With RainBoW ("Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings”), the German Meteorological Service (Deutscher Wetterdienst/DWD) launched a program to renew its weather warning system. The overarching goal of the program is to tailor weather warnings more strongly towards the needs of end users, thus enabling the recipients to make informed decisions using the information provided. Specifically, we are working on three fields of action to achieve this. First, we want to increase the forecasting horizon of weather warnings to inform users early on. For this, we are implementing a probabilistic warning trend covering up to seven days and showing the likelihood of a specific warning to occur. It should transition as seamlessly as possible into an actively distributed warning. Second, we want to improve the comprehensibility of our weather warnings. This includes strengthening the consistency of warning criteria across all warning elements. Beyond this, we intend to improve the perception by shifting the communication focus away from the meteorological description of an event towards the associated weather impacts. For the latter, besides restructuring the warning text towards a more prominent placement of recommendations for action, we also plan to establish a data-driven approach resulting in illustrative impact description to add to that text. Third, we want to individualize our weather warnings, since the warning criteria for the general public do not neccesarily meet the needs of specialized user groups (e.g. professional users). For specific weather-dependent use cases, hazard-inducing thresholds can be individual for the application at hand. To cover for this, we are developing a warning portal serving as a warning toolbox for end users, with the possibility to configure, receive and visualize warnings as needed. 

This contribution will give an overview over the goals of the RainBoW program at DWD, including specific updates on current developments in all three fields of action.

How to cite: Feige, K., Altnau, S., Anger, F., Baumgartner, M., Erhardt, B., Felsberg, A., Hoff, M., Klink, M., Kratzsch, T., Lambert, A., Leschzyk, D. K., März, B., Niebuhr, H., Noël, L., Riedl, K., Sauter, C., Schaab, R., Vogel, C., Wapler, K., and Hagedorn, R.: One step closer to the RainBoW: First results from the development of a new weather warning system at the German Meteorological Service, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-741, https://doi.org/10.5194/ems2024-741, 2024.

The ever-increasing complexity and data volumes of numerical weather prediction demands innovations in the analysis and synthesis of operational forecast data, in particular in the context of warnings of extreme events.

Here we discuss how dynamical thinking can offer directly applicable forecast information for early warnings of extreme events. We present the semi-automated framework “DOMINO” which allows identifying globally, in any variable of the ECMWF ERA5 reanalysis, the robust, and statistically significant dynamical precursor patterns to any type of meteorological event on the meso-α to synoptic scale O(200-2000 km). We call these patterns “event-prone regime” (EPR) and develop a scalar index which allows a very easy monitoring of “EPR activity”. Computing this index for all members of ECMWF’s medium- and extended-range ensemble forecasting system provides a massive simplification of the forecast information. This enables not only early warnings for the potential of an extreme events, but also allows us to derive the physical forecasting storyline for the unfolding of a given potential extreme. This storyline can help identify predictability barriers well before an extreme and assess when the ensemble spread reduces and thus when the forecast scenario becomes more reliable.

We will demonstrate this workflow in case studies in particular for the extreme north Italian flooding of May 2023. We show in ECMWF medium-range forecasts that an EPR perspective was able to identify the growing possibility of the Emilia-Romagna extreme event eight days beforehand – four days earlier than the direct precipitation forecast. Furthermore, we demonstrate that a cyclogenesis near New Foundland posed a predictability barrier. Only once the details about this cyclogenesis verified did all ensemble members converge towards the extreme event.  We conclude that dynamical precursors identified through the respective EPR prove well-suited for monitoring the potential unfolding of an extreme event in the medium-range as well as in the extended-range. In addition, through identifying and interpreting predictability barriers, the EPRs approach provides important additional guidance to forecasters in the early warning process. 

Dorrington, J., Wenta, M., Grazzini, F., Magnusson, L., Vitart, F., and Grams, C.: Precursors and pathways: Dynamically informed extreme event forecasting demonstrated on the historic Emilia-Romagna 2023 flood, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-415, 2024.

Dorrington, J., Grams, C., Grazzini, F., Magnusson, L. & Vitart, F. (2024) Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall. Quarterly Journal of the Royal Meteorological Society, 150(759), 776–795. Available from: https://doi.org/10.1002/qj.4622

Federico Grazzini, Joshua Dorrington, Christian M. Grams, George C. Craig, Linus Magnusson, Frederic Vitart  Grazzini, 2024: Improving forecasts of precipitation extremes over Northern and Central Italy using machine learning. In review for Quarterly Journal of the Royal Meteorological Society  Available from:  https://arxiv.org/html/2402.06542v1

How to cite: Dorrington, J., Grams, C. M., Grazzini, F., Magnusson, L., Vitart, F., and Wenta, M.: Increasing lead time for early warnings through dynamically informed forecasting of extreme events and monitoring predictability barriers , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-641, https://doi.org/10.5194/ems2024-641, 2024.

Weather warnings are often based on a catalog of hazard-related meteorological thresholds. However, there are weather-dependent applications that need warnings beyond these fixed criteria. In the course of the program RainBoW ("Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings") by the German Meteorological Service (DWD), a key field of action is to provide individualized weather warnings to account for specialized application-dependent needs. This will be realized through a warning portal ("DWD-Warnportal"), in which users with specific requirements may configure profiles with individual warning criteria matching their particular use case in terms of warning thresholds and considered areas.

A current light-weight prototype version of the DWD warning portal allows the automated evaluation of probabilistic warning information for a set of preconfigured profiles containing warning criteria that partly differ from the standard warnings of the DWD for the general public. To obtain a final product tailored to the needs of specialized users, access to the prototype was granted to a group of test users from various areas of expertise. This group includes, among others, experts from the field of disaster control, energy network operation, forestry offices, media and academic research. Their feedback through regular user workshops and questionnaires is then used as a guidance to prioritize which additional features should be implemented and in which order. As requested by the test users, the first individual aspect implemented in the prototype is the warning location, which may be defined by an address, geographical coordinates or a GeoJSON polygon. For future releases, it is planned to provide persistent user-configurable profiles, in which the warning thresholds may be adjusted to individual use cases.

How to cite: Riedl, K., Vogel, C., Reetz, B., Schaab, R., Noel, L., Niebuhr, H., and Feige, K.: Individualized weather warnings in the DWD warning portal prototype, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-687, https://doi.org/10.5194/ems2024-687, 2024.

Short-term variations in PV power are an increasingly important challenge for solar energy integration. By anticipating sudden changes in irradiance caused by passing clouds, all-sky imager-based solar nowcasting can help address this challenge. However, the utility of nowcasting systems is highly dependent on the quality of the forecast. While recent data-driven models have shown great potential in standard forecast metrics such as root-mean-square error (RMSE) and forecast skill, they tend to produce smoothed forecast curves and may not be well suited to detect ramps. An alternative data-driven approach lies in generative modeling. Instead of forecasting solar irradiance directly from available data, like radiometer measurements or sky images, we propose a two-step method to predict cloud dynamics and irradiance separately.

