New Failure Mode and Effects Analysis based on D Numbers Downscaling Method

Failure mode and effects analysis (FMEA) is extensively applied to process potential faults in systems, designs, and products. Nevertheless, traditional FMEA, classical risk priority number (RPN), acquired by multiplying the ratings of occurrence, detection, and severity, risk assessment, is not effective to process the uncertainty in FMEA. Many methods have been proposed to solve the issue but deficiencies exist, such as huge computing quality and the mutual exclusivity of propositions. In fact, because of the subjectivity of experts, the boundary of two adjacent evaluation ratings is fuzzy so that the propositions are not mutually exclusive. To address the issues, in this paper, a new method to evaluate risk in FMEA based on D numbers and evidential downscaling method, named as D numbers downscaling method, is proposed. In the proposed method, D numbers based on the data are constructed to process uncertain information and aggregate the assessments of risk factors, for they permit propositions to be not exclusive mutually. Evidential downscaling method decreases the number of ratings from 10 to 3, and the frame of discernment from 2^{10} to 2^3 , which greatly reduce the computational complexity. Besides, a numerical example is illustrated to validate the high efficiency and feasibility of the proposed method

Climate Change Impact Assessment for Surface Transportation in the Pacific Northwest and Alaska

WA-RD 772.

A relocatable ocean model in support of environmental emergencies

During the Costa Concordia emergency case, regional, subregional, and relocatable ocean models have been used together with the oil spill model, MEDSLIK-II, to provide ocean currents forecasts, possible oil spill scenarios, and drifters trajectories simulations. The models results together with the evaluation of their performances are presented in this paper. In particular, we focused this work on the implementation of the Interactive Relocatable Nested Ocean Model (IRENOM), based on the Harvard Ocean Prediction System (HOPS), for the Costa Concordia emergency and on its validation using drifters released in the area of the accident. It is shown that thanks to the capability of improving easily and quickly its configuration, the IRENOM results are of greater accuracy than the results achieved using regional or subregional model products. The model topography, and to the initialization procedures, and the horizontal resolution are the key model settings to be configured. Furthermore, the IRENOM currents and the MEDSLIK-II simulated trajectories showed to be sensitive to the spatial resolution of the meteorological fields used, providing higher prediction skills with higher resolution wind forcing.MEDESS4MS Project; TESSA Project; MyOcean2 Projectinfo:eu-repo/semantics/publishedVersio

Investigating the link between southern African droughts and global atmospheric teleconnections using regional climate models

Includes bibliographical referencesDrought is one of the natural hazards that threaten the economy of many nations, especially in Southern Africa, where many socio-economic activities depend on rain-fed agriculture. This study evaluates the capability of Regional Climate Models (RCMs) in simulating the Southern African droughts. It uses the Standardized Precipitation-Evapotranspiration Index (SPEI, computed using rainfall and temperature data) to identify 3-month droughts over Southern Africa, and compares the observed and simulated drought patterns. The observation data are from the Climate Research Unit (CRU), while the simulation data are from 10 RCMs (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF, and CRCM) that participated in the Regional Climate Downscaling Experiment (CORDEX) project. The study also categorizes drought patterns over Southern Africa, examines the persistence and transition of these patterns, and investigates the roles of atmospheric teleconnections on the drought patterns. The results show that the drought patterns can occur in any season, but they have preference for seasons. Some droughts patterns may persist up to three seasons, while others are transient. Only about 20% of the droughts patterns are induced solely by El Niño Southern Oscillation (ENSO), other drought patterns are caused by complex interactions among the atmospheric teleconnections. The study also reveals that the Southern Africa drought pattern is generally shifting from a wet condition to a dry condition, and that the shifting can only be captured with a drought monitoring index that accounts for temperature influence on drought. Only few CORDEX RCMs simulate the Southern African droughts as observed. In this regard, the ARPEGE model shows the best simulation. The best performance may be because the stretching capability of ARPEGE helps the model to eliminate boundary condition problems, which are present in other RCMs. In ARPEGE simulations, the stretching capability would allow a better interaction between large and small scale features, and may lead to a better representation of the rain producing systems in Southern Africa. The results of the study may be applied to improve monitoring and prediction of regionally-extensive drought over Southern Africa, and to reduce the socio-economic impacts of drought in the region

