Ensemble hindcasting of wind and wave conditions with WRF and WAVEWATCH III® driven by ERA5

Abstract

Abstract. When hindcasting wave fields of storm events with state-of-the-art wave models, the quality of the results strongly depends on the meteorological forcing dataset. The wave model will inherit the uncertainty of the atmospheric data, and additional discretization errors will be introduced due to a limited spatial and temporal resolution of the forcing data. In this study, we apply an atmospheric downscaling to (i) add regional details to the wind field, (ii) increase the temporal resolution of the wind fields, (iii) provide a more detailed representation of transient events such as storms and (iv) generate ensembles with perturbed atmospheric conditions, which allows for a flow-dependent and spatio-temporally variable uncertainty estimation. We test different strategies to generate an ensemble hindcast of a relatively strong storm event in February 2002 in the Baltic Sea. The Weather Research and Forecasting (WRF) model used for this purpose is driven by the ECMWF ERA5 reanalysis, and wind fields are passed to the third-generation wave model WAVEWATCH III®. A combination of initial conditions from the ERA5 ensemble of data assimilations and stochastic perturbations during runtime is identified as the most promising strategy. The final aim of the ensemble approach is to quantify the hindcast error, but this approach can also be used to generate alternative representations of historical extreme events to sample the recent climate and to increase the sample size for statistical studies, such as for civil engineering applications for coastal protection studies.

Swell Generation Under Extra‐Tropical Storms

“…This might result in biases in arrival times of swell events. The present analysis suggests that swell analysis will lead to a better representation of extreme surface wind speeds and hence also improve surface wave models (Cardone et al., 1996; Cavaleri, 2009; Durrant et al., 2013; Feng et al., 2006; P. A. Janssen & Bidlot, 2018; Osinski & Radtke, 2020; Ponce & Ocampo‐Torres, 1998; Stopa & Cheung, 2014).…”

“…The present analysis suggests that swell analysis will lead to a better representation of extreme surface wind speeds and hence also improve surface wave models (Cavaleri, 2009;Cardone et al, 1996;Ponce & Ocampo-Torres, 1998;Feng et al, 2006;Durrant et al, 2013;Stopa & Cheung, 2014;P. A. Janssen & Bidlot, 2018;Osinski & Radtke, 2020).…”

“…10.1029/2021JC017637 20 of 25 models (Cardone et al, 1996;Cavaleri, 2009;Durrant et al, 2013;Feng et al, 2006;P. A. Janssen & Bidlot, 2018;Osinski & Radtke, 2020;Ponce & Ocampo-Torres, 1998;Stopa & Cheung, 2014).…”

Numerical Simulation of Wind Wave Using Ensemble Forecast Wave Model: A Case Study of Typhoon Lingling

“…The wave forecast model to which the ensemble technique is applied provides prediction information through a field operation and is used for probabilistic wave forecast research for hazardous weather [9][10][11][12]. In the forecast model, although prediction uncertainty is affected by physical processes, initial conditions, and boundary conditions, it is known that the wave model results are directly affected by the prediction accuracy of sea surface winds by atmospheric models the most [13][14][15]. Although the result of the wave forecast model differs depending on the accuracy of the input sea surface wind, the prediction uncertainty cannot be determined by the deterministic forecast model, even though there is uncertainty in predicting the actual event, since the information is limited.…”

Model uncertainties of a storm and their influence on microplastics and sediment transport in the Baltic Sea

“…An application of ensemble forecasts to quantify the uncertainty in the morphological impact of storms was proposed by Baart et al (2011). Osinski et al (2016) applied a windstorm tracking algorithm to the operational ensemble forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) and demonstrated strong variation in the track as well as in the damage potential of the different realizations of historical storm events in the ensemble members. This range of uncertainty should also be reflected in the uncertainty in the transport of suspended matter.…”