Using novel denoising diffusion models [1], we show that realistic sequences of sky images can be generated. By conditioning video prediction on the latest acquired sky images, plausible future sky conditions are produced. In contrast to traditional methods that only predict cloud motion, changes in cloud shape can also be represented. Another advantage of diffusion-based video prediction is the versatility of possible outcomes. By introducing samples of random noise during inference, the model generates different outputs that vary depending on the conditioned input.

In the second step, we apply an irradiance model to the generated synthetic sky images. Each image is processed independently and returns a corresponding irradiance value. Thus, an irradiance distribution can be obtained from the samples of synthetic sky images for each lead time. As a result, the uncertainty of the forecast can be estimated, since a larger variation of synthetic sky images will lead to a larger distribution of corresponding irradiance.

We evaluate our novel generative nowcasting approach not only on standard forecast metrics, but especially on its ability to detect ramp events. Preliminary results already indicate that such a generative video prediction on sky images in combination with an irradiance model can overcome the problem of smoothed forecast curves [2]. Furthermore, the intermediate results of synthetic sky images enhance interpretability, and the generation of varying scenarios enables probabilistic forecasting.

[1] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. "Denoising diffusion probabilistic models." Advances in neural information processing systems 33 (2020): 6840-6851.

[2] Paletta, Quentin, Guillaume Arbod, and Joan Lasenby. "Benchmarking of deep learning irradiance forecasting models from sky images–An in-depth analysis." Solar Energy 224 (2021): 855-867.

How to cite: Fabel, Y., Schnaus, D., Nouri, B., Wilbert, S., Blum, N., Zarzalejo, L. F., Kowalski, J., and Pitz-Paal, R.: Leveraging Generative Models for ASI-based Solar Nowcasting, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-892, https://doi.org/10.5194/ems2024-892, 2024.

How to cite: Korpinen, A., Hieta, L., and Partio, M.: Thunder probability nowcast, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-471, https://doi.org/10.5194/ems2024-471, 2024.

Forecasting lightning is generally challenging for numerical weather models with parameterized convection. Nonetheless, for the aviation industry reliable lightning forecasts are needed for larger domains for which running convection permitting models is not feasible yet. For models with coarser resolution, parameterizations exist that derive the potential for lightning based on the model's convection scheme. One example is the subgrid-scale lightning potential index (LPI, Schröder et al. 2022). However, this index inherits model biases such as a biased onset of convection.  Furthermore, for ensemble prediction systems, the spread of the LPI is not calibrated either. Baumgartner et al. (2023) have shown that applying a neural network within the European domain can correct model errors like the wrong timing as well as the ensemble spread. Nevertheless, applying this approach to the global domain leads to further challenges, e.g. the different behaviour of the numerical model in different climatological regions of the world. From a technical point of view, a global product needs to be trained on significantly larger datasets. Furthermore, after an update of the numerical model usually only a few months of data are available for (re-)training which is inherently a biased dataset. 

In this work it is shown how to deal with these challenges by using the global ensemble prediction system of ICON. The global lightning data of Vaisala (GLD360, 3) serves as ground truth. A key component of the neural network is a climate feature that allows the neural network to differentiate between the different climatological regions. This is accompanied by features like solar elevation and local time. The benefits of these new features/predictors are shown using the Brier score. The diurnal cycle, as well as region dependent climatological biases, are corrected. It is also shown that even without the availability of training data for a whole year, the neural network can learn corrections e.g. from the northern hemisphere summer and apply them to the southern hemisphere summer and vice versa.

References:

(1) Schröder et al., 2022: Subgrid scale Lightning Potential Index for ICON with parameterized convection. Reports on ICON (10), DOI: https://doi.org/10.5676/dwd_pub/nwv/icon_010 .

(2) Baumgartner, M., Schröder, G., and Primo, C.: Forecasting lightning probabilities derived from the Lightning Potential Index using neural networks, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-34, https://doi.org/10.5194/ems2023-34, 2023.

(3) https://www.vaisala.com/en/products/systems/lightning/gld360

How to cite: Schröder, G., Baumgartner, M., Primo, C., and Theis, S.: Challenges in training neural networks for global lightning probability forecasts, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-475, https://doi.org/10.5194/ems2024-475, 2024.

In 2022, the German Meteorological Service (DWD) started the program RainBoW (“Risk-based, Application-oriented and INdividualizaBle delivery of Optimized Weather warnings”) aiming at a rework of the warning system. Among others, one important goal of the program is to extend the forecast horizon of warnings in order to inform authorities and the general public early-on about possible hazardous weather. This so-called “warning trend” is envisioned for all warning parameters, but has been explored first for wind gusts. In this contribution, the outline of the prototype for wind gusts, that is already available and running in a test mode, will be presented.

One of the main features of the prototype is its modularity, i.e. it consists of several different components which are tied together with modern messaging techniques. The wind gust prototype is based on data of DWD's inhouse model ICON, but is easily extensible to other data sources. It combines ensemble data from all ICON model versions to allow the construction of a warning trend up to 7 days. This comprises the local area version for Germany (ca. 2 km spatial resolution and forecast up to 48 hours), the EU version (ca. 13 km spatial resolution and forecast up to 120 hours) and the global version (ca. 26 km resolution and forecast up to 180 hours). After spatial and temporal aggregation of the individual model data, exceedance probabilities for given warning thresholds are computed and combined into a single, coherent warning trend dataset. Since this warning trend dataset changes with each new model run, an additional post-processing step is necessary to reduce the inherent jumpiness. Overall, the prototype thereby provides a smooth, automated wind gust warning trend.

How to cite: Felsberg, A., Koser, D., Brune, S., Breitenbach, B., Baumgartner, M., and Klink, M.: A prototype for automated wind gust warnings, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-645, https://doi.org/10.5194/ems2024-645, 2024.

With the changing climate, urban flash floods are likely to become more common and societies should be prepared for these events more than ever before. Heavy rainfall and insufficient drainage systems are the main contributors to the overflow of water on streets. Thus, information of meteorological and hydrological conditions are needed to be able to alert the public of urban flood events beforehand.

A new urban flash flood warning system was developed and piloted real-time in the framework of project HULEHENRI. The system consists of three main parts: 1) Weather radar based precipitation nowcasts are used as an input to 2) urban rainfall-runoff model, which simulates the changing water level on city streets. These flood forecasts are then 3) delivered to customers via web interface.

Good accuracy of radar based precipitation nowcasts is a key to accurate flood forecasts. To achieve best possible results multiple improvements were made to algorithms used to calculate the radar-based quantitative precipitation estimation (QPE) during this project. These improved both the reliability and time resolution of the estimates and included for example rain-gauge correction, attenuation correction and advection correction. For nowcasting we chose a model specifically tailored for urban scale precipitation nowcasting, the LINDA model, which is available via the Pysteps Python library.

