Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model

• URL: http://arxiv.org/abs/2409.20087v1
• Date: Mon, 30 Sep 2024 08:40:28 GMT
• Title: Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model
• Authors: Kianusch Vahid Yousefnia, Tobias Bölle, Christoph Metzl,
• Abstract summary: Thunderstorms have significant social and economic impacts due to heavy precipitation, hail, lightning, and strong winds. We develop SALAMA 1D, a deep neural network that directly infers the probability of thunderstorm occurrence from vertical profiles of ten atmospheric variables. SALAMA 1D is trained over Central Europe with lightning observations as the ground truth.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thunderstorms have significant social and economic impacts due to heavy precipitation, hail, lightning, and strong winds, necessitating reliable forecasts. Thunderstorm forecasts based on numerical weather prediction (NWP) often rely on single-level surrogate predictors, like convective available potential energy and precipitation rate, derived from vertical profiles of three-dimensional atmospheric variables. In this study, we develop SALAMA 1D, a deep neural network that directly infers the probability of thunderstorm occurrence from vertical profiles of ten atmospheric variables, bypassing single-level predictors. By training the model on convection-permitting NWP forecasts, we allow SALAMA 1D to flexibly identify convective patterns, with the goal of enhancing forecast accuracy. The model's architecture is physically motivated: sparse connections encourage interactions at similar height levels, while a shuffling mechanism prevents the model from learning non-physical patterns tied to the vertical grid. SALAMA 1D is trained over Central Europe with lightning observations as the ground truth. Comparative analysis against a baseline machine learning model that uses single-level predictors shows SALAMA 1D's superior skill across various metrics and lead times of up to at least 11 hours. Moreover, increasing the number of forecasts used to compile the training set improves skill, even when training set size is kept constant. Sensitivity analysis using saliency maps indicates that the model reconstructs environmental lapse rates and rediscovers patterns consistent with established theoretical understandings, such as positive buoyancy, convective inhibition, and ice particle formation near the tropopause, while ruling out thunderstorm occurrence based on the absence of mid-level graupel and cloud cover.

Related papers

• PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling [85.56969895866243]
  We propose an unsupervised postprocessing method to eliminate the blurriness without the requirement of training with the pairs of blurry predictions and corresponding ground truth. A zero-shot blur kernel estimation mechanism and an auto-scale denoise guidance strategy are introduced to adapt the unconditional correlations to any blurriness modes.
  arXiv  Detail & Related papers  (2024-10-08T08:38:23Z)
• 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)
• Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments with Global AI-based Weather Models [0.08271752505511926]
  Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. Recently released suite of AI-based weather models produces medium-range forecasts within seconds. We assess the forecast skill of three top-performing AI-models for convective parameters against reanalysis and ECMWF's operational numerical weather prediction model IFS.
  arXiv  Detail & Related papers  (2024-06-13T07:46:03Z)
• 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)
• Weather Prediction with Diffusion Guided by Realistic Forecast Processes [49.07556359513563]
  We introduce a novel method that applies diffusion models (DM) for weather forecasting. Our method can achieve both direct and iterative forecasting with the same modeling framework. The flexibility and controllability of our model empowers a more trustworthy DL system for the general weather community.
  arXiv  Detail & Related papers  (2024-02-06T21:28:42Z)
• 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)
• Scaling transformer neural networks for skillful and reliable medium-range weather forecasting [23.249955524044392]
  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)
• 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)
• A machine-learning approach to thunderstorm forecasting through post-processing of simulation data [0.0]
  Thunderstorms pose a hazard to society and economy, which calls for reliable thunderstorm forecasts. In this work, we introduce a Signature-based Approach of identifying Activity using MAchine Central learning (SALAMA), a feedforward neural network model for identifying thunderstorm occurrence in numerical weather prediction (NWP) data.
  arXiv  Detail & Related papers  (2023-03-15T16:21:15Z)
• SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture [5.28539620288341]
  We show that it is possible to produce an accurate precipitation nowcast using a data-driven neural network approach. We evaluate our approaches on a real-life datasets using precipitation maps from the region of the Netherlands and binary images of cloud coverage of France.
  arXiv  Detail & Related papers  (2020-07-08T20:33:10Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.