Advancing Meteorological Forecasting: AI-based Approach to Synoptic Weather Map Analysis

• URL: http://arxiv.org/abs/2411.05384v1
• Date: Fri, 08 Nov 2024 07:46:50 GMT
• Title: Advancing Meteorological Forecasting: AI-based Approach to Synoptic Weather Map Analysis
• Authors: Yo-Hwan Choi, Seon-Yu Kang, Minjong Cheon,
• Abstract summary: Our study proposes a novel preprocessing method and convolutional autoencoder model to improve the interpretation of synoptic weather maps. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions.
• Score: 3.686808512438363
• License:
• Abstract: As global warming increases the complexity of weather patterns; the precision of weather forecasting becomes increasingly important. Our study proposes a novel preprocessing method and convolutional autoencoder model developed to improve the interpretation of synoptic weather maps. These are critical for meteorologists seeking a thorough understanding of weather conditions. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions, marking a significant step forward in modern technology in meteorological forecasting. This comprises unsupervised learning models like VQ-VQE, as well as supervised learning models like VGG16, VGG19, Xception, InceptionV3, and ResNet50 trained on the ImageNet dataset, as well as research into newer models like EfficientNet and ConvNeXt. Our findings proved that, while these models perform well in various settings, their ability to identify comparable synoptic weather maps has certain limits. Our research, motivated by the primary goal of significantly increasing meteorologists' efficiency in labor-intensive tasks, discovered that cosine similarity is the most effective metric, as determined by a combination of quantitative and qualitative assessments to accurately identify relevant historical weather patterns. This study broadens our understanding by shifting the emphasis from numerical precision to practical application, ensuring that our model is effective in theory practical, and accessible in the complex and dynamic field of meteorology.

Related papers

• WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning [69.82211470647349]
  We introduce the first generalist weather foundation model (WeatherGFM) It addresses a wide spectrum of weather understanding tasks in a unified manner. Our model can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing.
  arXiv  Detail & Related papers  (2024-11-08T09:14:19Z)
• Multi-Source Temporal Attention Network for Precipitation Nowcasting [4.726419619132143]
  Precipitation nowcasting is crucial across various industries and plays a significant role in mitigating and adapting to climate change. We introduce an efficient deep learning model for precipitation nowcasting, capable of predicting rainfall up to 8 hours in advance with greater accuracy than existing operational models.
  arXiv  Detail & Related papers  (2024-10-11T09:09:07Z)
• 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)
• 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)
• Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with Heatmaps [30.771706309741656]
  Real-time detection and prediction of extreme weather protect human lives and infrastructure. Traditional methods rely on numerical threshold setting and manual interpretation of weather heatmaps with Geographic Information Systems (GIS) Our research redefines Extreme Weather Events Detection (EWED) by framing it as a Visual Question Answering (VQA) problem.
  arXiv  Detail & Related papers  (2024-06-14T08:46:44Z)
• Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models [3.332582598089642]
  The field of meteorological forecasting has undergone a significant transformation with the integration of large models. Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts.
  arXiv  Detail & Related papers  (2024-04-10T00:52:54Z)
• FengWu-4DVar: Coupling the Data-driven Weather Forecasting Model with 4D Variational Assimilation [67.20588721130623]
  We develop an AI-based cyclic weather forecasting system, FengWu-4DVar. FengWu-4DVar can incorporate observational data into the data-driven weather forecasting model. Experiments on the simulated observational dataset demonstrate that FengWu-4DVar is capable of generating reasonable analysis fields.
  arXiv  Detail & Related papers  (2023-12-16T02:07:56Z)
• 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)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• A case study of spatiotemporal forecasting techniques for weather forecasting [4.347494885647007]
  The correlations of real-world processes aretemporal, and the data generated by them exhibits both spatial and temporal evolution. Time series-based models are a viable alternative to numerical forecasts. We show that decompositiontemporal prediction models reduced computational costs while improving accuracy.
  arXiv  Detail & Related papers  (2022-09-29T13:47:02Z)

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.