The flood caused by rain has been calculated on a 2-meter grid using a surface flow model. Rain observations and forecasts based on weather radar data are available every five minutes in a 500 meter grid. This rainfall data is converted to a 2 m grid for use in the surface flow model. In addition to the digital elevation model (DEM), land use and soil data are used in the calculation. Infiltration into the soil is calculated from the soil data using the Green-Ampt method, and accurate land use data (2x2m grid) is used to determine the friction coefficient (Manning n). The main water drainage pipes are included in the model, but not the entire drainage system. Road network data is used to define road sections that may be flooded during the forecast period.

Development of the urban flood warning system was user-centered. Pilot customers, including for example public emergency services and logistics companies, were involved in the development process throughout the project. In this work we discuss the customers’ input and needs and the way we addressed them and the development that was made to each three parts of the system. We also show verification results during urban flash flood events that occurred during the pilot period and discuss possible changes that could be made to improve the warning system even further.

How to cite: Perttula, T., Pulkkinen, S., Huokuna, M., and Niemi, T.: New Urban Flash Flood Warning System Combines Radar-Based Nowcasts and Surface Flow Model, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-187, https://doi.org/10.5194/ems2024-187, 2024.

In order to overcome the legacy of perceiving lightning information as tertiary in importance for weather surveillance tasks, this study documents severe weather signatures observed by the largest Lightning Mapping Array (LMA) network operating in Europe. Weather offices like Servei Meteorològic of Catalunya (SMC) are taking advantage of total lightning data in real-time in the short-term forecast process, as well as for the automatic generation of severe weather warnings.

The joint venture of the Polytechnical University of Catalonia (UPC) and the SMC has allowed the deployment of a network of more than 20 LMA stations to cover Catalonia and its surroundings (NE of the Iberian Peninsula).

The LMA system locates the impulsive very high frequency (VHF) breakdown processes produced by the lightning channels. Each LMA station records the time and magnitude of the electromagnetic radiation received in successive 80 μs intervals and relays, in real-time, this information to the base station. With data from 5 or more stations, processed with the time-of-arrival technique, the system calculates the horizontal, vertical, and temporal location of each source. Typically, hundreds of sources per flash can be reconstructed, which in turn produces accurate three-dimensional lightning image maps. Indeed, these VHF sources then can be used for various operational products in either their raw form (i.e., source densities) or recombined into flashes.

Since lightning discharges tends to connect opposite charged parts of storm, plots of the density of LMA resolved sources reveal features of severe-storm structure, complementary to those revealed by the weather radar. Animations of lightning density can reveal strong updrafts (overshooting tops, V-shaped patterns), storm intensification (lightning jump) and early stages of a mesocyclone (transient minimum in lightning density, also named lightning “holes”).

In this work, LMA severe weather signatures are presented, corresponding to tornadic supercells and large hail events occurred during the 2023 thunderstorm season.

How to cite: Pineda, N., Rodríguez, O., Fabró, F., San Segundo, H., Montanyà, J., Romero, D., van der Velde, O. A., and López, J. A.: Operational use of the Lightning Mapping Array for tracking, nowcasting and warning about severe weather, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-233, https://doi.org/10.5194/ems2024-233, 2024.

DWD's new Seamless Integrated Forecasting system (SINFONY) is currently targeted to improve very-short-range forecasting of intense convective events from observation time up to 12h ahead for Germany.
There are different optimal forecast methods for different forecast lead times. The idea is to improve and combine them in an optimal way, including uncertainty information via ensembles.

For this, seamless ensemble forecast products in observation space are produced, i.e., radar reflectivity composites, precipitation fields and convective cell objects, as well as informations
on the probability  of hazards like heavy precipitation, hail, wind gusts and lightning. These hopefully contribute to improve DWD's meteorological warnings (forecasters, automated systems) as
well as warnings of the German flood forecasting authorities.

In the last seven years we developed in an interdisciplinary team 

1) radar Nowcasting ensembles for areal precipitation, reflectivity (STEPS-DWD) and convective
cell objects including hail and life cycle information (KONRAD3D-EPS) with good forecast quality up to 1-2 hours,

2) a new regional NWP ICON-ensemble model (ICON-RUC-EPS) with assimilation of 3D radar volumes, cell objects, Meteosat VIS and IR channels and hourly new
forecasts on the km-scale, whose quality exceeds Nowcasting after forecast hour 1-2,

3) to get the best of both worlds for our customers, optimal probabilistic and ensemble combinations ("blending") 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 hazards towards flexible “warn-on-objects.

4) Common Nowcasting and NWP verification systems to help continuously improve the SINFONY components.

For 2), efficient forward operators for radar volumes and visible/infrared satellite data enable
direct operational assimilation of these data in an LETKF framework. Advanced model physics (2-moment bulk microphysics with prognostic hail)
contribute to an improved forecast of convective clouds.

For 3), the ICON-RUC-EPS forecasts output simulated reflectivity volume scan ensembles of the German radar
network every 5’. Radar composites and KONRAD3D cell objects and their tracks are generated by the exact same methods as in the Nowcasting. These are seamlessly combined with
the STEPS-DWD- and KONRAD3D-EPS Nowcasts - resting upon the improvements for Nowcasting (1) and NWP (2).

Meanwhile the system has matured and is in the process of operational installation. A number of its components have been run continuously during
the last four convective seasons. This presentation will give a short overview on the system
status and its performance during the last years, as well as our future plans to enlarge lead time beyond 12h and to broaden the focus to other weather phenomena (renewables, air-traffic-related, winter), i.e., by integrating modern satellite data (MTG, IRS) into the system.

How to cite: Blahak, U. and the Team SINFONY: Current status of SINFONY – The combination of nowcasting and numerical weather prediction for forecasting convective events at DWD, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-178, https://doi.org/10.5194/ems2024-178, 2024.

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 for a number of applications. Historically the operational NWC and NWP forecasts are generated on separate IT-Infrastructures. To reduce data transfer between both infrastructures and to reduce the complexity of SINFONY those NWC components, which solely rely on NWP pre-products, are ported to the NWP infrastructure using software container.

With this aim in view a container image containing all relevant NWC components is created in a CICD oriented procedure. The respective containers are integrated into DWD’s development and operational code bases and executed on DWD’s HPC using apptainer. The integration into DWD’s development code base is completed already and currently used for further development of the data assimilation procedure.

A major innovation of SINFONY is the rapid update cycle (RUC), an hourly refreshing NWP procedure with a forecast range of 12 hours. Currently RUC is in a preoperational stage and subsequent calculations using NWC components together with the subsequent combination of RUC forecasts with NWC forecasts is still executed on the NWC infrastructure. The RUC will be implemented to the operational forecasting system at the end of 2024 and together with that our aim is to implement the containers to the RUC for two applications. At this step the containers have to meet even harder performance requirements and above that a reliable update and support workflow must be established, since it will then be part of Germanys national critical infrastructure.

