Meteorological Satellite Images Prediction Based on Deep Multi-scales Extrapolation Fusion

• URL: http://arxiv.org/abs/2209.11682v1
• Date: Mon, 19 Sep 2022 16:00:17 GMT
• Title: Meteorological Satellite Images Prediction Based on Deep Multi-scales Extrapolation Fusion
• Authors: Fang Huang, Wencong Cheng, PanFeng Wang, ZhiGang Wang, HongHong He
• Abstract summary: It is important to make accurate predictions for meteorological satellite images, especially the nowcasting prediction up to 2 hours ahead. Here we present a deep multiscales fusion method, to address the challenge of the nowcasting prediction. Experiments based on the FY-4A meteorological satellite data show that the proposed method can generate realistic prediction images.
• Score: 5.125401106179782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meteorological satellite imagery is critical for meteorologists. The data have played an important role in monitoring and analyzing weather and climate changes. However, satellite imagery is a kind of observation data and exists a significant time delay when transmitting the data back to Earth. It is important to make accurate predictions for meteorological satellite images, especially the nowcasting prediction up to 2 hours ahead. In recent years, there has been growing interest in the research of nowcasting prediction applications of weather radar images based on deep learning. Compared to the weather radar images prediction problem, the main challenge for meteorological satellite images prediction is the large-scale observation areas and therefore the large sizes of the observation products. Here we present a deep multi-scales extrapolation fusion method, to address the challenge of the meteorological satellite images nowcasting prediction. First, we downsample the original satellite images dataset with large size to several images datasets with smaller resolutions, then we use a deep spatiotemporal sequences prediction method to generate the multi-scales prediction images with different resolutions separately. Second, we fuse the multi-scales prediction results to the targeting prediction images with the original size by a conditional generative adversarial network. The experiments based on the FY-4A meteorological satellite data show that the proposed method can generate realistic prediction images that effectively capture the evolutions of the weather systems in detail. We believe that the general idea of this work can be potentially applied to other spatiotemporal sequence prediction tasks with a large size.

Related papers

• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
  We deal with air quality observations in a city-wide network of ground monitoring stations. We propose a conditioning block that embeds past and future covariates into the current observations. We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
  arXiv  Detail & Related papers  (2024-04-08T09:13:16Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Transformer-based nowcasting of radar composites from satellite images for severe weather [45.0983299269404]
  We present a Transformer-based model for nowcasting ground-based radar image sequences using satellite data up to two hours lead time. Trained on a dataset reflecting severe weather conditions, the model predicts radar fields occurring under different weather phenomena. The model can support precipitation nowcasting across large domains without an explicit need for radar towers.
  arXiv  Detail & Related papers  (2023-10-30T13:17:38Z)
• Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network [4.393292642453663]
  Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. We proposed warp and refine network (WR-Net) to solve this problem. WR-Net is divided into an optical flow warp component and a warp image refinement component.
  arXiv  Detail & Related papers  (2023-03-08T06:53:42Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• WeatherFusionNet: Predicting Precipitation from Satellite Data [0.0]
  We aim to predict high-resolution precipitation from lower-resolution satellite radiance images. A neural network called WeatherFusionNet is employed to predict severe rain up to eight hours in advance. We achieved 1st place in the NeurIPS 2022 Weather4Cast Core challenge.
  arXiv  Detail & Related papers  (2022-11-30T08:49:13Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Short-term precipitation prediction using deep learning [5.1589108738893215]
  We show that a 3D convolutional neural network using a single frame of meteorology fields is capable of predicting the precipitation spatial distribution. The network is developed based on 39-years (1980-2018) data of meteorology and daily precipitation over the contiguous United States.
  arXiv  Detail & Related papers  (2021-10-05T06:37:24Z)
• EarthNet2021: A large-scale dataset and challenge for Earth surface forecasting as a guided video prediction task [12.795776149170978]
  We frame Earth surface forecasting as the task of predicting satellite imagery conditioned on future weather. EarthNet2021 is a large dataset suitable for training deep neural networks on the task. Resulting forecasts will greatly improve over the spatial resolution found in numerical models.
  arXiv  Detail & Related papers  (2021-04-16T09:47:30Z)

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.