained in previous studies. A strategy based on minimizing the analysis error at the targeting time, as in the Ensemble Transform Kalman Filter (ETKF) approach of Bishop et al. (2001) gives the same results as the LEKF ensemble spread.

We will further explore these different adaptive strategies on the SPEEDY model (Molteni, 2003), which is a primitive equation global model with a simple but complete set of physical parameterizations. We will compare the LEKF-based strategies with the system used at NCEP for operational adaptive observations (ETKF), evaluating these strategies in selecting the optimal flight routes rather than single targets. With this realistic model we will explore the use of interactive adaptive observation strategies for satellite observing systems including “observing 10% of the area to obtain 90% of the impact” as required for future lidar wind instruments. The best strategies to follow for tropical and extratropical adaptive observations, which may be quite different, will be also explored.