In this study, we present Jua’s Vilhelm, an innovative high-resolution AI-based global precipitation forecasting system. Vihelm exhibits significant improvements over existing state-of-the-art numerical models for prediction of binary precipitation (precipitation events) of up to 48 hours in advance, demonstrating superior performance when compared to the well-established Integrated Forecast System (IFS) model. Leveraging a combination of cutting-edge deep learning techniques and an in-depth understanding of physics, Vilhelm generates high-resolution hourly global predictions for surface parameters on a 1x1km grid. Beyond precipitation, the model is particularly adept at forecasts for a number of additional critical surface parameters including wind speed, direction and air temperature. This study focuses on its performance on precipitation prediction, notably the speed in which forecasts can be produced. The model runs in a matter of seconds, enabling the execution of hundreds of ensemble runs within a small number of minutes, an accomplishment previously unattainable. To assess the performance of Jua Vilhelm against Numerical Weather Prediction (NWP) models, we selected the IFS model of the European Centre for Medium-Range Weather Forecasts (ECMWF) as a suitable comparison. The ECMWF’s ERA5 reanalysis dataset was employed as a benchmark for evaluating the Vilhelm model on a global scale, using its full 0.25-degree resolution. We benchmarked both models at 6-hour intervals for multiple initial conditions for one year. Various metrics (Accuracy, Precision, Recall, Heidke Skill Score, F1 Score, True Negative Rate and False Alarm Rate) were used for comparative analysis between IFS precipitation forecasts and Vilhelm’s model. Vilhelm’s precipitation forecasts showed better performance than IFS over all metrics for prediction of precipitation events. Furthermore, precision, recall and P-R Curve of Vilhelm model for prediction of precipitation events were calculated using SYNOP (Surface Synoptic Observations) data as ground truth data. The results demonstrated the high performance of Vilhelm model. The results showed that development of Jua Vilhelm marks a significant advancement in the field of weather forecasting, offering unprecedented accuracy and speed.

How to cite: Gabler, M. V., Wuillaud, J., Taheri Shahraiyni, H., Neupert, D., Grigoryev, A., Almeida, R., Galimzhanov, A., Hernandez, G. M., Daubinet, J. D., Ekhtiari, N., Song, R. J., Dudbridge, P., and Tarakci, E.: A Novel High-Resolution AI-based Global Precipitation Forecasting System, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-355, https://doi.org/10.5194/ems2023-355, 2023.

Application of a global downscaling method for 2m temperature is investigated, using forecasts of the ECMWF Integrated Forecast System. We work with a spatial resolution of 9km, which is destined to become the operational medium range ensemble resolution at ECMWF from June 2023. The downscaling technique is broadly based on Sheridan et al (Meteorological Applications, 2010), in which regression between model topographic elevation ("coarse grid") and model 2m temperature at an instant, in the vicinity of a gridbox in question, is the basis for lapse rate assignment for that gridbox. The rate so-derived is used to adjust temperatures onto a fine grid orography (e.g. 1km) for the said gridbox. Wherever, on the fine grid, elevation is out of the range of the coarse grid model topographic elevations in the vicinity then we use either extrapolation, for lower points, or model level data, for higher points. This overall approach should innately adapt to many meteorological scenarios - unstable, frontal, inversions, etc. - delivering much more weather-dependant lapse rate assignments for particular locations. The 2m temperatures so computed should verify much better than when using just a fixed rate assumption - e.g. -6.5K/km - as ECMWF does currently for it's very widely used meteogram products. The key attractions of this approach are: (i) the potential for large skill gains, (ii) that we exploit model "situational awareness" by indirectly incorporating the model's in-built physical processes, (iii) no training period or training data is needed, (iv) there is complete transparency for users because the lapse rate derivation is clearcut, and one can store lapse rate values on the grid in question.

In this presentation we will describe the approach and discuss the results of early investigations, using unstable and stable examples. The stronger and weaker aspects will be highlighted, discussing how to deal with coastal effects and addressing the all-important questions of search radius for defining 'the vicinity', and the regression format. One innate weaker point is the absence of any bias correction; a proposal for how this could also be built in in future, using weather-type dependant gridbox-scale bias correction from ECMWF's ecPoint post-processing method, for 2m temperature, will also be touched on.

How to cite: Höhlein, K. and Hewson, T.: 2m Temperature Downscaling with Dynamic Lapse Rates, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-301, https://doi.org/10.5194/ems2023-301, 2023.

