How to cite: Lerch, S., Freytag, J., Muschinski, T., and Allen, S.: Enhancing member-by-member post-processing with neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2005, https://doi.org/10.5194/egusphere-egu24-2005, 2024.

The possibility to use seasonal weather forecasts is of paramount importance in hydrological and socio-economical applications. However, current seasonal weather forecasts from global numerical weather prediction (NWP) models inherit systematic biases resulting from inaccurate representation and parameterization of local to global scale environmental processes. Therefore, the hydrological community frequently uses the quantile mapping (QM) statistical postprocessing for bias correction and downscaling of the meteorological inputs (i.e., daily precipitation and temperature) to hydrological models. The QM often assumes a linear and static relationship between quantiles of observed and simulated data over time. These limitations can be relaxed by employing a Neural Network (NN) based postprocessing method. In this context, the objective of this study is to compare the accuracy of QM and NN statistical postprocessing of ensemble seasonal weather forecasts over the Trentino-South Tyrol region (north-eastern Italian Alps), characterised by complex topography. 

The study uses the latest fifth-generation seasonal weather forecast system (SEAS5) total precipitation and 2m-temperature dataset produced by European Centre for Medium-Range Weather Forecast (ECMWF), available at a horizontal grid resolution of 0.125° x 0.125° with 25 ensemble members in a re-forecast period from 1981 to 2016. The respective reference dataset is a high-resolution gridded observation (250 m x 250 m) over the region of interest. The QM method derives a functional relationship between the variable of interest and the corresponding predictor, whereas the NN based methods can be used with a set of predictors to learn the linear and non-linear relationships in a data-driven way.

The analysis is divided into training (1981 – 2010, 30 years) and testing (2011 – 2016, 6 years) period to compare the cumulative ranked probability scores (CRPS) of both the statistical postprocessing methods. The statistical postprocessing is implemented univariately on the daily dataset (2m temperature and precipitation) over a month for each lead time. The raw forecasts and postprocessed forecasts are compared with particular focus on the effects of the forecast lead time and location, as well as diurnal and seasonal cycles in forecast performance. The postprocessed forecasts revealed a significant improvements compared to the raw forecasts.

How to cite: Uttarwar, S. B., Lerch, S., Avesani, D., and Majone, B.: Can neural networks outperform quantile mapping for post-processing seasonal weather forecast variables over the Alpine region?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6959, https://doi.org/10.5194/egusphere-egu24-6959, 2024.

The new programs of EUMETNET have now been launched for 5 years (2024-2028). Within this context, new activities on statistical postprocessing have been proposed, supported by 16 National Meteorological Services. One key activity that will continue is the benchmark of different statistical techniques for correcting weather forecasts. Another one is the development of ready-to-use ensemble calibration techniques that will be available to the community, in particular using machine learning techniques. Finally, several workshops will be organized on the topic during the duration of the project. In this poster, we will discuss the past achievement on statistical postprocessing using the benchmark developed in the context of the previous phase of the project, the current activities, and the future plans in comparing the statistical methods.

How to cite: Vannitsem, S. and Demaeyer, J.: The past, current and future activities on statistical postprocessing in the context of the European Meteorological Network (EUMETNET) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7878, https://doi.org/10.5194/egusphere-egu24-7878, 2024.

Precipitation nowcasting is essential for mitigating the negative effects of severe weather, necessitating accurate and timely forecasts. Our study introduces a novel Local Ensemble Transform Kalman Filter (LETKF)-based blending approach that effectively combines ensemble forecasts to provide cheap probabilistic nowcasts. Our method optimally weights ensemble probabilities by considering precipitation/radar observations as the ground truth. It shifts computed weights in time and refines neighborhood probabilities (NPs). The upscaling step for computing NPs introduces two free parameters, neighborhood size and precipitation rate, which can be selected based on the forecasters needs. Demonstrated within GeoSphere Austria's regional forecasting system using AROME ensemble forecasts over Austria, our technique was especially beneficial for short lead times of up to 2 hours. Longer lead times require to incorporate signal propagation, which is possible by shifting weights in space and time. Our study underscores the effectiveness of data assimilation techniques in enhancing ensemble blending. The proposed approach affordably improves the robustness and accuracy of short-term probabilistic forecasts and holds the potential for extending it to multi-model ensemble blending.

