ALBERT

Description: The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an “overtuning” to the “home-region”, which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the Köppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intra-seasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different climate types as measured by the probability density function (PDF) skill score method. Although REMO seems to perform best over its home domain in Europe (domain of development and testing), the model has simulated quite well the climate characteristics of other regions with the same set of parameterization options. Therefore, these results lead us to the conclusion that REMO is well suited for long-term climate change simulations to examine projected future changes in all these regions.

Description: The imbalance between energy resource availability, demand, and production capacity, coupled with inherent economic and environmental uncertainties make strategic energy resources planning, management, and decision-making a challenging process. In this paper, a descriptive approach has been taken to synthesize the world’s energy portfolio and the global energy balance outlook in order to provide insights into the role of Organization of Petroleum Exporting Countries (OPEC) in maintaining “stability” and “balance” of the world’s energy market. This synthesis illustrates that in the absence of stringent policies, i.e., if historical trends of the global energy production and consumption hold into the future, it is unlikely that non-conventional liquid fuels and renewable energy sources will play a dominant role in meeting global energy demand by 2030. This should be a source of major global concern as the world may be unprepared for an ultimate shift to other energy sources when the imminent peak oil production is reached. OPEC’s potential to impact the supply and price of oil could enable this organization to act as a facilitator or a barrier for energy transition policies, and to play a key role in the global energy security through cooperative or non-cooperative strategies. It is argued that, as the global energy portfolio becomes more balanced in the long run, OPEC may change its typical high oil price strategies to drive the market prices to lower equilibria, making alternative energy sources less competitive. Alternatively, OPEC can contribute to a cooperative portfolio management approach to help mitigate the gradually emerging energy crisis and global warming, facilitating a less turbulent energy transition path while there is time.

Description: The transferability of the regional climate model REMO with a standard setup over different regions of the world has been evaluated. The study is based on the idea that the modeling parameters and parameterizations in a regional climate model should be robust to adequately simulate the major climatic characteristic of different regions around the globe. If a model is not able to do that, there might be a chance of an “overtuning” to the “home-region”, which means that the model physics are tuned in a way that it might cover some more fundamental errors, e.g., in the dynamics. All simulations carried out in this study contribute to the joint effort by the international regional downscaling community called COordinated Regional climate Downscaling EXperiment (CORDEX). REMO has been integrated over six CORDEX domains forced with the so-called perfect boundary conditions obtained from the global reanalysis dataset ERA-Interim for the period 1989 to 2008. These six domains include Africa, Europe, North America, South America, West Asia and the Mediterranean region. Each of the six simulations was conducted with the identical model setup which allows investigating the transferability of a single model to regions with substantially different climate characteristics. For the consistent evaluation over the different domains, a new evaluation framework is presented by combining the Köppen-Trewartha climate classification with temperature-precipitation relationship plots and a probability density function (PDF) skill score method. The evaluation of the spatial and temporal characteristics of simulated precipitation and temperature, in comparison to observational datasets, shows that REMO is able to simulate the mean annual climatic features over all the domains quite reasonably, but still some biases remain. The regions over the Amazon and near the coast of major upwelling regions have a significant warm bias. Wet and dry biases appear over the mountainous regions and East Africa, respectively. The temperature over South America and precipitation over the tundra and highland climate of West Asia are misrepresented. The probable causes leading to these biases are discussed and ideas for improvements are suggested. The annual cycle of precipitation and temperature of major catchments in each domain are also well represented by REMO. The model has performed well in simulating the inter- and intra-seasonal characteristics of different climate types in different regions. Moreover, the model has a high ability in representing the general characteristics of different climate types as measured by the probability density function (PDF) skill score method. Although REMO seems to perform best over its home domain in Europe (domain of development and testing), the model has simulated quite well the climate characteristics of other regions with the same set of parameterization options. Therefore, these results lead us to the conclusion that REMO is well suited for long-term climate change simulations to examine projected future changes in all these regions.

Description: Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1–7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

Description: Uncertainty related to inventory data, growth models and timber price fluctuation was investigated in the assessment of forest property net present value (NPV). The degree of uncertainty associated with inventory data was obtained from previous area-based airborne laser scanning (ALS) inventory studies. The study was performed, applying the Monte Carlo simulation, using stand-level growth and yield projection models and three alternative rates of interest (3, 4 and 5%). Timber price fluctuation was portrayed with geometric mean-reverting (GMR) price models. The analysis was conducted for four alternative forest properties having varying compartment structures: (A) a property having an even development class distribution, (B) sapling stands, (C) young thinning stands, and (D) mature stands. Simulations resulted in predicted yield value (predicted NPV) distributions at both stand and property levels. Our results showed that ALS inventory errors were the most prominent source of uncertainty, leading to a 5.1–7.5% relative deviation of property-level NPV when an interest rate of 3% was applied. Interestingly, ALS inventory led to significant biases at the property level, ranging from 8.9% to 14.1% (3% interest rate). ALS inventory-based bias was the most significant in mature stand properties. Errors related to the growth predictions led to a relative standard deviation in NPV, varying from 1.5% to 4.1%. Growth model-related uncertainty was most significant in sapling stand properties. Timber price fluctuation caused the relative standard deviations ranged from 3.4% to 6.4% (3% interest rate). The combined relative variation caused by inventory errors, growth model errors and timber price fluctuation varied, depending on the property type and applied rates of interest, from 6.4% to 12.6%. By applying the methodology described here, one may take into account the effects of various uncertainty factors in the prediction of forest yield value and to supply the output results with levels of confidence.

