Nowcasting of thunderstorm has important implication on minimizing the loss of properties and life. Conventional approaches for such purpose using radiometer measured brightness temperature of only water vapor line usually tend to overestimate the occurrence probability. In this paper, we demonstrate that use of brightness values of oxygen absorption lines in addition with the water vapor absorption lines can provide unique signature of heavy rain. © 2012 IEEE.

S DAS

S TALUKDAR

R CHAKRABORTY

A MAITRA

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

Nowcasting of tropical rain using dual frequency atmospheric brightness temperatures at Kolkata

CODEC 2012 - 5th International Conference on Computers and Devices for Communication

Automatic nowcasting of convective initiation and thunderstorms has potential applications in several sectors including aviation planning and disaster management. In this paper, random forest based machine learning algorithm is tested for nowcasting of convective rain with a ground based radiometer. Brightness temperatures measured at 14 frequencies (7 frequencies in 22–31 GHz band and 7 frequencies in 51–58 GHz bands) are utilized as the inputs of the model. The lower frequency band is associated to the water vapor absorption whereas the upper frequency band relates to the oxygen absorption and hence, provide information on the temperature and humidity of the atmosphere. Synthetic minority over-sampling technique is used to balance the data set and 10-fold cross validation is used to assess the performance of the model. Results indicate that random forest algorithm with fixed alarm generation time of 30 min and 60 min performs quite well (probability of detection of all types of weather condition ∼90%) with low false alarms. It is, however, also observed that reducing the alarm generation time improves the threat score significantly and also decreases false alarms. The proposed model is found to be very sensitive to the boundary layer instability as indicated by the variable importance measure. The study shows the suitability of a random forest algorithm for nowcasting application utilizing a large number of input parameters from diverse sources and can be utilized in other forecasting problems. © 2017 COSPAR

Advances in Space Research

A random forest algorithm for nowcasting of intense precipitation events

Nowcasting of heavy rain events using microwave radiometer has been carried out at Kolkata (22.65°N, 88.45°E), a tropical location. Microwave radiometer can produce the temperature and humidity profiles of the atmosphere with fairly good accuracy. Definite changes are observed in temperature and humidity profiles before and at the onset of heavy rain events. Concurrent changes in the brightness temperatures (BT) at 22. GHz and 58. GHz are found to be suitable to nowcast rain. The time derivatives of brightness temperatures at 22. GHz and 58. GHz are used as inputs to the proposed nowcasting model. In addition, the standard deviation of the product of these time derivatives is also considered. The model has been developed using the data of 2011 and validated for rain events of 2012-2013 showing a prediction efficiency of about 90% with alarm generated about 25. min in advance. © 2014 Elsevier B.V.

Journal	CODEC 2012 - 5th International Conference on Computers and Devices for Communication
Publisher	-