NWP models have been a staple of modern weather forecasting for a long time. Although their skill is steadily improving, their limitations are still notable. An additional forecast improvement obtained by applying statistical post-processing for the locations where the measurements are available is thus a natural next step. Such an example of various NWPs improvements is achieved by the analog method.  For that reason, a potential post-processing contribution of the analog method is analyzed by verifying forecasts against the temperature measurements across the Republic of Croatia.

The raw NWP model used in this work is the ALADIN model with a 4 km horizontal resolution. The analyzed analog-based method includes weight optimization and the correction for more extreme values. Weight optimization aims to improve the process of similarity search by determining the ideal weight for each predictor variable, and prevents overfitting. The correction for more extreme values tends to improve forecasts on both ends of the temperature distribution by adding the correction factor if the starting NWP forecast exceeds the before-determined threshold.

The verification of the hourly temperature forecasts is performed using both the continuous and categorical approaches. In the categorical approach, the analysis is performed for both common and more extreme events. Considering the sensitivity of forecast accuracy on the terrain type, verification is also performed for different groups of stations. Results reveal the benefits of using the more advanced version of the analog method. Moreover, such an advanced analog method forecast outperforms raw NWP in most cases. When results are compared depending on the location, it is shown that the greater post-processing improvement can be seen in the continental than in the coastal area of Croatia.

Considering that the minimal and maximal daily values are a pragmatic way to provide forecasters with a quick and summarized overview, such forecasts are additionally examined. In addition to the statistical verification approach, a few interesting cases were also singled-out and analyzed more thoroughly. Finally, the amplitude of diurnal temperature variation seems to be more realistic for the analogs during heat or cold waves.

How to cite: Vujec, I. and Odak Plenković, I.: Analog-based post-processing of the operational temperature NWP in Croatia, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-441, https://doi.org/10.5194/ems2023-441, 2023.

Convection-permitting numerical weather prediction (NWP) models are a useful tool to forecast high-impact phenomena such as storms and heat waves. Ensemble prediction systems based on such models can then be used to quantify the associated forecast uncertainties. However, high-resolution ensemble forecasts come with a high computing cost; this limits the size of operational ensembles, and potentially reduces their relevance in critical situations.
In this work we propose a new and computationally efficient way to synthesize additional ensemble members of the kilometer-scale AROME model,based on deep generative models. For that purpose, state-of-the-art style-based generative adversarial networks (StyleGAN) and denoising diffusion probabilistic models (DDPM) are examined and compared on the joint generation of 10-meter wind and 2-meter temperature forecasts. We first show that these neural networks, once properly trained, create physically consistent, realistic, multivariate ensemble members. We then propose several methods to condition the generated members on the NWP model outputs 'of the day', in order to produce large hybrid physical/statistical ensembles at a small numerical cost. In particular, we show how to leverage the latent representations learnt by the networks to control the resulting ensemble statistics. A thorough evaluation of the resulting hybrid ensembles is proposed to assess their relevance and the new information they add with respect to small, pure NWP ensembles. Finally, we compare large, deep-learning-generated ensembles to a 875-member AROME forecast on a situation corresponding to a weather alert on the Mediterranean region, and evaluate the ability of generative approaches to provide an acceptable approximation of this large NWP ensemble.

How to cite: Brochet, C., Moldovan, G., Raynaud, L., and Plu, M.: Using state-of-the-art generative neural networks for high-resolution NWP ensemble emulation, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-402, https://doi.org/10.5194/ems2023-402, 2023.

The Met4Airports project aims to apply methods of machine learning (ML) and artificial intelligence (AI) in order to provide predictions of the weather impact on planning and control parameters relevant for air traffic management (ATM), such as values of single and average flight delays as well as capacity values of runways and en-route airspace sectors.

For this purpose, air traffic data such as pre-scheduled flight lists and up-to-date time stamps from the A-CDM system (Airport Collaborative Decision Making) are combined with meteorological forecast data from nowcasting and numerical weather prediction models, with the ML models being trained on corresponding historical data sets.

While different approaches for modeling of the air traffic system regarding temporal discretization and sampling were investigated and extensive feature engineering and feature importance studies were conducted, a major challenge was the selection and optimization of appropriate ML model architectures to process the 2-dimensional data input from NowCastMix-Aviation (NCM-A) and the ICON-D2 model. Within this scope, it was found that comparatively simple multilayer perceptrons (MLPs), for whose data input the 2D NCM/ICON arrays are pooled in advance, show a better performance for all use cases than convolutional neural networks (CNNs), which are a staple of modern image processing.

