Towards a predictive multi-phase model for alpine mass movements and process cascades

Alpine mass movements can generate process cascades involving different materials including rock, ice, snow, and water. Numerical modelling is an essential tool for the quantification of natural hazards, but state-of-the-art operational models reach their limits when facing unprecedented or complex events. Here, we advance our predictive capabilities for process cascades on the basis of a three-dimensional numerical model, coupling fundamental conservation laws to finite strain elastoplasticity. Through its hybrid Eulerian-Lagrangian character, our approach naturally reproduces fractures and collisions, erosion/deposition phenomena, and multi-phase interactions, which finally grant very accurate simulations of complex dynamics. Four benchmark simulations demonstrate the physical detail of the model and its applicability to real-world full-scale events, including various materials and ranging through four orders of magnitude in volume. In the future, our model can support risk-management strategies through predictions of the impact of potentially catastrophic cascading mass movements at vulnerable sites.

Updated GOES-13 Heliosat-2 Method for Global Horizontal Irradiation in the Americas

Increasing the utilization of renewable energy is at the center of most sustainability policies. Solar energy is the most abundant resource of this type on Earth, and optimizing its use requires the optimal estimation of surface solar irradiation. Heliosat-2 is one of the most popular methods of global horizontal irradiation (GHI) estimation. Originally developed for the Meteosat satellite, Heliosat-2 has been modified in previous work to deal with GOES-13 data and named here GOES_H2. This model has been validated through the computation of indicators and irradiation maps for the Guiana Shield. This article proposes an improved version of GOES_H2, which has been combined with a radiative transfer parameterization (RTP) and the McClear clear-sky model (MC). This new version, hereafter designated RTP_MC_GOES_H2, was tested on eight stations from the Baseline Surface Radiation Network, located in North and South America, and covered by GOES-13. RTP_MC_GOES_H2 improves the hourly GHI estimates independently of the type of sky. This improvement is independent of the climate, no matter the station, the RTP_MC_GOES_H2 gives better results of MBE and RMSE than the original GOES_H2 method. Indeed, the MBE and RMSE values, respectively, change from −11.93% to −2.42% and 23.24% to 18.24% for North America and from −4.35% to 1.79% and 19.97% to 17.37 for South America. Moreover, the flexibility of the method may allow to improve results in the presence of snow cover and rainy/variable weather. Furthermore, RTP_MC_GOES_H2 results outperform or equalize those of other operational models.

Model-Centric Fulfillment Operations and Maintenance Automation

As networks have evolved, there has been an evolution in how they are managed as well. This evolution has seen a move from manual configuration via command line interface (CLI) to script-based automation and eventually to a template-based approach with workflow to coordinate multiple templates and scripts. The next step in this evolution is the introduction of models to provide a more dynamic capability than is in place today. This chapter will discuss three major layers of modelling that should be considered during implementation of this approach: device models focused on the configuration of the hardware itself; service models focused on the customer or network facing services that leverage the hardware level configuration; and operational models focused on people, processes, and tools involved in application of device and service models. This includes the orchestration of activities with other tools, such as operational support systems (OSS) and business support systems (BSS).

Slovenia on the Way to Drone Delivery - What More Needs to be Done

Nowadays, it is impossible to bypass the fact that digitization, robotics and automation of work are becoming an increasingly important part of our living and business. It becomes crucial, especially in urban areas, to identify new operational models that could be applied for last mile deliveries, where increasing of city logistics sustainability is being the main goal. Drones have been widely acknowledged as a promising technology in many fields and industries, especially for the delivery of medical and aid packages in humanitarian and healthcare logistics. In this study, we present the project of Post of Slovenia, which aimed to implement first delivery drone to the fleet to access hard-to-access locations. Slovenian Post aims to create innovative, cost-efficient and market-led business environment for the development and take-up of new drone services and technologies within the Slovenian’s internal market. As the national legislation in this area is still relatively unregulated, Post of Slovenia has actively contacted with the national authorities, which are the drafters of the relevant legislation, in order to accelerate the introduction of delivery drones into Slovenian airspace.

