From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences

• URL: http://arxiv.org/abs/2506.12045v1
• Date: Sat, 24 May 2025 16:24:10 GMT
• Title: From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
• Authors: Kazuma Kobayashi, Samrendra Roy, Seid Koric, Diab Abueidda, Syed Bahauddin Alam,
• Abstract summary: TRON is trained on 22 years of simulation data and generalizes across 65,341 spatial locations.<n>TRON offers a domain-agnostic framework for scientific field reconstruction from sparse data, with applications in atmospheric modeling, geophysical hazard monitoring, and real-time environmental risk forecasting.
• Score: 0.38836072943850625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geophysics to public health and aerospace safety. Traditional approaches rely on physics-based simulators or dense sensor networks, both constrained by high computational cost, latency, or limited spatial coverage. We present the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture designed to infer continuous global scalar fields from sequences of sparse, non-uniform proxy measurements. Unlike recent forecasting models that operate on dense, gridded inputs to predict future states, TRON addresses a more ill-posed inverse problem: reconstructing the current global field from sparse, temporally evolving sensor sequences, without access to future observations or dense labels. Demonstrated on global cosmic radiation dose reconstruction, TRON is trained on 22 years of simulation data and generalizes across 65,341 spatial locations, 8,400 days, and sequence lengths from 7 to 90 days. It achieves sub-second inference with relative L2 errors below 0.1%, representing a >58,000X speedup over Monte Carlo-based estimators. Though evaluated in the context of cosmic radiation, TRON offers a domain-agnostic framework for scientific field reconstruction from sparse data, with applications in atmospheric modeling, geophysical hazard monitoring, and real-time environmental risk forecasting.

Related papers

• FuseTen: A Generative Model for Daily 10 m Land Surface Temperature Estimation from Spatio-Temporal Satellite Observations [3.344876133162209]
  Urban heatwaves, droughts, and land heatwaves are pressing and growing challenges in the context of climate change.<n>One of the most important variables for assessing and understanding these phenomena is Land Surface Temperature (LST)<n>We propose FuseTen to produce daily LST observations at a fine 10 m spatial resolution by fusing-basedtemporal observations from Landsat 8, and Terra MODIS.
  arXiv  Detail & Related papers  (2025-07-30T23:04:16Z)
• Conditional Diffusion Models for Global Precipitation Map Inpainting [0.0]
  Incomplete satellite-based precipitation presents a significant challenge in global monitoring.<n>In this study, we formulate the completion of precipitation map as a video inpainting task.<n>We propose a machine learning approach based on conditional diffusion models.
  arXiv  Detail & Related papers  (2025-07-28T02:26:36Z)
• Resolution Revolution: A Physics-Guided Deep Learning Framework for Spatiotemporal Temperature Reconstruction [5.6087513714958686]
  Current technology allows for hourly temperature observations at 2 km, but only every 16 days at 100 m, a gap further exacerbated by cloud cover.<n>Here, we present a physics-guided deep learning framework for temperature data reconstruction that integrates two data sources.<n>The proposed framework uses a convolutional neural network that incorporates the annual temperature cycle and includes a linear term to amplify the coarse Earth system model output into fine-scale temperature values observed from satellites.
  arXiv  Detail & Related papers  (2025-07-14T03:03:25Z)
• ReconMOST: Multi-Layer Sea Temperature Reconstruction with Observations-Guided Diffusion [48.540756751934836]
  ReconMOST is a data-driven guided diffusion model framework for multi-layer sea temperature reconstruction.<n>Our method extends ML-based SST reconstruction to a global, multi-layer setting, handling over 92.5% missing data.
  arXiv  Detail & Related papers  (2025-06-12T06:27:22Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [55.09326865401653]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• Towards real-time assessment of infrasound event detection capability using deep learning-based transmission loss estimation [5.601176010173589]
  Accurate modeling of infrasound transmission loss is essential for evaluating the performance of the International Monitoring System.<n>State-of-the-art propagation modeling tools enable transmission loss to be finely simulated using atmospheric models.<n>Recent studies made use of a deep learning algorithm capable of making transmission loss predictions almost instantaneously.<n>In this study, we address these limitations by using both wind and temperature fields as inputs to a neural network, simulated up to 130 km altitude and 4,000 km distance.
  arXiv  Detail & Related papers  (2025-06-03T09:49:12Z)
• Deep learning methods for modeling infrasound transmission loss in the middle atmosphere [5.842419815638353]
  We develop an optimized convolutional network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields.<n>The implemented model predicts TLs with an average error of 8.6 dB in the whole frequency band (0.1-3.2 Hz) and explored realistic atmospheric scenarios.
  arXiv  Detail & Related papers  (2025-06-02T13:10:29Z)
• Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture [9.955223104442755]
  We present a novel S-Temporal Graph Network architecture that specifically captures dependencies to forecast PM2.5 concentration.<n>Our model is based on an encoder-decoder architecture where the decoder parts leverage recurrent units (GRU) augmented with a graph neural network (Transformerv) to account for spatial diffusion.
  arXiv  Detail & Related papers  (2024-12-18T15:18:12Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• 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)
• 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)
• Federated Prompt Learning for Weather Foundation Models on Devices [37.88417074427373]
  On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing. This paper propose Federated Prompt Learning for Weather Foundation Models on Devices (FedPoD) FedPoD enables devices to obtain highly customized models while maintaining communication efficiency.
  arXiv  Detail & Related papers  (2023-05-23T16:59:20Z)
• Spatiotemporal modeling of European paleoclimate using doubly sparse Gaussian processes [61.31361524229248]
  We build on recent scale sparsetemporal GPs to reduce the computational burden. We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
  arXiv  Detail & Related papers  (2022-11-15T14:15:04Z)
• Earthformer: Exploring Space-Time Transformers for Earth System Forecasting [27.60569643222878]
  We propose Earthformer, a space-time Transformer for Earth system forecasting. The Transformer is based on a generic, flexible and efficient space-time attention block, named Cuboid Attention. Experiments on two real-world benchmarks about precipitation nowcasting and El Nino/Southerntemporaltion show Earthformer achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-07-12T20:52:26Z)

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.