4-9 September 2022, Bonn, Germany

OSA1.1

OSA1
Forecasting, nowcasting and warning systems 

This session presents and explores the increasingly sophisticated systems developed to aid, and often automate, the forecasting and warning process. The rapid proliferation of data available, including probabilistic and rapidly-updating NWP as well as a plethora of observations, combined with a growing appreciation of user needs and the importance of timely and relevant forecasts, has brought the development of these systems to the fore. The opportunities afforded by the WMO's HiWeather programme will also be discussed in this session.

Topics may include:
• Nowcasting systems
• Links to severe weather and severe weather impacts
• Automated first guess warning systems
• Post-processing techniques
• Seamless deterministic and probabilistic forecast prediction
• Use of machine learning and other advanced analytic techniques

Including Young Scientist Conference Award
Conveners: Timothy Hewson, Yong Wang | Co-conveners: Bernhard Reichert, Fulvio Stel
Orals
| Mon, 05 Sep, 16:00–17:30 (CEST)|Room HS 7, Tue, 06 Sep, 09:00–10:30 (CEST)|Room HS 7
Posters
| Tue, 06 Sep, 16:00–17:15 (CEST) | Display Tue, 06 Sep, 08:00–18:00|b-IT poster area

Orals: Mon, 05 Sep | Room HS 7

Chairperson: Bernhard Reichert
16:00–16:15
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EMS2022-10
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CC
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Onsite presentation
Gabriele Messori, Steve Jewson, and Sebastian Scher

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.

16:15–16:30
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EMS2022-108
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Onsite presentation
Riccardo Bonanno, Matteo Lacavalla, and Simone Sperati

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.

16:30–16:45
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EMS2022-254
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Onsite presentation
Stephen Moseley, Ken Mylne, Bruce Wright, Simon Jackson, Fiona Rust, Gavin Evans, Ben Ayliffe, Kat Hurst, Marcus Spelman, Ben Fitzpatrick, and Chris Sampson

IMPROVER (Integrated Model Post-Processing and Verification) has been developed by the Met Office as an open-source probability-based post-processing system to fully exploit our convection permitting, hourly cycling ensemble forecasts. Post-processed MOGREPS-UK model forecasts are blended with deterministic UKV model forecasts and data from the coarser resolution global ensemble, MOGREPS-G, to produce seamless probabilistic forecasts from now out to 7 days ahead. For precipitation, an extrapolation nowcast is also blended in at the start. Forecasts are converted to probabilities at the start, and all initial stages of post-processing are performed on gridded data, with site-specific forecasts extracted as a final step, helping to ensure consistency. Data are processed on a 10km global grid and on a 2km UK-centred grid.

This talk will briefly describe the post-processing sequence from raw NWP model data to fully-blended, gridded and spot forecasts as probabilities and percentiles of a broad range of meteorological diagnostics, with the application of physical and statistical post-processing techniques. The system became operational in early May 2022, and the this talk will focus on some of the work that has been undertaken in the last year to achieve this, including ensemble calibration of temperature and wind speed data at observed and non-observed sites. There will be discussion of some of the verification used to prove this system, as well as a brief look at the technical aspects of this complex system, the initial customers and collaborators and the planned future work, including the use of ECMWF forecast data to extend the range of IMPROVER out to 14 days.

How to cite: Moseley, S., Mylne, K., Wright, B., Jackson, S., Rust, F., Evans, G., Ayliffe, B., Hurst, K., Spelman, M., Fitzpatrick, B., and Sampson, C.: IMPROVER overview and updates, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-254, https://doi.org/10.5194/ems2022-254, 2022.

16:45–17:00
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EMS2022-293
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Online presentation
Peter Bissolli, Stefan Rösner, Maarit Roebeling, and Maya Körber

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.

17:00–17:15
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EMS2022-637
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CC
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Onsite presentation
Heiko Niebuhr, Renate Hagedorn, Kathrin Feige, and Matthias Jerg

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.

17:15–17:30
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EMS2022-220
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CC
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Onsite presentation
Joshua Dorrington, Christian Grams, Federico Grazzini, Laura Ferranti, Linus Magnusson, and Frederic Vitart

Numerical weather prediction models still struggle to accurately forecast extreme precipitation events, both due to their relative rarity and the length-scales of the processes involved, which fall below the grid scale and so must be parameterised. However, extreme precipitation events are by no means decoupled from the large-scale flow: the prevailing winds and wave-structures ‘set the scene’ for amplified or suppressed risk of an extreme, by controlling moisture availability and vertical stability. This is especially true in the mid-latitudes where frontal rainfall is dominant.

