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Cloud — Aerosol interaction during lightning activity over land and ocean: Precipitation pattern assessment 总被引：1，自引：0，他引：1
Jayanti Pal Sutapa Chaudhuri Arumita Roy Chowdhury Tanuka Bandyopadhyay 《Asia-Pacific Journal of Atmospheric Sciences》2016,52(3):251-261
The present study attempts to identify the land - ocean contrast in cloud - aerosol relation during lightning and non-lightning days and its effect on subsequent precipitation pattern. The thermal hypothesis in view of Convective Available Potential Energy (CAPE) behind the land - ocean contrast is observed to be insignificant in the present study region. The result shows that the lightning activities are significantly and positively correlated with aerosols over both land and ocean in case of low aerosol loading whereas for high aerosol loading the correlation is significant but, only over land. The study attempts to comprehend the mechanism through which the aerosol and lightning interact using the concept of aerosol indirect effect that includes the study of cloud effective radius, cloud fraction and precipitation rate. The result shows that the increase in lightning activity over ocean might have been caused due to the first aerosol indirect effect, while over land the aerosol indirect effect might have been suppressed due to lightning. Thus, depending on the region and relation between cloud parameters it is observed that the precipitation rate decreases (increases) over ocean during lightning (non-lightning) days. On the other hand during non-lightning days, the precipitation rate decreases over land. 相似文献
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Thunderstorms are the perennial feature of Kolkata (22° 32???N, 88° 20???E), India during the premonsoon season (April?CMay). Precise forecast of these thunderstorms is essential to mitigate the associated catastrophe due to lightning flashes, strong wind gusts, torrential rain, and occasional hail and tornadoes. The present research provides a composite stability index for forecasting thunderstorms. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant indices with threshold ranges for the prevalence of such thunderstorms. The analyses reveal that the lifted index (LI) within the range of ?5 to ?12?°C, convective inhibition energy (CIN) within the range of 0?C150?J/kg and convective available potential energy (CAPE) within the ranges of 2,000 to 7,000?J/kg are the most pertinent indices for the prevalence thunderstorms over Kolkata during the premonsoon season. A composite stability index, thunderstorm prediction index (TPI) is formulated with LI, CIN, and CAPE. The statistical skill score analyses show that the accuracy in forecasting such thunderstorms with TPI is 99.67?% with lead time less than 12?h during training the index whereas the accuracies are 89.64?% with LI, 60?% with CIN and 49.8?% with CAPE. The performance diagram supports that TPI has better forecast skill than its individual components. The forecast with TPI is validated with the observation of the India Meteorological Department during the period from 2007 to 2009. The real-time forecast of thunderstorms with TPI is provided for the year?2010. 相似文献
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Implementation of intuitionistic fuzzy logic to assess the predictability of severe thunderstorms 总被引：1，自引：0，他引：1
Sutapa Chaudhuri 《Asia-Pacific Journal of Atmospheric Sciences》2011,47(5):477-483
Thunderstorms are responsible for remarkable devastation when accompanied with lightning flashes, high wind gusts, torrential rain, hail and tornadoes. Weather hazards due to thunderstorms of such severe measure take place every year over Kolkata (22°32′N, 88°20′E), India during the pre-monsoon season (April–May). Prediction of severe thunderstorms is extremely important to cope with the devastations. However, forecasting severe thunderstorms is very difficult because the weather system is confined within a very small spatial-temporal scale. The network of observation systems is not adequate to detect such high frequency small scale weather. The purpose of the present study is to bring in the concept of Intuitionistic fuzzy logic as a decision — making technique to assess the predictability of severe thunderstorms over Kolkata in the premonsoon season. Different measures of entropies are used to extract the route of fuzziness. The intuitionistic fuzzy logic is implemented with ten years (1997–2006) observation of the occurrence/nonoccurrence of severe thunderstorms to assess the predictability. The result reveals that two consecutive severe thunderstorm days are highly probable after two consecutive non-thunderstorm days whereas the probability of severe thunderstorm is very less after three consecutive non-thunderstorm days during the pre-monsoon season over Kolkata. The result is compared with the box-and-whisker plot and validated with four years (2007–2010) observations of India Meteorological Department (IMD). 相似文献
