In the framework of the flagship EU Destination Earth initiative, the On-Demand Extremes digital twin project aims at delivering an event- and user-driven weather-induced engine for selected impact sectors. The core of the project lies in the design and development of an operational capability for an on-demand workflow on Europe's fastest supercomputers, efficiently exploiting large data volumes coming from observations and hectometric limited-area numerical weather prediction (NWP) simulations for the benefit of a variety of users. Within the project, air quality (AQ) is one of the chosen impact sectors to highlight the DT engine capabilities.

Poor air quality is a major cause of premature death and disease, and the single largest environmental health risk in Europe. AQ models are routinely run at meteorological and environmental agencies to identify contributions to air quality problems and assist in the design of effective strategies to reduce harmful air pollutants.

The talk will showcase the work done during the first phase of the project in the design and development of high-resolution HARMONIE-AROME NWP experiments within the Extremes DT infrastructure Prototype whose output is given as input for AQ model runs. Initially focusing on a historical summer heatwave case, the presentation will highlight the impact of higher resolution and other configuration parameters as well as the steps taken to deliver the NWP output variables to the AQ model developers on the target high-performance computing facilities.

Example use cases for air quality end users will be shown and next steps will be discussed in the context of the second phase of the project, which aims at delivering an integrated end-to-end, on-demand NWP-AQ workflow.

How to cite: Benacchio, T., Thorsen, S. B., Baordo, F., and Yang, X.: HARMONIE-AROME high-resolution experiments in the Destination Earth On-demand Extremes digital twin: an air quality models and end users perspective, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-550, https://doi.org/10.5194/ems2024-550, 2024.

This presentation introduces the limited-area version of the Korean Integrated Model (KIM). KIM is a global atmospheric weather prediction model which has been operational in producing medium-range forecasts for the Korea Meteorological Administration since 2019. In efforts to develop a high-resolution short-range forecasting system for the Korean Peninsula and East Asia, we developed a model framework capable of running KIM as a limited-area model (LAM). The LAM version shares model components with the global KIM model, including the cubed-sphere grid system, spectral element method solver, non-hydrostatic dynamical core, and scale-aware physics package; with the distinction that the model domain is confined to the first panel of the cubed-sphere. The related modules to accommodate this configuration in the model framework has been newly developed accordingly. Utilizing the Schmidt transformation of the cubed-sphere enables adjustment of the size and center position of the first panel through stretching and rotating the cube. The functionality of the LAM framework is demonstrated through idealized dynamics-only test runs nesting global KIM simulations. In real-case dynamical downscaling experiments refining 100 km global simulations to 25 km, the LAM exhibits stable simulations beyond 10 days of integration, closely aligning with global simulation results. Minimal generation of numerical spurious waves is observed, even without utilizing Newtonian-relaxations toward the global simulation near the lateral boundaries, suggesting the effectiveness of dynamical cores with spectral element solvers for dynamical downscaling. The presentation also addresses challenges related to high computational costs, sensitivities to varying domain size and resolution, and the model's applicability to operational kilometer-scale short-range weather prediction.

How to cite: Cho, H., Kim, J., Noh, I., and Jang, J.: Limited-Area Version of the Korea Integrated Model for the High-Resolution Regional Forecast System, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-586, https://doi.org/10.5194/ems2024-586, 2024.

The model’s mean orography acts as the boundary condition for the model dynamics and the drag from resolved orographic gravity waves can have a significant impact on the large-scale atmospheric circulation in weather and climate models. As we approach km-scale horizontal resolutions in global models, more of the orographic spectrum and the impact of orography becomes resolved. Benefits of increased resolution, for example better prediction of orographic rain, can only be harvested if the resolution of the mean orography field is also increased. However, even at kilometre scale, some of the orographic variance will not be represented on the model grid and must be parameterised.

