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 | Conference 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 zhihan.gao@connect.ust.hk Xingjian Shi Boson AI xshiab@connect.ust.hk Boran Han AWS boranhan@amazon.com Hao Wang AWS AI Labs howngz@amazon.com Xiaoyong Jin Amazon jxiaoyon@amazon.com Danielle Maddix AWS AI Labs dmmaddix@amazon.com Yi Zhu Boson AI yi@boson.ai Mu Li Boson AI mu@boson.ai Yuyang Wang AWS AI Labs yuyawang@amazon.com |
| 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. |