PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Authors: Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle Maddix, Yi Zhu, Mu Li, Yuyang (Bernie) Wang
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Specifically, we impose the law of conservation of energy in N-body MNIST and anticipated precipitation intensity in SEVIR. Experiments demonstrate the effectiveness of Pre Diff in handling uncertainty, incorporating domain-specific prior knowledge, and generating forecasts that exhibit high operational utility. |
| Researcher Affiliation | Collaboration | Zhihan Gao Hong Kong University of Science and Technology EMAIL Xingjian Shi Boson AI EMAIL Boran Han AWS EMAIL Hao Wang AWS AI Labs EMAIL Xiaoyong Jin Amazon EMAIL Danielle Maddix AWS AI Labs EMAIL Yi Zhu Boson AI EMAIL Mu Li Boson AI EMAIL Yuyang Wang AWS AI Labs EMAIL |
| Pseudocode | Yes | Algorithm 1 One training step of the knowledge alignment network Uϕ |
| Open Source Code | No | The paper provides a link for generating the N-body MNIST dataset, but not for the Pre Diff model's source code or the experiment code itself. "Code available at https://github.com/amazon-science/earth-forecasting-transformer/tree/main/src/earthformer/datasets/nbody". |
| Open Datasets | Yes | We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset. Dataset is available at https://sevir.mit.edu/ (for SEVIR) and Code available at https://github.com/amazon-science/earth-forecasting-transformer/tree/main/src/earthformer/datasets/nbody (for N-body MNIST). |
| Dataset Splits | Yes | We generate 20,000 sequences for training and 1,000 sequences for testing. (N-body MNIST) ... a real-world precipitation nowcasting benchmark SEVIR2 [55] (SEVIR test set implicitly used as a benchmark with standard splits). |
| Hardware Specification | Yes | All experiments are conducted on machines with NVIDIA A10G GPUs (24GB memoery). |
| Software Dependencies | No | The paper mentions optimizers (Adam, AdamW) and activation functions (SiLU, GELU) but does not provide specific version numbers for software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | Detailed configurations are shown in Table 10, Table 11 and Table 12 for the frame-wise VAE, the latent Earthformer-UNet and the knowledge alignment network, respectively. These tables list hyperparameters such as Learning rate, β1, β2, Weight decay, Batch size, Training epochs, Warm up percentage, and Learning rate decay. |