Deterministic weather forecasting models based on intelligent predictors: A survey
In one sentence. Survey classifying deterministic deep learning weather forecasting models and public datasets, with directions for future research.
Abstract
Weather forecasting is the practice of predicting the state of the atmosphere for a given location based on different weather parameters. Weather forecasts are made by gathering data about the current state of the atmosphere. Accurate weather forecasting has proven to be a challenging task for meteorologists and researchers. Weather information is essential in every facet of life like agriculture, tourism, airport system, mining industry, and power generation. Weather forecasting has now entered the era of Big Data due to the advancement of climate observing systems like satellite meteorological observation and also because of the fast boom in the volume of weather data. So, the traditional computational intelligence models are not adequate to predict the weather accurately. Hence, deep learning-based techniques are employed to process massive datasets that can learn and make predictions more effectively based on past data. The effective implementation of deep learning in various domains has motivated its use in weather forecasting and is a significant development for the weather industry. This paper provides a thorough review of different weather forecasting approaches, along with some publicly available datasets. This paper delivers a precise classification of weather forecasting models and discusses potential future research directions in this area.
Labels
- Topic
- Global medium-range NWP
- Type
- Review, survey or tutorial
- Method
- CNN / U-Net, RNN / LSTM / ConvLSTM, Transformer / ViT
- Time scale
- 6 h–3 days, 3–15 days
- Domain
- Global
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1016/j.jksuci.2020.09.009 OpenAlex:W3088671467
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.