Assimilating NOAA SST data into the BSH operational circulation model for the North and Baltic Seas: Inference about the data

Abstract

The operational ocean prediction model for the North and Baltic Seas of the German Maritime and Hydrographic Agency (BSH) is augmented with a multivariate data assimilation (DA) system. We report on the implementation and performance of the scheme which is based on ensemble forecasting. 2011. The quality of the predicted fields that were not assimilated (velocities, sea level and salinity) is preserved as is confirmed by independent in situ data.The results have required adjustment of the conditional data error statistics.The experiments conducted with different timing and frequency of data assimilation and variable forecasting periods show that the DA system corrects systematic model uncertainties and, due to memory to the corrections, improves prediction over periods of up to 5 days. The results also explicitly illustrate a lower quality of the AVHRR daytime product and reveal low informative influence of the data on the forecasting system when daytime SSTs are assimilated additionally to midnight observations.

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

“…Second-order exact filters aim at an analysis ensemble such that its mean, x a , and covariance, P a 5 [1/(m 2 1)]X 0 a X 0T a , exactly match some specified values. Both the ETKF and NETF can be described by the same square root filtering framework (Nerger et al 2012). The analysis ensemble, X a 5 X a 1 X 0 a , incorporates the observation by updating the prior mean and perturbations as follows:…”

“…Observation localization (OL; Hunt et al 2007) suppresses spurious correlations associated with distant locations and increases the rank of the analysis covariance by partitioning the state vector into subsets, the so-called local domains. Typically, a local domain contains all state variables at each grid point or in each vertical column (e.g., Houtekamer and Mitchell 1998;Losa et al 2012). Each local domain is updated independently using only a part of the global observation vector.…”

“…awi.de). In PDAF, numerous EnKF variants including the LETKF are already implemented and used for different applications (e.g., Losa et al 2012;Fournier et al 2013). Because of the similarity of the LETKF and NETF equations (see section 2), almost the same implementation is used.…”

“…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.…”

Assimilating NOAA SST data into BSH operational circulation model for the North and Baltic Seas: Part 2. Sensitivity of the forecast's skill to the prior model error statistics

“…It pertains not only the implementation of DA algorithms but also the approximation of the error statistics (Counillon et al, 2009;Janjić et al, 2011;Fu et al, 2011;Simon and Bertino, 2012;Lermusiaux, 2007), which in each case demands a study on its own. This is in full measure related to the development of a DA system for the operational forecasting model of the North and Baltic Seas run by the German Federal Maritime and Hydrographic Agency (BSH), which was described in Losa et al (2012). The DA system is based on Singular Evolutive Interpolated Kalman filter (SEIK, Pham, 2001;Pham et al, 1998).…”