Simulation of model uncertainty using multidimensional Langevin processes in the NOAA Unified Forecast System (UFS)

<p>Numerical weather prediction (NWP) systems nowadays need to be capable of providing not only high-quality deterministic forecasts, but also information about forecast uncertainty.&#160; The ensemble forecast technique is commonly used to provide an estimation of forecast uncertainty.&#160; Since a great deal of the forecast uncertainty comes from dynamical and physical processes not resolved or explicitly represented numerically, there is a need to correctly quantify and simulate the uncertainty associated with these processes as required by the ensemble forecast technique.</p><p>To address this need, we have developed a new stochastic physics scheme for simulating the uncertainty in parameterizations in the NOAA Unified Forecast System (UFS).&#160; This scheme is derived from the connection in mathematical physics between the Mori-Zwanzig formalism and multidimensional Langevin processes.&#160; It follows the correspondence principle, a philosophical guideline for new theory development, such that it can be shown that the previously implemented stochastic uncertainty quantification schemes in the UFS are particular cases of this scheme.&#160; We will show how we have used this scheme to simulate uncertainty at the process level of unresolved dynamics and physics in the UFS.&#160; We will also present a preliminary performance comparison of previously-implemented stochastic physics schemes with the newly-developed process-level scheme in the UFS medium-range ensemble prediction</p>