Initial simulations for Destination Earth’s global Digital Twin at 4.4 km horizontal resolution have shown a negative wind bias over Eastern Asia and increasing forecast error compared to the operational 9 km model, indicating that the higher resolution of resolved orography causes additional small horizontal-scale orographic gravity waves breaking above the mid-latitude jet, which affects the global circulation. Therefore, we focused on improving the mean orography processing and sub-grid scale orography parameterisations with the aim to find a scale-independent formulation that maintains good forecast skill at operational model resolutions and profits from increased resolution at kilometre scale.

The processing of the source data was simplified and harmonised across resolutions using conservative interpolation of the source dataset.  A new source dataset for surface elevation with 30 m resolution is used. A small increase to the spectral filtering of mean orography showed an improvement in forecast skill over the Tibetan plateau, and the updated fields describing sub-grid orographic features yield a more consistent behaviour across different resolutions. However, the sub-grid orography has significantly changed, and the parameters of the orographic parameterisation schemes needed to be optimised again considering an appropriate formulation across resolutions. We present a new approach to this parameter re-tuning, using Bayesian parameter optimisation, which enables an efficient workflow for simultaneously optimising several interdependent parameters.

How to cite: Sützl, B., van Niekerk, A., Beljaars, A., Maciel, P., Choulga, M., Janoušek, M., Vannière, B., Forbes, R., Balsamo, G., Sandu, I., and Dueben, P.: Optimising orography for global high-resolution simulations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-259, https://doi.org/10.5194/ems2024-259, 2024.

Atmospheric flows display phenomena on a very wide range of spatial scales that interact with each other. Many strongly localized features, such as complex orography, can only be modelled correctly if a very high spatial resolution is employed, especially in the lower troposphere, while larger scale features such as high/low pressure systems and stratospheric flows can be adequately resolved on much coarser meshes. The insufficient resolution of orographic features is compensated in numerical weather predictions (NWP) and climate models by subgrid-scale orographic drag parameterizations, which are essential for an accurate description of atmospheric flows with models using feasible resolutions. The interplay between resolved and parameterized orographic effects is critical, since many operational models employ resolutions in the so-called 'grey zone', for which some orographic effects are well resolved while others still require parameterization. Global simulations without drag parameterization have shown that the increase in forecast skill for increasing atmospheric resolution was mainly due to the improved representation of the orography. 

Because of these considerations, NWP is an apparently ideal framework for adaptive numerical approaches. However, mesh adaptation strategies have only slowly found their way into the NWP literature, due to limitations of earlier numerical methods, concerns about the accuracy of the representation of atmospheric wave phenomena for variable resolution meshes, and the complexity of an efficient parallel implementation for non-uniform or adaptive meshes. We present a quantitative assessment of the static local mesh refinement capabilities of a recently proposed IMEX-DG method [1] to a number of benchmarks for atmospheric flows over both idealized and real orography. We show that simulations with adaptive meshes around orography can increase the accuracy of the local flow description without affecting the larger scales, thereby significantly reducing the overall number of degrees of freedom compared to uniform mesh simulations [2, 3]. Importantly, no spurious reflections arise at internal boundaries separating mesh regions with different resolution and correct values for the momentum flux are retrieved. Both on idealised benchmarks and on test cases over real orographic profiles, simulations using non-conforming meshes correctly reproduce the larger scale, far-field response with meshes that are relatively coarse over most of the domain. This supports the idea that locally refined meshes can be an effective tool to reduce the dependence of NWP on parametrizations of orographic effects.

[1] G. Orlando, T. Benacchio, and L. Bonaventura. “An IMEX-DG solver for atmospheric dynamics simulations with adaptive mesh refinement”. Journal of Computational and Applied Mathematics 427 (2023), p. 115124.

[2] G. Orlando, T. Benacchio, and L. Bonaventura. "Robust and accurate simulations of flows over orography using non-conforming meshes". 2024. arXiv:2402.07759.

