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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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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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Chaudhuri Sutapa Goswami Sayantika Middey Anirban Das Debanjana Chowdhury S. 《Natural Hazards》2015,78(2):1369-1385
Natural Hazards - Forecasting, with precision, the location of landfall and the height of surge of cyclonic storms prevailing over any ocean basin is very important to cope with the associated... 相似文献
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Meta-heuristic ant colony optimization technique to forecast the amount of summer monsoon rainfall: skill comparison with Markov chain model 总被引：3，自引：1，他引：2
Sutapa Chaudhuri Sayantika Goswami Debanjana Das Anirban Middey 《Theoretical and Applied Climatology》2014,116(3-4):585-595
Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5–35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012. 相似文献
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Sutapa Chaudhuri Sayantika Goswami Anirban Middey 《Theoretical and Applied Climatology》2014,117(1-2):61-71
As the world's highest and largest plateau, the Qinghai–Xizang Plateau has experienced a greater warming than the Northern Hemisphere and global averages. This warming has been reported to exhibit an elevation-dependent pattern. However, the finding involved plenty of uncertainties caused by the spatially limited datasets and complex topography. Here, we explored an approach integrating satellite-derived LST data and ground records to generate a spatially continuous air temperature dataset for the plateau grasslands from 2003 to 2012, and then examined influences of elevation/topography on temperature change trends. The derived temperature dataset was validated to be closely correlated with field-station records. Based on the derived spatially continuous temperature datasets, we found an opposite change trend of annually average temperature between Qinghai and Xizang Province. The contrasted trend was obvious in daytime and more so in summer season. By analyzing the temperature trend in relation to elevation, we found an enhanced temperature change trend in higher elevation than in lower elevation for autumn nights and winter temperatures, while the temperature change trends for other seasons were more evident in lower elevation areas. The varying temperature change trends as regulated by elevation implies that temperate grasslands have experienced a more rapid temperature change than alpine grasslands during the past decade. 相似文献
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