The development of DWD's new Seamless INtegrated FOrecastiNg sYstem (SINFONY) has matured and a number of components have been run  continuously in near-realtime on a daily basis during the last two convective seasons. This presentation will give a short overview on the system components itself and their current status, and will report on the results of this years convective season.

There are different "optimal" forecast methods for different forecast lead times and different weather phenomena.
Focusing on precipitation and convective events up to some hours ahead, we developed

• a) radar Nowcasting ensembles for areal precipitation, reflectivity and convective cell objects,
• b) a regional ICON-ensemble model with extensive data assimilation of high-resolution remote sensing data (3D radar volume scans of radial winds and reflectivity, cell objects, Meteosat VIS channels and lightning) and hourly new rapid update cycle forecasts (SINFONY-RUC-EPS) on the km-scale,
• c) optimal combinations of Nowcasting and NWP ensemble forecasts in observation space, which constitute the seamless forecasts of the SINFONY. Gridded combined precipitation and reflectivity ensembles are targeted towards hydrologic warnings. Combined Nowcasting- and NWP cell object ensembles help evolve DWD's warning process for convective events towards a flexible "warn-on-objects",
• d) systems for common Nowcasting and NWP verification of precipitation, reflectivity and objects. In particular the cell object based verification will provide new insights into the representation of deep convective cells in the model.

For b), new innovative and efficient forward operators for radar volume scans and visible satellite data (SEVIRI-VIS) enable
direct operational assimilation of these data in an LETKF framework. In late summer 2022, we finally could also activate the SEVIRI-VIS data in the daily RUC. Advanced model physics (2-moment bulk cloud mircophysics) contribute to an improved forecast of convective clouds. A stochastic PBL scheme has been developed, but is not yet in daily use.

For c), the SINFONY-RUC-EPS outputs simulated reflectivity volume scan ensembles of the entire German radar network every 5' online during its forecast runs. Ensembles of composites and cell object tracks are generated by the same compositing and cell detection- and tracking methods/software packages which are applied to the observations.

Considerable improvements in the RUC compared to the 2021 season lead to the situation that RUC starts to outperform Nowcasting already after about 1 hour lead time, both for areal Nowcasting and cell object Nowcasting. Life-cycles of simulated convective cells proved to be surprisingly realistic, although some problems in details of cell organization and cell size remain and need to be looked at.

Other presentations from SINFONY team members will give more details about particular SINFONY components.

How to cite: Blahak, U. and the Team SINFONY: Current status of SINFONY - the combination of Nowcasting and Numerical Weather Prediction on the convective scale at DWD, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-254, https://doi.org/10.5194/ems2023-254, 2023.

How to cite: Clare, M. and Haiden, T.: Creating skillful and reliable probabilistic forecasts using machine learning, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-199, https://doi.org/10.5194/ems2023-199, 2023.

Ensemble simulations have become a standard in numerical weather prediction (NWP). However, ensemble simulations generate large amounts of data, and their comprehensive analysis remains challenging. We introduce a feature-based NWP ensemble analysis method based on the well-established conceptual model of atmospheric fronts. Recent developments in front detection techniques have enabled a reliable, robust, and objective identification of three-dimensional (3-D) frontal structures in NWP data.

We advance detection of individual front features towards front-feature-based time series analysis and ensemble clustering. We track 3-D fronts of a selected cyclone system to create time series of frontal attributes. These frontal attributes characterize properties of the tracked front, for example, the maximum strength of the temperature gradient across the frontal zone, the average slope of the 3-D frontal structure or associated upward motion. The obtained time series provide a compact overview of the development of front characteristics. For ensemble analysis, we generate such time series for the cyclone system as represented in the different ensemble members. Time series distance measures including dynamic time warping can then be utilized to analyze similarities and differences of frontal development in the ensemble, for example, for front-feature-based ensemble cluster analysis. Also, a time window similarity search enables users to select a specific event of interest (for example, a sudden increase in frontal strength) in one of the ensemble members and to search for similar events in other members.

Integrated in the 3-D visual analysis framework Met.3D, our approach facilitates a comprehensive analysis of the spatiotemporal development of 3-D atmospheric fronts and thus contributes to the challenge of rapidly analyzing large ensemble weather predictions.  

How to cite: Beckert, A. and Rautenhaus, M.: Ensemble analysis of spatiotemporal attributes derived from objectively identified three-dimensional atmospheric fronts, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-466, https://doi.org/10.5194/ems2023-466, 2023.