How to cite: Zacharuk, M., Ulbrich, S., Josipovic, L., Welzbacher, C., and Blahak, U.: Docker container in DWD's Seamless INtegrated FOrecastiNg sYstem (SINFONY), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-581, https://doi.org/10.5194/ems2024-581, 2024.

The Hong Kong Observatory (HKO) has been developing severe weather forecast products for the aviation community in order to provide early and reliable alerts for advance flight route, deviation and flow control planning. This study presents an 8-hour seamless significant convection forecast for the Asia-Pacific region, created by blending the extrapolation-based satellite nowcast with precipitation forecast from either the in-house regional HKO-WRF model or the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution model, smoothly transitioning to just the NWP models thereafter.

Our 1-year verification suggested that the blended forecast benefited from heavy reliance on satellite extrapolation at early forecast hours, progressively shifting towards the NWP models as forecast lead time increased, particularly after the fourth hour when the intensity-corrected NWP model forecast outperformed the extrapolation. This study also demonstrated the advantage of the salient cross dissolve (SalCD) technique, which minimised the misses raised from linear blending. In view of the capability of the NWP models, latitude-dependent weights were developed to capture the more rapidly evolving convection at lower latitudes. A specific profile for tropical cyclones was introduced, considering that NWP models performed better with the spiral bands. The blended forecast exhibited improved accuracy compared to using only satellite extrapolation or the NWP output within the forecast period. The findings revealed the potential benefit of blending to enhance accuracy while allowing a seamless transition to the NWP models for longer lead times. In addition to the deterministic blended forecast, probability prediction was explored by perturbing the weights and combining satellite extrapolation with individual ECMWF ensemble members for supporting the decision-making of aviation users.

How to cite: Cheung, O.-P., Ng, Y.-L., Leung, C. Y.-Y., and Kok, M.-H.: Seamless significant convection forecast for aviation, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-632, https://doi.org/10.5194/ems2024-632, 2024.

How to cite: Bessenbacher, V., Beusch, L., Bhend, J., Mahlstein, I., Nerini, D., and Spirig, C.: First experiences with a seamless weather forecast for severe weather products at MeteoSwiss, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-423, https://doi.org/10.5194/ems2024-423, 2024.

Destination Earth initiative of the European Commission is creating several digital replicas (digital twins) covering different aspects of the Earth system and based on state-of-the-art simulations and observations. One of the digital twins aims to develop two components of Earth's Digital Twin on Weather-induced Extremes (Extremes DT), with both continuous global high-resolution forecasts, developed by ECMWF and on-demand regional simulations, funded by ECMWF and led by Météo-France.

The Global Continuous Extremes DT is engineered to forecast extreme weather events worldwide with unparalleled precision, providing continuous km-scale global high-resolution forecasts. Currently, the Continuous (Global) Extremes DT uses ECMWF's Integrated Forecasting System (IFS) cycle 48r1 with approximately 4.4 km grid-spacing (TCo2559). On the other side, the On-Demand Extremes (DT) offers configurable digital twin engines for forecasting environmental extremes at a sub-km scale regionally and when necessary. It aims at providing an on-demand workflow with co-design of very high-resolution predictions about extreme weather events combined with decision making support for impact sectors. This On-Demand Extremes DT aims to run a hectometric scale forecasting models with a typical resolution of 750 or 500 m, exploring a finer resolution of up to 200 m for special applications where it is meaningful. In the first phase, capability demonstration aims to explore the added values with co-designing workflow combining the continuous and the on-demand Extremes DT, event- or user-driven solutions with weather forecasting on the one hand, and impact sectors on the other hand. As part as this activity is the focus by examining a range of carefully selected high-impact weather events in both Extremes DT to understand the synergies and the added values that each one provides inside of the Extremes DT.

In this presentation, we will showcase a range of extreme weather events where we demonstrate the predictability capacity of both, the Continuous and the On-Demand extremes DT, and the added value of progressive increment of model horizontal resolution. These cases will be focus on European severe weather, like Mediterranean and Atlantic cyclones and the extreme values in surface variables like wind gusts or precipitation, especially convective precipitation, which is generally better represented at sub-grid km-scale. These selected cases will also provide some insights of how useful is to use the Continuous global Extremes DT to initialise the On-Demand DT compared to the current 9km deterministic IFS.

How to cite: Gascón, E., Yang, X., Benacchio, T., Baordo, F., Alerskans, E., Vannière, B., and Sandu, I.: Maximizing added value in extreme weather forecasting: insights from case studies with the Continuous Global and On-Demand Extremes Digital Twins within the Destination Earth initiative, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-272, https://doi.org/10.5194/ems2024-272, 2024.

The last ten to fifteen years have seen a marked growth in the range of forecasting operations-focused Operations-to-Research (O2R) activities at the Met Office. The range and maturity of many of these activities has developed such that in more recent years dedicated resource has been drawn from the chief operational meteorologist community to lead, manage and develop these activities. The goal is to ensure an operational, forecasting-focused perspective is represented in the O2R/R2O (Research to Operations) space.

Here, an overview of the range of activities O2R is active in is presented. Activities can be categorized as follows: process groups providing governance and steering, model evaluation and testbed co-organisation.

A number of process groups exist within the Met Office to draw on senior representation from key areas across the organisation to communicate and seek feedback on plans for requirements, research, development and delivery of new capabilities and upgrades. Interests here extend across a wide range of activities including the interests of NWP and post-processed outputs and some of the associated IT frameworks. O2R are invited to ensure that the plans align with forecasters present and future requirements.

Model evaluation activities effectively fall into two categories, one providing subjective analysis of proposed future model packages and formulations and the other a retrospective assessment of model output. The former process supplements objective evaluation and verification of these packages and formulations, providing an operational perspective as to what they mean to and how they could be used by the forecasting community and how they could be impacted by their introduction. The latter category reviews a log of model performance called the Daily Forecast Assessment, completed by operational teams and then retrospectively assessed if model performance is considered particularly poor. These assessments are reviewed by O2R or O2R subgroups and Met Office Science and informs both the Met Office’s formal model characteristics documentation and model development processes.

Since 2013 the Met Office has run a series of testbeds aimed at allowing operations and researchers to come together to evaluate new tools and share experience, the results then helping drive future developments of forecasting tools. In recent years, O2R has taken a leading role in organising testbeds and delivering their results.

Other O2R activities include helping develop and test new forecasting tools, representation of the operational perspective to internal groups taking a more detailed look at specific elements of model performance, evaluation of impactful weather events to consider model performance, public service warning strategy and verification and forecasting best practice.