[3] G. Orlando, T. Benacchio, and L. Bonaventura. "Impact of curved elements for flows over orography with a Discontinuous Galerkin scheme". 2024. arXiv: 2404.09319.

How to cite: Orlando, G., Benacchio, T., and Bonaventura, L.: Robust and accurate simulations of flows over orography using non-conforming variable resolution meshes, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-301, https://doi.org/10.5194/ems2024-301, 2024.

Numerical simulation of the atmosphere plays a crucial role in understanding and anticipating extreme weather events. Steady advances in computing power have made it possible to increase the complexity and range of scales represented by numerical simulation. However, the advent of heterogeneous computing architectures with multi-core central processing units (CPUs) and graphics processing units (GPUs) requires atmospheric codes to be adapted.

Here we describe the adaptation of the Meso-NH non-hydrostatic mesoscale atmospheric model of the French research community. The Fortran code is ported to GPUs by including OpenACC directives to the most computationally expensive parts of the code. This approach allows running the same code on CPUs and on hybrid CPUs/GPUs architectures. To guarantee the accuracy of the port and the absence of bugs, measures have been taken to ensure bit-to-bit reproducibility between executions on these two architectures. A critical point lies in the atmospheric pressure solver, which requires the inversion of an elliptic equation. A geometric multigrid inversion algorithm is integrated, because the fast Fourier transforms approach used in the original version of the code becomes inefficient with a high number of GPUs. Currently, the code runs on different GPU-NVIDIA and GPU-AMD platforms and scales efficiently up to at least 1,024 GPUs, achieving a 3x increase in energy efficiency compared to CPUs only.

First scientific applications focus on the simulation of extreme weather events across scales as part of a Grand Challenge GPU pilot project on the AMD-based Adastra supercomputer of GENCI (same architecture as Frontier, the 1^st exascale supercomputer). Three representative storms are simulated: a North Atlantic windstorm associated with a mid-latitude cyclone, a Mediterranean convective storm characterized as a derecho, and a mesoscale convective system over the Amazon rainforest. Representation of the North Atlantic storm requires downscaling from the synoptic cyclone scale (>100 km) down to local wind gust formation (<1 km). Inversely, the representation of the Amazon storm requires upscaling from the local triggering of convective cells (<1 km), which organize and maintain the system at the mesoscale (>100 km). Finally, the Mediterranean storm involves both up- and downscaling. We show that Meso-NH successfully represents the cascade of scales for the three representative storms for horizontal grid spacing down to 100 m and grid size up to 4096x4096x128 points.

Porting Meso-NH to GPUs opens up new opportunities to simulate extreme weather events across scales and paves the way for future European exascale supercomputers.

How to cite: Escobar, J., Wautelet, P., Pianezze, J., Chaboureau, J.-P., Dauhut, T., Barthe, C., Brumer, S., and Pantillon, F.: Porting the Meso-NH atmospheric model to GPU architectures allows simulating extreme weather events across scales, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-726, https://doi.org/10.5194/ems2024-726, 2024.

NOAA is undertaking the development of a Seasonal Forecast System version 1 (SFSv1) to replace the current Climate Forecast System version 2 (CFSv2) due to its limitations and the need for enhanced forecast accuracy. The SFS's development is primarily based on the capabilities of the NOAA Global Ensemble Forecast System (GEFSv13) which utilizes a non-hydrostatic Finite-volume dynamical core and is tailored for extended-range forecasting at a 25 km resolution. SFSv1, intended to operate at approximately 100 km, with a target resolution of about 50 km, requires careful examination to ensure the effectiveness of the existing physics suite and dynamics at these resolutions. 

Within the resolution range of 50 km to 100 km, the hydrostatic assumption remains the hydrostatic assumption retains its viability, offering reduced computational costs and intimating that the non-hydrostatic alternative may be superfluous. To comprehensively evaluate these alternatives, sensitivity experiments with both AMIP-type and fully coupled experiments, have been conducted. Adjustments to the schemes and parameters governing artificial dissipation, horizontal advection, and vertical remapping have been made to align with the hydrostatic option.

