OSA1.1

Weather forecasts, seasonal forecasts and climate projections can in principle help their users make "good" decisions, but using the information they provide in a optimal way is far from easy. Decisions that users may need to make include whether to act now or wait for the next forecast, or select which of a series of lagged forecasts to use, when using a forecast has both costs and benefits. In this presentation, I will provide an overview of some tools that may be used to support sound decision-making based on weather forecasts. I will first present an extension of the cost-loss model applied to weather forecasts to help users "decide when they should decide". This boils down to the question of whether to make a decision now, on the basis of the current weather forecast, or to wait for the next forecast before making the decision. The later forecast is hopefully more accurate, but delaying the decision may lead to higher costs, and may thus not be the optimal choice. An analysis of this problem shows that better decisions can be made if information describing potential forecast changes is made available.  I will next explore a number of ways in which such forecast information can be presented, from changes in forecast values to changes in forecast skill. I will specifically consider unconditional forecast metrics, namely information about forecast changes that can be derived from analysis of historical forecasts. I conclude by arguing that forecast providers should consider presenting forecast change information in order to help forecast users make better decisions.

How to cite: Messori, G., Jewson, S., and Scher, S.: Decide now or wait for the next forecast?, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-10, https://doi.org/10.5194/ems2022-10, 2022.

This study shows the potential of an alert system addressed to the prediction of failures for underground distribution lines for the Milan urban area. In the summer season, one of the most widespread causes of failure in urban areas is due to a degradation of the insulating material that constitutes the joints between the underground cables. Causes are due both to the age of the joint and to the repeated stress conditions to which the electrical component can be subjected related to the abnormal and frequent increases in cable temperature during summer heat waves. In particular, the high electrical load for cooling, the high soil temperatures and the low moisture content prevent the heat dispersion of the cable (soil drying-out phenomenon). The predictors of such a system are atmospheric and soil meteorological variables that show a clear correlation with the daily failure rate. Another important variable in this forecasting system is the electrical load which represents a considerable stress source for underground power lines. In this alert system, weather forecasts come from the ECMWF IFS model, while load and fault prediction systems are based on a Random Forest machine learning method. These systems are trained with forecast predictors related to atmospheric and soil variables and using as predictands the time series of electrical load and failure provided by the distribution company of Milan. The warning system for the city of Milan is therefore defined by linking together the weather forecast to the load and fault forecasting systems. The performance of the alert system was evaluated by means of a k-fold cross validation, training the forecast system on the time series excluding one year at a time and using it each time as a test set. The system demonstrates the ability to identify quite satisfactorily the main failure events associated with summer heat waves for the period 2010-2019, albeit with an underestimation of failure peaks. This alert system can be a valuable support for the management of the distribution network in urban areas. In fact, the reliability of the electricity service in the perspective of resilience requires planner tools but also operational strategies to face this problem. This tool makes it possible to prepare sufficiently in advance all the measures necessary to mitigate the effects and reduce the time necessary to restore the energy supply service.

How to cite: Bonanno, R., Lacavalla, M., and Sperati, S.: An alert system for faults critical conditions on underground distribution lines for Milan urban area, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-108, https://doi.org/10.5194/ems2022-108, 2022.

The German Meteorological Service (Deutscher Wetterdienst, DWD) hosts the Regional Climate Centre Network of the WMO Regional Association (RA) VI Region (Europe and Middle East). One of its mandatory functions is the issuance of Climate Watch Advisories (CWA). These are early warning advisories on weather and climate events in the extended forecast range (2-4 weeks), such as heat and cold waves, heavy precipitation periods and drought, all within Europe / RA VI. The advisories are based on expert assessments of climate monitoring and extended forecast results. Users of these advisories are other National Meteorological and Hydrological services (NMHSs) in the RA VI Region. It is up to the NMHSs to turn CWAs into tailored national advisories or warnings to their end users.

One of the main criteria for issuing CWAs is the existence of any expected impact, and the advisories should also contain impact information (e.g. flooding, impact on health or agriculture). In such a large area like Europe / the RA VI Region, the impact can be very different from country to country due to the very heterogeneous climate within the Region, different infrastructure and different level of vulnerability.

To gather the impact information, an appropriate geodatabase has been established at DWD, the so-called KRONER (Knowledge Database on EuROpeaN Climate ExtRemes). Taken from various sources in the web and especially from data and reports of NMHSs, the following event information is archived as data base records: start and end time of the event, event category (flood, landslide, heat wave, etc.), related low- or high-pressure system, place (country, various administrative levels), description of the event and related impact. The data base is filled daily, and the content is checked for relevance for Climate Watch purposes by applying specific warning criteria (sufficient duration, spatial extension, extreme intensity and impact).

