Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (3rd Edition)

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 746

Special Issue Editors


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Guest Editor
1. Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO), University of Oklahoma, Norman, OK 73072, USA
2. National Severe Storms Laboratory (NSSL), National Oceanic & Atmospheric Administration, Norman, OK 73072, USA
Interests: radar data assimilation; regional NWP; convective-allowing model; high-performance computing
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Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the first Special Issue entitled “Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition)” (https://www.mdpi.com/journal/atmosphere/special_issues/YV418U1IXT) published in Atmosphere in 2023 and will cover all aspects of hazardous weather prediction issues.

Recently, short-range (0–6 hour) weather forecasts have made significant progress in hazardous weather events, including in predictions of tornados, hails, flash floods, damaging winds, etc. This is due to advances in data assimilation (DA) algorithms and the application of radar/satellite observation data, the development of convective-allowing models (CAMs), the utilization of high-performance computers and the development of AI techniques. This Special Issue seeks submissions on the following topics related to the improvement of forecasts, warnings and decision support for high-impact thunderstorm events:

  • CAM development and application;
  • DA algorithms and application for new observation datasets;
  • High-performance computing in DA and CAMs;
  • Applications of machine learning and AI techniques for hazardous event prediction;
  • Developments in verification methods and data for hazardous events;
  • Applications of other computing techniques for hazardous weather systems, such as workflow development, software management, etc.

Dr. Feifei Shen
Dr. Yunheng Wang
Guest Editors

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Keywords

  • radar data assimilation
  • regional numerical weather prediction
  • convective-allowing model
  • probabilistic hazard information
  • high-performance computing
  • machine learning and artificial intelligence
  • objective verification

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Published Papers (1 paper)

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Research

19 pages, 5158 KiB  
Article
Impact of Background Error Length Scale Tuning in WRF-3DVAR System on High-Resolution Radar Data Assimilation for Typhoon Doksuri Simulation
by Weidi Zhai, Feifei Shen, Jing Liu, Haiyan Fei, Liu Yi, Shen Wan and Xiaolin Yuan
Atmosphere 2025, 16(6), 679; https://doi.org/10.3390/atmos16060679 - 3 Jun 2025
Viewed by 325
Abstract
To improve the prediction of Typhoon Doksuri (2023), this paper explores how variations in horizontal scale factors used in assimilating radar-derived wind velocities influence the performance of numerical simulations and forecasts. Using the WRF-ARW model in conjunction with the WRF-3DVAR data assimilation system, [...] Read more.
To improve the prediction of Typhoon Doksuri (2023), this paper explores how variations in horizontal scale factors used in assimilating radar-derived wind velocities influence the performance of numerical simulations and forecasts. Using the WRF-ARW model in conjunction with the WRF-3DVAR data assimilation system, two assimilation configurations were tested with horizontal length scale factors of 1.0 and 0.25. Results show that a reduced length scale facilitates a more detailed reconstruction of mesoscale features, including the typhoon’s eye and inner-core circulation, leading to improved accuracy in short-term intensity and structure forecasts. The experiment utilizing the 0.25 length scale exhibited a tighter warm core, stronger cyclonic wind bands, and a better representation of the vortex’s three-dimensional structure. However, this configuration also led to growing forecast deviations in the latter stages, likely due to imbalances introduced by excessive localization. In contrast, the 1.0-scale experiment produced smoother but less accurate structures and demonstrated larger track deviations. These findings highlight a key trade-off between localized observational influence and long-term forecast stability. The study underscores the importance of optimizing horizontal scale parameterization in variational assimilation to enhance the forecasting accuracy of high-impact tropical cyclones and offers practical insights for operational forecasting systems in regions frequently affected by typhoon activity. Full article
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