Vibrational Excitons in CH-Stretching Fundamental and Overtone Vibrational Circular Dichroism Spectra

A set of vibrational circular dichroism (VCD) spectra in the CH-stretching fundamental region for about twenty compounds belonging to the class of essential oils was empirically analyzed by the use of a sort of vibrational exciton mechanism, involving three centers. Through a general formula applicable to many coupled dipole oscillators, the rotational strengths of the previously identified vibrational excitons are evaluated. The results are then critically reviewed by the use of recent ab initio methodology, as applied to selected molecules of the original set. Further insight is gained by model calculations adding up the contribution of the coupling between electric dipole moments associated with normal mode behavior and that of the polarizability from polarizable groups. The former part is responsible for the excitonic behavior of the VCD spectra. For the same selected molecules we have also investigated whether some excitonic behavior is taking place in the second overtone region, and have concluded that this is not the case

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through copula-based integration of gridded observation data

Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over- or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer function-based statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971–2000. The REGNIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971–1985) and validation (1986–2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10% (wet bias) to −1% (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32% March–April–May (MAM), −15% June–July–August (JJA), 4% September–October–November (SON) and 28% December–January–February (DJF) to 16% (MAM), −11% (JJA), −1% (SON) and −3% (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions

Copula-based assimilation of radar and gauge information to derive bias corrected precipitation fields

This study addresses the problem of combining radar information and gauge measurements. Gauge measurements are the best available source of absolute rainfall intensity albeit their spatial availability is limited. Precipitation information obtained by radar mimics well the spatial patterns but is biased for their absolute values. <br><br> In this study copula models are used to describe the dependence structure between gauge observations and rainfall derived from radar reflectivity at the corresponding grid cells. After appropriate time series transformation to generate "iid" variates, only the positive pairs (radar >0, gauge >0) of the residuals are considered. As not each grid cell can be assigned to one gauge, the integration of point information, i.e. gauge rainfall intensities, is achieved by considering the structure and the strength of dependence between the radar pixels and all the gauges within the radar image. Two different approaches, namely <i>Maximum Theta</i> and <i>Multiple Theta</i>, are presented. They finally allow for generating precipitation fields that mimic the spatial patterns of the radar fields and correct them for biases in their absolute rainfall intensities. The performance of the approach, which can be seen as a bias-correction for radar fields, is demonstrated for the Bavarian Alps. The bias-corrected rainfall fields are compared to a field of interpolated gauge values (ordinary kriging) and are validated with available gauge measurements. The simulated precipitation fields are compared to an operationally corrected radar precipitation field (RADOLAN). The copula-based approach performs similarly well as indicated by different validation measures and successfully corrects for errors in the radar precipitation