For the future, it is planned to transform this subjective Climate Watch system (based on expert assessment) into an objective automatized system. This new system will contain the check of warning criteria based on gridded data of preselected forecast models, the automatized generation of CWAs using text modules, automatic inclusion of impact information taken from the KRONER database, automatic visualisation of warned areas using geodata layers and automatic distribution to the users (the NMHSs).

How to cite: Bissolli, P., Rösner, S., Roebeling, M., and Körber, M.: The Climate Watch System in WMO RA VI, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-293, https://doi.org/10.5194/ems2022-293, 2022.

To meet the individual needs requested by many expert users of DWD products, the idea of a new customizable branch for our future warning system was born. Based on the prospects of a fully automatic warning system, the possibility of individual warning information could be realized by giving users the option to configure their own parameters and settings for warnings and reports. To investigate and test such a system both internally and together with our users, and also to determine the meteorological and technical requirements and challenges, a prototype named Kassandra (acronym for "configurable automated weather warning information based on individual user profiles and requirements" (translated from German)) has been developed at DWD during the last years.

By using a configurable web portal, Kassandra gives users the ability to create and manage their own profiles, which consist of warning location, period, weather element and criteria and also probabilistic or time-based thresholds. Using the wide range of available EPS models at DWD, the user configurations are periodically analyzed. Warnings or reports are generated and sent to the users by mail or messenger (PUSH) in case of transgression. The system also gives users the possibility for PULL requests at any time by using the Kassandra web portal or web API. Especially the web API enables users to include individual warning information into their own automated processes.

In this presentation the Kassandra system and its concepts are shown and also the opportunity to try it out is given at the conference.

How to cite: Niebuhr, H., Hagedorn, R., Feige, K., and Jerg, M.: Kassandra: A Prototype of a Customizable, Probabilistic Warning and Information System, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-637, https://doi.org/10.5194/ems2022-637, 2022.

How to cite: Vujec, I. and Odak Plenković, I.: Kalman Filter Post-Processing of the Wind Speed and Gusts NWP and Analog-Based Forecasts: Performing Sensitivity Tests, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-15, https://doi.org/10.5194/ems2022-15, 2022.

Accurate predictions of heavy precipitation in India are vital for impact-orientated forecasting, and an essential requirement for mitigating the impact of damaging flood events. Operational forecasts from non-convection-permitting models can have large biases in the intensities of heavy precipitation, and while convection-permitting models can perform better, their operational use over large areas is not yet feasible. Statistical postprocessing can reduce these biases for relatively little computational cost, but few studies have focused on postprocessing forecasts of monsoonal rainfall.

As part of the UK Weather and Climate Science for Service Partnership India (WCSSP India), the HEavy Precipitation Forecast Postprocessing over India (HEPPI) project has evaluated and compared two popular postprocessing methods: Univariate Quantile Mapping (UQM) and Ensemble Model Output Statistics (EMOS). The project focuses on the suitability of the methods for postprocessing heavy rainfall in India. Both methods are applied to daily precipitation in the National Centre for Medium Range Weather Forecasting (NCMWF) 12km forecast for the 2018 and 2019 monsoon seasons. The evaluation is based on day 1 forecasts and fitting the methods individually for each location.

UQM leads by construction to precipitation distributions close to the observed ones, while EMOS optimises the spread of the postprocessed ensemble without guaranteeing realistic rainfall distributions, and it is not a priori clear which method is better suited for practical applications. The methods are therefore compared with respect to several aspects: local distributions, representation of temporal variability using the Continuous Ranked Probability Score, ensemble spread using Rank Histograms, and exceedance of heavy precipitation thresholds using Brier Scores, Reliability Diagrams, and Receiver Operating Characteristics curves.

EMOS performs not only best, as expected, with respect to correcting under- or overdispersive ensembles, but also with respect to scores for temporal variability, both for the whole range of rainfall values and specifically for heavy rainfall. UQM performs best, as expected, with respect to the local precipitation distributions. The ROC results are inconclusive and location dependent, although both postprocessing methods consistently outperform the raw forecast. These findings are independent of the choice of gridded precipitation data sets used for model fitting and validation.

We recommend EMOS for operational application, as from a user perspective a good performance in forecasting values at a given time, in particular heavy precipitation events, can be expected to be more important than achieving a close match between the forecasted and observed local precipitation distributions.

How to cite: Widmann, M., Angus, M., Orr, A., and Leckebusch, G.: Postprocessing of precipitation forecasts over India with Quantile Mapping and Ensemble Model Output Statistics, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-126, https://doi.org/10.5194/ems2022-126, 2022.