How to cite: Suri, D.: Operational Meteorologist-Led O2R Activities at the Met Office (or What I Do When I’m Not Forecasting Weather), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-433, https://doi.org/10.5194/ems2024-433, 2024.

Ensemble clustering is an efficient ensemble post-processing approach that distils an ensemble forecast into its prevalent forecast scenarios by grouping similar ensemble members together - something which is increasingly important in a world where ensemble data volumes are rapidly increasing. The application of a suitable clustering method combined with appropriate forecast visualisation allows a forecaster to effectively focus on the key possible outcomes, simplify the message and characterise and communicate forecast uncertainty more easily. For example, output may be presented as (1) probabilities of each cluster occurring, (2) representative or central members from each cluster showing alternative forecast directions, and (3) probabilities of threshold exceedance under each cluster.

Operationally, the Met Office runs a probabilistic weather pattern forecasting tool called Decider (Neal et al., 2024), where ensemble members are clustered according to their allocation to one of 30 predefined weather patterns (circulation types). This allows for changes in the large-scale circulation to be identified at a range of lead times and is useful for many downstream applications where the same weather patterns have been related to specific impacts. To be used alongside this, a prototype feature-based clustering approach is being trialled, which is the focus of this talk. Here, k-medoids clustering is applied to Fractions Skill Score (FSS) distances between members, using identified features in each member. These features may represent areas of hazardous weather or mesoscale or synoptic features, such as areas of heavy rainfall, damaging wind speeds, or weather fronts. The methods being trialled follow those initially developed by Boykin (2022) in PhD work at the University of Reading in collaboration with the Met Office, and include several post-processing steps. These steps are designed to (1) determine spatial distances between objects and cluster on those distances, (2) identify an optimal number of clusters, (3) identify windows of interest where clusters become more distinct, and (4) identify representative members within each cluster, within the time window, to provide plausible forecast scenarios or evolutions with associated probabilities. Multiple configurations of the prototype feature-based clustering approach have been trialled and early results will be discussed.

References

Boykin, K.A. (2022) Extracting likely scenarios from Ensemble Forecasts in real time. PhD in Atmosphere, Oceans and Climate, Department of Meteorology, University of Reading, DOI: 10.48683/1926.00111270.

Neal, R., Robbins, J., Crocker, R., Cox, D., Fenwick, K., Millard, J., Kelly, J. (2024) A seamless blended multi-model ensemble approach to probabilistic medium-range weather pattern forecasts over the UK. Meteorological Applications, 31(1), e2179.

How to cite: Neal, R., Titley, H., Sherwin, R., Willington, S., Moseley, S., Roberts, N., Boykin, K., Methven, J., and Frame, T.: Operational and prototype ensemble clustering post-processing approaches at the Met Office, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-351, https://doi.org/10.5194/ems2024-351, 2024.

The Canadian Meteorological Service's road surface model METRo is used by the DWD to simulate road surface temperatures at around 1,500 road weather stations (RWS). The modelled road temperature from METRo, together with other meteorological parameters from Model Output Statistics (MOS), is displayed on operational websites of winter services such as Autobahn GmbH.

The MOS provides a combined statistical interpretation of the IFS-HRES (ECMWF) and ICON (DWD) model forecasts at individual weather stations. The technique is based on multiple linear regressions by minimizing the Root Mean Square Error (RMSE). A wide range of model variables are used as predictors, including unobserved variables, as well as surface observations, precipitation radar and lightning detection for nowcasting.

The MOS is used to apply forecasted variables, such as air temperature, dew point, and precipitation parameters, to the METRo. The METRo then calculates the pavement temperature for the next seven days.

The RWS provide temperature data from federal roads, country roads, and international airports. The data undergo automated plausibility checks and are used to train the MOS and in METRo's data assimilation.

The MOS also calculates pavement temperatures. However, it has not been used operationally in the past due to a lack of quality testing. Thanks to years of automatically quality-assured measurement data at the RWS, the MOS can simulate the road temperature fairly well.

The RMSE was used to evaluate the accuracy of the METRo and MOS forecasts. It was based on 2.2 million quality-assured measured and modelled pavement temperatures at around 1,100 RWS stations between November 2023 and January 2024.

The RMSE was averaged over forecast times covering the next day and night, which is crucial for winter services. METRo's RMSE is 1.5°C, indicating high prediction accuracy given the pavement temperature measurement. MOS predictions are even better, with a calculated RMSE of 1.3°C. The largest differences between the models occur at noon and at night. Both models' forecasts are more accurate in regions of Germany where the data quality and quantity of observed pavement temperature at the RWS is higher.

Automated plausibility checks are crucial, as demonstrated by experiences with pavement forecasting in operational and pre-operational mode. If such checks are in place, a MOS can outperform a physical model, even with parameters that are difficult to observe. This is especially relevant for future developments in the field of AI.

How to cite: Trepte, S. and Schmitz, R.: Differences in modelled pavement temperature at German Road Weather Stations, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-148, https://doi.org/10.5194/ems2024-148, 2024.

Abstract: The 19th Asian Games has been held in Hangzhou, China from 23 September to 8 October 2023 and 22 to 28 October 2023 for Asian Para Games. Heavy rainfall and strong gust wind during the Asian Games have high impact on relevant events, especially on those outdoor activities. How to combine forecast uncertainty and to provide more reliable forecast and early warning services was still one of the major scientific and social challenges. WMO RA II Focal Points on Research intend to initiate a WMO RA II Research Development Project / 19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application (HangzhouRDP). The Scientific objectives were as follows:

1) Understand the impact of multi-scale initial errors and model errors on the prediction of high-resolution models and ehe forecast uncertainty of local severe convective weather.

2) Demonstrate the improvement of forecasting and early warning capabilities at sub-kilometer scale and minute scale by utilizing the uncertainty information from ensemble forecast.

3) Develop convective-scale ensemble prediction, postprocessing and verification method at sub-kilometer  meter and minute scale.

4) Share the experience gained with RA II members through training courses.

The project has conducted demonstration and application by developing convective-scale Ensemble Prediction System (EPS, including 1 km deterministic model and 3 km ensemble model) and using minute scale multi-source observations, gain a deep understanding toward the influence of multi-scale initial errors and model errors on high-resolution model forecasts, understand the forecast uncertainty of local severe weather event, demonstrate the improvement of forecast and early warning services of weather events at hundred-meters scale and minute scale brought by uncertainly information of ensemble forecast, and provide technical methods and references for RA II members on carrying out forecast and early warning services of high-impact weather at hundred-meters scale and minute scale.The project was designed to be jointly led by Zhejiang Meteorological Bureau (ZMB) and Centre for Earth System Modeling and Prediction of China Meteorological Administration (CEMC), along with the participation of National Meteorological Centre (NMC), CMA Huafeng Group, and WMO Regional Training Centre Beijing (CMA Training Centre, CMATC). The project expected to run for two years, including system development, data transmission test, ensemble prediction system construction in half a year, and case study and forecast evaluation of societal/economic impact in one and a half years. The project focused on improving 0-36 h forecast and early warning capabilities of local rainfall and wind using uncertainty information from ensemble forecast.