The ensemble prediction system based on the Korean Integrated Model (KIM) has been in operation at Korea Meteorological Administration (KMA) since October 2021. KIM is KMA’s new generation global model which is based on the cubed sphere grid system and has 12km horizontal resolution and 91 vertical levels. It was developed over 9 years from 2011 to 2019 and operationally launched in April 2020. KIM-based ensemble forecast system consists of 50 perturbation members (25 members for long-range forecast) and 1 control member. Four-dimensional LETKF (Local Ensemble Transform 
Kalman Filter) with additive and RTPS inflation scheme is used to make initial perturbation.
The performance of KIM-based ensemble system was evaluated. It is generally more skillful compared to the KIM deterministic global model and shows similar performance with UM-based ensemble system which KMA is operating. An increase in the number of ensemble members results in an overall improvement in prediction performance, especially at higher latitudes. Details of results from KIM ensemble system and impacts of increased ensemble size will be discussed.
Multi-model ensemble is another type of ensemble prediction system KMA is operating. KMA’s multi-model ensemble prediction system that merges six global domain models(KIM, UM, ECMWF global and global ensemble models) is used for short and medium-range forecast. Multi-model ensemble mean shows better performance than the ECMWF ensemble model, which is the best member model. 
In addition, multi-model ensemble system that incorporate regional models in addition to global models is used for impact-based forecast of heat waves and cold waves.  Impact-based forecast for high risk level(`warning` and `alarm`) is improved by using multi-model ensemble system.

How to cite: Shin, H., Kim, E. J., Park, J. I., Yun, S., Ha, J.-C., and Kwon, Y.-C.: Various approaches using ensemble prediction system in KMA, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-415, https://doi.org/10.5194/ems2023-415, 2023.

Subseasonal predictions are gaining more and more attention and importance in many applications, e.g. agriculture or energy&consumption predictions. To bridge the gap between those two temporal horizons and their drivers is, however, a challenge. Several attempts have been made in recent years to improve the numerical weather predictions but they to come at a high computation cost resulting in coarse spatial resolutions.  In the past decade, significant advances were made in improving the S2S and seasonal prediction using mainly numerical weather prediction models (NWP) and in some cases climate models for generating the predictions. Recently, the application of these models in real time forecasting through the S2S Real-Time Pilot Initiative (Robbins et al., 2020) was evaluated and is ongoing. There are, however, drawbacks. Computational costs for performing one forecast cycle are high (RAM, storage, ensemble for uncertainty) and limit the spatial, and to some extent temporal, resolution which are currently roughly 1.5° in spatial and at most 6-hourly in temporal resolution. Both resolutions are not sufficient for small scale renewable production sites. To overcome this, post-processing can be applied using statistical and machine leraning methods.

In this study, statistical (EPISODES, GMOS, SAMOS) and machine learning methods (U-net, random forest) are used to downscale and post-process the coarse subseasonal ensemble predictions for temperature and precipitation. The domain in centred on Austria with a spatial resolution of 1 km  using the INCA analysis as target fields. Evaluation against INCA and point observations show the skills of all methods and highlight the need for additional downscaling.

How to cite: Schicker, I., Papazek, P., Dabernig, M., and Schellander-Gorgas, T.: SSEA- Statistical and machine learning based post-processing for high-resolution subseasonal ensemble predictions, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-467, https://doi.org/10.5194/ems2023-467, 2023.

Wind power systems' maintenance, deployment, and management, as well as the balance between energy supply and demand, are highly dependent on wind speeds and their temporal variability. Wind speed prediction at the sub-seasonal time scale presents a challenge since the skill of surface wind prediction sharply declines after two weeks. Nevertheless, the predictability of large-scale variables is higher than that of surface variables at this time scale. Goutham et al. (2022) improved the skill of surface wind speed forecasts by downscaling 500 hPa geopotential height (Z500)t forecasts using redundancy analysis based on a linear framework. Leveraging their work, we investigate whether Convolutional Neural Networks (CNNs) can be used to further improve the skill of subseasonal wind speed predictions over Europe.

