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Dutta D.,Kohima Science College | Sharma S.,Kohima Science College | Kannan B.A.M.,Mausam Bhawan | Venketswarlu S.,Mausam Bhawan | And 3 more authors.
Indian Journal of Radio and Space Physics | Year: 2012

A study is carried out to investigate the sensitivity of Z-R relations and spatial variability of error in a Doppler Weather Radar (DWR) measured rain intensity. For this purpose, observations from a DWR at Satish Dhawan Space Centre, SHAR(13.66°N, 80.23°E) and five units of automatic tipping bucket rain gauges around the DWR are utilized. It is found that rain with intensity > 20 mm h-1 occurs for 16% of the total rain time but its contribution is 53% to the total rainfall. Sensitivity of Z-R relations are examined with the help of Z-R relations developed by two different approaches. One set of relations are developed from Joss Waldvogel Disdrometer (JWD) observations of rain drop size distribution (ZJWD-RJWD) and other set of relations are developed with the help of combined use of DWR and rain gauge observations (ZDWR-RRG). Performance of the DWR is improved with (ZDWR-RRG) relation. Using ZDWR-RRG relations for Z ≤ 30 dBZ and Z > 30 dBZ, the root mean square error (rmse) and bias for DWR measured rain intensity is reduced by 28% & 39% and 33% & 74%, respectively. The bias and error between DWR and rain gauge measured rain intensity are found to decrease with respect to decrease in distance between the rain gauges and DWR.

Dutta D.,Kohima Science College | Sharma S.,Kohima Science College | Das J.,Indian Statistical Institute | Gairola R.M.,Meteorology and Oceanography Group
Advances in Space Research | Year: 2012

The present study emphasize the development of a region specific rain retrieval algorithm by taking into accounts the cloud features. Brightness temperatures (T bs) from various TRMM Microwave Imager (TMI) channels are calibrated with near surface rain intensity as observed from the TRMM - Precipitation Radar. It shows that T b-R relations during exclusive-Mesoscale Convective System (MCS) events have greater dynamical range compared to combined events of non-MCS and MCS. Increased dynamical range of T b-R relations for exclusive-MCS events have led to the development of an Artificial Neural Network (ANN) based regional algorithm for rain intensity estimation. By using the exclusive MCSs algorithm, reasonably good improvement in the accuracy of rain intensity estimation is observed. A case study of a comparison of rain intensity estimation by the exclusive-MCS regional algorithm and the global TRMM 2A12 rain product with a Doppler Weather Radar shows significant improvement in rain intensity estimation by the developed regional algorithm. © 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Dutta D.,Kohima Science College | Sarma D.K.,Kohima Science College | Konwar M.,Kohima Science College | Sharma S.,Kohima Science College | And 5 more authors.
Indian Journal of Radio and Space Physics | Year: 2010

A soft computing model for nowcasting of Yes/No rain situations, with a lead time of 2 h, is developed over DWR station at Satish Dhawan Space Centre (SDSD), Shriharikota (13.66°N, 80.23°E) using Doppler weather radar (DWR) reflectivity imageries. Primarily, precipitating systems of mesoscale, i.e. meso-gamma (2-20 km), meso-beta (20-200 km) and meso-alpha (200-2000 km) are considered for the present study. The main components of soft computing approach are: analysis of two-dimensional reflectivity imageries from DWR and utilization of artificial neural network (ANN) for training of input/output data. About 15 input and one output parameters are extracted from radar imageries. The image analysis and training of ANN are carried out on MATLAB platform. After training of ANN, 91 and 77% results are matched with the observed values for No rain and Yes rain situations, respectively. The probability of detection (POD) for the nowcasting of Yes/No rain situations is found to be 0.84. The significant improvement in the nowcasting of Yes/No rain situations is observed by the developed methodology as compared to linear multivariable regression method. The POD of Yes/No rain for other two locations, namely Chennai (12.99°N, 80.18°E) and Tiruvallur (13.09°N, 79.57°E) are found to be 0.81 and 0.77, respectively. Overall, reasonably good results are obtained by the newly developed soft computing model.

Sarma A.C.,Patkai Christian College | Deshamukhya A.,Assam University | Narayana Rao T.,Clouds and Convective Systems Group | Sharma S.,Kohima Science College
Meteorological Applications | Year: 2016

A study was carried out to investigate the rain drop size distribution (DSD) characteristics during strong bright band (SBB), weak bright band (WBB) and no bright band (NBB) regimes of stratiform rain by using an L-band wind profiler and Joss-Waldvogel disdrometer at Gadanki (13.5°N, 79.20°E), a tropical station in India. The stratiform events with SBB (bright band width >0.49 km) are associated with larger mean drops (Dm) at ground level compared with WBB and NBB situations. Different shapes of raindrop DSDs during these three situations suggest different microphysical processes involved in the evolution of rain DSDs. During SBB regimes, the raindrop spectrum, which is found to be governed predominantly by an aggregation process, is size controlled. On the other hand, during WBB regimes, the raindrop spectrum, which is found to be governed predominantly by a riming process, is number controlled. A new rain DSD parameterization scheme, P (Z, N0 *), is proposed in the framework of multi-parameter radar observation. The considered parameters are radar reflectivity factor (Z) and normalized scale parameter (N0 *). These two parameters are found to be mutually independent. Reasonable improvement in the estimation of rain intensity is observed by the developed P(Z, N0 *) scheme compared with P(Z) and P(Z, Dm). The rain DSDs simulated by the developed P(Z, N0 *) scheme are in good agreement with the observed spectrum. © 2016 Royal Meteorological Society.

Choudhury L.,Gauhati University | Barman P.,Kohima Science College | Sarma R.,Darrang College
Indian Journal of Public Health Research and Development | Year: 2013

Objective: In this paper, it is an attempt to estimate the life expectancy at birth for the smaller north eastern states of India for 2001-05. Materials and Methods: Regression technique is used to estimate the life expectancy at birth, which requires only two data elements: the crude death rate and the proportion of the population aged 65 years and above. The regression model underlying the life expectancy estimates is constructed by using state level data for 16 bigger states of India. Result: The result indicates that the life expectancy at birth for males (females) varies from 57.03(58.09) for Meghalaya to the maximum of 68.92(71.12) for Manipur. Conclusion: Majority of the smaller north eastern states of India have recorded a higher life expectancy at birth compared to the national average. Interestingly state situated far away from the mainstream of the country shows better life expectancies.

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