Characterization of the model error in ICON-D2-EPS using a flow-dependent partial SDE

Abstract

<p>The growing share of renewable energy in power generation increases the impact of the weather on the stability of the power grid.<br>Especially prior to severe weather events, not only high-quality weather forecasts but also information about forecast uncertainties is needed by the transmission system operators (TSOs) to prepare stability provisions. <br>To this end, in the research project <em>gridcast</em> the German Meteorological Service (DWD) aims at an improved representation of the inherent model error in its recently introduced convection-permitting ensemble prediction system ICON-D2-EPS.</p><p>We describe the model error using the following stochastic ansatz: The tendency equations for a set of relevant variables for power prediction like temperature, and zonal and meridional winds are extended by an additive tendency error approximated by the solution of a partial stochastic differential equation (SDE). This SDE consists – similar to an Ornstein-Uhlenbeck equation – of a damping term and a random field. However, the SDE is augmented with an additional diffusion term that ensures spatial correlations.<br>Each of the three terms has a strength parameter that is assumed to be a function of (possibly different) flow-dependent predictor variables. Hence the relative importance of the three terms varies in space and time according to the respective weather conditions.<br>The functional form of the parameters can be approximated from past estimates of the model error based on ICON-D2 ensemble forecasts.</p><p>We present theoretical properties of the SDE and motivate its choice as representation of the model error. Furthermore, we investigate a method to determine the parameters of the SDE and apply this method to the operational ICON-D2-EPS at DWD for the model error of relevant forecast variables.<br>First numerical results along the development of the scheme are presented.</p>