The idea that explicitly resolved large-scale circulation patterns, may serve as potentially skilful precursors to extreme precipitation has been explored in a number of studies, investigating precursor patterns to flooding in particular geographical areas, in both process-oriented and regime-based approaches. However, such insights are not yet easily extensible to other regions, or available to the operational meteorologist.

 

We will report on the development of a new flexible tool to identify the precursors of extreme events, assess their usefulness as predictors, and produce statistical forecasts of increased event probability. This will allow the leveraging of physical insight to add value to traditional NWP. We will use results found for UK and Northern Italy extreme precipitation as motivational examples, exploring time-scales ranging from the medium range out to the sub-seasonal. Development of this tool is driven by applications to operational meteorology, but we also see considerable opportunities for application in an academic setting, by streamlining studies of large-scale physical processes and enabling the use of precursor chains in model validation and bias analysis.

How to cite: Dorrington, J., Grams, C., Grazzini, F., Ferranti, L., Magnusson, L., and Vitart, F.: A tool to identify large-scale dynamical precursors to European extreme precipitation, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-220, https://doi.org/10.5194/ems2022-220, 2022.

Orals: Tue, 06 Sep | Room HS 7

Chairperson: Fernando Prates
09:00–09:15
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EMS2022-15
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Young Scientist Conference Award
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Onsite presentation
Ivan Vujec and Iris Odak Plenković

Weather forecasting is based on the NWP models for a long time. Although their skill is constantly improving, their limitations are still substantial. This is especially the case in the complex terrain, even when referring to the high-resolution mesoscale models. Besides, NWP models require massive computational resources, and their requirements grow excessively with the refinement of the spatiotemporal scale. Thus, in addition to the further model development, the forecast improvement can be obtained by statistical post-processing of raw NWP forecasts for the locations where measurements are available. The post-processing methods analyzed in this work are based on the analog and Kalman filter (KF) approach. 

The focus of this study is to perform a sensitivity test to find the optimal value of the variance ratio r in the KF algorithm for different forecasts. The KF algorithm is applied to generate four different forecasts: KF (KF applied to the raw NWP time-series), KFAN (KF applied to the analog-method time-series), KFAS (KF applied to NWP forecasts in analog space), and KF-KFAS (KF applied to the KFAS time-series). The post-processing is applied to the point-based predictions of wind speed and wind gust. The forecasting range is 72 hours, and the measurements include 61 locations across the Republic of Croatia.  

The wind forecasts are analyzed considering the variables as both continuous and categorical. The continuous verification relies on RMSE decomposition, spectral analysis, and quantile-quantile plot. The categorical verification includes the equitable threat score, frequency bias measure, extremal dependence index, and frequency measure. The verification is conducted to assess the improvement for both the overall data and for the relatively extreme events. The results suggest that for the KF and KFAS forecasts the r-value of 0.01 is recommended, whereas for the KFAN and KF-KFAS forecasts the r-value should be set to 0.001. Even though different r-value implementations yield certain trade-offs, the proposed r-values are considered optimal since they lead to excellent results for the overall data, and the results remain adequate even for strong wind. 

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.

09:15–09:30
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EMS2022-126
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Onsite presentation
Martin Widmann, Michael Angus, Andrew Orr, and Gregor Leckebusch

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.

09:30–09:45
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EMS2022-251
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Onsite presentation
Alan Demortier, Olivier Caumont, Vivien Pourret, and Marc Mandement

The rise of connected objects, such as personal weather stations (PWSs), and the availability of their data, have opened up a new field of possibilities for real-time weather observation. For instance, the Netatmo PWS network measures temperature, relative humidity and pressure near the surface at high spatial density over France with around 50,000 stations. Nevertheless, such stations have measurement errors, e.g. pressure sensor has vertical calibration and drifting issues, thus they need to be processed. The objective here is to assess the relevance of assimilating these pressure observations into an operational kilometre-scale numerical weather prediction system such as AROME-France, which uses a three-dimensional variational (3D-Var) data assimilation scheme. And, despite the rich literature describing efficient correction methods, two specific criteria have to be taken into account in our study. First the data need to be coherent with standard weather station (SWS) observations actually assimilated and secondly the processing method has to respect near-real time limitations.

