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Applications of Convolutional-Recurrent Neural Networks in Weather Forecasting

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dc.contributor.advisor Kumar, Bipin en_US
dc.contributor.advisor Chattopadhyay, Rajib en_US
dc.contributor.author SAMANTA, ARYA en_US
dc.date.accessioned 2021-08-23T04:01:30Z
dc.date.available 2021-08-23T04:01:30Z
dc.date.issued 2021-06
dc.identifier.citation 52 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/6175
dc.description.abstract In this thesis, we use convolutional long short-term memory based neural network archi- tectures to predict certain basic dynamics of weather variables. First, rainfall dynamics are studied across direct and iterative forecasting methodologies from India-restricted data set. Next, we develop models capable of predicting dynamics over a global cube- sphere grid and as a proof of concept study the dynamics of geopotential height while comparing them on a benchmark dataset namely WeatherBench. Experiments indicate that ConvLSTM2D-based models are advantageous for iterative forecasting than direct forecasting than conv2D-based models by slight margins and need further development. When applied to cube-sphere grid, CubeSphereConvLSTM did perform better than the benchmark CNN iterative model but did not beat the best operational systems. en_US
dc.language.iso en en_US
dc.subject Machine Learning in Atmospheric Scienes en_US
dc.subject Spatiotemporal Forecasting en_US
dc.subject Convolution Recurrent NNs en_US
dc.subject Medium Range Weather Forecasting en_US
dc.title Applications of Convolutional-Recurrent Neural Networks in Weather Forecasting en_US
dc.type Thesis en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Data Science en_US
dc.contributor.registration 20161157 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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