How to cite: Chen, J., Yao, M., and Chen, F.: The introduction of a WMO RA II Research Development Project /19th Hangzhou Asian Games Research Development Project on Convective-scale Ensemble Prediction and Application (HangzhouRDP), EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-10, https://doi.org/10.5194/ems2024-10, 2024.

The Met Office have routinely been producing surface analysis charts operationally since 1872. Early charts bear little resemblance to current operational analyses and evolved through the late 19th and early 20th centuries to take on board evolving concepts and notions, for example Bergen School frontal theory, with fronts starting to appear on these charts in the early 1930s. These charts, and subsequent charts can be found online in Met Office publications such as the Daily Weather Report.

Originally hand-drawn based on surface observations, as forecaster workstations developed in the 1980s and 1990s, production of these charts began to shift to become a wholly workstation-based graphical production process. Currently, the analyst - the duty Principle or Expert Operational Meteorologist in the Met Office's Expert Weather Hub - blends observational and model data to produce the surface pressure field and then uses a range of observational data supplemented by NWP to aid drawing the fronts. Conceptual models considered now extend beyond the Norwegian Cyclone Model to, for example, incorporate Shapiro-Keyser and Browning and Roberts Cyclone Models.

Meanwhile, improvements in satellite and radar imagery and NWP resolution have allowed analyses to become increasingly detailed, revealing structures and detail otherwise not present or immediately obvious in more conventional surface observations. This has undoubtedly led to an increase in detail in Met Office analyses. Concurrently, the scale of some features on the analyses can be relatively small, especially around the UK; this is quite deliberate and necessary as these charts are both used operationally and provide the UK's official weather analysis.

Inspired by questions about how these charts are made from a number of delegates at the EMS Annual Meeting in 2023, this presentation will offer a brief history of the operational surface analysis chart at the Met Office and, as one of the team of operational meteorologists producing these charts, then focus on using real world examples to illustrate the processes by which these human-produced charts are prepared.

That these charts remain human-produced, rather than automated and wholly-objective, is important to appreciate - this is felt to aid continuity between charts, can offer an indication of feature development and for many producing these charts maintains a link between the current and forecast weather situation, i.e. is a necessary part of the forecast process, and on a more romantic level for some of us these charts provide a link with our forefathers and pioneers.

How to cite: Suri, D.: The Met Office's Operational Surface Analysis Chart, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-154, https://doi.org/10.5194/ems2024-154, 2024.

The frequency and intensity of natural disasters, including high winds, floods, and droughts, are being significantly altered by climate change. This increase in activity not only causes immediate damage but also increases the long-term socioeconomic risks associated with these extreme events. As a result, the field of impact-based forecasting has emerged as an important area of research. In contrast to traditional weather forecasting, which is primarily concerned with meteorological elements such as temperature, precipitation, and wind speed, impact-based forecasting aims to predict the potential socioeconomic impacts of weather events in order to better prepare for and mitigate risks. Currently, the majority of research on impact forecasting is focused on major natural disasters such as floods and typhoons, which can cause significant damage to infrastructure and communities. However, there is still a significant gap in research on the impact of high winds on vehicles, particularly in the transportation sector. Although damage in this sector is generally low compared to more catastrophic events, the increasing frequency of high winds due to climate change requires a deeper understanding and assessment of the risks to vehicle safety. In response to this need, we developed three specific indices to assess the risk to vehicles in high wind conditions. These indices were carefully calculated and analysed to identify areas vulnerable to wind-related vehicle accidents. The results effectively recreated areas where these accidents have occurred in the past, confirming the usefulness of the indices in real-world scenarios. Through this research, we hope to lay the groundwork for an objective, data-driven assessment of the risks that high winds pose to drivers, which can better inform policy decisions, improve driver safety measures, and ultimately reduce the number of road accidents and fatalities caused by severe weather. 

Acknowledgement

This research was support by a (2022-MOIS63-002(RS-2022-ND641012)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Choo, K.-S., Choi, S.-C., and Kim, B.-S.: A Study on the Development of Wind Disaster Impact Model Based on Weather GIS Big Data, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-625, https://doi.org/10.5194/ems2024-625, 2024.

Early detection of extreme meteorological events in Numerical Weather Predictions (NWP) brings an intrinsically high societal and economical value by providing precious lead time for putting effective countermeasures in place. For this reason, an increasing number of initiatives are arising to develop the underlying technological solutions. One prominent drive in Europe is the EU flagship initiative Destination Earth (DestinE) to develop highly accurate digital models of the Earth at global scale (Digital Twins).

Under the DestinE initiative, the European Centre for Medium-Range Weather Forecasts (ECMWF) is developing a mechanism that allows extending data processing functionalities of a NWP model "on-the-fly" (while the model data still resides in memory). This mechanism is based on a software called Plume that allows the development of model functionalities as plugins. Plume plugins are separate software libraries that are loaded dynamically at the beginning of the NWP simulation, access the model data in memory and perform data processing tasks like extreme event detection. This very early access of data in memory through plugins has many advantages, including reducing the detection lead time, avoiding writing output data to disk unnecessarily and promoting collaborative development of plugins to be run with a NWP model.  

The object of this presentation is the development of a Plume plugin that performs Tropical Cyclone detection and tracking. The underlying detection algorithm is based on a Machine Learning model that analyses NWP model data in memory and performs the Tropical Cyclone detection at each iteration of the model. In addition, the plugin notifies an external notification system (Aviso) whenever an extreme event is detected (according to pre-defined criteria, for example a tropical cyclone with intensity above a threshold). This whole mechanism could be used for notifying and triggering appropriate downstream workflows in response of detected events. The Tropical Cyclone detection plugin prototype is demonstrated within the ECMWF Integrated Forecasting System (IFS).  

In summary, the Plume plugin system can add on-the-fly data processing capabilities to a NWP model through plugins and can be used to implement various data processing tasks including early extreme event detection. Furthermore, a plugin architecture preserves a modular structure of additional NWP data processing functionalities  and guarantees a collaborative development environment.

How to cite: Bonanni, A., Hawkes, J., Sarmany, D., and Quintino, T.: Early Detection of Extreme Events through NWP Plugins, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-175, https://doi.org/10.5194/ems2024-175, 2024.

Sting jets (SJ) have been a research interest in the extratropical cyclone community for the last two decades. An SJ is a descending airstream that can develop in Shapiro-Keyser-type cyclones, bringing high winds to the surface. As extratropical cyclones are usually accompanied by high wind gusts and heavy precipitation, they are considered one of the most hazardous weather phenomena in Europe, especially during wintertime. While winds are often associated with the warm or cold jet (WJ and CJ, respectively), cold-frontal convection or cold-sector winds, SJs are less common but –if occurring– are often found to have caused the highest gusts.

