A curated UK rain radar data set for training and benchmarking nowcasting models

• URL: http://arxiv.org/abs/2512.17924v1
• Date: Mon, 08 Dec 2025 16:11:40 GMT
• Title: A curated UK rain radar data set for training and benchmarking nowcasting models
• Authors: Viv Atureta, Rifki Priansyah Jasin, Stefan Siegert,
• Abstract summary: This paper documents a data set of UK rain radar image sequences for use in statistical modeling and machine learning methods for nowcasting.<n>The main dataset contains 1,000 randomly sampled sequences of length 20 steps (15-minute increments) of 2D radar intensity fields of dimension 40x40.<n>Additional atmospheric and geographic features are made available, including date, location, mean elevation, mean wind direction and speed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper documents a data set of UK rain radar image sequences for use in statistical modeling and machine learning methods for nowcasting. The main dataset contains 1,000 randomly sampled sequences of length 20 steps (15-minute increments) of 2D radar intensity fields of dimension 40x40 (at 5km spatial resolution). Spatially stratified sampling ensures spatial homogeneity despite removal of clear-sky cases by threshold-based truncation. For each radar sequence, additional atmospheric and geographic features are made available, including date, location, mean elevation, mean wind direction and speed and prevailing storm type. New R functions to extract data from the binary "Nimrod" radar data format are provided. A case study is presented to train and evaluate a simple convolutional neural network for radar nowcasting, including self-contained R code.

Related papers

• RadarGen: Automotive Radar Point Cloud Generation from Cameras [64.69976771710057]
  We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery.<n>RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form.<n>We show that RadarGen captures characteristic radar measurement distributions and reduces the gap to perception models trained on real data.
  arXiv  Detail & Related papers  (2025-12-19T18:57:33Z)
• Towards Foundational Models for Single-Chip Radar [49.896124982717716]
  mmWave radars are compact, inexpensive, and durable sensors that work regardless of environmental conditions, such as weather and darkness.<n>This comes at the cost of poor angular resolution, especially for inexpensive single-chip radars, which are typically used in automotive and indoor sensing applications.<n>We train a foundational model for 4D single-chip radar, which can predict 3D occupancy and semantic segmentation with quality that is typically only possible with much higher resolution sensors.
  arXiv  Detail & Related papers  (2025-09-15T22:06:17Z)
• DRO: Doppler-Aware Direct Radar Odometry [11.042292216861762]
  A renaissance in radar-based sensing for mobile robotic applications is underway.<n>We propose a novel SE(2) odometry approach for spinning frequency-modulated continuous-wave radars.<n>Our method has been validated on over 250km of on-road data sourced from public datasets.
  arXiv  Detail & Related papers  (2025-04-29T01:20:30Z)
• Rainfall regression from C-band Synthetic Aperture Radar using Multi-Task Generative Adversarial Networks [0.0]
  The paper introduces a data-driven approach to estimate precipitation rates from Synthetic Aperture Radar (SAR) at a spatial resolution of 200 meters per pixel. It exploits the full NEXRAD archive to look for potential co-locations with Sentinel-1 data. The resulting model demonstrates improved accuracy in rainfall estimates and the ability to extend its performance to scenarios up to 15 m/s.
  arXiv  Detail & Related papers  (2024-11-05T20:06:50Z)
• SparseRadNet: Sparse Perception Neural Network on Subsampled Radar Data [5.344444942640663]
  Radar raw data often contains excessive noise, whereas radar point clouds retain only limited information. We introduce an adaptive subsampling method together with a tailored network architecture that exploits the sparsity patterns. Experiments on the RADIal dataset show that our SparseRadNet exceeds state-of-the-art (SOTA) performance in object detection and achieves close to SOTA accuracy in freespace segmentation.
  arXiv  Detail & Related papers  (2024-06-15T11:26:10Z)
• Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar [62.51065633674272]
  We introduce Radar Fields - a neural scene reconstruction method designed for active radar imagers. Our approach unites an explicit, physics-informed sensor model with an implicit neural geometry and reflectance model to directly synthesize raw radar measurements. We validate the effectiveness of the method across diverse outdoor scenarios, including urban scenes with dense vehicles and infrastructure.
  arXiv  Detail & Related papers  (2024-05-07T20:44:48Z)
• A Benchmark Dataset for Tornado Detection and Prediction using Full-Resolution Polarimetric Weather Radar Data [4.1241397159763835]
  This study introduces a new benchmark dataset, TorNet, to support development of Machine Learning algorithms in tornado detection and prediction. A novel deep learning (DL) architecture capable of processing raw radar imagery without the need for manual feature extraction is studied. Despite not benefiting from manual feature engineering or other preprocessing, the DL model shows increased detection performance compared to non-DL and operational baselines.
  arXiv  Detail & Related papers  (2024-01-26T21:47:39Z)
• Diffusion Models for Interferometric Satellite Aperture Radar [73.01013149014865]
  Probabilistic Diffusion Models (PDMs) have recently emerged as a very promising class of generative models. Here, we leverage PDMs to generate several radar-based satellite image datasets. We show that PDMs succeed in generating images with complex and realistic structures, but that sampling time remains an issue.
  arXiv  Detail & Related papers  (2023-08-31T16:26:17Z)
• Semantic Segmentation of Radar Detections using Convolutions on Point Clouds [59.45414406974091]
  We introduce a deep-learning based method to convolve radar detections into point clouds. We adapt this algorithm to radar-specific properties through distance-dependent clustering and pre-processing of input point clouds. Our network outperforms state-of-the-art approaches that are based on PointNet++ on the task of semantic segmentation of radar point clouds.
  arXiv  Detail & Related papers  (2023-05-22T07:09:35Z)
• End-to-end system for object detection from sub-sampled radar data [18.462990836437626]
  We present an end-to-end signal processing pipeline that relies on sub-sampled radar data to perform object detection in vehicular settings. We show robust detection based on radar data reconstructed using 20% of samples under extreme weather conditions. We generate 20% sampled radar data in a fine-tuning set and show 1.1% gain in AP50 across scenes and 3% AP50 gain in motorway condition.
  arXiv  Detail & Related papers  (2022-03-08T08:02:33Z)
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion [52.59664614744447]
  We present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and high definition (HD) maps. automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous velocity measurements.
  arXiv  Detail & Related papers  (2020-10-02T00:13:00Z)

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.