Authors:
Priya Sharma
1
;
Ashish Patel
1
;
Pratik Shah
1
and
Soma Senroy
2
Affiliations:
1
Department of Computer Science and Technology, India Indian Intitute of Information Technology, Vadodara, India
;
2
India Meteorological Department, India
Keyword(s):
Weather Forecast, U-Net, Time Series, NWP, Climate, CNN, IMD, Diurnal Temperature, ConvLSTM.
Abstract:
Weather forecasting is an important task for the meteorological department as it has a direct impact on the day-to-day lives of people and the economy of a country. India is a diverse country in terms of geographical conditions like rivers, terrains, forests, and deserts. For the weather forecasting problem, we have taken the state of Madhya Pradesh as a case study. The current state of the art for weather forecasting is numerical weather prediction (NWP), which takes a long time and a lot of computing power to make predictions. In this paper, we have introduced a data-driven model based on a deep convolutional neural network, i.e., U-Net. The model takes weather features as input and nowcasts those features. The climate parameters considered for weather forecasting are 2m-Temperature, mean sea level pressure, surface pressure, wind velocity, model terrain height, intensity of solar radiation, and relative humidity. The model can predict weather parameters for the next 6 hours. The r
esults are encouraging and satisfactory, given the acceptable tolerances in prediction.
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