Using multi-decadal simulations, we investigate the relationship between the quasi-biennial oscillation (QBO) and the Madden-Julian oscillation (MJO) in the Global Ocean Mixed Layer configuration of the Met Office Unified Model (MetUM-GOML1) at two horizontal resolutions (approximately 200 and 90 km at the equator). MetUM-GOML1 produces a weak and insignificant correlation between QBO winds and mean MJO amplitude in boreal winter, in contrast to the significant anti-correlation in reanalysis. While reanalysis shows the easterly QBO favors stronger Maritime Continent MJO activity, MetUM-GOML1 displays stronger West Pacific MJO activity. The biased QBO-MJO relationship in MetUM-GOML1 may be due to weak QBO-induced temperature anomalies in the tropical tropopause layer, or to errors in MJO vertical structure.
Skillful weather forecasting on sub-seasonal timescales is important to enable users to make cost-effective decisions. Forecast skill can be expected to be mediated by the prediction of atmospheric flow patterns, often known as weather regimes, over the relevant region. Here, we show how the Grosswetterlagen (GWL), a set of 29 European weather regimes, can be modulated by the extra-tropical teleconnection from the Madden-Julian Oscillation (MJO). Together, these GWL regimes represent the large-scale flow characteristics observed in the four North Atlantic-European classical weather regimes (NAE-CWRs), while individually capturing synoptic scale flow details. By matching each GWL regime to the nearest NAE-CWR, we reveal GWL regimes which occur during the transition stages between the NAE-CWRs and show the importance of capturing the added synoptic detail of GWL regimes when determining their teleconnection pattern from the MJO. The occurrence probabilities of certain GWL regimes are significantly changed 10-15 days after certain MJO phases, exhibiting teleconnection patterns similar to their NAE-CWR matches but often with larger occurrence anomalies, over fewer consecutive MJO phases. These changes in occurrence probabilities are likely related to MJO-induced changes in the persistence and transition probabilities. Other GWL regimes are not significantly influenced by the MJO. These findings demonstrate how the MJO can modify the preferred evolution of the NAE atmospheric flow, which is important for sub-seasonal weather forecasting.
The background error covariance matrix plays a vital role in any data assimilation system. Proper specification, which is determined by the forecast system set‐up, is often required. Previous studies have investigated its relevance in various global and regional numerical weather prediction (NWP) systems; however, very few have explored it in tropical NWP systems. Here, we present and evaluate the structures of the background error covariance matrix for a tropical convective‐scale NWP system. A total of 12 background error covariance matrices are modelled using differences between pairs of forecasts of different lengths but valid at the same time, based on the application of the vertical‐first and horizontal‐first transform order formulations on six permutations of the training data. Through pseudo‐single observation tests, we extract and test the sensitivity of their structures to the training data period (seasons), forecast lag and transform order. The structures typically exhibit more dependence on forecast lag and transform order; horizontal‐first transform order covariances had structures with shorter horizontal length‐scales for wind and larger wind background error standard deviations. We also note that some covariances had horizontal and vertical structures with stronger mass–wind coupling, closely resembling an equatorial Kelvin wave. To assess the performance of each of the covariances, 12 month‐long data assimilation trials in May 2018 (characterised by frequent occurrences of localised thunderstorm events) are performed. We show improved short‐range precipitation forecasts in trials using some of the covariances compared to the current operational covariance. These covariances generally have structures with weak mass–wind coupling, shorter horizontal length‐scales for wind and larger wind background error standard deviations, compared to other covariances which led to poorer forecasts. These may be desirable factors when modelling the background error covariance matrix for tropical convective‐scale data assimilation systems.
Abstract. Hybrid ensemble-variational data assimilation (DA) methods have gained significant traction in recent years. These methods aim to alleviate the limitations and maximise the advantages offered by ensemble or variational methods. Most existing hybrid applications focus on the mid-latitudinal context; almost none have explored its benefits in the tropical context. In this article, hybrid ensemble-variational DA is introduced to a tropical configuration of a simplified non-hydrostatic convective-scale fluid dynamics model (the ABC model, named after its three key parameters: the pure gravity wave frequency A, the controller of the acoustic wave speed B, and the constant of proportionality between pressure and density perturbations C), and its existing variational framework, the ABC-DA system. The hybrid ensemble-variational DA algorithm is developed based on the alpha control variable approach, often used in numerical weather prediction. Aspects of the algorithm such as localisation (used to mitigate sampling error caused by finite ensemble sizes) and weighting parameters (used to weight the ensemble and climatological contributions to the background error covariance matrix) are implemented. To produce the flow-dependent error modes (ensemble perturbations) for the ensemble-variational DA algorithm, an ensemble system is also designed for the ABC model which is run alongside the hybrid DA system. A random field perturbations method is used to generate an initial ensemble which is then propagated using the ensemble bred vectors method. This setup allows the ensemble to be centred on the hybrid control analysis. Visualisation software has been developed to focus on the diagnosis of the ensemble system. To demonstrate the hybrid ensemble-variational DA in the ABC-DA system, sensitivity tests using observing system simulation experiments are conducted within a tropical framework. A 30-member ensemble was used to generate the error modes for the experiments. In general, the best performing configuration (with respect to the “truth”) for the hybrid ensemble-variational DA system used an 80%/20% weighting on the ensemble-derived/climatological background error covariance matrix contributions. For the horizontal wind variables though, full weight on the ensemble-derived background error covariance matrix (100%/0%) resulted in the smallest cycle-averaged analysis root mean square errors, mainly due to large errors in the meridional wind field when contributions from the climatological background error covariance matrix were involved, possibly related to a sub-optimal background error covariance model. The ensemble bred vectors method propagated a healthy-looking DA-centred ensemble without bimodalities or evidence of filter collapse. The ensemble was under-dispersive for some variables but for others, the ensemble spread approximately matched the corresponding root mean square errors. Reducing the number of ensemble members led to slightly larger errors across all variables due to the introduction of larger sampling errors into the system.
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