How to cite: Weissmann, M., Köhldorfer, S., Necker, T., and Kann, A.: Optimal blending of ensemble forecasts for probabilistic precipitation nowcasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11694, https://doi.org/10.5194/egusphere-egu24-11694, 2024.

Modern weather forecasts are typically in the form of an ensemble of forecasts obtained from multiple runs of numerical weather prediction models. Ensemble forecasts are usually biased and affected by dispersion errors, and they should be statistically corrected to gain accuracy. This is often done following a two-step approach: first, we correct the univariate forecasts, and then, we reconstruct the dependence structure non-parametrically via empirical copulas. The parametric correction of the dependence structure is limited to Gaussian copula-based methods. In this work, we propose a novel approach based on a more general parametric class of copulas called Archimedean copulas. We test the new method in both a simulated scenario and a case-study setting for multi-site temperature forecasts from the ALADIN-LAEF ensemble system in Austria. Our findings show that the state-of-the-art non-parametric techniques perform well in the simulation study. However, Archimedean copulas outperform the existing techniques, especially Gaussian copula approaches, and output well-calibrated forecasts in the real-case study. Our analysis demonstrates the usefulness of including advanced parametric copula methods in the post-processing context and the need of a more realistic simulated framework to test new methodology.

How to cite: Perrone, E., Flos, M., and Schicker, I.: Copula-based statistical post-processing for multi-site temperature forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18642, https://doi.org/10.5194/egusphere-egu24-18642, 2024.

The amount of wind and solar energy fed into the European power grid increases rapidly and with the transition to a fossil fuel-free energy production, relying heavily on renewable energy sources, more accurate predictions for both high-resolution temporal and spatial scales are needed to ensure, most of all, grid stability. This is even more the case with extreme events, both extremes in weather across the nowcasting to weeks ahead time scale and combined and non-necessarily extreme weather, events such as Dunkelflaute or longer lasting solar/wind droughts. Accurate, frequently updated and especially on-demand available predictions of the expected power production are needed. Post-processing methods enable targeted forecasts of meteorological parameters both at site-location and regional level, which can server for a conversion to power production, particularly a direct conversion of NWP predictions and observations to power production.

For an on-demand extreme digital twin forecasting system, fast and transferable post-processing methods, able to account for the upper/lower bounds of the respective distributions are needed. Furthermore, they need to be able to either generate on-the-fly (semi-)synthetic power production data or a reduced set of both observation and NWP input data. The latter is essential when moving towards hyper-resolution NWP simulations with only a limited set of training data available.

Within the on-demand extreme digital twin initiative, several post-processing methods, statistical and (deep) machine learning, were implemented and applied to selected use cases for on/offshore wind and solar production extreme events. Here, we demonstrate (i) the capability of the Kalman filter, the analogs method, IrradPhyDNet, a sequence-2-sequence LSTM, Random Forest, and other machine learning/statistical methods in extreme event prediction, (ii) evaluate the methods skills by using heterogeneous (multiple NWP models, observations, climatologies, etc.) and varying length input data and real/(semi-)synthetic power data as target, and (iii) present a workflow for an on-demand prediction for wind and solar energy production including user-interaction.

How to cite: Schicker, I., Papazek, P., Gfäller, P., Odak Plenkovic, I., Vujec, I., Kann, A., and Horvath, K.: Post-processing for an on-demand extremes digital twin – a multi-model approach for wind and solar energy production, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19254, https://doi.org/10.5194/egusphere-egu24-19254, 2024.