In a first phase, a pre-existing correction and quality control method was adapted.  The correction is based on the interpolation of the SWS observations at PWS's locations (called reference interpolation) in order to subtract an average temporal bias. Then, a data quality control is applied in order to remove the PWS time series whose values deviate too far from the reference interpolation time series, with a non-stationary threshold. In this way, we ensured the quality of the data and the scale of their representativeness. In a second phase, two experiments assimilating the PWS observations were carried out. In a first experiment, PWS biases with respect to the model's background during a selected time window are corrected before PWS observations are quality controlled by the operational screening. In a second experiment, the pre-processing method previously described is used. A thinning at 1.3 km scale is added in order to reduce the high density in the cities which could deteriorate the analysis.
Those configurations serves as a benchmark to beat, as it is clear that some adjustments must be optimized both on the quantification of the observation error, and on the kind and scale of the thinning.

Results show that, on average over the month of August 2020, the PWSs help to bring closer the analysis to SWSs. In addition, some convective events have been studied, showing the limits of the current assimilation methods and the progress to be made.

How to cite: Demortier, A., Caumont, O., Pourret, V., and Mandement, M.: Added value of assimilating surface pressure observations from personal weather stations in AROME-France, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-251, https://doi.org/10.5194/ems2022-251, 2022.

09:45–10:00
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EMS2022-505
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Onsite presentation
Samer Chaaraoui, Sebastian Houben, and Stefanie Meilinger

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.

10:00–10:15
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EMS2022-377
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CC
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Onsite presentation
Christoph Spirig, Jonas Bhend, Stephan Hemri, Jan Rajczak, Daniele Nerini, Lionel Moret, and Mark A. Liniger

MeteoSwiss is currently implementing a new NWP postprocessing suite for providing automated local weather forecasts to the general public. As these forecasts are nowadays mainly accessed via smartphone app, we aimed at global postprocessing approaches, that is, optimizing forecasts not only at observation sites but at any location in Switzerland. The system takes advantage of both regional area and global NWP ensemble models with different forecast horizons for providing seamless probabilistic predictions over two weeks leadtime. Finally, the postprocessing suite also considers operational aspects such as robustness towards missing or delayed input data or the ability to cope with limited reforecasts records for training.

Both ensemble model output statistics (EMOS) and machine learning (ML) methods are applied for postprocessing the target parameters temperature, precipitation, wind and cloud cover. Forecast skill in terms of CRPS improves by up to 30% compared to direct model output, with largest benefits for temperature and wind in areas of complex orography and only marginal gains for precipitation during seasons with a high fraction of convective situations. The postprocessing of multiple NWP sources not only allows seamless forecasts, but also proved more skillful than single-model postprocessing. EMOS postprocessing performed well even in case only short reforecast records were available, but was outperformed by ML approaches given sufficient training data. While this general-purpose postprocessing suite improves forecasts overall, it showed weaknesses in some warning-relevant weather situations. Future developments will aim at extending its applicability to these less frequent situations, target further parameters, and extending the use of ML methods.

How to cite: Spirig, C., Bhend, J., Hemri, S., Rajczak, J., Nerini, D., Moret, L., and Liniger, M. A.: Lessons learnt from implementing a postprocessing suite for probabilistic seamless weather forecasts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-377, https://doi.org/10.5194/ems2022-377, 2022.

10:15–10:30
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EMS2022-514
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Onsite presentation
Vjeran Magjarević and Lidija Srnec

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.

Display time: Tue, 6 Sep 08:00–Tue, 6 Sep 18:00

Posters: Tue, 6 Sep, 16:00–17:15 | b-IT poster area

Chairperson: Fernando Prates
P9
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EMS2022-225
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Onsite presentation
Iris Odak Plenkovic, Irene Schicker, Markus Dabernig, Alexander Kann, and Aitor Atencia

Analogies between similar past forecasts, measurements, or analyses are a potentially useful tool when the training dataset is long enough, thus enabling an adequate identification of true analogs. Reducing the number of degrees of freedom in the matching procedure makes the analog-based method an excellent candidate for point-based post-processing. However, accurate forecasts at remote locations are used to drive many user-specific applications (e.g., road temperature forecasts along an entire roadway or wind speed for windfarms). For that reason, besides the point-based post-processing for the measuring sites, there is also an increasing demand for gridded (2D) products. The latter is a direct motivation for the adaptation of analog-based method to produce gridded output based on an analysis.

