The State-of-the-Art of Battery Electric City Buses

Over the last decade, many manufacturers brought battery electric city buses to the market. By organizing and categorizing the reported specifications of more than 100 of these vehicles, this paper aims to create an overview of the state-of-the-art. The used information is obtained from manufacturer websites and news sources. The results indicate that Lithium-ion Iron Phosphate is the most often used battery cell chemistry. Based on the reported range and battery capacity, the energy consumption is 1.3 kWh/km on average. Therefore, at an occupancy rate of 38% (seated), battery electric buses offer the same energy consumption per person as an average electric passenger car. The current lack of standardization in the reported range makes direct a comparison of individual vehicles difficult

The State-of-the-Art of Battery Electric City Buses

Over the last decade, many manufacturers brought battery electric city buses to the market. By organizing and categorizing the reported specifications of more than 100 of these vehicles, this paper aims to create an overview of the state-of-the-art. The used information is obtained from manufacturer websites and news sources. The results indicate that Lithium-ion Iron Phosphate is the most often used battery cell chemistry. Based on the reported range and battery capacity, the energy consumption is 1.3 kWh/km on average. Therefore, at an occupancy rate of 38% (seated), battery electric buses offer the same energy consumption per person as an average electric passenger car. The current lack of standardization in the reported range makes direct a comparison of individual vehicles difficult

Enhancing temporal correlations in EOF expansions for the reconstruction of missing data using DINEOF

DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of missing data in geophysical fields, such as those produced by clouds in sea surface temperature satellite images. A technique to reduce spurious time variability in DINEOF reconstructions is presented. The reconstruction of these images within a long time series using DINEOF can lead to large discontinuities in the reconstruction. Filtering the temporal covariance matrix allows to reduce this spurious variability and therefore more realistic reconstructions are obtained. The approach is tested in a three years sea surface temperature data set over the Black Sea. The effect of the filter in the temporal EOFs is presented, as well as some examples of the improvement achieved with the filtering in the SST reconstruction, both compared to the DINEOF approach without filtering

Detection of Gravitational Redshift on the Solar Disk by Using Iodine-Cell Technique

With an aim to examine whether the predicted solar gravitational redshift can be observationally confirmed under the influence of the convective Doppler shift due to granular motions, we attempted measuring the absolute spectral line-shifts on a large number of points over the solar disk based on an extensive set of 5188-5212A region spectra taken through an iodine-cell with the Solar Domeless Telescope at Hida Observatory. The resulting heliocentric line shifts at the meridian line (where no rotational shift exists), which were derived by finding the best-fit parameterized model spectrum with the observed spectrum and corrected for the earth's motion, turned out to be weakly position-dependent as ~ +400 m/s near the disk center and increasing toward the limb up to ~ +600 m/s (both with a standard deviation of sigma ~ 100 m/s). Interestingly, this trend tends to disappear when the convectiveshift due to granular motions (~-300 m/s at the disk center and increasing toward the limb; simulated based on the two-component model along with the empirical center-to-limb variation) is subtracted, finally resulting in the averaged shift of 698 m/s (sigma = 113 m/s). Considering the ambiguities involved in the absolute wavelength calibration or in the correction due to convective Doppler shifts (at least several tens m/s, or more likely up to <~100 m/s), we may regard that this value is well consistent with the expected gravitational redshift of 633 m/s.Comment: 28 pages, 12 figures, electronic materials as ancillary data (table3, table 4, ReadMe); accepted for publication in Solar Physic

Ensemble perturbation smoother for optimizing tidal boundary conditions by assimilation of High-Frequency radar surface currents - application to the German Bight

High-Frequency (HF) radars measure the ocean surface currents at various spatial and temporal scales. These include tidal currents, wind-driven circulation, density-driven circulation and Stokes drift. Sequential assimilation methods updating the model state have been proven successful to correct the density-driven currents by assimilation of observations such as sea surface height, sea surface temperature and in-situ profiles. However, the situation is different for tides in coastal models since these are not generated within the domain, but are rather propagated inside the domain through the boundary conditions. For improving the modeled tidal variability it is therefore not sufficient to update the model state via data assimilation without updating the boundary conditions. The optimization of boundary conditions to match observations inside the domain is traditionally achieved through variational assimilation methods. In this work we present an ensemble smoother to improve the tidal boundary values so that the model represents more closely the observed currents. To create an ensemble of dynamically realistic boundary conditions, a cost function is formulated which is directly related to the probability of each boundary condition perturbation. This cost function ensures that the boundary condition perturbations are spatially smooth and that the structure of the perturbations satisfies approximately the harmonic linearized shallow water equations. Based on those perturbations an ensemble simulation is carried out using the full three-dimensional General Estuarine Ocean Model (GETM). Optimized boundary values are obtained by assimilating all observations using the co-variances of the ensemble simulation