Meteorological feature studies have been performed, aimed at determining the size of the relevant area around the airports, the required spatial resolution of the weather forecasting data and the relevant meteorological model prediction parameters. Additionally, also systematic hyperparameter studies were performed on all considered ML models.

Ultimately, the developed preliminary prototypes not only have shown to provide viable impact predictions on a set of thunderstorm days which were selected for historical process testing, but apparently also yield advantages over the already available information from the A-CDM system, which constitute a comparative baseline. A detailed insight on the validation and testing of the ML model predictions is given by Knigge et al. [1].  Furthermore, it was found that the temporal and spatial accuracy of the weather forecast provided by the ICON-D2 model at lead times of up to 24 hours is generally good enough for the impact prediction on ATM target parameters.

The project is funded by the German Federal Ministry for Digital and Transport (BMDV), coordinated by Deutscher Wetterdienst (DWD) and developed in close cooperation with the project partners ask – Innovative Visualisierungslösungen GmbH, Deutsche Flugsicherung (DFS), Fraport AG and Flughafen München GmbH (FMG). 

[1] Knigge et al. Testing and validation of forecasts for weather-induced operating restrictions in air traffic management based on Machine Learning models, OSA2.4 Reducing weather risks to transport: air, sea and land, submitted for EMS 2023.

How to cite: Koser, D., Knigge, C., Beckmann, B.-R., Zinkhan, D., Beumer-Aftahi, H., Müller, B., Melzer, A., Breitruck, I., Seitz, S., and Estrella, H.: Development and optimization of Machine Learning methods to predict weather-induced operating restrictions in air traffic management, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-274, https://doi.org/10.5194/ems2023-274, 2023.

Developing hectometric scale numerical weather prediction (NWP) models requires an accurate, high-resolution physiography dataset to represent surface heterogeneity in calculating surface-atmosphere exchanges. Land cover is one of the main components of physiography datasets. Unfortunately, the resolution of land cover datasets currently used in NWP is too coarse to fulfil the needs of hectometric NWP.

High-resolution remote sensing imagery and machine learning techniques have enabled the emergence of 10 m resolution global land cover maps such as  the Environmental Systems Research Institute 2020 (ESRI2020), European Space Agency (ESA) WorldCover, and the second generation Coordination of Information on the Environment land cover  (CLC+). These dataset labels are too generic to be directly implemented into NWP. Other high-resolution datasets can provide information about specific themes, such as the Copernicus dominant leaf type (DLT) 2018 and the Global map of Local Climate Zones, or national/regional information, such as the Swedish national land cover database 2018. While plenty of high-resolution physiography resources exist, none cover NWP needs.

This work aims at developing a high-resolution version of ECOCLIMAP-SG, the land cover map used operationally at Met Éireann, using supervised machine learning techniques. Supervised machine-learning techniques are widely used for land cover mapping but require a reference dataset. Here we propose to develop a reference dataset using multiple data sources and bagging multiple decision trees. We will complement this dataset with artificial (or synthetic) data using Geometric Synthetic Minority Oversampling Technique (G-SMOTE). Eventually, we will compare supervised machine-learning techniques such as convolutional neural networks or random forests.

How to cite: Bessardon, G., Rieutord, T., and Gleeson, E.: Developement of a land cover map for hectometric numerical weather prediction using machine learning, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-482, https://doi.org/10.5194/ems2023-482, 2023.

Fog is one of the severe meteorological phenomena, causing difficulties in transportation activities by air, road, or sea. Improving fog prediction methods is of great importance to society as a whole. Our study presents a fog forecast at the Poprad-Tatry Airport, Slovakia, where we used various machine learning algorithms (support vector machine, decision trees, k-nearest neighbors) to predict fog with visibility below 300 m for a lead time of 30 minutes. This research was carried out in the framework of the SESAR research project PJ.04-29.2. The novelty of the study is represented by the fact that beyond the standard meteorological variables as predictors, the forecast models also make use of information on visibility obtained through remote camera observations. The cameras help observe visibility using tens of landmarks at various distances and directions from the airport. The best-performing model achieved a score level of 0.89 (0.23) for the probability of detection (false alarm ratio). One of the most important findings of the study is that the predictor, defined as the minimum camera visibilities from eight cardinal directions, helps improve the performance of the constructed machine learning models in terms of an enhanced ability to forecast the initiation and dissipation of fog, i.e., the moments when a no-fog event turns into fog and vice versa. Camera-based observations help overcome the drawbacks of automated sensors (which predominantly measure points) and human observers (which have lower frequency observations but are more complex), and offer a viable solution for certain situations, such as the recent periods of the COVID-19 pandemic.

