Polarimetric weather radars significantly enhance the capability to infer the properties of scatterers within a resolution volume. Previous studies have identified several consistently seen polarimetric signatures in supercells observed in the central United States. Nearly all of these studies used data collected by fixed-site S-and C-band radars. Because there are few polarimetric mobile radars, relatively little has been documented in high-resolution polarimetric data from mobile radars. Compared to S and C bands, there has been very limited examination of polarimetric signatures at X band.The primary focus of this paper is on one signature that has not been documented previously and one that has had little documentation at X band. The first signature, seen in at least seven supercell datasets collected by a mobile, X-band, polarimetric radar, consists of a narrow band of locally reduced reflectivity factor Z H and differential reflectivity, typically near the location where the hook echo ''attaches'' to the main body of the storm echo. No consistent pattern is seen in radial velocity V R or copolar cross correlation r HV . The small size of this feature suggests a significant heterogeneity in precipitation microphysics, the cause and impact of which are unknown. The greater resolution and the scattering differences at X band compared to other frequencies may make this feature more apparent. The second signature consists of anomalously low r HV in areas of high Z H along the left section (relative to storm motion) of the bounded weak-echo region. Examples of other polarimetric signatures at X band are provided.
Solar Tower technology has gained considerable momentum over the past decade. Unlike the parabolic trough, Solar Tower has a lot of variants in terms of type of receivers, working fluids, power cycles, size of heliostats, etc. Most of the literature available on this technology does not address in great depths, details of various parameters associated with tower technology. A detailed examination of plant parameters is required in order to perform a potential assessment, design basis or feasibility analysis. This paper aims to assess the principal parameters of existing plants, namely, solar to electric conversion efficiency, mirror and land area per MW e of equivalent capacity, packing density, field layout configuration, receiver size, tower height and gross costs of plants, wherever data is available. Based on this global review of existing plants, it is observed that, the annual solar to electric conversion efficiencies has an average value of 16% and an average packing density of about 20%. Since most of the existing plants have been constructed for demonstration purposes, the true potential of this technology has not yet been realised. Using this assessment as a basis, the technical, financial and policy drivers and barriers for adopting tower technology in India are discussed. It is seen that based on indigenisation prospects, tower technology with external cylindrical or cavity receivers with storage could be adopted. The role and significance of this technology is brought out in the context of the Jawaharlal Nehru National Solar Mission (JNNSM) in order to achieve grid-connected solar power. It is estimated that around 1800 MW of grid connected Solar Tower plants could come up under this mission by 2022.
The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigated in this study. The performance metrics of the errors are analysed such as the root mean square error (RMSE), mean absolute percentage error (MAPE), and mean square root (MSE). According to the findings, the model behaves consistently at the specified time and place in the experiment. Forecasters utilizing neural network models will have better accuracy if they use techniques like EN, FFN, and GRN having the RMSE of 0.25, 0.37, and 0.45, respectively.
Abstract-Millimeter wave radars have been widely used for atmospheric remote sensing and tracking hard-targets from airborne platforms. For these radars, the product of the unambiguous range and Doppler velocity is limited by the radar wavelength. This work focuses on a novel method to extend the Nyquist rate of millemeter radars, which uses frequency diversity pulse-pairs for Doppler phase estimation. Two short pulses with center-frequencies of f1 followed by f2 are transmitted during the first pulse repetition interval (PRI). During the next PRI, the pulses transmitted are in the order f2 followed by f1 respectively. There are two mechanisms for error reduction. First, the "beat" phases of the f1/f2 and f2/f1 pairs cancel out in the expected value sense. Second, since the f1/f2 and f2/f1 phase estimates are highly anti-correlated, the sum of the two phase estimates has a much smaller variance than the individual phase estimates. Based on Monte-Carlo simulations, the feasibility of this method is demonstrated herein. Ongoing data analysis is discussed.
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