We address the question of parametrizing the subgrid scales in simulations of geophysical flows by applying stochastic mode reduction to the one-dimensional stochastically forced shallow-water equations. The problem is formulated in physical space by defining resolved variables as local spatial averages over finite-volume cells and unresolved variables as corresponding residuals. Based on the assumption of a time-scale separation between the slow spatial averages and the fast residuals, the stochastic mode reduction procedure is used to obtain a low-resolution model for the spatial averages alone with local stochastic subgrid-scale parametrization coupling each resolved variable only to a few neighbouring cells. The closure improves the results of the low-resolution model and outperforms two purely empirical stochastic parametrizations. It is shown that the largest benefit is in the representation of the energy spectrum. By adjusting only a single coefficient (the strength of the noise) we observe that there is a potential for improving the performance of the parametrization, if additional tuning of the coefficients is performed. In addition, the scale-awareness of the parametrizations is studied.
Many subgrid-scale (SGS) parameterizations in climate models contain empirical parameters and are thus data dependent. In particular, it is not guaranteed that the SGS parameterization still helps the model to produce the correct climate projection in the presence of an external perturbation (e.g., because of climate change). Therefore, a climate dependence of tuning parameters is proposed, using the fluctuation–dissipation theorem (FDT). The FDT provides an estimation of the changes in the statistics of a system, caused by a small external forcing. These estimations are then used to update the SGS parameterization. This procedure is tested for a toy atmosphere given by a quasigeostrophic three-layer model (QG3LM). We construct a low-order climate model for this toy atmosphere, based on a reduced number of its empirical orthogonal functions (EOFs), equipped with either an empirical deterministic or an empirical stochastic SGS parameterization. External forcings are considered that are either a local anomalous heat source in the extratropics or a global dynamical forcing represented by individual EOF patterns. A quasi-Gaussian variant of the FDT is able to successfully update the SGS parameterization leading to an improvement in both amplitude and correlation between the low-order climate model and the QG3LM, in case of a perturbed system. The stochastic closure exhibits nearly no improvement compared to the deterministic parameterization. The application of a more sophisticated non-Gaussian FDT algorithm (i.e., the blended short-time/quasi-Gaussian FDT) yields only marginal improvement over the simple quasi-Gaussian FDT.
<p>A primary goal of the upcoming Rapid Update Cycle (RUC) at the German weather service is to close the gap between nowcasts (NWC) and numerical weatherp prediction (NWP) by adding cloud- and precipitation-related observations to the operational data assimilation. However, the NWP and NWC worlds differ not just by timescale but also more fundamentally in their approach: &#160;while NWCs deal with individual convective cells, i.e. coherent objects whose positions and physical features are tracked, NWP systems and their associated data assimilation deal with gridded information, i.e. pixels of data.</p><p>To bridge these two worlds, we have developed a unique aproach of assimilating nowcast objects into an NWP model. The crux of the idea is to identify objects first, and then map the individual physical features of each object onto a regular model grid. In this talk we explore two ways of implementing this idea: The first defines objects simply by whether or not the observed radar reflectivity exceeds a given threshold, and then assimilates the gridded fraction of gridpoints that meet this criterion within a given spatial scale. The second approach defines objects using a more complex cell identification and tracking algorithm, and then grids the associated cell attributes (e.g. cell area) based on the distance of each gridpoint from the object centroid. We then go on to show how both of these approaches allow us to assimilate object-based information into an ensemble filter, focusing in particular on the difficulties of such an unconventional observation operator, as well as the possible complimentarity to conventional radar reflectivity assimilation.</p>
<p>At Deutscher Wetterdienst (DWD), the SINFONY project has been set up to develop a seamless ensemble prediction system for convective-scale forecasting with forecast ranges of up to 12 hours. It combines Nowcasting (NWC) techniques with numerical weather prediction (NWP) in a seamless way. So far NWC and NWP run on two different IT-Infrastructure levels. Due to the data transfer between both infrastructures, this separation slows down SINFONY, makes it complex and prone to disturbances. These disadvantages are solved by applying the interconnected part of the SINFONY on one single architecture using a Docker Container.</p> <p>With this aim in view a Docker-Container of the respective NWC components is created and executed on the infrastructure of NWP, the high performance linux computing cluster (HPC) of DWD. In test applications we already observed a speed up of roughly 20% by using the Container on the HPC-cluster instead of using NWC-Tools on the initial NWC IT-Architecture. The Container is already implemented in DWD&#8217;s experimental tool BACY for the assimilation cycle.</p> <p>A major innovation of SINFONY is the rapid update cycle (RUC), an hourly refreshing NWP procedure with a Forecast range of 8 hours, which will be extended to 12 hours soon. The container will be implemented to the RUC and used for the subsequent combination of NWP and NWC forecasts.</p> <p>In the presentation I will explain what a container is and discuss opportunities and risks of this technology. I will introduce how building the Container is integrated to the CICD procedures at DWD, how and where the Container is implemented to BACY and discuss latest results for the implementation to the RUC.</p>
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