Some aspects of daily rainfall distribution over India during the south-west monsoon season

This paper presents the results of an analysis of the daily rainfall at 365 Indian stations for the 80-year period, 1901-1980. The rainfall data relate to the south-west monsoon season June to September (122 days), which accounts for the major part of the annual rainfall over most parts of the country. The coefficient of variation (CV) of the daily rainfall series varies between 100 and 230 at individual stations, with nearly half the number of stations having CV values in the range 130-150. The number of days of significant rainfall (days with rainfall greater than the mean intensity per rain-day) constitute about 30 of the total number of rain-days and account for about 75 of the seasonal rainfall at almost all the stations

Long range prediction of Indian summer monsoon rainfall

The search for new parameters for predicting the all India summer monsoon rainfall (AISMR) has been an important aspect of long range prediction of AISMR. In recent years NCEP/NCAR reanalysis has improved the geographical coverage and availability of the data and this can be easily updated. In this study using NCEP/NCAR reanalysis data on temperature, zonal and meridional wind at different pressure levels, few predictors are identified and a prediction scheme is developed for predicting AISMR. The regression coefficients are computed by stepwise multiple regression procedure. The final equation explained 87 of the variance with multiple correlation coefficient (MCC), 0.934. The estimated rainfall in the, El-Niño year of 1997 was -1.7 as against actual of 4.4. The estimated rainfall deficiency in both the recent deficient years of 2002 and 2004 were -19.5 and -8.5 as against observed -20.4 and -11.5 respectively

Quantitative precipitation forecasting over Narmada Catchment

Quantitative precipitation forecasting (QPF) has been attempted over the Narmada Catchment following a statistical approach. The catchment has been divided into five sub-regions for the development of QPF models with a maximum lead-time of 24 hours. For this purpose the data of daily rainfall from 56 raingauge stations, twice daily observations on different surface meteorological parameters from 28 meteorological observatories and upper air data from 11 aerological stations for the nine monsoon seasons of 1972-1980 have been utilized. The horizontal divergence, relative vorticity, vertical velocity and moisture divergence are computed using the kinematic method at different pressure levels and used as independent variables along with the rainfall and surface meteorological parameters. Multiple linear regression equations have been developed using the stepwise procedure separately with actual and square root and log-transformed rainfall using 8-year data (1972-1979). When these equations were verified with an independent data for the monsoon season of 1980, it was found that the transformed rainfall equations fared much better compared to the actual rainfall equations. The performance of the forecasts of QPF model compared to the climatological and persistence forecasts has been assessed by computing the verification scores using the forecasts for the monsoon season of 1980

Thermo-Chromic Response of Polymer Stabilized Cholesteric Liquid Crystal for Thermal Imaging

Cholesteric liquid crystal (Ch-LC) exhibits many remarkable optical properties due to formation of a macroscopic helical structure. A low amount of monomer (5wt.%) is dispersed into cholesteric liquid crystal and get polymerized under UV radiations to form polymer stabilized cholesteric texture (PSCT). The thermo-chromic response made this device suitable for the developing applications in thermal imaging. Temperature based measurements of PSCT exploits the key property of some polymer stabilized cholesteric liquid crystals (PSCLC) to reflect definite colors at specific temperatures. The selective color of PSCT texture shifts with raise in temperature from 30oC to 85oC, which can be utilized in thermal imaging applications

Effect of Cationic Surfactant Head Groups on Synthesis, Growth and Agglomeration Behavior of ZnS Nanoparticles

Colloidal nanodispersions of ZnS have been prepared using aqueous micellar solution of two cationic surfactants of trimethylammonium/pyridinium series with different head groups i.e., cetyltrimethylammonium chloride (CTAC) and cetyltrimethylpyridinium chloride (CPyC). The role of these surfactants in controlling size, agglomeration behavior and photophysical properties of ZnS nanoparticles has been discussed. UV–visible spectroscopy has been carried out for determination of optical band gap and size of ZnS nanoparticles. Transmission electron microscopy and dynamic light scattering were used to measure sizes and size distribution of ZnS nanoparticles. Powder X-ray analysis (Powder XRD) reveals the cubic structure of nanocrystallite in powdered sample. The photoluminescence emission band exhibits red shift for ZnS nanoparticles in CTAC compared to those in CPyC. The aggregation behavior in two surfactants has been compared using turbidity measurements after redispersing the nanoparticles in water. In situ evolution and growth of ZnS nanoparticles in two different surfactants have been compared through time-dependent absorption behavior and UV irradiation studies. Electrical conductivity measurements reveal that CPyC micelles better stabilize the nanoparticles than that of CTAC

