Dynamical seasonal forecasts are afflicted with biases, including seasonal ensemble precipitation forecasts from the new ECMWF seasonal forecast system 5 (SEAS5). In this study, biases have been corrected using empirical quantile mapping (EQM) bias correction (BC). We bias correct SEAS5 24-h rainfall accumulations at seven monthly lead times over the period 1981–2010 in Java, Indonesia. For the observations, we have used a new high-resolution (0.25°) land-only gridded rainfall dataset [Southeast Asia observations (SA-OBS)]. A comparative verification of both raw and bias-corrected reforecasts is performed using several verification metrics. In this verification, the daily rainfall data were aggregated to monthly accumulated rainfall. We focus on July, August, and September because these are agriculturally important months; if the rainfall accumulation exceeds 100 mm, farmers may decide to grow a third rice crop. For these months, the first 2-month lead times show improved and mostly positive continuous ranked probability skill scores after BC. According to the Brier skill score (BSS), the BC reforecasts improve upon the raw reforecasts for the lower precipitation thresholds at the 1-month lead time. Reliability diagrams show that the BC reforecasts have good reliability for events exceeding the agriculturally relevant 100-mm threshold. A cost/loss analysis, comparing the potential economic value of the raw and BC reforecasts for this same threshold, shows that the value of the BC reforecasts is larger than that of the raw ones, and that the BC reforecasts have value for a wider range of users at 1- to 7-month lead times.

Abstract. Mediterranean Tropical-Like Cyclones, called “medicanes”, present a multiscale nature and their track and intensity have been recognized as highly sensitive to large-scale atmospheric forcing and to diabatic heating as represented by the physical parameterizations in numerical weather prediction. Here, we analyse the structure and investigate the predictability of medicanes with the aid of the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) ensemble forecasting system with 25 perturbed members at 9 km horizontal resolution (compared to the 16 km operational resolution). The IFS ensemble system includes the representation of initial uncertainties from the ensemble data assimilation (EDA) and a recently developed uncertainty representation of the model physics with perturbed parameters (Stochastically Perturbed Parameterizations, SPP). The focus is on three medicanes, Ianos, Zorbas and Trixie that have been among the strongest in recent years. In particular, we have carried out separate ensemble simulations with initial perturbations, full physics SPP, and with a reduced set of SPP, where only convection is perturbed to highlight the convective nature of medicanes. It is found that compared to the operational analysis and satellite rainfall data, the forecasts reproduce the tropical-like features of these cyclones. Furthermore, the SPP simulations compare to the initial condition perturbation ensemble, in terms of tracking, intensity, precipitation and more generally in terms of ensemble skill and spread. Moreover, the study confirms that similar processes are at play in the development of the investigated three medicanes, in that the predictability of these cyclones is linked not only to the prediction of the precursor events (namely the deep cut-off low) but also to the interaction of the upper-level dynamically driven Potential Vorticity (PV) streamer with the tropospheric PV anomaly that is driven by surface heating and stratiform and convective condensational heating.

<p>Analogies between similar past forecasts, measurements, or analyses are a potentially useful tool when the training dataset is long enough, thus enabling an adequate identification of true analogs. Reducing the number of degrees of freedom in the matching procedure makes the analog-based method an excellent candidate for point-based post-processing. However, accurate forecasts at remote locations are used to drive many user-specific applications (e.g., road temperature forecasts along an entire roadway or wind speed for windfarms). For that reason, besides the point-based post-processing for the measuring sites, there is also an increasing demand for gridded (2D) products. The latter is a direct motivation for the adaptation of analog-based method to produce gridded output based on an analysis.</p><p>In this work, the control member of the ECMWF ensemble forecast is used as a raw forecast as input to the analog method, whereas the gridded INCA analysis fields are used similar to the observations in the point-based analog approach. All experiments use wind speed and direction variables as predictors, normalized by standard deviation. The domain is defined by ECMWF resolution and INCA domain size, covering Austria. The first experiment (EX1) is based on the simplest transfer from point-based to gridded products: treating every grid point as an independent location. Alternatively, an average error on the entire field is used to choose the most similar histor