The verification of atmosphere-only sensitivity experiments indicates that the hydrostatic option exhibits comparable performance compared to non-hydrostatic in terms of the prediction of large-scale environmental patterns. It also shows superior performance in predicting the quasi-biennial zonal wind oscillation at the stratosphere. However, further model refinement is deemed necessary, particularly when coupled with the ocean model to mitigate cold SST biases over the NINO3.4 region. Ongoing investigations are also addressing other issues arising from the transition to the hydrostatic option for SFS.

How to cite: Zhou, X. and Yang, F.: Hydrostatic Seasonal Forecast System Development within the Unified Forecast System at NOAA, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-846, https://doi.org/10.5194/ems2024-846, 2024.

Several studies have shown the added value of convection-permitting climate modelling, in particular regarding extreme events, including heat waves and high precipitation events (Lind et al., 2020, Fosser et al., 2024). HCLIM consortium develops a high resolution climate model, with a non-hydrostatic version using AROME physics. Current evaluation has shown that HCLIM is a robust tool able to add value when reproducing the behaviour of the climate system over complex areas (Belusic et al., 2020).

AEMET has been a member of HCLIM since its foundation and has participated in the effort to develop the model and provide a complete and adaptable tool. In particular, we present some results on the contributions from AEMET: as many basin resources depend significantly on groundwater, works focused on improving the surface component with a new watertable parametrization. On the other hand, AEMET has contributed actively to the development of a more detailed aerosol scheme. An active line of development is currently underway, focused on the development of a fully ocean-coupled model.

Future plans include the generation of high resolution climate simulations over Spain, assessing the added value of the model and exploring different strategies for the generation of high resolution climate scenarios feeding National Adaptation Plan. Some preliminary results, evaluating the added value against CORDEX simulations, are shown. Additionally, several tests comparing different strategies regarding model configuration and the impact of the domain size and nesting strategy have been conducted, giving insight into designing the strategy for the generation of an ensemble of simulations.

References:

Belušić, Danijel & de Vries, Hylke & Dobler, A. & Landgren, Oskar & Lind, Petter & Lindstedt, David & Pedersen, Rasmus & Sánchez-Perrino, Juan & Médus, Erika & Ulft, Bert & Wang, Fuxing & Andrae, Ulf & Batrak, Yurii & Kjellström, Erik & Lenderink, Geert & Nikulin, Grigory & Pietikäinen, Joni-Pekka & Rodriguez-Camino, Ernesto & Samuelsson, Patrick & Wu, Minchao. (2020). HCLIM38: a flexible regional climate model applicable for different climate zones from coarse to convection-permitting scales. Geoscientific Model Development. 13. 1311-1333. 10.5194/gmd-13-1311-2020. http://dx.doi.org/10.5194/gmd-13-1311-2020

Fosser, G., Gaetani, M., Kendon, E.J. et al. Convection-permitting climate models offer more certain extreme rainfall projections. npj Clim Atmos Sci 7, 51 (2024). https://doi.org/10.1038/s41612-024-00600-w

Lind, P., Belušić, D., Christensen, O.B. et al. Benefits and added value of convection-permitting climate modeling over Fenno-Scandinavia. Clim Dyn 55, 1893–1912 (2020). https://doi.org/10.1007/s00382-020-05359-3

How to cite: Prieto Rico, I., Sánchez Perrino, J. C., and Rodríguez Guisado, E.: Main regional climate modelling development at AEMET using HCLIM, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1108, https://doi.org/10.5194/ems2024-1108, 2024.

How to cite: Schröttle, J., Lupu, C., and Burrwos, C.: Approaching the sub-mesoscale globally at 10 min temporal resolution through assimilating clear-sky radiances measured by geostationary satellites, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-109, https://doi.org/10.5194/ems2024-109, 2024.

