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|Title:||Gravity waves generated by thunderstorms||Authors:||Naren Athreyas, Kashyapa Bramha||Keywords:||Engineering::Aeronautical engineering::Instruments
Science::Physics::Meteorology and climatology
|Issue Date:||2019||Source:||Naren Athreyas, K. B. (2019). Gravity waves generated by thunderstorms. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Gravity waves are considered to be one of the integral parts of the atmosphere which are responsible for energy and momentum distribution among various layers and regions in the atmosphere. These can be generated from various sources such as earthquakes, volcanoes and thunderstorms which can create disturbances in the atmosphere. Thunderstorms are one of the phenomena which occur quite frequently in the tropical region. Thunderstorms are known to generate a wide range of waves including electromagnetic, sound and gravity waves. As it is known, thunderstorms are one of the strongest disturbances in the atmosphere involving huge amount of energy, therefore the gravity wave perturbations created by this can carry large amount of energy and momentum into the middle atmosphere which can affect the circulation and constitution of the layer where the wave dissipates. This thesis is focused on the quantifying the gravity wave perturbations created by the latent heat inside a thunderstorm which is the driving force of the storm, as these relationships have not been quantified based on a large-scale statistical study. Multi-disciplinary approaches have been used in order to develop a gravity wave-latent heat model by studying the ten-year radiosonde data from Singapore. The gravity waves are detected in the stratosphere using the radiosonde profiles, and the source of these waves are traced back using ray tracing technique. The sources are classified into thunderstorms using global outgoing radiation maps and these thunderstorms sources are further analysed for the latent heat generation. Since the latent heat is not a measurable quantity, it is realised using a cloud-resolving model. The simple linear model developed is applicable to the South-East Asia region. The model is able to estimate waves phase velocities and wave amplitudes which are validated using the radiosonde in Singapore when the thunderstorm is in the close vicinity of the station. The model shows reasonable performance with mean estimation error between 14-23% and standard deviation of estimation between 11-19%. vi Since the cloud-resolving models are computationally expensive, it becomes quite impractical to perform these quantifications on a large/global-scale. Therefore, satellite-based latent heat estimations are deemed to be necessary. The latent heat estimations are developed for CloudSat’s radar and MODIS imaging spectroradiometer which can be used to develop gravity wave models on a global scale. The cloud resolving models are used to develop a database of thunderstorms valid for a region (eg. South-east Asia). The database of reflectivity profiles of thunderstorms simulated using a cloud-resolving model is used for comparing with CloudSat radar reflectivity profiles of thunderstorms using Bayesian Monte-Carlo approach to determine the vertical profiles of latent heat. The performance of the algorithm is evaluated using the simultaneous cloud-resolving model simulations near Singapore in 2016. The estimated latent heat profiles are used for training a Bayesian neural network with cloud inputs from MODIS on-board Aqua. This approach exploits the synergy between the CloudSat and Aqua satellites which are on the similar orbit with 60 s time lag. The gravity wave models are developed using satellite based latent heat estimations for South-East Asia and West Africa regions with validation performed using local radiosonde profiles. The gravity wave models developed using satellites also show reasonable performance which gives encouragement and hope for developing global relationships. The models developed here represent the interactions that affect circulation and constituency of the atmosphere which can be used for improving the weather models.||URI:||https://hdl.handle.net/10356/85056
|DOI:||10.32657/10220/49183||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
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