Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones

• URL: http://arxiv.org/abs/2109.12029v1
• Date: Fri, 24 Sep 2021 15:33:29 GMT
• Title: Identifying Distributional Differences in Convective Evolution Prior to Rapid Intensification in Tropical Cyclones
• Authors: Trey McNeely, Galen Vincent, Rafael Izbicki, Kimberly M. Wood, and Ann B. Lee
• Abstract summary: Tropical cyclone (TC) intensity forecasts are issued by human forecasters every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC structure leading up to the rapid intensification of a storm.
• Score: 4.925967492198013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tropical cyclone (TC) intensity forecasts are issued by human forecasters who evaluate spatio-temporal observations (e.g., satellite imagery) and model output (e.g., numerical weather prediction, statistical models) to produce forecasts every 6 hours. Within these time constraints, it can be challenging to draw insight from such data. While high-capacity machine learning methods are well suited for prediction problems with complex sequence data, extracting interpretable scientific information with such methods is difficult. Here we leverage powerful AI prediction algorithms and classical statistical inference to identify patterns in the evolution of TC convective structure leading up to the rapid intensification of a storm, hence providing forecasters and scientists with key insight into TC behavior.

Related papers

• Leveraging data-driven weather models for improving numerical weather prediction skill through large-scale spectral nudging [1.747339718564314]
  This study illustrates the relative strengths and weaknesses of physics-based and AI-based approaches to weather prediction. A hybrid NWP-AI system is proposed, wherein GEM-predicted large-scale state variables are spectrally nudged toward GraphCast predictions. Results indicate that this hybrid approach is capable of leveraging the strengths of GraphCast to enhance the prediction skill of the GEM model.
  arXiv  Detail & Related papers  (2024-07-08T16:39:25Z)
• 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)
• 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 a training-free extreme value enhancement strategy named ExEnsemble, which increases the variance of pixel values and improves the forecast robustness. 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)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• 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)
• Scaling transformer neural networks for skillful and reliable medium-range weather forecasting [24.02355555479722]
  We introduce Stormer, a state-of-the-art performance on weather forecasting with minimal changes to the standard transformer backbone. At the core of Stormer is a randomized forecasting objective that trains the model to forecast the weather dynamics over varying time intervals. On WeatherBench 2, Stormer performs competitively at short to medium-range forecasts and outperforms current methods beyond 7 days.
  arXiv  Detail & Related papers  (2023-12-06T19:46:06Z)
• A Distributed Approach to Meteorological Predictions: Addressing Data Imbalance in Precipitation Prediction Models through Federated Learning and GANs [0.0]
  classification of weather data involves categorizing meteorological phenomena into classes, thereby facilitating nuanced analyses and precise predictions. It's imperative that classification algorithms proficiently navigate challenges such as data imbalances. Data augmentation techniques can improve the model's accuracy in classifying rare but critical weather events.
  arXiv  Detail & Related papers  (2023-10-19T21:28:20Z)
• Residual Diffusion Modeling for Km-scale Atmospheric Downscaling [51.061954281398116]
  A cost-effective downscaling model is trained from a high-resolution 2-km weather model over Taiwan. textitCorrDiff exhibits skillful RMSE and CRPS and faithfully recovers spectra and distributions even for extremes. Downscaling global forecasts successfully retains many of these benefits, foreshadowing the potential of end-to-end, global-to-km-scales machine learning weather predictions.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models [13.331224394143117]
  Uncertainty quantification is crucial to decision-making. dominant approach to representing uncertainty in weather forecasting is to generate an ensemble of forecasts. We propose to amortize the computational cost by emulating these forecasts with deep generative diffusion models learned from historical data.
  arXiv  Detail & Related papers  (2023-06-24T22:00:06Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Structural Forecasting for Tropical Cyclone Intensity Prediction: Providing Insight with Deep Learning [1.7587442088965226]
  Tropical cyclone (TC) intensity forecasts are ultimately issued by human forecasters. Our proposed framework leverages deep learning to provide forecasters with something neither end-to-end prediction models nor traditional intensity guidance does.
  arXiv  Detail & Related papers  (2020-10-07T21:01:06Z)

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.