Diagnosis of systematic analysis increments by using normal modes

Abstract

This paper applies the normal-mode functions for the three-dimensional diagnosis of systematic analysis increments in the operational systems of ECMWF and NCEP, in the NCEP/NCAR reanalyses and in the ensemble data assimilation system DART/CAM which is developed at NCAR. Non-zero systematic increments are interpreted as the analysis system bias. The main region of tropospheric biases in all systems is the Tropics; most of the large-scale tropical bias resides in the unbalanced (inertio-gravity) motion with the eastward-propagating component being dominant in some datasets. The magnitudes of tropospheric wind-field biases in July 2007 were in the range between 1 m s −1 and 2 m s −1 , illustrating the importance of diagnosing analysis systems in the Tropics where magnitudes of the large-scale variability are of the same order. The systematic increments integrated over the whole models' atmosphere appear predominantly balanced; this balance is associated with biases in the zonally averaged balanced state and, in case of ECMWF and NCEP, with the biases at the model top levels. Spectra of analysis increments averaged over one month show that, on average, 20% to 45% of total energy in increment fields belongs to the inertio-gravity motions.

The intricacies of identifying equatorial waves

“…The projection can be done using global fields in $$$ \sigma $$$‐ (e.g., Žagar et al ., 2009b; Blaauw and Žagar, 2018) or $$$ p $$$‐coordinates (Castanheira and Marques, 2015; Marques and Castanheira, 2018). It can be applied to full fields or anomalies; for example, with respect to the mean seasonal cycle or differences between analyses and forecasts (e.g., Žagar et al ., 2010; 2016).…”

CAFE60v1: A 60-year large ensemble climate reanalysis. Part I: System design, model configuration and data assimilation.

“…Localization removes spurious long-range correlations present in the ensemble estimated background error covariance, and has also been shown to improve the consistency between ensemble spread and error. Disadvantages to horizontal covariance localization have been reported with regard to weather prediction, where localization can remove information due to large-scale flow dependent inhomogeneities, creating potential imbalances (Kepert 2009;Zagar et al 2010); however, the large length scales used in CAFE60v1 mitigate this effect. When only sparsely distributed observational data that are heterogeneous in both space and time are available, such as is the case for the ocean in the early period of the 1960s and prior to Argo, the specified radius of influence can often lead to little observational influence on remote but data-sparse regions.…”