Wave ensemble forecast system for tropical cyclones in the Australian region

Forecasting of waves under extreme conditions such as tropical cyclones is vitally important for many offshore industries, but there remain many challenges. For Northwest Western Australia (NW WA), wave forecasts issued by the Australian Bureau of Meteorology have previously been limited to products from deterministic operational wave models forced by deterministic atmospheric models. The wave models are run over global (resolution 1/4^°) and regional (resolution 1/10^°) domains with forecast ranges of +?7 and +?3 day respectively. Because of this relatively coarse resolution (both in the wave models and in the forcing fields), the accuracy of these products is limited under tropical cyclone conditions. Given this limited accuracy, a new ensemble-based wave forecasting system for the NW WA region has been developed. To achieve this, a new dedicated 8-km resolution grid was nested in the global wave model. Over this grid, the wave model is forced with winds from a bias-corrected European Centre for Medium Range Weather Forecast atmospheric ensemble that comprises 51 ensemble members to take into account the uncertainties in location, intensity and structure of a tropical cyclone system. A unique technique is used to select restart files for each wave ensemble member. The system is designed to operate in real time during the cyclone season providing +?10-day forecasts. This paper will describe the wave forecast components of this system and present the verification metrics and skill for specific events.     

Balance-aware covariance localisation for atmospheric and oceanic ensemble Kalman filters

Covariance localisation is used in many implementations of the ensemble Kalman filter (EnKF) but has been shown by Lorenc and by Kepert to significantly degrade the main balances in the atmosphere and ocean. Kepert recently introduced an improved form of localisation that reduced or eliminated this problem. This paper presents an extension to that approach, in which the background state is decomposed into balanced and unbalanced parts as part of the localisation. This new balance-aware localisation is shown to be a slight improvement on the earlier work of Kepert and a substantial improvement on the standard Schur-product localisation. Balance-aware localisation also enables the use of some sets of alternative analysis variables that do not work well with conventional localisation in the EnKF. It is shown using identical-twin experiments with a global spectral shallow-water model and no separate initialisation step that analysis to geopotential, streamfunction and velocity potential is slightly more accurate than is analysis to geopotential and the wind components. Analysis to unbalanced (instead of total) geopotential, streamfunction and velocity potential leads to slightly less accurate but significantly better balanced analyses than the other choices of analysis variables. If nonlinear normal modes initialisation is incorporated in the analysis cycling, then the conventional localisation becomes the most accurate method. However, initialisation may be undesirable or unavailable, and the comparison of system performance without localisation is useful since it helps improve understanding of the balance issues in EnKF-based assimilation systems.     

Hindcasting of tropical cyclone winds and waves

Ocean Dynamics - This paper describes a series of hindcast simulations of 17 tropical cyclones over the northwest shelf region of Australia. Tropical cyclone track and vortex details were obtained...     

The impact of the assimilation of scatterometer winds on surface wind and wave forecasts

Recent work has demonstrated that surface marine winds from the Bureau of Meteorology's operational Numerical Weather Prediction (NWP) systems are typically underestimated by 5 to 10%. This is likely to cause significant bias in modelled wave fields that are forced by these winds. A simple statistical adjustment of the wind components is shown to reduce the observed bias in Significant Wave Height considerably. The impact of increasing the vertical resolution of the NWP model and assimilating scatterometer data into the model is assessed by comparing the resulting forecast wind and waves to observations. It is found that, in general, the inclusion of scatterometer observations improves the accuracy of the surface wind forecasts. However, most of the improvement is shown to arise from the increased number of vertical levels in the atmospheric model, rather than directly from the use of the observations. When the wave model is forced with surface winds from the NWP model that includes scatterometer data, it is found that the scatterometer assimilation does not reduce the systematic bias in surface wave forecasts, but that the random errors are reduced.