To answer this question, this study proposes a non-linear statistical sub-seasonal ensemble forecasting method for the boreal winter over Europe based on applying a supervised learning model to dynamic forecasts. Specifically, the proposed statistical CNN model regresses weekly-mean surface wind speeds from dynamic forecasts of Z500. The dynamical forecasts of surface wind speed from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the statistical forecasts from Redundancy Analysis (RA, Goutham et al. 2022) are selected as benchmarks for comparison, known for their superiority to climatology in different domains, lead times, and assessment indicators. Initially, we access the skill of a deterministic version of the network, utilizing ERA5 reanalysis data (trained for 15 years and tested for 5 years) to regress wind speed from geopotential height, find that the network is, on average, more skillful than RA based on the root mean squared error. Subsequently, the same network (without retraining) is applied to the subseasonal predictions from the ECMWF  covering the boreal winters from 2015 to 2022, with the same variables. Several indicators, including the anomaly correlation coefficient, continuous ranked probability score, and corresponding skill scores, compare the skill of the statistical forecasts (RA and CNN) and the dynamical forecasts (ECMWF). This comparison aims to determine if the statistical CNN has superior skill in the primary wind energy-producing countries in Europe and whether different models exhibit specific spatiotemporal patterns of skill in the sub-seasonal range. This study also investigates the performance of statistical and dynamical wind speed forecasts under normal and extreme conditions by analyzing the probability density distribution of ensemble members at given areas. Our preliminary findings reveal that statistical forecasts exhibit superior skill in normal

How to cite: Tian, G., Le Coz, C., Alexandre Charantonis, A., Tantet, A., Goutham, N., and Plougonven, R.: Convolutional neural network downscaling to improve sub-seasonal wind-speed predictions in Europe, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-321, https://doi.org/10.5194/ems2023-321, 2023.

The repeated and severe droughts that have affected central Europe over the last years (2018, 2019, 2020, 2022) have triggered the need for sub-seasonal to seasonal forecasts of subsurface water resources. Such long-term forecasts are needed in several sectors such as agriculture, forestry, and water resources, to allow for elaborating and implementing management strategies able to deal with the reduced water resources.

In this context, we have developed a high-resolution (0.6km) monitoring and forecasting system of the terrestrial water cycle with the integrated, physics-based hydrologic model ParFlow/CLM over Germany and the surrounding regions. This model setup simulates the surface and 3D subsurface state and fluxes down to 60m depth, thereby covering the variably saturated zone as well as the saturated zone of shallow groundwater bodies. We use the seasonal 50-member ensemble forecast SEAS from the European Centre for Medium-Range Weather Forecasts (ECMWF) as atmospheric forcing for ParFlow/CLM. We calculate these forecasts over the whole available lead time, i.e., seven months, at the beginning of each meteorological season (March, June, September, and December).

In this study, we analyse the ability for the ParFlow/CLM seasonal forecasts to predict the evolution of water resources, and in particular total subsurface storage, water table depth, and groundwater recharge, during the summer drought of 2022. To evaluate the forecasting skill, the 50-member ensemble seasonal forecasts are compared with a reference time series simulated by forcing ParFlow/CLM by the first 24h of each daily HRES deterministic forecast from ECMWF, which has been validated against observations in a previous study.

Finally, we provide some examples on how these seasonal forecasts can be synthesized to assess the risk of water resources depletion due to atmospheric drought conditions, thus providing useful information for society, and, in particular, the agricultural, forestry, and water management sectors.

How to cite: Belleflamme, A., Goergen, K., Hammoudeh, S., Wagner, N., and Kollet, S.: Assessment of forecast skill of seasonal forecasts with the hydrologic model ParFlow/CLM to predict subsurface water resources under atmospheric drought conditions in central Europe, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-348, https://doi.org/10.5194/ems2023-348, 2023.

Seasonal prediction uses ensemble forecasting to sample the distribution of possible climate outcomes in the upcoming term given the slowly-varying constraints on the atmosphere. However, translating the members’ distribution of a seasonal forecast into meaningful information is a challenge climate services are often faced with. When a large ensemble spread makes the forecast difficult to interpret, highlighting the competing signals from which the uncertainty arises may bear added value to end users. In order to do so, we present an approach to extract alternative seasonal forecast scenarios over Europe (in temperature, precipitation and atmospheric circulation) from ensemble seasonal forecasts. The aim of the scenarios is to refine the ensemble analysis beyond the usual forecast products (e.g ensemble mean, tercile probabilities), and to provide additional guidance for preparation of the seasonal forecast bulletins routinely issued at Météo-France.