In this work, the control member of the ECMWF ensemble forecast is used as a raw forecast as input to the analog method, whereas the gridded INCA analysis fields are used similar to the observations in the point-based analog approach. All experiments use wind speed and direction variables as predictors, normalized by standard deviation. The domain is defined by ECMWF resolution and INCA domain size, covering Austria. The first experiment (EX1) is based on the simplest transfer from point-based to gridded products: treating every grid point as an independent location. Alternatively, an average error on the entire field is used to choose the most similar historical field (EX2). The latter generates comparable results to the EX1 when using training datasets of the same length. In addition to these experiments, a simplification of the procedure for choosing the best analogs using empirical orthogonal functions (EOF) is also tested (EX3). The training data is used to calculate EOFs for EX3, and training is thus saved as EOFs and corresponding principal components (PCs) time series. Results show that using only a few EOFs might capture the majority of the variance in the training. The best match to the current raw forecast is determined using PCs. Even though EX3 is not as skillful as EX1 and EX2, the approach using EOFs is notably more computationally efficient. For that reason, it is possible to include longer training than for EX1 and EX2. Moreover, the setup can be further optimized to improve results (e.g., increasing the number of predictors or implementing a predictor-weighting strategy), which is a natural next step in future work.

How to cite: Odak Plenkovic, I., Schicker, I., Dabernig, M., Kann, A., and Atencia, A.: The analog-based gridded data post-processing, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-225, https://doi.org/10.5194/ems2022-225, 2022.

P10
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EMS2022-705
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Onsite presentation
Reinhold Hess

Reliable forecasts of heavy precipitation are one of the most difficult challenges in weather forecasting. Highest precipitation rates of convective events usually realise more likely in the surrounding of rain gauges rather than exactly above their small funnels and are therefore not always detected by this observation system. Precipitation rates of more than 15 mm per hour are captured only about once a year at each rain gauge within Germany and more extreme events are even less frequent. Statistical forecasts of these point observations often underestimate maximum rain rates and result in low probabili­ties for the occurrence of heavy precipitation at a given location.

The spatial coverage of DWD’s radar network allows to detect local precipitation events in the vicinity of synoptic stations and increases representativity and predictive skill of statistical forecasts of heavy convection. The suggested approach derives spatial 95%-quantiles in circular surroundings of 40 km radius as an innovative forecast product in order to support forecasters to rate upcoming heavy precipative events. 95%-quantiles of rain gauge adjusted radar precipitation are chosen in order to estimate the maximum precipitation amount in the circular surroundings, whilst they are robust against spurious spikes in the radar data.

For statistical training several years of data are used in a model output statistics (MOS) approach that is based on numerical ensemble forecasts of the COSMO-DE/2-EPS of DWD. Resulting statistical forecasts are presented for different relevant precipitation scenarios. Probabilities for heavy precipitation rates show enhanced signals for upcoming convective and also stratiform events compared to corresponding statistical forecasts based on conventional synoptic observations. Verifications of raw COSMO-DE/2-EPS forecasts are likewise included.

Main focus, however, are the relevance and acceptance of the presented new radar product for forecasters and the public with regard to weather warnings of heavy precipitation.

How to cite: Hess, R.: A radar based statistical forecast product for heavy precipitation, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-705, https://doi.org/10.5194/ems2022-705, 2022.

P11
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EMS2022-518
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Onsite presentation
Andreas Beckert, Lea Eisenstein, Timothy Hewson, Annika Oertel, George Craig, and Marc Rautenhaus

Atmospheric fronts are a widely used conceptual model in meteorology, most encountered as two-dimensional (2-D) front lines, e.g., on surface analysis charts. The three-dimensional (3-D) dynamical structure of fronts is commonly sketched in 3-D illustrations of idealized weather systems in atmospheric science textbooks. Only recently the feasibility of objective detection and visual analysis of “real” 3-D frontal structures within numerical weather prediction (NWP) data has been proposed, and such approaches are not yet widely known in the atmospheric community. In our work, we investigate the benefit of objective 3-D front analysis for case studies of atmospheric dynamics and forecasting. Our technique builds on a recent gradient-based detection approach, combined with modern 3-D interactive visual analysis techniques, all integrated into the open-source meteorological visualization framework Met.3D. Comparison of detected 3-D frontal structures with 2-D fronts from surface analysis charts of weather services show agreement and augment the surface charts by additional vertical information. In our presentation, we show case studies of extratropical cyclones and their frontal dynamics. Examples include joint interactive visual analysis of 3-D fronts and warm conveyor belt trajectories, and development of the 3-D frontal structure of the characteristic stages of a Shapiro-Keyser cyclone. We also demonstrate the benefit of our technique for comparative analysis of frontal dynamics in different numerical weather prediction model simulations, e.g., of different resolution and simulations with parameterised and permitted convection. We argue that the presented approach has large potential to be beneficial for complex studies of atmospheric dynamics and for operational weather forecasting.