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In operational forecast, the stability indices either individually or in combination are utilized to assess the predictability of local severe storms over a region. The objective of the present study is to identify such stability indices to assess the predictability of Bordoichila of Guwahati, India, during the pre-monsoon season (April–May) aiming to formulate a composite stability index using the most pertinent indices for nowcasting Bordoichila with considerable precision. Bordoichila, meaning the angry daughter of Assam, represents local severe storms of Guwahati during the pre-monsoon season. Precise forecast of Bordoichila is essential to mitigate the associated catastrophe over Guwahati. The forecast quality detection parameters are computed with the available indices during the period from 1997 to 2006 to select the most relevant stability indices for the prevalence of Bordoichila. The method of normal probability distribution is implemented to identify the threshold ranges of the selected indices. The stability indices that are selected with appropriate ranges are lifted index, Showalter index (SI), cross total index (CTI), vertical total index, totals total, convective available potential energy, convective inhibition energy, SWEAT and bulk Richardson number. The forecast skill scores are estimated with the selected indices. The best predictor indices identified for the prevalence of Bordoichila are the cross total index (CTI) and Showalter index (SI). A composite stability index, Bordoichila prediction index, is formulated with CTI and SI with proper weightages. The forecast with BPI is validated with the observations of India Meteorological Department for the year 2007 and is implemented for real-time forecast for the years 2009 and 2011. 相似文献
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The coastal regions, deltas, and estuaries are severely affected by the sea level rise and cyclonic activities and climate changes. Sundarban delta is one of the most mysterious landscapes in the world, which has successively evolved due to sediment accumulation by the great Ganga and Brahmaputra river system. The area is characterized by low-lying islands and a flat topography coupled with macro-tidal activities, powerful surges, and seasonal cyclonic events. All these conditions put together this landscape defenseless to frequent flood and erosion. Since the last hundred years, the face of Sundarban has been changed remarkably from wildest to human-occupied territory by protecting this low-lying flat plain from tidal inundation through artificial embankment. In this background, the current study attempts to highlight the spatial extent and magnitudes of internal risk factors of the region using the composite vulnerability index. Coastal vulnerability defines a system’s openness to flood and erosion risk due to hydrogeomorphic exposures and socio-economic susceptibility in conjunction with its capacity/incapacity to be resilient and to cope, recover, or adapt to an extent. Coastal vulnerability assesses the potential risk from erosion and flooding of any low-lying coastal region due to its physiographical and hydrological exposures, socio-economic and political susceptibility, and resilience capacity. A natural system affects the socio-economic scenario of any region. Hence, multidimensional databases can be more effective to understand the extent of exposure, susceptibility, and resilience of any system. To throw some light on the situation of vulnerability of western estuarine Sundarban, between Muriganga and Saptamukhi interfluve, the composite vulnerability index has been carried out to delineate the magnitude and spatial extent of vulnerability with the help of quantitative techniques and geospatial tools. The estuarine tracts and coastal parts of the Ganga delta are two of the most densely populated areas in the world. The study highlights the critical situation of the population under different potential risk classes residing in the study area with the intention of suggesting some proper course of action of planning and management to conserve coastal communities in their original habitat. 相似文献
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An attempt is made in this study to develop a model to forecast the cyclonic depressions leading to cyclonic storms over North Indian Ocean (NIO) with 3 days lead time. A multilayer perceptron (MLP) model is developed for the purpose and the forecast quality of the model is compared with other neural network and multiple linear regression models to assess the forecast skill and performances of the MLP model. The input matrix of the model is prepared with the data of cloud coverage, cloud top temperature, cloud top pressure, cloud optical depth, cloud water path collected from remotely sensed moderate resolution imaging spectro-radiometer (MODIS), and sea surface temperature. The input data are collected 3 days before the cyclogenesis over NIO. The target output is the central pressure, pressure drop, wind speed, and sea surface temperature associated with cyclogenesis over NIO. The models are trained with the data and records from 1998 to 2008. The result of the study reveals that the forecast error with MLP model varies between 0 and 7.2 % for target outputs. The errors with MLP are less than radial basis function network, generalized regression neural network, linear neural network where the errors vary between 0 and 8.4 %, 0.3 and 24.8 %, and 0.3 and 32.4 %, respectively. The forecast with conventional statistical multiple linear regression model, on the other hand, generates error values between 15.9 and 32.4 %. The performances of the models are validated for the cyclonic storms of 2009, 2010, and 2011. The forecast errors with MLP model during validation are also observed to be minimum. 相似文献
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Sutapa Chaudhuri Debashree Dutta Sayantika Goswami Anirban Middey 《Natural Hazards》2013,65(1):97-113
The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models. 相似文献
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