The raw forecasts from a numerical weather prediction (NWP) model cannot be directly used because of systematic biases. Statistical calibration is performed to produce reliable and accurate ensemble forecasts. However, this is usually done on a grid-cell by grid-cell basis, followed by the use of empirical copula to embed a realistic spatial structure in the calibrated ensemble members. One drawback of these approaches is that it is difficult to select the empirical copula. In this paper, we propose Convolutional Neural Network (CNN) based models for post-processing precipitation forecast fields and generating ensemble forecasts. Unlike the traditional approaches which are applied to individual grid-cells, the model is applied to the whole precipitation field. Monte-Carlo (MC) dropouts are used to estimate uncertainty and generate ensemble forecasts. These ensemble forecasts preserve the inherent spatial structure, thereby eliminating the need for ensemble reordering. The model is applied to NWP forecasts of Brisbane drainage basin in eastern Australia. It is evaluated on all precipitation events, including no, low and high precipitation amounts. The results show that, for all levels of precipitation, the ensemble forecasts are skillful at both the grid-cell and basin scale, and the uncertainty is estimated reliably.

How to cite: Siffat, S. A., Wang, Q. J., Whan, K., and Weyer, E.: Statistical post-processing and generation of spatially correlated precipitation forecasts with convolutional neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4610, https://doi.org/10.5194/egusphere-egu24-4610, 2024.

In aviation meteorology, as well as in water and road transportation, the accurate and
reliable prediction of visibility is of utmost importance. Despite various meteorological
services offering ensemble forecasts for visibility, the predictive accuracy and reliability for
this parameter are notably lower compared to variables like temperature or wind speed.
Therefore, it is strongly recommended to implement some form of calibration, typically
involving the estimation of the predictive distribution through parametric or non-parametric
methods, including machine learning techniques. The World Meteorological Organization
suggests that visibility observations should be reported in discrete values, turning the
predictive distribution into a discrete probability law. Consequently, the calibration process
can be simplified to a classification problem. This study investigates the predictive
performance of locally, semi-locally, and regionally trained proportional odds logistic
regression (POLR) and multilayer perceptron (MLP) neural network classifiers using
visibility ensemble forecasts from the European Centre for medium-range weather
forecasts. The findings reveal that while climatological forecasts surpass the raw
ensemble, post-processing leads to a substantial improvement in forecast skill. Overall,
POLR models exhibit superiority over their MLP counterparts.

Reference
Baran, S., Lakatos, M., Statistical post-processing of visibility ensemble forecasts.
Meteorol. Appl. 30 (2023), paper e2157, doi:10.1002/met.2157.

*Research is supported by the ÚNKP-23-3 New National Excellence Program of the
Hungarian Ministry for Culture and Innovation from the source of the National Research,
Development and Innovation Fund and the Hungarian National Research, Development
and Innovation Office under Grant No. K142849.

How to cite: Nagy-Lakatos, M. and Baran, S.: Machine learning-based discrete post-processing of visibility ensemble forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5541, https://doi.org/10.5194/egusphere-egu24-5541, 2024.

Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of forecasts for extreme events, which are often of particular interest due to the impact they have on forecast users. In this work, we reinforce previous results related to the deficiencies of proper scoring rules when evaluating forecasts for extreme outcomes, demonstrating that classes of scoring rules cannot distinguish between forecasts with the incorrect tail behaviour. Alternative methods to evaluate forecasts for extreme events are therefore required. To this end, we introduce several notions of tail calibration for probabilistic forecasts, which allow forecasters to assess the reliability of their predictions for extreme outcomes. We study the relationships between these different notions, and provide several examples. We then demonstrate how these tools can be applied in practice by implementing them in a case study on European precipitation forecasts.

How to cite: Allen, S., Koh, J., and Ziegel, J.: Tail calibration of probabilistic forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6281, https://doi.org/10.5194/egusphere-egu24-6281, 2024.