How to cite: Beckert, A., Eisenstein, L., Hewson, T., Oertel, A., Craig, G., and Rautenhaus, M.: Interactive detection and visual analysis of 3-D fronts in NWP data, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-518, https://doi.org/10.5194/ems2022-518, 2022.

P12
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EMS2022-717
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Onsite presentation
Heiko Niebuhr, Renate Hagedorn, Kathrin Feige, and Matthias Jerg

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.

During this poster session the opportunity to try out Kassandra is given. More information about the Kassandra system and its concepts are shown as an oral presentation in another session.

How to cite: Niebuhr, H., Hagedorn, R., Feige, K., and Jerg, M.: Tryout of Kassandra: A Prototype of a Customizable, Probabilistic Warning and Information System, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-717, https://doi.org/10.5194/ems2022-717, 2022.

P13
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EMS2022-553
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Onsite presentation
Uros Kalabic, Michael Chiu, Saleh Nabi, Steven Mitts, Jeffrey Norell, Bosko Telenta, and Zivorad Radonjic

Short-term weather forecasting, i.e., nowcasting, is an important area of research, with application to logistics, insurance, and environmental and social governance. More accurate forecasting is required to achieve nowcasting, which implies the need for improvement in either the acquisition of weather data or the development of better forecasting algorithms. While much of the literature focuses on algorithmic improvements, we consider how one may incentivize better data acquisition. Our idea is to implement a market-based incentive scheme that rewards meteorological data acquisition in proportion to how useful that data is in improving weather forecasts.

Our scheme incentivizes optimal placement of meteorological sensors by issuing blockchain-backed digital currency in direct proportion to the sensitivity of the weather forecast to raw data inputs. The scheme consists of an algorithm whose outputs are weather forecasts and whose inputs are data from weather sensors. The algorithm issues digital tokens in direct proportion to the sensitivity of outputs, i.e., the errors in forecasting, to inputs, i.e., sensor data. The issuance has the effects of incentivizing better sensor placement and improved sensor quality. Placement is incentivized because poor placement results in a lower forecast sensitivity; for example, this could happen due to placement near a non-representative microclimate, causing an aberration, or near another, well-placed sensor, resulting in redundancy. Quality is incentivized because higher certainty results in higher forecast sensitivity. With improved sensor placement and quality, the scheme therefore incentivizes better weather forecasts.

The incentivization therefore encourages better data assimilation and data availability and also enables the development of solutions that encourage better forecasting with market mechanisms. For example, a digital token may be traded between market participants and tokens may be issued by some authority to incentivize improved data acquisition. This latter example can be extended to a situation where an authority seeks to improve data acquisition in an area with low monitoring frequency, such as the world's oceans.

We present the results of a demonstration performed using a network of three Intellisense weather stations with forecasts based on a modification of the NAM‑NMM, performing nowcasts for locations in Torrance, California. The model was implemented in an oracle that performs forecasts and uses the results of the forecasts to issue rewards on the Solana blockchain testnet. The three weather stations were place in two different locations, with the two sensors placed in the same location having different update frequencies, resulting in higher uncertainty in the data coming from the lower-fidelity sensor. The two sensors in the same location cannibalize each other's earnings, and the higher-fidelity sensor is rewarded with the larger share of earnings.

How to cite: Kalabic, U., Chiu, M., Nabi, S., Mitts, S., Norell, J., Telenta, B., and Radonjic, Z.: Distributed weather sensing incentivization, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-553, https://doi.org/10.5194/ems2022-553, 2022.

P14
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EMS2022-367
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Onsite presentation
Benjamin Owen, Belinda Trotta, Jaiping Liu, Thomas Gale, Anja Schubert, and Gary Weymouth

Improvements to skill (and other characteristics) of quantitative probabilistic rainfall forecasting for weather and hydrological purposes are a high priority at the Australian Bureau of Meteorology (the Bureau), highlighted by a series of major floods in eastern Australia in early 2022.  Post-processed ensemble numerical weather prediction (NWP) rainfall guidance is key to increasing forecast automation in routine conditions and providing better guidance for high impact rainfall events. 

