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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

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. Source

Pradhan D.,Building Radar | Mitra A.,Mausam Bhawan | De U.K.,Jadavpur University
Indian Journal of Radio and Space Physics

A study of five tropical cyclones in the Bay of Bengal has been conducted to estimate the pressure drop in the eye of the cyclone and associated storm surge height using Doppler velocity data. A new value of constant K (13.637) has been found in the empirical relation Vmax = K(P-Pc) between maximum velocity (Vmax) and central pressure drop (P-Pc) in terms of maximum radial velocity measured by the Doppler Weather Radar (DWR) for coastal region of India. The present study provides an alternate method for estimating central pressure drop and expected storm surge height associated to a tropical cyclone. The study also reveals that the storm surge height estimated from Doppler velocity measurements for these cyclones is very close to the actual occurrence. The results of pressure drop estimates from Doppler velocity are in close agreement with the satellite estimates. It is also observed that DWR estimates are sometimes better than those from satellite. However, the limitation of DWR is the limited range of observation (400 km) and shorter duration of cyclone tracking. Source

Mohapatra M.,Mausam Bhawan
Journal of Earth System Science

Hazards associated with tropical cyclones (TCs) are long-duration rotatory high velocity winds, very heavy rain, and storm tide. India has a coastline of about 7516 km of which 5400 km is along the mainland. The entire coast is affected by cyclones with varying frequency and intensity. Thus classification of TC hazard proneness of the coastal districts is very essential for planning and preparedness aspects of management of TCs. So, an attempt has been made to classify TC hazard proneness of districts by adopting a hazard criteria based on frequency and intensity of cyclone, wind strength, probable maximum precipitation, and probable maximum storm surge. Ninety-six districts including 72 districts touching the coast and 24 districts not touching the coast, but lying within 100 km from the coast have been classified based on their proneness. Out of 96 districts, 12 are very highly prone, 41 are highly prone, 30 are moderately prone, and the remaining 13 districts are less prone. This classification of coastal districts based on hazard may be considered for all the required purposes including coastal zone management and planning. However, the vulnerability of the place has not been taken into consideration. Therefore, composite cyclone risk of a district, which is the product of hazard and vulnerability, needs to be assessed separately through a detailed study. © Indian Academy of Sciences. Source

Kumar A.,National Center for Medium Range Weather Forecasting | Mitra A.K.,National Center for Medium Range Weather Forecasting | Bohra A.K.,National Center for Medium Range Weather Forecasting | Iyengar G.R.,National Center for Medium Range Weather Forecasting | Durai V.R.,Mausam Bhawan
Meteorological Applications

The prediction of Asian summer monsoon rainfall on various space and time scales is still a difficult task. Compared to the mid-latitudes, proportional improvement in the skill in prediction of monsoon rainfall in medium range has been less in recent years. Global models and data assimilation techniques are being further improved for monsoons and the tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and reducing the model uncertainties. As major centres are exchanging the model output in near real time, MME is a viable, inexpensive, way for enhancing the forecast skill. Apart from a simple ensemble mean, the MME predictions of large-scale monsoon precipitation in the medium range was carried out during the 2009 monsoon at NCMRWF/MoES, India. The neural network weights were obtained and a neural network was trained based upon forecast data from four global models for the 2007 and 2008 monsoons in order to develop the multi-model ensemble system. The skill score for country and sub-regions, indicates that a multi-model ensemble forecast has a higher skill than individual model forecasts and also higher skill than the simple ensemble mean in general. Although the skill of the global models falls beyond day 3, a significant improvement could be seen by employing the MME technique up to day 5. © 2011 Royal Meteorological Society. Source

Singh K.K.,Mausam Bhawan | Mall R.K.,Banaras Hindu University | Singh R.S.,National Institute of Disaster Management | Srivastava A.K.,Indian Institute of Sugarcane Research
Journal of Agrometeorology

The sugarcane crop growth simulation model was calibrated and validated in Eastern Uttar Pradesh (UP) region of Indo-Gangetic Plains of India using 12 years field experiment data conducted in several places. The results reveal that the CANEGRO Sugarcane model satisfactorily simulated the potential growth and yield of sugarcane crop. The model simulates the stalk height, stalk fresh mass and sucrose yield within ±15% of range in comparison to the observed values. Therefore the validated CANEGRO Sugarcane model can be further used for applications such as prediction of crop growth, phenology, water management, potential and actual yields, performance of sugarcane under climate variability and change scenarios etc. The model may also be used to improve and evaluate the current practices of sugarcane growth management to achieve enhanced cane production and sugar recovery. Source

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