ABSTRACT Studies on seasonal dynamics of white fly (Bemesia tabaci) on tomato (Solanum esculentum var. Pusa Ruby) revealed that it appeared first during the 13th and reached maximum during 21st standard meteorological week. A positive correlation between adult population and abiotic factors viz. temperature (maximum and minimum) and sunshine hours was observed, whereas humidity (maximum and minimum) and rainfall showed a negative correlation with it. Taken together, the key weather parameters studied, caused 89.00 per cent variation in whitefly population (R2 value). Combination of carbofuran (soil application) + imidacloprid (seed treatment ) + imidacloprid (foliar application) proved significantly superior and caused maximum reduction in whitefly population followed by imidacloprid (seed treatment) + thiomethoxam (spray), imidacloprid (seed treatment) + imidacloprid (spray), imidacloprid (seed treatment) + dimetheoate (spray), carbofuran (soil application) + malathion (spray), and imidacloprid (seed treatment) + yellow sticky traps. The highest cost benefit ratio of 1:25.04 was recorded in case of carbofuran (soil application) + imidacloprid (seed treatment ) + imidacloprid (foliar application) followed by 1:22.38 for imidacloprid (seed treatment) + thiomethoxam (spray) ; 1:21.81 for imidacloprid (seed treatment) + imidacloprid (spray); 1:19.27 imidacloprid (seed treatment) + dimetheoate (spray); 1:19.48 carbofuran (soil application) + malathion (spray), and 1:8.33 for imidacloprid (seed treatment) + yellow sticky traps. The soil application of carbofuran + seed treatment with imidacloprid and three foliar sprays of imidacloprid at fortnight interval starting 40 days after transplanting is found effective and is advised for whitefly management in susceptible tomato cultivars.

In this paper, impact of assimilation of Indian Doppler Weather Radar (DWR) data has been assessed by numerical weather prediction system (ARPS) at 9 km horizontal resolution. The radial velocity and reflectivity observations from two DWR stations namely, Chennai (Lat. 13.0° N and Long. 80.0° E) and Machilipatnam (Lat. 16.5° N and Long. 81.3° E) are assimilated using the ARPS Data Assimilation System (ADAS) and cloud analysis scheme of the model. Two case studies selected are 1) Bay of Bengal Tropical Cyclone Ogni of October 2006 and 2) A local thunderstorm event of 5 June 2009 over the southeast parts of India. The study shows that the model at 9 km resolution with the assimilation of DWR observations (Chennai) could simulate mesoscale features suchas: number of cells, spiral rain band structure, location of the center, strengthening of the lower tropospheric winds and northerly movement of the small size cyclonic storm in the analysis as well as in the forecasts. The model with DWR assimilation could retain the intensity of the cyclone up to 6 hours of forecasts. Thereafter the cyclone showed a weakening trend when it was drifting away from the radar site. In case of thunderstorm, the model with the DWR assimilation could capture the convective precipitation in the right location. The DWR assimilation could realistically reproduce the development process and south-westward movement of thunderstorm cells.

En este trabajo se determina el impacto de la asimilación de datos del radar meteorológico hindú (DWR) por medio del sistema meteorológico de predicción numérica (ARPS) a una resolución horizontal de 9 km. La velocidad radial y las observaciones de reflectividad de dos estaciones DWR, Chennai (Lat. 13.0° N, Long. 80.0° E) y Machilipatnam (Lat. 16.5° N, Long. 81.3° E) se asimilan utilizando el sistema de asimilación de datos (ADAS) ARPS y el esquema de análisis de nubosidad del modelo. Se seleccionaron dos casos de estudio, 1) el ciclón tropical Ogni de octubre de 2006 en la Bahía de Bengala, y 2) la tormenta eléctrica local del 5 de junio de 2009 sobre parte del suroeste de la India. El estudio muestra que el modelo a una resolución de 9 km con la asimilación de observaciones DWR de Chennai, pudo simular características de mesoescala como: número de células, estructura de la banda de lluvia espiral, localización del centro, fortalecimieno de los vientos troposféricos bajos y el movimiento hacia el norte de la pequeña tormenta ciclónica, tanto en el análisis como en el pronóstico. El modelo con la asimilación de DWR pudo retener la intensidad del ciclón hasta las seis horas de pronóstico. Después de esto el ciclón se debilitó al alejarse del sitio del radar. En el caso de la tormenta eléctrica, el modelo con la asimilación DWR pudo capturar la precipitación convectiva en la localización correcta. La asimilación DWR pudo reproducir de manera realista el proceso de desarrollo y el movimiento hacia el sur de las células de la tormenta.