Recently, all leading meteorological centers release ensemble forecasts that vary in terms of ensemble size and spatial resolution, even when covering the same area. These factors significantly impact the forecast accuracy and computational resources required. In the last few years, the plans of upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9 km resolution and a 51-member ensemble with 18 km resolution induced an extensive study of the forecast skill of both raw and post-processed dual-resolution predictions comprising ensemble members of different horizontal resolutions.

We investigate the predictive performance of the censored shifted gamma (CSG) [1] ensemble model output statistic (EMOS) approach for statistical post-processing with the help of dual-resolution 24h precipitation accumulation ensemble forecasts over Europe with various forecast horizons. The high-resolution operational 50-member ECMWF ensemble is supplemented by a 200-member low-resolution (29-km grid) experimental forecast. The various dual-resolution combinations, which are equivalent in computational cost to the operational ensemble, show improved forecast skill after EMOS post-processing compared with raw ensemble combinations [3]. Additionally, the differences between these combinations are significantly reduced as a result of this post-processing technique. Moreover, the semi-locally trained CSG EMOS is fully able to catch up with the state-of-the-art quantile mapping [2] and provides an efficient alternative without requiring additional historical data essential in determining the quantile maps.

References:

[1] Baran, S. and Nemoda, D. (2016). Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting. Environmetrics 27, 280–292.
[2] Gascón, E., Lavers, D., Hamill, T. M. , Richardson, D. S., Ben Bouallègue, Z., Leutbecher, M. and Pappenberger, F. (2019). Statistical postprocessing of dual-resolution ensemble precipitation forecasts across Europe. Quart. J. Roy. Meteor. Soc. 145, 3218–3235.
[3] Szabó, M., Gascón, E. and Baran, S. (2023) Parametric post-processing of dual-resolution precipitation forecasts. Weather Forecast., 38(8), 1313–1322.

How to cite: Lakatos-Szabó, M., Gascón, E., and Baran, S.: On statistical calibration of dual-resolution precipitation forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7702, https://doi.org/10.5194/egusphere-egu24-7702, 2024.

How to cite: Kıvrıl, H., François, B., Schmeits, M., Whan, K., van der Kooij, E., and Squintu, A.: Statistical Post-Processing for Wind Gust and Precipitation Extremes: Insights from a Pre-Operational System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10787, https://doi.org/10.5194/egusphere-egu24-10787, 2024.

As renewable energy sources continue to account for an increasing proportion of Belgium's energy production, decision making in renewable energy production increasingly relies on accurate numerical weather prediction forecasts. For general applications, forecast validation often focuses on direct comparisons to observations for the whole domains of interest, while in this study we assess model performance specifically related to renewable energy productions. We perform extended verification of relevant variables (wind speed, temperature, solar radiation, etc.) from multiple high-resolution deterministic and ensemble weather forecast models operated in Belgium for the period of May 2021 - June 2023. The forecasts are verified with observational datasets collected from on- and offshore weather stations, masts, lidars, and wind farm observations to comprehensively understand the capabilities of the models, making use of various deterministic and probabilistic skill scores. The results show that during lead times up to two days, although verification metrics differ among models, there are systematic errors in their forecasts for different observation sites. Such errors can often be eliminated by post-processing techniques. Therefore, we extend our verification dataset, with post-processed forecasts corrected by several methods including member-by-member and AI-based approaches. The results of this work will lead to an enhanced understanding of current forecasting skills of the operational models, help to evaluate the effectiveness of goal-oriented post-processing methods, and provide a reference for Belgian sustainable energy stakeholders.

How to cite: Meng, R., Van Poecke, A., Smet, G., Demaeyer, J., Tabari, H., Hellinckx, P., Van den Bergh, J., and Termonia, P.: Enhancing Renewable Energy Forecasting: A Comprehensive Evaluation of Weather Forecast Models and Post-Processing Methods for Belgium, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16157, https://doi.org/10.5194/egusphere-egu24-16157, 2024.