# Machine Learning Methods in Weather and Climate Applications: A Survey
## 5 Short-Term Weather Forecast
### Model Design
This comprehensive process captures the complexity of weather systems and serves as a robust method for weather prediction [77]. While the sophistication of NWP allows for detailed simulations of global atmospheric states, one cannot overlook the intensive computational requirements of such models. Even with the formidable processing capabilities of contemporary supercomputers, a ten-day forecast simulation can necessitate several hours of computational engagement. **MetNet. ** MetNet [46] is a state-of-the-art weather forecasting model that integrates the functionality of CNN, LSTM, and auto-encoder units. The CNN component conducts a multi-scale spatial analysis, extracting and abstracting meteorological patterns across various spatial resolutions. In parallel, the LSTM component captures temporal dependencies within the meteorological data, providing an in-depth understanding of weather transitions over time [42]. Autoencoders are mainly used in weather prediction for data preprocessing, feature engineering, and dimensionality reduction to assist more complex prediction models in making more accurate and efficient predictions. This combined architecture permits a dynamic and robust framework that can adaptively focus on key features in both spatial and temporal dimensions, guided by an embedded attention mechanism [78; 79]. MetNet consists of three core components as shown in Figure 2: Spatial Downsampler, Temporal Encoder (ConvLSTM), and Spatial Aggregator. In this architecture, the Spatial Downsampler acts as an efficient encoder that specializes in transforming complex, high-dimensional raw data into a more compact, low-dimensional, information-intensive form. This process helps with feature extraction and data compression. The Temporal Encoder, using the ConvLSTM (Convolutional Long Short-Term Memory) model, is responsible for processing this dimensionality-reduced data in the temporal dimension. One of the major highlights of ConvLSTM is that it combines the advantages of CNNs and LSTM. The advantage of ConvLSTM is that it combines the advantages of CNN and LSTM, and is able to consider the localization of space in time series analysis simultaneously, increasing the model's ability to perceive complex time and space dependencies. The Spatial Aggregatorplays the role of an optimized, high-level decoder. Rather than simply recovering the raw data from its compressed form, it performs deeper aggregation and interpretation of global and local information through a series of axial self-attentive blocks, thus enabling the model to make more accurate weather predictions. These three components work in concert with each other to form a powerful and flexible forecasting model that is particularly well suited to handle meteorological data with a high degree of spatio-temporal complexity. The operational workflow of MetNet begins with the preprocessing of atmospheric input data, such as satellite imagery and radar information [80]. Spatial features are then discerned through the CNN layers, while temporal correlations are decoded via the LSTM units. This information is synthesized with the attention mechanism strategically emphasizing critical regions and timeframes, leading to short-term weather forecasts ranging from 2 to 12 h [79]. MetNet's strength lies in its precise and adaptive meteorological predictions, blending spatial and temporal intricacies, and thus offering an indispensable tool for refined weather analysis [46]. **FourCastNet. ** In response to the escalating challenges posed by global climate change and the increasing frequency of extreme weather phenomena, the demand for precise and prompt weather forecasting has surged. High-resolution weather models serve as pivotal instruments in addressing this exigency, offering the ability to capture finer meteorological features, thereby rendering more accurate predictions [81; 82]. Against this backdrop, FourCastNet [47] has been conceived, employing ERA5, an atmospheric reanalysis dataset.