Probabilistic Spatial Interpolation of Sparse Data using Diffusion Models

• URL: http://arxiv.org/abs/2506.00033v1
• Date: Mon, 26 May 2025 21:19:09 GMT
• Title: Probabilistic Spatial Interpolation of Sparse Data using Diffusion Models
• Authors: Valerie Tsao, Nathaniel W. Chaney, Manolis Veveakis,
• Abstract summary: We propose a conditional data imputation framework that reconstructs full temperature fields from as little as 1% observational coverage.<n>We validate our framework over the Southern Great Plains, focusing on afternoon temperature fields during the summer months of 2018-2020.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large underlying assumption of climate models today relies on the basis of a "confident" initial condition, a reasonably plausible snapshot of the Earth for which all future predictions depend on. However, given the inherently chaotic nature of our system, this assumption is complicated by sensitive dependence, where small uncertainties in initial conditions can lead to exponentially diverging outcomes over time. This challenge is particularly salient at global spatial scales and over centennial timescales, where data gaps are not just common but expected. The source of uncertainty is two-fold: (1) sparse, noisy observations from satellites and ground stations, and (2) internal variability stemming from the simplifying approximations within the models themselves. In practice, data assimilation methods are used to reconcile this missing information by conditioning model states on partial observations. Our work builds on this idea but operates at the extreme end of sparsity. We propose a conditional data imputation framework that reconstructs full temperature fields from as little as 1% observational coverage. The method leverages a diffusion model guided by a prekriged mask, effectively inferring the full-state fields from minimal data points. We validate our framework over the Southern Great Plains, focusing on afternoon (12:00-6:00 PM) temperature fields during the summer months of 2018-2020. Across varying observational densities--from swath data to isolated in-situ sensors--our model achieves strong reconstruction accuracy, highlighting its potential to fill in critical data gaps in both historical reanalysis and real-time forecasting pipelines.

Related papers

• Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation [4.430758443755128]
  Appa is a score-based data assimilation model producing global atmospheric trajectories at 0.25-degree resolution and 1-hour intervals.<n>Our results establish latent score-based data assimilation as a promising foundation for future global atmospheric modeling systems.
  arXiv  Detail & Related papers  (2025-04-25T22:14:29Z)
• A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation [23.504915709396204]
  We present a novel generative framework that uses a score-based diffusion model trained on high-resolution reanalysis data to capture the statistical properties of local weather dynamics.<n>We demonstrate that the model generates spatially and temporally coherent weather dynamics that align with global climate output.
  arXiv  Detail & Related papers  (2024-12-19T19:47:35Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales [5.427841765899196]
  We demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather.<n>By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained.<n>It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground-based, and satellite remote sensing data streams.
  arXiv  Detail & Related papers  (2024-06-19T10:28:11Z)
• 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)
• DiffDA: a Diffusion Model for Weather-scale Data Assimilation [19.336483240566142]
  We propose DiffDA as a denoising diffusion model capable of assimilating atmospheric variables using predicted states and sparse observations. Acknowledging the similarity between a weather forecast model and a denoising diffusion model dedicated to weather applications, we adapt the pretrained GraphCast neural network as the backbone of the diffusion model.
  arXiv  Detail & Related papers  (2024-01-11T14:11:12Z)
• 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)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• 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)
• PriSTI: A Conditional Diffusion Framework for Spatiotemporal Imputation [35.62945607302276]
  We propose a conditional diffusion framework for Stemporal imputation with the prior modeling, named PriSTI. PriSTI outperforms existing imputation methods in various missing patterns of different real-world data, and effectively handles scenarios such as high missing rates and sensor failure.
  arXiv  Detail & Related papers  (2023-02-20T03:52:53Z)
• 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)

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.