Remote Sensing of Hydro-Meteorology

Flood/drought, risk management, and policy: decision-making under uncertainty. Hydrometeorological extremes and their impact on human–environment systems. Regional and nonstationary frequency analysis of extreme events. Detection and prediction of hydrometeorological extremes with observational and model-based approaches. Vulnerability and impact assessment for adaptation to climate change

Predictability of marine ecosystems in a changing climate

Ecosystem predictability is the basis for ecosystem management. To study the influence of climate on the variability and predictability of marine ecosystems, various climate indices are related to ecosystem descriptors. Various aspects influencing ecosystem predictability are being discussed. It is shown that predictability of marine ecosystems is altered by large-scale transitions in the atmosphere. A multivariate climate descriptor is developed to compensate for the increase in non-linearity due to a regime shift in 2001/2002 and the resulting decrease in predictability.Die Vorhersagbarkeit von Ökosystemen ist die Grundlage für erfolgreiches Ökosystemmanagement. Um den Einfluss des Klimas auf die Vorhersagbarkeit mariner Ökosysteme zu untersuchen, werden Klimaindizes mit Deskriptoren mariner Ökosysteme in Beziehung gesetzt. Die die Vorhersagbarkeit von Ökosystemen beeinflussenden Faktoren werden diskutiert. Es wird gezeigt, dass großskalige Veränderungen in der Atmosphäre die Vorhersagbarkeit von Ökosystemen beeinflussen. Ein multivariater Index wird entwickelt um die Abnahme der Vorhersagbarkeit durch einen Regime Shift in 2001/2002 zu kompensieren

The effect of climate variability on wheat in Iran

In this study, we investigated the impact of temperature variability on wheat phenology in Iran. Temperature is the most appropriate climate variable affecting wheat production wheat in cultivation under irrigation in Iran. To that aim, an effective and potentially scalable statistical downscaling method is developed for temperature and growing degree days (GDD) of wheat. Statistical downscaling quantitatively establishes statistical links between the large-scale reanalysis or climate model and regional climate data. GDD is the atmospheric energy that a plant utilizes to grow over the phenological phases until the harvesting stage. The GDD values are calculated during the growth period from the phenological dates and the daily mean temperature data of observations and reanalysis. The underlying database in downscaling comprises the ERA-40 reanalysis for the global scale and observations of local daily temperature and annual GDD of wheat at 16 synoptic stations for the period 1961-2001 for the regional scale. For the quantitative analysis of the statistical downscaling, we used the linear regression model (LR) and multiple regression model (MR). The LR is implemented using the ERA-40 fingerprints (FP) of local variability by squared correlation coefficients between the variable at ERA-40 grid points and each station. The MR technique is performed to relate the large-scale information at the neighboring grid points to the stations data. Extending the usual downscaling, we implement a weather generator (WG) providing realizations of the local temperatures and GDD by adding Gaussian random noise with expectation zero and the variance between the downscaled values and the observations. ERA-40 reanalysis well represents the local daily temperature and the annual GDD. From the analysis of 2m temperature, FPs are more localized in warm seasons than cold seasons. FP statistical downscaling seems to perform best for annual GDD and it is particularly beneficial for the annual GDD. Whereas, the MR calculated robust results for daily mean temperature time series. The quality of the WGs is assessed along with verification score such as the continuous ranked probability score, CRPS. The local temperature time series through WGs are more realistic and well represented than the deterministic downscaling. As a next step, the probabilistic wheat model is developed. It represents the probabilistic relations between the phenological and climate parameters. The basic idea of the model is to interpret a survival function which is based on the normal distribution, on a time scale which is defined by lifetime or growth duration for wheat. The probabilistic phenological model is adjusted by the survival analysis (SA) considering the risk in interpreting the maturity time of wheat. SA is a statistical method to study the occurrence and timing of event which here is the ripening time of wheat from the random variable of ripening dates. In summary, we believe that the probabilistic phenological model have the potential to reduce the vulnerability of agricultural production system and can increase the food security in the region