A Probabilistic Approach to the Drag-Based Model

The forecast of the time of arrival of a coronal mass ejection (CME) to Earth is of critical importance for our high-technology society and for any future manned exploration of the Solar System. As critical as the forecast accuracy is the knowledge of its precision, i.e. the error associated to the estimate. We propose a statistical approach for the computation of the time of arrival using the drag-based model by introducing the probability distributions, rather than exact values, as input parameters, thus allowing the evaluation of the uncertainty on the forecast. We test this approach using a set of CMEs whose transit times are known, and obtain extremely promising results: the average value of the absolute differences between measure and forecast is 9.1h, and half of these residuals are within the estimated errors. These results suggest that this approach deserves further investigation. We are working to realize a real-time implementation which ingests the outputs of automated CME tracking algorithms as inputs to create a database of events useful for a further validation of the approach.Comment: 18 pages, 4 figure

Projecting the Medium-Term: Outcomes and Errors for GDP Growth

The focus of this paper is the evaluation of a very popular method for potential output estimation and medium-term forecasting? the production function approach?in terms of predictive performance. For this purpose, a forecast evaluation for the three to five years ahead predictions of GDP growth for the individual G7 countries is conducted. To carry out the forecast performance check a particular testing framework is derived that allows the computation of robust test statistics given the specific nature of the generated out-of sample forecasts. In addition, medium-term GDP projections from national and international institutions are examined and it is assessed whether these projections convey a reliable view about future economic developments and whether there is scope for improving their predictive content. --Potential output,projections,forecast evaluation

Reliability of Dynamic Load Scheduling with Solar Forecast Scenarios

This paper presents and evaluates the performance of an optimal scheduling algorithm that selects the on/off combinations and timing of a finite set of dynamic electric loads on the basis of short term predictions of the power delivery from a photovoltaic source. In the algorithm for optimal scheduling, each load is modeled with a dynamic power profile that may be different for on and off switching. Optimal scheduling is achieved by the evaluation of a user-specified criterion function with possible power constraints. The scheduling algorithm exploits the use of a moving finite time horizon and the resulting finite number of scheduling combinations to achieve real-time computation of the optimal timing and switching of loads. The moving time horizon in the proposed optimal scheduling algorithm provides an opportunity to use short term (time moving) predictions of solar power based on advection of clouds detected in sky images. Advection, persistence, and perfect forecast scenarios are used as input to the load scheduling algorithm to elucidate the effect of forecast errors on mis-scheduling. The advection forecast creates less events where the load demand is greater than the available solar energy, as compared to persistence. Increasing the decision horizon leads to increasing error and decreased efficiency of the system, measured as the amount of power consumed by the aggregate loads normalized by total solar power. For a standalone system with a real forecast, energy reserves are necessary to provide the excess energy required by mis-scheduled loads. A method for battery sizing is proposed for future work.Comment: 6 pager, 4 figures, Syscon 201

Precise cosmological parameter estimation using CosmoRec

We use the new cosmological recombination code, CosmoRec, for parameter estimation in the context of (future) precise measurements of the CMB temperature and polarization anisotropies. We address the question of how previously neglected physical processes in the recombination model of Recfast affect the determination of key cosmological parameters, for the first time performing a model-by-model computation of the recombination problem. In particular we ask how the biases depend on different combinations of parameters, e.g. when varying the helium abundance or the effective number of neutrino species in addition to the standard six parameters. We also forecast how important the recombination corrections are for a combined Planck, ACTPol and SPTpol data analysis. Furthermore, we ask which recombination corrections are really crucial for CMB parameter estimation, and whether an approach based on a redshift-dependent correction function to Recfast is sufficient in this context.Comment: 12 pages, 7 figures, submitted to MNRA

Surface atmospheric pressure excitation of the translational mode of the inner core

Using hourly atmospheric surface pressure field from ECMWF (European Centre for Medium-Range Weather Forecasts) and from NCEP (National Centers for Environmental Prediction) Climate Forecast System Reanalysis (CFSR) models, we show that atmospheric pressure fluctuations excite the translational oscillation of the inner core, the so-called Slichter mode, to the sub-nanogal level at the Earth surface. The computation is performed using a normal-mode formalism for a spherical, self-gravitating anelastic PREM-like Earth model. We determine the statistical response in the form of power spectral densities of the degree-one spherical harmonic components of the observed pressure field. Both hypotheses of inverted and non-inverted barometer for the ocean response to pressure forcing are considered. Based on previously computed noise levels, we show that the surface excitation amplitude is below the limit of detection of the superconducting gravimeters, making the Slichter mode detection a challenging instrumental task for the near future

Predicting Upper Atmospheric Weather Conditions Utilizing Long-Short Term Memory Neural Networks for Aircraft Fuel Efficiency

Aviation fuel is a major component of the Air Force (AF) budget, and vital for the core mission of the AF. This study investigated the viability of LSTMs to increase the accuracy of deterministic NWP models, while also investigating the ability to reduce model generation time. Increased forecast accuracy for wind speeds could be implemented into existing flight path models to further increase fuel efficiency, while reduced modeling times would allow flight planners to generate a flight plan in rapid response situations. The most viable model consisted of an ensemble of six LSTMs trained o six coordinates. The model\u27s error was on average 1.2 m/s higher than the deterministic NWP with a computation time of 1.85 s. The LSTM generated a flight path that was on average 14.2 min slower for an approximately 7 hour 32 min flight. This forecast generation took seconds to complete compared to hours from the deterministic model. While the LSTM architecture in this study was not able to increase forecast accuracy, the speed at which it generates an approximately close forecast can be an integral tool for flight planners in the future

Forecasting the Index of Financial Safety (IFS) of South Africa using neural networks

This paper investigates neural network tools, especially the nonlinear autoregressive model with exogenous input (NARX), to forecast the future conditions of the Index of Financial Safety (IFS) of South Africa. Based on the time series that was used to construct the IFS for South Africa (Matkovskyy, 2012), the NARX model was built to forecast the future values of this index and the results are benchmarked against that of Bayesian Vector-Autoregressive Models. The results show that the NARX model applied to IFS of South Africa and trained by the Levenberg-Marquardt algorithm may ensure a forecast of adequate quality with less computation expanses, compared to BVAR models with different priors