On the detection of human influence in extreme precipitation over India

Climate change is expected to influence extreme precipitation which in turn might affect risks of pluvial flooding. Recent studies on extreme rainfall over India vary in their definition of extremes, scales of analyses and conclusions about nature of changes in such extremes. Fingerprint-based detection and attribution (D&A) offer a formal way of investigating the presence of anthropogenic signals in hydroclimatic observations. There have been recent efforts to quantify human effects in the components of the hydrologic cycle at large scales, including precipitation extremes. This study conducts a D&A analysis on precipitation extremes over India, considering both univariate and multivariate fingerprints, using a standardized probability-based index (SPI) from annual maximum one-day (RX1D) and five-day accumulated (RX5D) rainfall. The pattern-correlation based fingerprint method is used for the D&A analysis. Transformation of annual extreme values to SPI and subsequent interpolation to coarser grids are carried out to facilitate comparison between observations and model simulations. Our results show that in spite of employing these methods to address scale and physical processes mismatch between observed and model simulated extremes, attributing changes in regional extreme precipitation to anthropogenic climate change is difficult. At very high (95%) confidence, no signals are detected for RX1D, while for the RX5D and multivariate cases only the anthropogenic (ANT) signal is detected, though the fingerprints are in general found to be noisy. The findings indicate that model simulations may underestimate regional climate system responses to increasing human forcings for extremes, and though anthropogenic factors may have a role to play in causing changes in extreme precipitation, their detection is difficult at regional scales and not statistically significant. (C) 2015 Elsevier B.V. All rights reserved.

Ultra sensitive NO2 gas detection using the reduced graphene oxide coated etched fiber Bragg gratings

We report a simple and highly sensitive methodology for the room temperature NO2 gas sensing using reduced graphene oxide (RGO) coated clad etched fiber Bragg grating (eFBG). A significant shift (>10 pm) is observed in the reflected Bragg wavelength (lambda(B)) upon exposing RGO coated on the surface of eFBG to the NO2 gas molecules of concentration 0.5 ppm. The shift in Bragg wavelength is due to the change in the refractive index of RGO by charge transfer from the adsorbing NO2 molecules. The range of NO2 concentration is tested from 0.5 ppm to 3 ppm and the estimated time taken for 50% increase in Delta lambda(B) ranges from 20 min (for 0.5 ppm) to 6 min (for 3 ppm). (C) 2015 Elsevier B.V. All rights reserved.