Description: Single-shaft gas turbines are sensitive to frequency changes which might affect the grid stability during large frequency drops. This paper presents a new control system that uses steam injection as an auxiliary input to improve the transient performance of the gas turbine during frequency drops. Steam injection is beneficial because it reduces the peak temperature in the combustion chamber and augments the output power by increasing the mass flow through the turbine. The use of this auxiliary input is based on the event-based control approach. It means that during the frequency drop, the controller exploits the steam injection to help the main control loop recover the frequency and when the frequency reaches its predefined value, the system will return to its normal operation. The performance of the proposed control algorithm is investigated under different scenarios and the results show that the application of steam injection improves the performance of the regular control algorithm significantly, especially near full load condition.

Description: With the rapid increase in computational power of mobile devices the amount of ambient intelligence-based smart environment systems has increased greatly in recent years. A proposition of such a solution is described in this paper, namely real time monitoring of an electrocardiogram (ECG) signal during everyday activities for identification of life threatening situations. The paper, being both research and review, describes previous work of the authors, current state of the art in the context of the authors’ work and the proposed aforementioned system. Although parts of the solution were described in earlier publications of the authors, the whole concept is presented completely for the first time along with the prototype implementation on mobile device—a Windows 8 tablet with Modern UI. The system has three main purposes. The first goal is the detection of sudden rapid cardiac malfunctions and informing the people in the patient’s surroundings, family and friends and the nearest emergency station about the deteriorating health of the monitored person. The second goal is a monitoring of ECG signals under non-clinical conditions to detect anomalies that are typically not found during diagnostic tests. The third goal is to register and analyze repeatable, long-term disturbances in the regular signal and finding their patterns.

Description: The use of global navigation satellite system (GNSS) antenna arrays for applications such as interference counter-measure, attitude determination and signal-to-noise ratio (SNR) enhancement is attracting significant attention. However, precise antenna array calibration remains a major challenge. This paper proposes a new method for calibrating a GNSS antenna array using live signals and an inertial measurement unit (IMU). Moreover, a second method that employs the calibration results for the estimation of steering vectors is also proposed. These two methods are applied to the receiver in two modes, namely calibration and operation. In the calibration mode, a two-stage optimization for precise calibration is used; in the first stage, constant uncertainties are estimated while in the second stage, the dependency of each antenna element gain and phase patterns to the received signal direction of arrival (DOA) is considered for refined calibration. In the operation mode, a low-complexity iterative and fast-converging method is applied to estimate the satellite signal steering vectors using the calibration results. This makes the technique suitable for real-time applications employing a precisely calibrated antenna array. The proposed calibration method is applied to GPS signals to verify its applicability and assess its performance. Furthermore, the data set is used to evaluate the proposed iterative method in the receiver operation mode for two different applications, namely attitude determination and SNR enhancement.

Description: Electric Vehicle (EV) technology is expected to take a major share in the light-vehicle market in the coming decades. Charging of EVs will put an extra burden on the distribution grid and in some cases adjustments will need to be made. On the other hand, EVs have the potential to support the grid under various conditions. This paper studies possible potential and applications of Vehicle to Grid (V2G) services, including active power services, which discharge the EV batteries, and power quality services, which do not engage the battery or require only small amounts of battery charge. The advantages and disadvantages of each service and the likelihood that a given service will be effective and beneficial for the grid in the future are discussed. Further, the infrastructure cost, duration, and value of V2G services are compared qualitatively.

Description: We investigate the reasons for the opposite climate change signals in precipitation between the regional climate model REMO and its driving earth system model MPI-ESM over the greater Congo region. Three REMO simulations following three RCP scenarios (RCP 2.6, RCP 4.5 and RCP 8.5) are conducted, and it is found that the opposite signals, with REMO showing a decrease and MPI-ESM an increase in the future precipitation, diverge strongly as we move from a less extreme to a more extreme scenario. It has been shown that REMO simulates a much higher number of extreme rainfall events than MPI-ESM. This results in higher surface runoff and thus less soil infiltration, which leads to lower amounts of soil moisture in REMO. This further leads to less moisture recycling via evapotranspiration, which in turn results in less precipitation over the region. In the presence of a strong radiative forcing, the hydrological cycle becomes less intense in REMO and a downward trend in hydrological variables is observed. Contrary to this, the higher amounts of soil-moisture due to the lack of extreme rainfall events in MPI-ESM enhance the hydrological cycle. In the presence of strong radiative forcing, higher amounts of soil moisture result in increased evapotranspiration which in turn results in the higher amount of precipitation. It is concluded that the land-atmosphere coupling over the Congo region is very sensitive to the change in soil moisture amounts, which is likely to play a major role in global warming conditions. Therefore, adequate and improved representation of soil processes in climate models is essential to study the effects of climate change. However, the better representation of extreme rainfall events in REMO compared to MPI-ESM can be regarded as an added value of the model.