How to cite: Ivica, L., Bartok, J., Sisan, P., Bartokova, I., Malkin Ondik, I., and Gaal, L.: Machine Learning-Based Fog Nowcasting for Aviation with the Aid of Camera Observations, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-129, https://doi.org/10.5194/ems2023-129, 2023.

Renewable Energy is gaining more importance in tackling globally growing energy demands. However, time-series obtained form production sites are often limited in time-horizon and also different in nature due to present technology in power plants. Due to their high resource demands high-resolution NWPs, such as AROME, can often not be fully stored long-term and change significantly in each update cycle, making them a challenging input for data hungry deep learning approaches. Still, in short- and medium range forecasts selected NWP parameters often improve forecast results of machine learning approaches.

In this study, we investigate how to setup a synthetically generated/extended training dataset based on diverse spatially and temporary heterogeneous inputs. For practical reasons, we concentrate on a solar power production case study. Data considered includes NWP models (e.g.: AROME, ECMWF), satellite data and products (e.g.:  CAMS), radiation time series from remote sensing, and observation time-series. In particular, we present a machine learning methodology on extending time-series from AROME in accordance with close observations.  Our synthetic data generator learns from long observation time series, and longer available but still coarse ECMWF. It yields both synthetic observations for missing data in the observation, synthetic production optimized for a relatively new established site, as well as a prolongation of numeric model data for studied renewable energy sites.

Results are evaluated by our deep learning framework (e.g.: LSTM) and cross-validation or achieved data of the data-set. The combination of real and synthetic data generally offers benefits in forecasting by more complex machine learning methods relying on sufficiently long training data-sets.

How to cite: Papazek, P. and Schicker, I.: Synthetic Data Generation for Deep Learning based Renewable Energy Forecasts, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-394, https://doi.org/10.5194/ems2023-394, 2023.

Forecasting solar energy is crucial for enabling its better integration into the energy mix. Short-term forecasts (up to 6h) have various applications, including grid integration and management, energy trading, energy storage management, and microgrid management. Photovoltaic (PV) production is highly variable and directly depends on solar irradiance reaching the Earth's surface. The stochastic component of solar irradiance variability is primarily related to its attenuation by the clouds.

Currently, cloud motion analysis methods applied to geostationary meteorological satellite images yield better results than Numerical Weather Prediction (NWP) models for short-term irradiance forecasting. Yet, their performance is limited in particular situations, such as convective or unstable cloud conditions. Deep Learning methods may overcome these limitations because of their ability to automatically extract complex spatiotemporal cloud patterns.

Prior to implementing a Deep Learning method, it is essential to analyse the data in order to understand its spatiotemporal characteristics. Clustering cloud patterns might allow us to better characterize cloud cover evolutions in distinct weather situations, thereby enabling the development of more accurate short-term solar energy forecasting models. Our research establishes a foundation for partition-based forecasts using multiple models, each model being adjusted to the distinct traits of each group.

In this work, we conduct a comparative analysis between traditional feature extraction techniques (e.g., statistical, textural, and temporal features) and Deep-Learning-based feature extraction (e.g., autoencoding) to evaluate their ability to discriminate specific cloud pattern evolutions (e.g., advections, convection, multi-layer, appearance/disappearance). Additionally, we assess and compare several clustering methods (e.g., K-Means and its variations, Hierarchical Clustering, Gaussian Mixture Model) using the extracted features as the input. Then, we evaluate the quality of the final partitioning using quantitative criteria and studying its physical consistency. Finally, we conduct an extensive study of the partitioning results, highlighting useful insights about the data such as the average duration of a given meteorological phenomenon. For this study, we use cloud albedo images derived from the HRV channel of the Meteosat Second Generation’s (MSG) SEVIRI instrument and focus on the Paris area.

The proposed feature extraction method enables us to perform clustering analysis that effectively distinguishes identifiable meteorological situations and cloud pattern evolutions. This is preliminary work for the development of a partition-based Deep Learning model for short-term solar energy forecasting, with the goal of addressing complex and diverse forecast scenarios using multiple models.

How to cite: Chea, N., Cros, S., Guillon, S., Badosa, J., Tuomiranta, A., and Haeffelin, M.: Cloud patterns clustering in geostationary satellite imagery: Investigating traditional and Deep-Learning-based feature extraction techniques for solar energy forecast, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-434, https://doi.org/10.5194/ems2023-434, 2023.