A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS

The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals

Experimental and neural network approach to effective electrical conductivity of carbon nanotubes dispersed chiral nematic liquid crystals

Single walled carbon nanotubes (SWCNT’s) doped cholesteric liquid crystal composite has been prepared and characterized for their electrical responses. Also theoretically, an artificial neural network (ANN) approach has been trained for predicting the effective electrical conductivity of these composites. The ANN models are based on a feedforward backpropagation (FFBP) network with such training functions as the adaptive learning rate (GDX), gradient descent with adaptive learning rate (GDA), gradient descent (GD), conjugates gradient with Powell-Beale restarts (CGB), one-step secant (OSS), and Levenberg–Marquardt (LM), and training algorithms run at the uniform threshold transfer functions-Tangent sigmoid (TANSIG) and pure linear (PURELIN) for 1000 epochs. Our modeling confirms that the expected effective electrical conductivity by different training functions of ANN is in higher agreement with the experimental results of SWCNT doped CLC composites

Studies on spatial pattern of NDVI over Indiaand its relationship with rainfall, air temperature, soil moisture adequacy and ENSO

The changes in spatial distribution of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) are studied for different seasons in India for the period 1982 to 2000. The inter-annual variability of All India NDVI (AINDVI) has been studied and related to rainfall, air temperature, soil moisture adequacy, Southern Oscillation Index (SOI) and Nino 3 Sea Surface Temperature (Nino 3 SST) to understand the influence of these variables on vegetal cover. The results show that the NDVI is high during the south-west (June to September) monsoon and retreat (October and November) seasons where in major crop seasons Kharif and Rabi take place over India. The trend of AINDVI is increasing and displayed higher values during La Niña and lower in El Niño episodes. The correlation between rainfall and NDVI of All India is not significant on monthly basis (+0.13) but it is more prominent when the cumulative annual amounts of rainfall are involved (+0.61). NDVI responded very well to the variations of soil moisture adequacy (SAD) which enumerates the strongest correlation (+0.73) of crop performance with NDVI. This significant strong correlation inferred that SAD can be taken as the indicator for the NDVI variations rather rainfall. The linear regression analysis of AINDVI and the ENSO indices revealed the strong impact of sea surface temperatures than SOI on vegetation pattern over India

Studies on spatial pattern of NDVI over india and its relationship with rainfall, air temperature, soil moisture adequacy and ENSO

The changes in spatial distribution of Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) are studied for different seasons in India for the period 1982 to 2000. The inter-annual variability of All India NDVI (AINDVI) has been studied and related to rainfall, air temperature, soil moisture adequacy, Southern Oscillation Index (SOI) and Nino 3 Sea Surface Temperature (Nino 3 SST) to understand the influence of these variables on vegetal cover. The results show that the NDVI is high during the south-west (June to September) monsoon and retreat (October and November) seasons where in major crop seasons Kharif and Rabi take place over India. The trend of AINDVI is increasing and displayed higher values during La Niña and lower in El Niño episodes. The correlation between rainfall and NDVI of All India is not significant on monthly basis (+0.13) but it is more prominent when the cumulative annual amounts of rainfall are involved (+0.61). NDVI responded very well to the variations of soil moisture adequacy (SAD) which enumerates the strongest correlation (+0.73) of crop performance with NDVI. This significant strong correlation inferred that SAD can be taken as the indicator for the NDVI variations rather rainfall. The linear regression analysis of AINDVI and the ENSO indices revealed the strong impact of sea surface temperatures than SOI on vegetation pattern over India