Convection-permitting ensemble prediction systems (EPS) are often underdispersive. To build a more reliable EPS, it is crucial to represent not only uncertainty in initial and lateral boundary conditions (IBC) but also uncertainty arising from model deficiencies. Uncertainty representations like stochastic parameterizations can add substantial variability to deterministic forecasts. However, it has been shown that when additional model uncertainty representations are implemented into an existing EPS that includes IBC uncertainty, they increase the total ensemble variance only slightly. This study aims to quantify this redundancy in ensemble variance by introducing a new "variability budget" framework. The framework decomposes the total ensemble variance into the sum of individual variances and their correlations, measuring the efficiency in increasing the total variance in relation to pre-existing variance.

The variance budget framework is applied to a convective scale EPS based on operational IBC uncertainties, augmented by two model uncertainty representations, the physically-based stochastic perturbations scheme (PSP) and microphysical parameter perturbations (MPP). By adding these two model uncertainty representations we find a marginal increase in total variance of wind, temperature and humidity at various heights. In particular, PSP introduces variability at convection initiation, while MPP prolongs the lifetime of variance. Both model uncertainties work in sub regions of areas influenced by IBC uncertainty and impact convective activity especially at small scales. Since the impact of PSP and MPP is mostly negatively correlated with the existing impact, this only leads to a slight increase in the total variance. A flow-dependent assessment based on the strength of convective forcing reveals that the model uncertainties show larger variances in weakly-forced conditions but with stronger negative correlations. This negative correlation is primarily attributed to random displacements of convection, with a stronger effect during weak forcing when convection is more intermittent compared to strong forcing. 

How to cite: Matsunobu, T., Keil, C., and Craig, G.: A Variance Budget to estimate the Growth and Interaction of Uncertainties on Convective Scales , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-353, https://doi.org/10.5194/ems2024-353, 2024.

Natural hazards associated with mesoscale processes such as summertime convective events pose a considerable threat to people and property. Yet, forecasting such events remains a challenge, even for the latest generation of high-resolution, convective-scale numerical weather prediction models. Their forecast quality in such cases will likely benefit from improved initial conditions in form of an improved data assimilation analysis. Thus, the assimilation of high-resolution measurements of the lower troposphere has a high potential to enhance the predictability of convective conditions. For example, recent studies by the German Weather Service (DWD) in Aachen and Lindenberg suggest a positive influence of additional Doppler wind lidars (DWLs) on the analysis. However, a thorough investigation of the impact of additional ground-based observations in complex terrain is still lacking.

The ‘Swabian MOSES’ campaign took place from June to August 2023 in the German Black Forest mountain range and deployed a spatially distributed network of instruments to observe the dynamic and thermodynamic characteristics of the lower troposphere. Among them was a network of 12 DWLs, which together have never been used for data assimilation experiments before. Here, we present a 3-months campaign re-analysis dataset that uses a wealth of remote sensing and in-situ campaign observations. These data are added to the regional forecasting system of the DWD, which employs the non-hydrostatic model ICON at 2 km resolution (ICON-D2) and the Kilometer Scale Ensemble Data Assimilation system (KENDA) with 40 ensemble members that uses a Local Ensemble Transform Kalman Filter. We assimilate additional vertical profiles of the horizontal wind retrieved from the DWLs, targeted radiosoundings released from two sites during intensive observation periods, ground based zenith path-delay observations from a (not yet operational) German-wide network of Global Navigation Satellite Systems receivers, and 2-meter temperature and relative humidity as well as surface pressure observations.

In this contribution, we present our experimental setup, address challenges associated with the assimilation of non-operational observations, and demonstrate how different observations influence the campaign re-analysis. Moreover, we compare the campaign re-analysis with a quasi-operational reference re-analysis without additional observations. Our main focus is on the representation of convective summertime conditions.

