STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation Forecasting

• URL: http://arxiv.org/abs/2409.06732v1
• Date: Fri, 6 Sep 2024 10:28:52 GMT
• Title: STAA: Spatio-Temporal Alignment Attention for Short-Term Precipitation Forecasting
• Authors: Min Chen, Hao Yang, Shaohan Li, Xiaolin Qin,
• Abstract summary: Short-term precipitation forecasting model based on ontemporal alignment, with SATA as the temporal alignment module, STAU as the temporal alignment feature extractor. Based on satellite and ERA5 data, our model achieves improvements of 12.61% in terms of RMSE, in comparison with the state-of-the-art methods.
• Score: 9.177158814568887
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is a great need to accurately predict short-term precipitation, which has socioeconomic effects such as agriculture and disaster prevention. Recently, the forecasting models have employed multi-source data as the multi-modality input, thus improving the prediction accuracy. However, the prevailing methods usually suffer from the desynchronization of multi-source variables, the insufficient capability of capturing spatio-temporal dependency, and unsatisfactory performance in predicting extreme precipitation events. To fix these problems, we propose a short-term precipitation forecasting model based on spatio-temporal alignment attention, with SATA as the temporal alignment module and STAU as the spatio-temporal feature extractor to filter high-pass features from precipitation signals and capture multi-term temporal dependencies. Based on satellite and ERA5 data from the southwestern region of China, our model achieves improvements of 12.61\% in terms of RMSE, in comparison with the state-of-the-art methods.

Related papers

• Precipitation Nowcasting Using Diffusion Transformer with Causal Attention [3.9468501770612576]
  Current deep learning methods fall short in establishing effective dependencies between conditions and forecast results. We propose a Precipitationcasting Using Diffusion Transformer with Causal Attention model.
  arXiv  Detail & Related papers  (2024-10-17T08:10:41Z)
• Uncertainty-aware segmentation for rainfall prediction post processing [0.7646713951724011]
  We explore uncertainty-aware deep learning models for post-processing daily cumulative quantitative precipitation forecasts. Our study compares different state-of-the-art models, and we propose a variant of the well-known SDE-Net. Our results show that all deep learning models significantly outperform the average baseline NWP solution.
  arXiv  Detail & Related papers  (2024-08-28T16:31:40Z)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• Learning Robust Precipitation Forecaster by Temporal Frame Interpolation [65.5045412005064]
  We develop a robust precipitation forecasting model that demonstrates resilience against spatial-temporal discrepancies. Our approach has led to significant improvements in forecasting precision, culminating in our model securing textit1st place in the transfer learning leaderboard of the textitWeather4cast'23 competition.
  arXiv  Detail & Related papers  (2023-11-30T08:22:08Z)
• Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
  We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting" Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks. We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
  arXiv  Detail & Related papers  (2023-10-02T17:13:16Z)
• Long-term drought prediction using deep neural networks based on geospatial weather data [75.38539438000072]
  High-quality drought forecasting up to a year in advance is critical for agriculture planning and insurance. We tackle drought data by introducing an end-to-end approach that adopts a systematic end-to-end approach. Key findings are the exceptional performance of a Transformer model, EarthFormer, in making accurate short-term (up to six months) forecasts.
  arXiv  Detail & Related papers  (2023-09-12T13:28:06Z)
• RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting [30.277213545837924]
  Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. In this work, we consider the time-series data as a random realization from a nonlinear state-space model. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings.
  arXiv  Detail & Related papers  (2021-06-10T21:49:23Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• STAS: Adaptive Selecting Spatio-Temporal Deep Features for Improving Bias Correction on Precipitation [27.780513053310223]
  We propose an end-to-end deep-learning BCoP model named Spatio-Temporal feature Auto-Selective (STAS) model to select optimal ST regularity from EC. Experiments on an EC public dataset indicate that STAS shows state-of-the-art performance on several criteria of BCoP, named threat scores (TS)
  arXiv  Detail & Related papers  (2020-04-13T07:00:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.