Recently, we introduced RAMEFI (RAndom-forest-based MEsoscale wind Feature Identification), the first objective and flexible identification tool for high-wind features within cyclones, using a probabilistic random forest. RAMEFI can be applied to observational and gridded datasets independently of spatial distribution based on eight surface parameters. However, it is challenging to distinguish the SJ from the CJ in surface parameters alone, as their characteristics are similar such that the two features have been combined in RAMEFI so far. Nevertheless, the origin and potential for damage differ. While the SJ is a descending air stream originating within the cloud head, the CJ stays at low levels throughout its lifetime. With the descent, the SJ brings high momentum from mid-levels down to the top of the boundary layer or even to the surface. This commonly creates higher winds and gusts and, thus, a separate detection is desirable.

In this work, we aim to extend RAMEFI to identify potential SJs, especially focusing on output from high-resolution numerical weather prediction models. This way we want to create a suitable alternative to the data-intensive and computationally expensive computation of Lagrangian trajectories, the most established SJ identification method. Our approach is based on a simple detection of a coherent three-dimensional region of high winds. We further use relative humidity to ensure the proximity to the cloud head following previous literature and gust speeds to connect the region to actual surface impact. Initial results using model simulations and the deterministic forecast from the German weather service (ICON-EU) show promise, with a high hit and low false alarm rate. The method provides a basis for further refinement and increasing robustness, such that it can be used on different models and resolutions.

How to cite: Eisenstein, L., Pinto, J. G., and Knippertz, P.: Towards a low-cost approach to identify sting jets in numerical models, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-646, https://doi.org/10.5194/ems2024-646, 2024.

The expanding user base of meteorological forecasts underscores the growing demand for accurate forecast interpretation, particularly among aviation professional users who rely on various applications and models. Meteorologists often face the questions not only about the reliability of these forecasts but also about the changes users observe during forecast updates. This work proposes a framework of indices aimed at providing users with relevant forecast insights tailored to their specific needs. These indices consider factors such as overall suitability for activities, mission requirements, and the reliability of forecast products. For instance, the Warning Related Source Suitability Index (WSSI) assesses the suitability of TAF forecasts in predicting conditions that meet warning threshold values. Vehicle-related feasibility index (VRFI) presents probability that vehicle with its limits will be able to accomplish the mission. The Source Uncertainty Index (SUI) describes the spread characteristics of the product at a specific time and estimates its ongoing uncertainty when continuously observed by the user. These indexes might be expanded or improved, providing potential for the novel interpretation framework.

Our research involved testing various methods for determining probabilities from numerical model archives, professional landing forecasts and TAF forecasts. The results indicate the superiority of machine learning methods, which achieved accuracy rates up to 10% higher than conditional probability (98% vs 88%) for aviation indices. 

The index framework will help simplify the output of increasingly sophisticated systems and evaluate only relevant information from meteorological big data databases. In the area of decision-making processes, the machine-readable nature of these indices supports integration into automated systems. This enables fast and informed decision making in real time. Overall, this open index system aims to enhance the understanding and trustworthiness of forecast predictions, delivering to both amateur and professional users.

How to cite: Sládek, D.: Forecast Clarity: A User-Focused Index Framework , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-596, https://doi.org/10.5194/ems2024-596, 2024.

MeteoSwiss has recently introduced multi-model ensemble postprocessing for the automatic production of its local forecasts. While this has allowed to streamline forecast production and resulted in improved forecasts compared with the legacy system, the inclusion of data-driven approaches in the forecasting process also poses unique challenges:

The statistical postprocessing produces probabilistic forecasts that are well calibrated. This often implies that forecast spread is increased compared with the underlying NWP forecasts requiring adaptation of the forecast visualizations and products. For example, producing deterministic weather symbols (pictograms) to summarize the weather evolution throughout the day is a challenge in particular during convective situations when spatio-temporal uncertainty in the forecast is very pronounced even a few hours ahead. A careful retuning of the decision tree to produce weather symbols was a key requirement for the successful introduction of postprocessed forecasts of precipitation and cloud cover.

Initially, the statistical postprocessing has been optimized for general-purpose rather than high-impact weather. In cases of weather warnings there may be notable inconsistencies between the automated forecast based on statistical postprocessing and the official warnings produced by the forecasters. Often, the statistical postprocessing underestimates the intensity of the event whereas the high-resolution NWP better reflects the observed situation. While we are currently further refining our postprocessing to adapt it to severe events, discrepancies between automated forecasts and manually tailored information will remain a communicative challenge. 

Finally, the operational  and organizational challenges of running data-driven approaches are not to be underestimated. Data-driven approaches have to be constantly maintained and monitored to avoid adverse impacts of erroneous observations and other sources of data drift. Furthermore, NWP development cycles and reforecasts for testing need to be co-designed to provide sufficient data for re-training of statistical approaches. Also, the added complexity poses a challenge when seeking to understand unexpected forecast behavior and respond to end-user feedback. While NWP characteristics are quite well known by all development and forecasting teams, knowledge on the specifics of the postprocessing is less well developed in the organization. 

While statistical postprocessing is a key component in automated forecasting, careful design of the system with a keen eye on operational constraints is necessary to manage the additional complexity. With the recent advent and proliferation of data-driven approaches in weather forecasting, we expect such considerations to become increasingly important.

How to cite: Bhend, J., Spirig, C., Nerini, D., Schaer, M., and Moret, L.: Operational multi-model ensemble postprocessing: key challenges and lessons learned, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-637, https://doi.org/10.5194/ems2024-637, 2024.

The MET Nordic dataset, developed by the Norwegian Meteorological Institute (MET Norway), offers high-resolution (1 km) near-surface meteorological variables for Scandinavia, Finland, and the Baltic countries. Derived through statistical post-processing of numerical model outputs, MET Nordic serves two main purposes: historical weather reconstruction and real-time weather monitoring with short-term forecasts. This presentation focuses on the real-time stream (MET Nordic RT), which updates hourly with a 20-minute latency and maintains an operational archive dating back to 2012.

The near-surface variables included in MET Nordic RT are: two-metre temperature, precipitation, air pressure at sea level, relative humidity, wind speed and direction, solar global radiation, long-wave downwelling radiation, cloud area fraction. The dataset combines data from the MetCoOp Ensemble Prediction System (MEPS) and diverse observational inputs, including -for temperature and precipitation- crowdsourced data from consumer-grade weather stations managed by citizens. The integration of such opportunistic data sources has enhanced the precision of reconstruction analysis and short-term forecasts, particularly temperature predictions in regions with sparse professional meteorological stations.

