# Machine Learning Methods in Weather and Climate Applications: A Survey
## 6 Medium-to-Long-Term Climate Prediction
### Model Design
Structurally characterized by hierarchical convolutional layers, CNNs possess the unique ability to articulate complex multi-scale spatial features across disparate scales, commencing with global coarse-grained characteristics and progressively refining to capture intricate regional details. An exemplar implementation of this approach was demonstrated by [PERSON] et al. [104], wherein a CNN comprised three convolutional layers with spatial kernels of varying counts (50, 25, and 10, respectively). The transformation process began with the recalibration of ERA-Interim reanalysis data to a 2\({}^{\circ}\) regular grid, elevating it  
Figure 7: CycleGAN flow chart. to 0.5\({}^{\circ}\)[106; 107; 108]. This configuration allowed the CNN to translate global atmospheric patterns into high-resolution regional specificity [109; 110]. The nuanced translation from global to regional scales, achieved through sequential convolutional layers, not only amplifies the spatial resolution but also retains the contextual relevance of climatic variables [111; 112]. The first convolutional layer captured global coarse-grained features, with subsequent layers incrementally refining these into nuanced regional characteristics. By the terminal layer, the CNN had effectively distilled complex atmospheric dynamics into a precise, high-resolution grid [113; 114]. This enhancement fosters a more robust understanding of regional climatic processes, ushering in an era of precision and flexibility in climate modeling. The deployment of this technology affirms a pivotal advancement in the field, opening new possibilities for more granulated, precise, and comprehensive examination of climatic processes and future scenarios [115; 116; 117]. The introduction of CNNs thus represents a transformative approach shown in Figure 8 to bridging the resolution gap inherent to traditional GCMs, with substantial implications for future climate analysis and scenario planning. **NNCAM. ** The design and implementation of the Neural Network Community Atmosphere Model (NNCAM) are architected to leverage advancements in machine learning for improved atmospheric simulations. The architecture is a nuanced blend of traditional General Circulation Models (GCMs), specifically the Super-Parameterized Community Atmosphere Model (SPCAM), and cutting-edge machine learning techniques like Residual Deep Neural Networks (ResDNNs). * Reference Model: SPCAM. SPCAM serves as the foundational GCM and is embedded with Cloud-Resolving Models (CRMs) to simulate microscale atmospheric processes like cloud formation and convection. SPCAM is employed to generate \"target simulation data\", which serves as the training baseline for the neural networks. The use of CRMs is inspired by recent advancements in data science, demonstrating that machine learning parameterizations can potentially outperform traditional methods in simulating convective and cloud processes. * Neural Networks: ResDNNs, a specialized form of deep neural networks, are employed for their ability to approximate complex, nonlinear relationships. The network comprises multiple residual blocks, each containing two fully connected layers with Rectified Linear Unit (ReLU) activations. ResDNNs are designed to address the vanishing and exploding gradient problems in deep networks through residual connections, offering a stable and effective gradient propagation mechanism. This makes them well-suited for capturing the complex and nonlinear nature of atmospheric processes. Figure 8: DeepESD structure. * Subgrid-Scale Physical Simulator. Traditional parameterizations often employ simplified equations to model subgrid-scale processes, which might lack accuracy. In contrast, the ResDNNs are organized into a subgrid-scale physical simulator that operates independently within each model grid cell. This simulator takes atmospheric states as inputs and outputs physical quantities at the subgrid scale, such as cloud fraction and precipitation rate.