Accurate estimations of Surface Solar Irradiance (SSI) are of high interest in domains as varied as climatology, solar energy, architecture, and agriculture. SSI estimations derived from meteorological satellites enable continuous spatial and temporal coverage and have thus become an important source of information standardly used for the planning, operation, and forecast of the production of PV power systems. To infer SSI from satellite images is, however, not straightforward; since the eighties, multiple satellite-based retrieval approaches have been proposed, from the earlier cloud index methods to physically based ones. Recent approaches are emerging based on machine learning (ML), inferring a direct data-driven model between images acquired from satellite and SSI ground measurements. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is not to propose a new ML-based method but to better understand the promises and limitations of this new coming family of methods.

To do so, we implement simple multi-layer-perceptron models with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements. To test the model's ability to generalize in time and space, we use different locations and time periods for training and testing. How we allocate measurement stations to each group is also a crucial factor. To understand our model behavior, we study two setups. In the first setup, stations are randomly assigned to each group, resulting in distinct but spatially interlaced training and test stations. In the second setup, we enforced strict and large geographical separation, allowing us to evaluate the model's performance in locations outside its training area. In both cases, the performance of the ML-based retrieval model is compared to that of the operational CAMS radiation service (CRS), which is based on Heliosat-4, a state-of-the-art physical retrieval model.

Our results show that the data-driven model’s performance can be much better than CRS but is very dependent on the training set, raising problems of generalization. Indeed, in the first setup, the ML model has a Root Mean Square Error (RMSE) almost 20% lower than CRS, but in the second training setup – when training and test stations are geographically separated, CRS RMSE is only 4% higher. Perhaps more critically, in the first setup, the ML model RMSE is lower or comparable to that of CAMS for all test stations but in the setup enforcing geographical separation, the ML model underperforms dramatically for several test locations.

ML models have great potential for satellite retrieval, but their inability to generalize in certain configurations could be critical and hinder their deployment to regions with sparse measurement networks. A hybrid approach combining data-driven and physical models seems to be of interest for further research activities.

How to cite: Verbois, H., Becquet, V., Saint-Drenan, Y.-M., Gschwind, B., and Blanc, P.: Promises and limitations of machine-learning-based methods for satellite retrieval of solar surface irradiance, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-460, https://doi.org/10.5194/ems2023-460, 2023.

Even though sub-seasonal weather forecasts are crucial for the decision making in many sectors including agriculture, public health and renewable energy production, sub-seasonal predictions are hardly used due to their low skill. We propose several post-processing approaches based on convolutional neural networks (CNNs) to improve and calibrate sub-seasonal forecasts from numerical weather prediction (NWP) models. All proposed methods work directly with the spatial NWP forecasts and are therefore able to retain spatial dependencies in the forecasts. Moreover, these methods have the potential to exploit the predictive information in the spatial structure of the NWP forecasts.

The proposed post-processing models use forecast fields of multiple meteorological variables as input, and produce global probabilistic tercile forecasts for biweekly aggregates of temperature and precipitation for weeks 3-4 and 5-6, as commonly done in sub-seasonal to seasonal prediction. The model architectures and the training strategy are optimized to deal with the low signal-to-noise ratio in sub-seasonal forecasts and the limited amount of training data. Half of the tested architectures use the well-known UNet architecture specifically designed for image segmentation. The remaining architectures are based on a standard CNN as typically used for image classification, with the difference that it estimates coefficient values for a set of basis functions to provide spatial predictions.

All post-processing methods improve precipitation and temperature forecasts for both lead times. Improvements increase with lead time and are larger for precipitation, for which the NWP forecasts show no skill. Our best performing model is based on the UNet architecture and trained directly on the global NWP forecasts. The post-processed forecasts are substantially less sharp than the respective probabilistic forecasts from ECMWF for all tested methods. We demonstrate that the post-processed forecasts are well calibrated in contrast to the ECMWF benchmark using a calibration simplex, a reliability diagram for probabilistic tercile forecasts. Thus, our proposed post-processing methods are able to derive a reliable uncertainty estimate based only on ensemble mean NWP forecasts.

How to cite: Horat, N. and Lerch, S.: Deep learning for post-processing global probabilistic forecasts on sub-seasonal time-scales, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-211, https://doi.org/10.5194/ems2023-211, 2023.

How to cite: Liu, Y., Schilperoort, B., Kalverla, P., Vijverberg, S., van Ingen, J., Alidoost, S., Verhoeven, S., and Coumou, D.: Data-driven s2s forecasting with s2spy & lilio, EMS Annual Meeting 2023, Bratislava, Slovakia, 4–8 Sep 2023, EMS2023-501, https://doi.org/10.5194/ems2023-501, 2023.