This study describes the automatic quality control system employed to vet incoming data, ensuring reliability in statistical processing. The system uses a range of validation techniques—ranging from basic range checks to sophisticated spatial consistency tests via Bayesian inference—to mitigate the risks posed by the inherently variable quality of crowdsourced data. Our findings underscore the importance of treating such data as a network to capitalize on its dense, high-resolution coverage. 

The quality control software, Titanlib, is important to this process and it is freely available for use at https://github.com/metno/titanlib.

The integration of data from Netatmo’s crowdsourced network into MET Nordic can be regarded as a success story. This now heavily influences MET Norway's work on upgrading our main quality control system through the internal project CONFIDENT.

How to cite: Neuville, A., Nipen, T. N., Seierstad, I. A., Båserud, L., and Lussana, C.: Integrating and validating crowdsourced data for improved weather predictions in MET Nordic, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-465, https://doi.org/10.5194/ems2024-465, 2024.

Aedes-borne diseases, such as dengue, Zika and Chikungunya, pose a grave threat to millions of people worldwide each year. Aware of potential compound effects regarding other important diseases, such as COVID-19, it has become imperative for health authorities to maintain a detailed surveillance of key environmental variables that can trigger epidemic episodes. While disease transmission is generally conditioned by multiple socio-economic factors, the environmental suitability for vectors and viruses to proliferate is a necessary –although not sufficient– condition that needs to be closely monitored and forecasted. As such, a comprehensive service that allows stakeholders to analyse and visualise environmental suitability on affected hotspots is crucial for communities to better prepare in the case of present and future outbreaks.

The newest version of the Aedes-borne Diseases Environmental Suitability (AeDES2) climate-and-health service is a next-generation, fully-operational monitoring system that reproduces and improves the previous version (Muñoz et al., 2020), broadening both the temporal and spatial scope while simultaneously enhancing both observational and forecasting quality. With AeDES2, users can consult the historical evolution of the environmental suitability values on any grid point of interest, as well as the expected future evolution up to three seasons in advance. Aside from the environmental suitability values, health authorities can additionally analyse the estimated incidence or percentage of population at risk threshold –a key indicator for governing bodies to trigger the implementation of control measures to reduce the spread of the disease in an affected population.

AeDES2 incorporates four state-of-the-art environmental suitability models, considering both epidemiological factors for transmission probability and climate variables such as temperature values. On the monitoring side, AeDES2 provides a continuously updated monthly historical sequence of environmental suitability values by generating an ensemble with multiple observational references, hence providing uncertainty estimates in the monitoring system, an improvement over the previous version. On the prediction side, still under development, AeDES2 builds on its predecessor’s pattern-based multi-model calibration approach, assimilating new Machine Learning calibration methods such as neural networks, aiming to reliably reproduce key non-linear patterns that are used as predictors in the cross-validated forecast system.

How to cite: Corvillo Guerra, J., Torralba, V., González Romero, C., Llabrés-Brustenga, A., Riviere-Cinnamond, A., and G. Muñoz, Á.: AeDES2.0: An enhanced climate-and-health service for monitoring and forecasting environmental suitability of Aedes-borne disease transmission in hotspots , EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-1061, https://doi.org/10.5194/ems2024-1061, 2024.

Visualization is an important and ubiquitous tool in the daily work of weather forecasters and atmospheric researchers to analyse data from simulations and observations. The domain-specific meteorological visualization tool Met.3D (documentation including installation instructions available at https://met3d.readthedocs.org) is an open-source effort to make interactive, 3-D, feature-based, and ensemble visualization techniques accessible to the meteorological community. Since the public release of version 1.0 in 2015, Met.3D has been used in multiple visualization research projects targeted at atmospheric science applications, and has evolved into a feature-rich visual analysis tool facilitating rapid exploration of gridded atmospheric data. The software is based on the concept of “building a bridge” between “traditional” 2-D visual analysis techniques and interactive 3-D techniques powered by modern graphics hardware. It allows users to analyse data using combinations of feature-based displays (e.g., atmospheric fronts and jet streams), “traditional” 2-D maps and cross-sections, meteorological diagrams, ensemble displays, and 3-D visualization including direct volume rendering, isosurfaces and trajectories, all combined in an interactive 3-D context.

In the past year, we have been able to significantly advance the Met.3D code base (available at https://gitlab.com/wxmetvis/met.3d) to make the tool more stable, usable, and to integrate visualization techniques not commonly available in other visualization tools. In this presentation, we present recent updates to the software and describe the status of our effort towards achieving a fully documented version 2.0. We demonstrate how Met.3D can be used for forecasting tasks, with examples including the utility of interactive visual analysis of 3-D cloud fields in combination with feature-based displays, and interactive visual analysis of trajectory data.

How to cite: Rautenhaus, M., Fischer, C., Vogt, T., Beckert, A., and Fuchs, S.: Towards Met.3D version 2: Rapid exploration of numerical weather prediction data by means of interactive 3-D visualization, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-946, https://doi.org/10.5194/ems2024-946, 2024.

How to cite: Grazzini, F., Dorrington, J., and Grams, C. M.: A hybrid approach to predict and explain extreme precipitation events over Northern Italy, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-759, https://doi.org/10.5194/ems2024-759, 2024.

The Croatian Meteorological and Hydrological Service (DHMZ) has a long experience in post-processing wind speed and wind gust variables. One of the most used methods at DHMZ is the analog method. Although notable improvements in wind speed forecasts have been observed, one of the disadvantages of the analog method is its accuracy in forecasting high wind speed episodes, especially when using an analog ensemble for deterministic post-processing (HRAN). Since onshore parts of Croatia regularly experience violent winds several times a year, leading to disruptions in electricity production and traffic, accurate high wind speed forecasts hold significant importance. 

This study investigates the impact of reducing the analog ensemble size on HRAN accuracy. Analysing wind speed forecasts from onshore locations in Croatia, we compare raw model output and HRAN with predictor weight optimisation. In the analysis, we use ALADIN-HR NWP, tuned for wind across Croatian territory, providing us with forecasts 72 hours ahead. Utilising measures for continuous forecast verification, the overall forecast quality is inspected, while the categorical approach is used for examining forecast performances for more extreme events. Generally, the highest enhancements are achieved with an analog ensemble size of 15 members. Since high winds are rare, compared to low and moderate winds, employing larger ensembles often results in overall error reduction. However, our study shows how fewer than 15 ensemble members can provide more favourable results for the highest wind categories, causing less pronounced underestimations. Thus, varying ensemble size might be an optimal way to address these issues. In the ongoing work, besides wind speed forecasts, the analog method is also used to improve visibility forecasts, focusing on low-visibility events often caused by fog.

How to cite: Lozuk, J., Odak Plenković, I., and Vujec, I.: Enhancing High Wind Speed Event Prediction through Adaptive Analog Ensemble Sizing, EMS Annual Meeting 2024, Barcelona, Spain, 1–6 Sep 2024, EMS2024-843, https://doi.org/10.5194/ems2024-843, 2024.