A comparison of three methods for downscaling daily precipitation in the Punjab region

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K-nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue-type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright (C) 2011 John Wiley & Sons, Ltd.

10 Years of Click Chemistry: Synthesis and Applications of Ferrocene-Derived Triazoles

Click chemistry has played a significant role as a rapid and versatile strategy for conjugating two molecular fragments under very mild reaction conditions. Introduction of ferrocene-derived triazole systems using click chemistry has attracted enormous interest in various fields due to its potential applications in electrochemical techniques for detection and sensing. The present discussion focuses on the synthesis of ferrocene-triazole and the importance of using a CuAAC reaction for such conjugation. Applications of ferrocene-based click reactions in conjugate chemistry, asymmetric catalysis, medicinal chemistry, host-guest interactions, and materials chemistry have been highlighted.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Physical chemistry of mixed conducting zirconia ceramics

Suivant la pression partielle d'oxygène, la zircone peut être conducteur ionique ou électronique. Mise au point de méthodes de mesures de f.é.m. permettant de s'affranchir des sources d'erreur introduites par ces propriétés.

Physical chemistry of the reduction of calcium oxide with aluminium in vacuum

The physical chemistry of "aluminothermic" reduction of calcium oxide in vacuum is analyzed. Basic thermodynamic data required for the analysis have been generated by a variety of experiments. These include activity measurements in liquid AI-Ca alloys and determination of the Gibbs energies of formation of calcium aluminates. These data have been correlated with phase relations in the Ca-AI-0 system at 1373 K. The various stages of reduction, the end products and the corresponding equilibrium partial pressures of calcium have been established from thermodynamic considerations. In principle, the recovery of calcium can be improved by reducing the pressure in the reactor. However,, the cost of a high vacuum system and the enhanced time for reduction needed to achieve higher yields makes such a practice uneconomic. Aluminum contamination of calcium also increases at low pressures. The best compromise is to carry the reduction up to the stage where 3CaO-Al,O, is formed as the product. This corresponds to an equilibrium calcium partial pressure of 31.3 Pa at 1373 K and 91.6 Pa at 1460 K. Calcium can be extracted at this pressure using mechanical pumps in approximately 8 to 15 hr, depending on the size and the fill ratio of the retort and porosity of the charge briquettes.

Colloidal calcium nanoparticles: digestive ripening in the presence of a capping agent and coalescence of particles under an electron beam

The nanochemistry of calcium remains unexplored, which is largely due to the inaccessibility of calcium nanoparticles in an easy to handle form by conventional methods of synthesis as well as its highly reactive and pyrophoric nature. The synthesis of colloidal Ca nanoparticles by the solvated metal atom dispersion (SMAD) method is described. The as-prepared Ca-THF nanoparticles, which are polydisperse, undergo digestive ripening in the presence of a capping agent, hexadecyl amine (HDA) to afford highly monodisperse colloids consisting of 2-3 nm sized Ca-HDA nanoparticles. These are quite stable towards precipitation for long periods of time, thereby providing access to the study of the nanochemistry of Ca. Particles synthesized in this manner were characterized by UV-visible spectroscopy, high resolution electron microscopy, and powder X-ray diffraction methods. Under an electron beam, two adjacent Ca nanoparticles undergo coalescence to form a larger particle.