ACE Metric: Advection and Convection Evaluation for Accurate Weather Forecasting

• URL: http://arxiv.org/abs/2406.04678v1
• Date: Fri, 7 Jun 2024 06:49:59 GMT
• Title: ACE Metric: Advection and Convection Evaluation for Accurate Weather Forecasting
• Authors: Doyi Kim, Minseok Seo, Yeji Choi,
• Abstract summary: We propose the advection and convection Error (ACE) metric to assess how well models predict advection and convection. We have validated the ACE evaluation metric on the WeatherBench2 and MovingMNIST datasets.
• Score: 7.016835396874093
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recently, data-driven weather forecasting methods have received significant attention for surpassing the RMSE performance of traditional NWP (Numerical Weather Prediction)-based methods. However, data-driven models are tuned to minimize the loss between forecasted data and ground truths, often using pixel-wise loss. This can lead to models that produce blurred outputs, which, despite being significantly different in detail from the actual weather conditions, still demonstrate low RMSE values. Although evaluation metrics from the computer vision field, such as PSNR, SSIM, and FVD, can be used, they are not entirely suitable for weather variables. This is because weather variables exhibit continuous physical changes over time and lack the distinct boundaries of objects typically seen in computer vision images. To resolve these issues, we propose the advection and convection Error (ACE) metric, specifically designed to assess how well models predict advection and convection, which are significant atmospheric transfer methods. We have validated the ACE evaluation metric on the WeatherBench2 and MovingMNIST datasets.

Related papers

• 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)
• 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)
• How far are today's time-series models from real-world weather forecasting applications? [22.68937280154092]
  WEATHER-5K is a comprehensive collection of observational weather data that better reflects real-world scenarios. It enables a better training of models and a more accurate assessment of the real-world forecasting capabilities of TSF models. We provide researchers with a clear assessment of the gap between academic TSF models and real-world weather forecasting applications.
  arXiv  Detail & Related papers  (2024-06-20T15:18:52Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs [14.095897879222676]
  We present ClimODE, a continuous-time process that implements key principle of statistical mechanics. ClimODE models precise weather evolution with value-conserving dynamics, learning global weather transport as a neural flow. Our approach outperforms existing data-driven methods in global, regional forecasting with an order of magnitude smaller parameterization.
  arXiv  Detail & Related papers  (2024-04-15T06:38:21Z)
• ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
  We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast. We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples. Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
  arXiv  Detail & Related papers  (2024-02-02T10:34:13Z)
• 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)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• 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)
• Smart Weather Forecasting Using Machine Learning:A Case Study in Tennessee [2.9477900773805032]
  We present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models. The accuracy of the models is good enough to be used alongside the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-08-25T02:41:32Z)

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.