Evaluation of Nine Operational Models in Forecasting Different Types of Synoptic Dust Events in the Middle East

This study investigates four types of synoptic dust events in the Middle East region, including cyclonic, pre-frontal, post-frontal and Shamal dust storms. For each of these types, three intense and pervasive dust events are analyzed from a synoptic meteorological and numerical simulation perspective. The performance of 9 operational dust models in forecasting these dust events in the Middle East is qualitatively and quantitatively evaluated against Terra-MODIS observations and AERONET measurements during the dust events. The comparison of model AOD outputs with Terra-MODIS retrievals reveals that despite the significant discrepancies, all models have a relatively acceptable performance in forecasting the AOD patterns in the Middle East. The models enable to represent the high AODs along the dust plumes, although they underestimate them, especially for cyclonic dust storms. In general, the outputs of the NASA-GEOS and DREAM8-MACC models present greater similarity with the satellite and AERONET observations in most of the cases, also exhibiting the highest correlation coefficient, although it is difficult to introduce a single model as the best for all cases. Model AOD predictions over the AERONET stations showed that DREAM8-MACC exhibited the highest R2 of 0.78, followed by NASA_GEOS model (R2 = 0.74), which both initially use MODIS data assimilation. Although the outputs of all models correspond to valid time more than 24 h after the initial time, the effect of data assimilation on increasing the accuracy is important. The different dust emission schemes, soil and vegetation mapping, initial and boundary meteorological conditions and spatial resolution between the models, are the main factors influencing the differences in forecasting the dust AODs in the Middle East.

Factors Determining the Subseasonal Prediction Skill of Summer Extreme Rainfall Over Southern China

The occurrence of summer extreme rainfall over southern China (SCER) is closely related with the boreal summer intraseasonal oscillation (BSISO). Whether the operational models can reasonably predict the BSISO evolution and its modulation on SCER probability is crucial for disaster prevention and mitigation. Here, we find that the skill of subseasonal-to-seasonal (S2S) operational models in predicting the first component of BSISO (BSISO1) might play an important role in determining the forecast skill of SCER. The systematic assessment of reforecast data from the S2S database show that the ECMWF model performs a skillful prediction of BSISO1 index up to 24 days, while the skill of CMA model is about 10 days. Accordingly, the SCER occurrence is correctly predicted by ECMWF (CMA) model at a forecast lead time of ~14 (6) days. The diagnostic results of modeled moisture processes further suggest that the anomalous moisture convergence (advection) induced by the BSISO1 activity serves as the primary (secondary) source of subseasonal predictability of SCER. Once the operational model well predicts the moisture convergence anomaly in the specific phases of BSISO1, the higher skill for the probability prediction of SCER is obtained. The present study implies that a further improvement in predicting the BSISO and its related moisture processes is crucial to facilitating the subseasonal prediction skill of SCER probability.

Optimal Coordination of Over-Current Relays in Microgrids Using Principal Component Analysis and K-Means

Microgrids (MGs) are decentralized systems that integrate distributed energy resources and may operate in grid-connected or islanded modes. Furthermore, MGs may feature several topologies or operative scenarios. These characteristics bring about major challenges in determining a proper protection coordination scheme. A new optimal coordination approach for directional over-current relays (OCRs) in MGs is proposed. In this case, a clustering of operational models is carried out by means of a K-means algorithm hybridized with the principal component analysis (PCA) technique. The number of clusters is limited by the number of setting groups of commercially available relays. The results carried out on a benchmark IEC microgrid evidence the applicability and effectiveness of the proposed approach.

ACCOMPLISHMENTS OF NOAA’S AIRBORNE HURRICANE FIELD PROGRAM AND A BROADER FUTURE APPROACH TO FORECAST IMPROVEMENT

Since 2005, NOAA has conducted the annual Intensity Forecasting Experiment (IFEX), led by scientists from the Hurricane Research Division at NOAA’s Atlantic Oceanographic andMeteorological Laboratory. They partner with NOAA’s Aircraft Operations Center, who maintain and operate the WP-3D and G-IV Hurricane Hunter aircraft, and NCEP’s National Hurricane Center and Environmental Modeling Center, who task airborne missions to gather data used by forecasters for analysis and forecasting and for ingest into operational numerical weather prediction models. The goal of IFEX is to improve tropical cyclone (TC) forecasts using an integrated approach of analyzing observations from aircraft, initializing and evaluating forecast models with those observations, and developing new airborne instrumentation and observing strategies targeted at filling observing gaps and maximizing the data’s impact in model forecasts. This summary article not only highlights recent IFEX contributions towards improved TC understanding and prediction, but also reflects more broadly on the accomplishments of the program during the 16 years of its existence. It describes how IFEX addresses high-priority forecast challenges, summarizes recent collaborations, describes advancements in observing systems monitoring structure and intensity, as well as in assimilation of aircraft data into operational models, and emphasizes key advances in understanding of TC processes, particularly those that lead to rapid intensification. The article concludes by laying the foundation for the “next generation” of IFEX as it broadens its scope to all TC hazards, particularly rainfall, storm-surge inundation, and tornadoes, that have gained notoriety during the last few years after several devastating landfalling TCs.