The Bureau's existing NWP post-processing system has markedly increased weather rainfall forecast skill in recent years. However, to enable further forecast improvements and greater integration of rainfall processing for weather and hydrological purposes, a more general approach with fewer calibration steps was required. To these ends, we have developed 'RainForests': a multi-ensemble rainfall processing system, utilising gradient boosted decision tree (GBDT) ensembles for forecast calibration.

RainForests is inspired by the ECPoint method of Hewson and Pillosu (2021). RainForests, like ECPoint, is a non-parametric and generally non-local method which uses decision trees to create situation-dependent error distributions for each input (analogous to an extension of Bayesian Joint Probabilities) that can be used to calibrate grid-scale rainfall guidance to point-scale.

Key features of RainForests:

  • Uses a series of GBDT ensembles to construct the error distribution in place of a single manually trained decision tree. This produces robust outputs which are near-continuous relative to inputs, allows for rapid retraining on new data or addition of feature variables, and utilises open source GBDT software.
  • Uses additive error (delta = obs - forecast) in place of the forecast error ratio. This allows for calibration when forecast rainfall is zero. Error distributions vary with forecast rainfall amount (and other predictors).
  • Models are trained and verified using both rain gauge and gauge-calibrated radar data, each of which have their uncertainties, strengths and weaknesses. Basic Bureau and RainForests QC is applied to the data to reduce gross errors.
  • Error distributions are pooled for each NWP model (e.g. ECMWF ensemble), from around 100 individual input ensemble and deterministic ('ensemble of one') members in total. Resulting calibrated probability distributions for each model are then blended in probability space.

Additionally, RainForests uses and contributes to capabilities in the Integrated Model post-PROcessing and VERification (IMPROVER) system being developed in collaboration with the UK Met Office.

Initial RainForests outputs have comparable skill to the Bureau's existing post-processing system.  Improvements are planned and will be reported on.  It is also planned to provide calibrated rainfall ensemble members, derived from RainForests outputs, to downstream applications.  This aims to support production of ensemble multiple-variable indices, hydrological applications and aggregation of rainfall forecasts in space and time.

How to cite: Owen, B., Trotta, B., Liu, J., Gale, T., Schubert, A., and Weymouth, G.: RainForests: A novel Machine Learning approach to calibrating rainfall forecasts, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-367, https://doi.org/10.5194/ems2022-367, 2022.

P15
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EMS2022-482
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Online presentation
Margarida Belo-Pereira

Aviation turbulence remains one of the leading causes of weather-related aviation accidents. Therefore, turbulence prediction is a major concern of aviation forecasters.

The performance of several turbulence diagnostics derived from ECMWF forecasts is evaluated over Portuguese Flight Information Regions (FIR) and surrounding areas, for the period February 2020 to April 2021, excluding May and June. In addition, the algorithm developed and used operationally by aviation meteorologists at IPMA to forecast moderate and severe turbulence over Portuguese FIRs is also discussed. The forecasts were compared with turbulence observations from special air reports and DEVG data from AMDARs received at the Portuguese MWO.

The objective verification approach in this paper uses not only the Relative Operating Characteristic curves but also novel measures such as the recently proposed Symmetric Extreme Dependence Index (SEDI) and Symmetric Extreme Dependence Index (SEDS). These measures are particularly suitable for assessing the forecasting skill of rare, such as moderate or greater turbulence, which accounts for 1.6% of the total data.

The vertical wind shear (VWS), DUTTON, Brown and Ellrod indices outperform the other turbulence diagnostics. In addition, VWS performs the best in terms of all verification measures. It has been found that adding a Richardson number function to these five turbulence diagnostics improves the performance of aviation turbulence forecasting. Consequently, the operational index combines these five diagnostics with the Richardson number.

Prediction of moderate and severe turbulence depends on the choice of the optimal threshold. However, this optimal threshold varies with the verification measure used. The results show that TSS and SEDI achieve a higher value for lower thresholds compared to SEDS. This is because when the contingency table becomes dominated by the correct predictions of non-events, both TSS and SEDI penalize under-prediction more than over-prediction.

Finally, the performance of the operational turbulence index and compared with the WAFS product is also illustrated for two turbulence episodes. 

How to cite: Belo-Pereira, M.: Aviation Turbulence Forecasting over the Portuguese Flight Information Regions: Objective Verification and case study, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-482, https://doi.org/10.5194/ems2022-482, 2022.

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