Gold nanoparticles on polarizable surfaces as Raman scattering antennas.

Surface plasmons supported by metal nanoparticles are perturbed by coupling to a surface that is polarizable. Coupling results in enhancement of near fields and may increase the scattering efficiency of radiative modes. In this study, we investigate the Rayleigh and Raman scattering properties of gold nanoparticles functionalized with cyanine deposited on silicon and quartz wafers and on gold thin films. Dark-field scattering images display red shifting of the gold nanoparticle plasmon resonance and doughnut-shaped scattering patterns when particles are deposited on silicon or on a gold film. The imaged radiation patterns and individual particle spectra reveal that the polarizable substrates control both the orientation and brightness of the radiative modes. Comparison with simulation indicates that, in a particle-surface system with a fixed junction width, plasmon band shifts are controlled quantitatively by the permittivity of the wafer or the film. Surface-enhanced resonance Raman scattering (SERRS) spectra and images are collected from cyanine on particles on gold films. SERRS images of the particles on gold films are doughnut-shaped as are their Rayleigh images, indicating that the SERRS is controlled by the polarization of plasmons in the antenna nanostructures. Near-field enhancement and radiative efficiency of the antenna are sufficient to enable Raman scattering cyanines to function as gap field probes. Through collective interpretation of individual particle Rayleigh spectra and spectral simulations, the geometric basis for small observed variations in the wavelength and intensity of plasmon resonant scattering from individual antenna on the three surfaces is explained.

Using fractal downscaling of satellite precipitation products for hydrometeorological applications

The objective of spatial downscaling strategies is to increase the information content of coarse datasets at smaller scales. In the case of quantitative precipitation estimation (QPE) for hydrological applications, the goal is to close the scale gap between the spatial resolution of coarse datasets (e.g., gridded satellite precipitation products at resolution L × L) and the high resolution (l × l; L»l) necessary to capture the spatial features that determine spatial variability of water flows and water stores in the landscape. In essence, the downscaling process consists of weaving subgrid-scale heterogeneity over a desired range of wavelengths in the original field. The defining question is, which properties, statistical and otherwise, of the target field (the known observable at the desired spatial resolution) should be matched, with the caveat that downscaling methods be as a general as possible and therefore ideally without case-specific constraints and/or calibration requirements? Here, the attention is focused on two simple fractal downscaling methods using iterated functions systems (IFS) and fractal Brownian surfaces (FBS) that meet this requirement. The two methods were applied to disaggregate spatially 27 summertime convective storms in the central United States during 2007 at three consecutive times (1800, 2100, and 0000 UTC, thus 81 fields overall) from the Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 precipitation product (~25-km grid spacing) to the same resolution as the NCEP stage IV products (~4-km grid spacing). Results from bilinear interpolation are used as the control. A fundamental distinction between IFS and FBS is that the latter implies a distribution of downscaled fields and thus an ensemble solution, whereas the former provides a single solution. The downscaling effectiveness is assessed using fractal measures (the spectral exponent β, fractal dimension D, Hurst coefficient H, and roughness amplitude R) and traditional operational scores statistics scores [false alarm rate (FR), probability of detection (PD), threat score (TS), and Heidke skill score (HSS)], as well as bias and the root-mean-square error (RMSE). The results show that both IFS and FBS fractal interpolation perform well with regard to operational skill scores, and they meet the additional requirement of generating structurally consistent fields. Furthermore, confidence intervals can be directly generated from the FBS ensemble. The results were used to diagnose errors relevant for hydrometeorological applications, in particular a spatial displacement with characteristic length of at least 50 km (2500 km2) in the location of peak rainfall intensities for the cases studied. © 2010 American Meteorological Society.