The accurate forecasting of solar radiation plays an important role for predictive control applications for energy systems with a high share of photovoltaic (PV) energy. Especially off-grid microgrid applications using predictive control applications can benefit from forecasts with a high temporal resolution to address sudden fluctuations of PV-power. However, cloud formation processes and movements are subject to ongoing research. For now-casting applications, all-sky-imagers (ASI) are used to offer an appropriate forecasting for aforementioned application. Recent research aims to achieve these forecasts via deep learning approaches, either as an image segmentation task to generate a DNI forecast through a cloud vectoring approach to translate the DNI to a GHI with ground-based measurement (Fabel et al., 2022; Nouri et al., 2021), or as an end-to-end regression task to generate a GHI forecast directly from the images (Paletta et al., 2021; Yang et al., 2021). While end-to-end regression might be the more attractive approach for off-grid scenarios, literature reports increased performance compared to smart-persistence but do not show satisfactory forecasting patterns (Paletta et al., 2021). This work takes a step back and investigates the possibility to translate ASI-images to current GHI to deploy the neural network as a feature extractor. An ImageNet pre-trained deep learning model is used to achieve such translation on an openly available dataset by the University of California San Diego (Pedro et al., 2019). The images and measurements were collected in Folsom, California. Results show that the neural network can successfully translate ASI-images to GHI for a variety of cloud situations without the need of any external variables. Extending the neural network to a forecasting task also shows promising forecasting patterns, which shows that the neural network extracts both temporal and momentarily features within the images to generate GHI forecasts.

References

Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmospheric Measurement Techniques, 15, 797–809, https://doi.org/10.5194/amt-2021-1, 2022.

Nouri, B., Blum, N., Wilbert, S., and Zarzalejo, L. F.: A Hybrid Solar Irradiance Nowcasting Approach: Combining All Sky Imager Systems and Persistence Irradiance Models for Increased Accuracy, Solar RRL, 2100442, https://doi.org/10.1002/solr.202100442, 2021.

Paletta, Q., Arbod, G., and Lasenby, J.: Benchmarking of deep learning irradiance forecasting models from sky images – An in-depth analysis, Solar Energy, 224, 855–867, https://doi.org/10.1016/j.solener.2021.05.056, 2021.

Pedro, H. T. C., Larson, D. P., and Coimbra, C. F. M.: A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods, Journal of Renewable and Sustainable Energy, 11, 36102, https://doi.org/10.1063/1.5094494, 2019.

Yang, H., Wang, L., Huang, C., and Luo, X.: 3D-CNN-Based Sky Image Feature Extraction for Short-Term Global Horizontal Irradiance Forecasting, Water, 13, 1773, https://doi.org/10.3390/w13131773, 2021.

How to cite: Chaaraoui, S., Houben, S., and Meilinger, S.: End to End Global Horizontal Irradiance Estimation Through Pre-trained Deep Learning Models Using All-Sky-Images, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-505, https://doi.org/10.5194/ems2022-505, 2022.

Cold waves or cold spells, similar to warm ones, affect people causing increased mortality and morbidity. Thus, it is state obligation to organize and ensure the on time heat wave, but also the cold wave, warning system.

Similar to the heat wave warnings, issuing the cold waves warnings in Croatia is the responsibility of DHMZ - Croatian Meteorological and Hydrological Service (national weather service). However, while the criteria for issuing warnings on heat waves and the levels of these warnings are the same for National system and Meteoalarm system, the criteria for issuing warnings about the low temperature are not the same in those two systems. The mechanism by which the human body reacts to extreme cold is different from the reaction to extreme heat. Low temperatures greatly affect people, but also can significantly affect the energy supply, especially electricity, the freezing of liquids, etc. In other words, thresholds that become critical to health in some regions can differ significantly from the temperature values that can affect the energy sector i.e. economy, traffic, etc.

At present, criteria for the occurrence of cold waves that can be dangerous to human health in Croatia is determined using mortality data and air temperature data for the period 1983-2008. The three degrees of danger are determined by the degree of increase in mortality. As these is determined for just eight different areas in Croatia, and Croatia is geographically quite diverse country, there is a need to develop alarm on more detailed spatial scale. DHMZ is working on introducing the new system of cold alarm based on different, spatially more variable, thresholds for extreme temperatures.

In this presentation we will compare the use of the existing criteria for the occurrence of cold waves (based on the mortality and air temperature data thresholds) and the possible calculation of the cold waves occurance using some of the cold indices (e.g. ECACWDI, ECACWFI) - climate indices based on the daily temperature parameters.

How to cite: Magjarević, V. and Srnec, L.: Past, present and the possible future of cold wave alarm in Croatia, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-514, https://doi.org/10.5194/ems2022-514, 2022.