How to cite: Thomas, J., Geppert, G., Reich, H., Steinert, T., Anlauf, H., Keller, J., Knippertz, P., and Oertel, A.: A campaign re-analysis for ‘Swabian MOSES 2023’: how do high-resolution observations change the analysis of a convective-scale data assimilation system?, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-853, https://doi.org/10.5194/ems2024-853, 2024.

We formulate a novel spatial forecast verification methodology grounded in the geometric principles underlying optimal transport (OT). In its original form OT seeks to minimise the transport of mass between two distributions in space, providing a cost of transportation. Canonical examples come from logistics, such as finding the optimal route to distribute bread from bakeries to cafes. Since its initial formulation, OT theory has found many varying applications from signal processing and economics to meteorology and machine learning, notably through the famed Wasserstein distance. Its success is first due to Kantorovich’s dual formulation and more recently due to novel algorithms and GPU compute. Which combined allow regularised OT problems to be solved efficiently.  

In this work we consider a precipitation field as a measure in 2D space and compute the unbalanced OT distance between a 2D observation and forecast field. By leveraging this geometric formulation, we find a summary metric (the objective), which itself has constituent parts indicating performance. Additionally, the methodology allows us to form transport maps. These maps highlight regions in the field which require the most transport to align with the observation. Alongside providing a visual representation of the error, this provides physical insight into transport error as the map will traverse along geodesics of the underlying space.  This has direct implications for operational forecasters, giving clear, easy to understand illustrations of error, whilst simultaneously providing important information to researchers at forecasting services. This research is supported by UKRI NERC, Imperial’s Grantham Institute and the MET Office UK.  

How to cite: Francis, J., Cotter, C., and Mittermaier, M.: Optimal Transport Tools for Spatial Forecast Verification, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-63, https://doi.org/10.5194/ems2024-63, 2024.

Location errors in precipitation forecasts are ubiquitous in high-resolution weather forecasts due to the misplacement of convective cells but also of mesoscale or synoptic-scale features such as convergence lines or low-pressure systems. However, those kinds of forecast errors never appear in isolation and are usually mixed with intensity errors and more generally with systematic model biases. Therefore, correctly disentangling the contribution of location errors in verification scores is challenging, but also essential to advance forecast quality due to a couple of reasons. Firstly, location errors will incur a double penalty in traditional point-by-point verification (one false alarm event and one missed event). As a result, traditional metrics have the undesirable property of penalizing more a forecast with a correct feature in the wrong location than a forecast that misses the feature. In second place, once a forecast has a location error, traditional metrics are insensitive to the magnitude of the displacement. Hence, traditional metrics are not good at detecting improvements in the size of location errors (i.e. they lack discrimination). Both problems imply that intrinsically better forecasts do not necessarily get better scores.

Here we showcase some novel ways to tackle those two issues from different perspectives. Regarding the first issue, we present a new decomposition of the Mean Squared Error into three positive definite terms, one of which is linked to the amount of double penalty. Then we show how this allows screening forecasts for their levels of double penalty.

Regarding the second issue, recent advances in spatial verification techniques have enabled estimating location errors of global precipitation forecasts by approximating the Wasserstein distance between unbiased fields. This technique constructs a transport plan to move all the precipitation water in the forecasts to the correct locations according to observations, from which a mean location error is computed. We have extended this technique to be able to work with biased fields and at the same time curtail the location errors to prevent large displacement errors that result from biases in regions far away. This enables us to detect improvements at global or regional scale in ECMWF forecasts.

Finally, we have also shown that there is a relationship between the travel plan employed to compute the mean location error and the Mean Absolute Error. This allows us to go back to the first issue again and assess what proportion of MAE is contributed by location errors of a certain scale.

How to cite: Lledó, L., Skok, G., and Haiden, T.: Quantifying double-penalty effects and mitigating their impact on forecast verification, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-50, https://doi.org/10.5194/ems2024-50, 2024.