SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
- URL: http://arxiv.org/abs/2406.16955v2
- Date: Fri, 28 Jun 2024 19:51:25 GMT
- Title: SRViT: Vision Transformers for Estimating Radar Reflectivity from Satellite Observations at Scale
- Authors: Jason Stock, Kyle Hilburn, Imme Ebert-Uphoff, Charles Anderson,
- Abstract summary: We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery.
This work aims to enhance short-term convective-scale forecasts of high-impact weather events and aid in data assimilation for numerical weather prediction over the United States.
- Score: 0.7499722271664147
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a transformer-based neural network to generate high-resolution (3km) synthetic radar reflectivity fields at scale from geostationary satellite imagery. This work aims to enhance short-term convective-scale forecasts of high-impact weather events and aid in data assimilation for numerical weather prediction over the United States. Compared to convolutional approaches, which have limited receptive fields, our results show improved sharpness and higher accuracy across various composite reflectivity thresholds. Additional case studies over specific atmospheric phenomena support our quantitative findings, while a novel attribution method is introduced to guide domain experts in understanding model outputs.
Related papers
- FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - 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) - Multi-Modal Learning-based Reconstruction of High-Resolution Spatial
Wind Speed Fields [46.72819846541652]
We propose a framework based on Vari Data Assimilation and Deep Learning concepts.
This framework is applied to recover rich-in-time, high-resolution information on sea surface wind speed.
arXiv Detail & Related papers (2023-12-14T13:40:39Z) - 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) - 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) - Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan.
textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes.
Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
arXiv Detail & Related papers (2023-09-24T19:57:22Z) - Attention-Based Scattering Network for Satellite Imagery [0.0]
We leverage the scattering to extract high-level features without additional trainable parameters.
Experiments show promising results on estimating tropical cyclone intensity and predicting the occurrence of lightning from satellite imagery.
arXiv Detail & Related papers (2022-10-21T18:25:34Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z) - Predicting Landsat Reflectance with Deep Generative Fusion [2.867517731896504]
Public satellite missions are commonly bound to a trade-off between spatial and temporal resolution.
This hinders their potential to assist vegetation monitoring or humanitarian actions.
We probe the potential of deep generative models to produce high-resolution optical imagery by fusing products with different spatial and temporal characteristics.
arXiv Detail & Related papers (2020-11-09T21:06:04Z) - Development and Interpretation of a Neural Network-Based Synthetic Radar
Reflectivity Estimator Using GOES-R Satellite Observations [0.02578242050187029]
This research aims to develop techniques for assimilating GOES-R Series observations in precipitating scenes.
A convolutional neural network (CNN) is developed to transform GOES-R radiances and lightning into synthetic radar reflectivity fields.
arXiv Detail & Related papers (2020-04-16T19:57:00Z)
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.