A Deep Learning Approach to Radar-based QPE

• URL: http://arxiv.org/abs/2402.09846v1
• Date: Thu, 15 Feb 2024 10:05:18 GMT
• Title: A Deep Learning Approach to Radar-based QPE
• Abstract summary: We propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations.
• Score: 0.11184789007828977
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this study, we propose a volume-to-point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location-specific precipitations. In contrast to QPE methods based on the Z-R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013-2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume-to-point framework performed comparably well in general cases and excelled in detecting heavy-rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.

Related papers

• High-resolution Calibrated Probabilistic Hourly Precipitation from a Deterministic Forecast [0.0]
  The method predicts the hourly probability of no precipitation plus the distribution of positive precipitation from a weighted mixture of two Gamma distributions.<n>The neural network is trained on patches of numerical weather prediction (NWP) hourly precipitation from The Weather Company's convection-permitting GRAF model.<n>The network outputs distributional parameters for each model grid point.
  arXiv  Detail & Related papers  (2026-08-13T01:03:52Z)
• Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data [49.62628745057007]
  We propose a simulation-based inference approach to terrain parameter inversion from radar sounder data.<n>Synthetic observations from a GPU-based simulator are used to train a neural network-based density estimator for neural posterior estimation.<n>We demonstrate that our NPE model is well calibrated on simulated data and transferable to real Mars radar profiles.
  arXiv  Detail & Related papers  (2026-05-05T08:41:02Z)
• Improvements to the post-processing of weather forecasts using machine learning and feature selection [0.0]
  This study aims to develop and improve machine learning-based post-processing models for precipitation, temperature, and wind speed predictions.<n>By incorporating meteorological variables from grid points surrounding the target locations as input features and applying feature selection based on correlation analysis, we found that the LightGBM-based models achieved lower RMSE.<n>Because precipitation has a highly skewed distribution with many zero cases, we additionally examined Tweedie-based loss functions and event-weighted training strategies for precipitation forecasting.
  arXiv  Detail & Related papers  (2026-04-21T11:17:29Z)
• Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting [52.53742727060265]
  Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets.<n>Here, we introduce Earth System Foundation Model (ESFM), a fully open model building on the 3Dwin UNet backbone of the pioneering model.<n>We present findings using dense gridded data (ERA5, CMIP6), regionally masked dense data, sparse gridded MODIS satellite data, and station data.<n>Results demonstrate competitive or superior performance relative to state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2026-04-20T11:40:39Z)
• Echo State Networks for Time Series Forecasting: Hyperparameter Sweep and Benchmarking [51.56484100374058]
  We evaluate whether a fully automatic, purely feedback-driven ESN can serve as a competitive alternative to widely used statistical forecasting methods.<n>Forecast accuracy is measured using MASE and sMAPE and benchmarked against simple benchmarks like drift and seasonal naive and statistical models.
  arXiv  Detail & Related papers  (2026-02-03T16:01:22Z)
• STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules [1.887852744008007]
  Space-time in situ postprocessing (STIPP) is a machine learning model that generates consistent weather forecasts for a network of station locations.<n>By leveraging a proper scoring rule for training, STIPP contributes to ongoing work-driven atmospheric models supervised only with distribution marginals.
  arXiv  Detail & Related papers  (2026-01-06T10:07:20Z)
• Towards a Spatiotemporal Fusion Approach to Precipitation Nowcasting [0.44940580193534]
  We propose a data fusion approach for precipitation nowcasting by integrating data from meteorological rain gauge stations in Rio de Janeiro.<n>We employ numerical weather deep learning architecture called STConvS2S, leveraging a structured dataset covering a 9 x 11 grid.<n>Among the tested configurations, the fusion-based model achieves an F1-score of 0.2033 for heavy precipitation events (greater than 25 mm/h) at a one-hour lead time.
  arXiv  Detail & Related papers  (2025-05-25T18:27:19Z)
• UNet with Axial Transformer : A Neural Weather Model for Precipitation Nowcasting [0.06906005491572399]
  We develop a novel method that employs Transformer-based machine learning models to forecast precipitation. This paper represents an initial research on the dataset used in the domain of next frame prediciton.
  arXiv  Detail & Related papers  (2025-04-28T01:20:30Z)
• WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models [11.016845506758841]
  We introduce WeatherReal, a novel benchmark dataset for weather forecasting derived from global near-surface in-situ observations. This paper details the sources and processing methodologies underlying the dataset, and illustrates the advantage of in-situ observations in capturing hyper-local and extreme weather. Our work aims to advance the AI-based weather forecasting research towards a more application-focused and operation-ready approach.
  arXiv  Detail & Related papers  (2024-09-14T08:53:46Z)
• 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)
• GPTCast: a weather language model for precipitation nowcasting [0.0]
  GPTCast is a generative deep-learning method for ensemble nowcast of radar-based precipitation. We employ a GPT model as a forecaster to learn precipitation dynamics using tokenized radar images.
  arXiv  Detail & Related papers  (2024-07-02T09:25:58Z)
• Generative Data Assimilation of Sparse Weather Station Observations at Kilometer Scales [5.453657018459705]
  We demonstrate the viability of score-based data assimilation in the context of realistically complex km-scale weather. By incorporating observations from 40 weather stations, 10% lower RMSEs on left-out stations are attained. 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)
• Short-term Precipitation Forecasting in The Netherlands: An Application of Convolutional LSTM neural networks to weather radar data [0.0]
  The research exploits the combination of Convolutional Neural Networks (CNNs) layers for spatial pattern recognition and LSTM network layers for modelling temporal sequences. The model was trained and validated on weather radar data from the Netherlands. Results indicate high accuracy in predicting the direction and intensity of precipitation movements.
  arXiv  Detail & Related papers  (2023-12-02T18:13:45Z)
• 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)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• Physics Informed Shallow Machine Learning for Wind Speed Prediction [66.05661813632568]
  We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in Italy. We train supervised learning algorithms using the past history of wind to predict its value at a future time. We find that the optimal design as well as its performance vary with the location.
  arXiv  Detail & Related papers  (2022-04-01T14:55:10Z)
• 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)

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.