The seasonal forecast scenarios are determined with a hierarchical clustering of the ensemble members, based on their forecast temperature at 2-m (T2m). The dissimilarity between two members is defined from the spatial correlation between their respective maps of T2m anomalies – relative to model climatology – over a European domain (29.5°W-40.5°E; 30.5°N-70.5°N, land grid points only). The subsequent dissimilarity matrix across the ensemble feeds the clustering algorithm that groups members into clusters eventually defining the scenarios. The seasonal outcomes corresponding to these scenarios are then described through several diagnostics, e.g composites on sensible climate variables (T2m, precipitation), composites on atmospheric circulation variables (Z500, V200), and analysis through modes of variability and weather regimes. In addition, we provide a description of how scenarios diverge in the course of forecast integration and identify teleconnections related to each scenario. Finally, we also assess the skill of the seasonal forecasts assuming that only the subset of members representing the most likely scenario is retained.

This methodology has been implemented to the Copernicus Climate Change Services (C3S) real-time seasonal forecasts across the past year for experimental purposes, and it is shown to be a relevant complement for the preparation of the Météo-France operational seasonal bulletins.

How to cite: Specq, D., Li, S., Batté, L., Viel, C., and Gayrard, F.: Multiple scenarios of climate anomalies over Europe in ensemble seasonal forecasts, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-31, https://doi.org/10.5194/ems2023-31, 2023.

The current Météo-France seasonal prediction system (MF System 8) has 25 members for hindcast from 1993 to 2016 and 51 members for real-time forecast. In order to investigate the benefits of increasing the ensemble size within our Copernicus Climate Change Services (C3S) seasonal prediction contract, we extended the system 8 hindcast to 51 members for the four main start dates (February, May, August, November). We compare the forecast skill between the official 25-member hindcast and the 51-member extended hindcast.

We focus on the European region at the lead-time 1 for the next trimester. To describe the performance of forecasts, we use correlation and relative operating characteristic (ROC) on the mean 2-meter temperature (T2M) and the mean precipitation (RR) averaged over Europe. Similarly, we also evaluate the forecast of modes of variability (East Atlantic, North Atlantic Oscillation, and Scandinavian Blocking) which impact the European climate.

The scores with 51 members are similar and not necessarily better than with 25 members. Moreover, we use 1000 random draws of 25 members out of 51 to determine the uncertainty of the official forecast scores. These scores can be at the edge of the confidence interval, while the 51-member scores are close to the median of the 1000 random draws. We did the same analysis for different regions that there is less uncertainty on the scores in the Tropics (e.g. Northeast Brazil) than in the mid-latitudes (e.g. Europe). These results suggest that it is not necessary to increase the ensemble size for verification of the seasonal forecasts beyond the available 25 members.

How to cite: Li, S., Specq, D., Batté, L., and Viel, C.: Benefits of increasing the reforecast ensemble size of the Météo-France seasonal prediction system, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-32, https://doi.org/10.5194/ems2023-32, 2023.

Numerical weather prediction models have systematic errors and biases, which can be reduced through statistical correction methods. The most intuitive approach to correcting weather forecasts is to establish a statistical relationship between the forecast and the corresponding observations. Once the relationship is established, it can be used to correct future forecasts. These approaches, also known as statistical adaptation, are used on a daily basis to improve the quality of operational forecasts. Many methods have been proposed in the literature to calibrate ensemble forecasts, and these can be divided into two main groups: parametric (that has a hypothesis on the underlying distribution) and non-parametric methods. One of the most widespread and used methods of the parametric group is the so-called Ensemble Model Output Statistics (EMOS).

We show the performance of EMOS for the post-processing of probabilistic temperature forecasts at 2m. This method assumes that the underlying distribution is Gaussian (in the case of 2m T), and the parameters to be estimated are therefore the mean and the standard deviation. First results were obtained using the ECMWF operational forecast ensemble against SYNOP observations. Further studies were carried out to investigate the reliability of the estimated parameters using not only the raw ensemble data, but also the ECMWF deterministic operational forecast HRES and the control member. The results showed an improvement, especially for the shorter lead-times. The improvement was measured using weather scores for forecast verification and performance: bias, CRPS (Continuous Ranked Probability Score) and CRPSS (Continuous Ranked Probability Skill Score). These post-processed forecasts will serve as a reference for future studies.

How to cite: Aleksovska, I.: Post-processing ensemble forecasts using Ensemble Model Output Statistics (EMOS), EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-619, https://doi.org/10.5194/ems2023-619, 2023.