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Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and...
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Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition)

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

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 6636

Special Issue Editors

Dr. Feifei Shen

E-Mail Website
Guest Editor
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), Nanjing University of Information Science & Technology, Nanjing 211544, China
Interests: doppler weather radar data assimilation; satellite remote sensing observation data assimilation; integrated variational hybrid assimilation system development; wind, solar and other renewable energy research
Special Issues, Collections and Topics in MDPI journals
Dr. Yunheng Wang

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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” (https://www.mdpi.com/journal/atmosphere/special_issues/Hazardous_Weather_Prediction) published in Atmosphere in 2021 and will cover all aspects of hazardous weather prediction issues.

Short-range (0–6 hour) weather forecasts have recently made significant progress for hazardous weather events including tornados, hails, flash floods, damaging winds, etc. This is highly accredited to the 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 that are 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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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 (5 papers)

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Research

14 pages, 3053 KiB  
Article
Analysis of the Micro-Physical Characteristics of the Sea of Clouds Phenomena in Jiuxian Mountain Based on Multiple Source Observations
by Si Cheng, Zilun Lin, Jianding Zhou, Geng Han, Zhenhao Chen and Qingbo Yang
Atmosphere 2024, 15(2), 207; https://doi.org/10.3390/atmos15020207 - 6 Feb 2024
Viewed by 764
Abstract
The micro-physical characteristics of a typical sea of clouds process in Jiuxian Mountain are investigated by comprehensively analyzing parameters that delineate the micro-physical characteristics of clouds and atmospheric stratification based on data from a cloud radar, wind profiler, meteorological gradient observation in high [...] Read more.
The micro-physical characteristics of a typical sea of clouds process in Jiuxian Mountain are investigated by comprehensively analyzing parameters that delineate the micro-physical characteristics of clouds and atmospheric stratification based on data from a cloud radar, wind profiler, meteorological gradient observation in high mountains, and other observations. The results show that water vapor condenses into cloud particles via an entrained and mixing process accompanied by an updraft originating from orographic uplift. During the thickening stage of the sea of clouds, atmospheric motion within the clouds is featured as “downdraft on the top—updraft on the bottom”. The zero vertical velocity area is located closely to the maximum of liquid water content. The thermal inversion layer is formed during the maintenance stage; however, the enhancement of inversion on the cloud top could suppress updraft in areas with a high liquid water content. The values mainly concentrate on the cloud top, and repetitively lifting and falling processes caused by the atmospheric upward and downward motion are in favor of the coalescence growth of cloud particles, which result in the persistence of strong radar echo. At the dissipation stage, warming on the cloud top is greater than that on the cloud bottom due to the short-wave absorption of clouds as the solar radiation enhances. As a result, the inversion layer thickens and elevates, evaporation caused by heating outweighs the condensation caused by cooling, a strong radar echo band descends from the top to the middle part of clouds, a sea of clouds dissipates gradually as cloud particles evaporates, and the particle size and concentration number of cloud particles decrease simultaneously. Full article
(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition))
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21 pages, 12639 KiB  
Article
Numerical Simulation of Charge Structure Evolution during the Feeder-Type Cells Merging
by Jie Deng, Fengxia Guo, Jing Sun, Zeyi Wu, Zhou Liu, Xian Lu, Ke Chen and Qingyuan Wang
Atmosphere 2023, 14(10), 1588; https://doi.org/10.3390/atmos14101588 - 20 Oct 2023
Viewed by 909
Abstract
Formation of the multipolar charge structure during feeder-type cell merging has important consequences in severe convective weather. This study used the Weather Research and Forecasting model with electrification and discharge parameterization schemes to simulate the feeder-type cell merging process in the tail of [...] Read more.
Formation of the multipolar charge structure during feeder-type cell merging has important consequences in severe convective weather. This study used the Weather Research and Forecasting model with electrification and discharge parameterization schemes to simulate the feeder-type cell merging process in the tail of a squall line that occurred on 27 June 2020 in Hubei Province (China). The results showed that the two cells involved in the merging process were at different life stages, but that the distribution of the inductive charging zones in the parent and child cells was broadly the same as that of the non-inductive charging zones. The charging zones were restricted to the mixed-phase region (between the 0 and −40 °C layers) with a cloud water content of >0.2 g/kg in the updraft zone, and the magnitude of the inductive charging rate was slightly smaller than that of the non-inductive charging rate. The differences in the vertical wind shear between the parent and child cells caused differences in the content, charge number, and polarity of the hydrometeors, which resulted in obvious differences in the charge structure characteristics between the two cells. Overall, the cloud droplets, ice, snow, and graupel were the main charged hydrometeors in the cells, whereas the rain and hail had little charge. Full article
(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition))
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30 pages, 6991 KiB  
Article
Investigation of Land–Atmosphere Coupling during the Extreme Rainstorm of 20 July 2021 over Central East China
by Yakai Guo, Changliang Shao and Aifang Su
Atmosphere 2023, 14(10), 1474; https://doi.org/10.3390/atmos14101474 - 23 Sep 2023
Cited by 4 | Viewed by 1406
Abstract
In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather [...] Read more.
In this study, a rainstorm of the type experienced on 20 July 2021 over central East China was simulated using the first-generation Chinese Reanalysis datasets and Global Land Data Assimilation System datasets, and the Noah land surface model coupled with the advanced weather research and forecasting model. Based on this, the gridded planetary boundary layer (PBL) profiles and ensemble states within soil perturbations were collected to investigate the typical land–atmosphere coupling chain during this modeled rainstorm by using various local coupling metrics and introduced ensemble statistical metrics. The results show that (1) except for the stratospheric thermodynamics and the surface temperature over mountain areas, the main characteristics of the mid-low atmospheric layers and the surface have been well captured in this modeled rainstorm; (2) the typical coupling intensity is characterized by the dominant morning moistening, an early afternoon weak PBL warming factor of around 2, a noontime buoyant mixing temperature deficit around 274 K, daytime PBL and surface latent flux contributions of around 100 and 280 W/m2, respectively, and significant afternoon soil-surface latent flux coupling; and (3) an overall negative soil–rainfall relationship can be identified from the ensemble metrics in which the moist static energy is more significant than PBL height, and this is consistent with the significance of daytime surface moistening indicated by local coupling metrics. Taking the multi-process chain in chronological order, the wet soil contributes greatly to daytime moisture evaporation, which then increases the early noon PBL warming and enhances the noon period buoyant mixing within weak moist heating; however, this is suppressed by large-scale forcing such as the upper southwestern inflows of rainstorms, which further significantly shapes the spatial distribution of the statistical metrics. These quantitatively described local daytime couplings highlight the potential local application of promoting public weather forecasting efforts, while the high spatial differences in the coupling indicate the more applicable threshold diagnoses within finer-scale spatial investigations. Full article
(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition))
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25 pages, 3294 KiB  
Article
Comparative Evaluation of Rainfall Forecasts during the Summer of 2020 over Central East China
by Yakai Guo, Changliang Shao and Aifang Su
Atmosphere 2023, 14(6), 992; https://doi.org/10.3390/atmos14060992 - 7 Jun 2023
Cited by 4 | Viewed by 1158
Abstract
By using various skill scores and spatial characteristics of spatial verification methods and traditional techniques of the model evaluation tool, the gridded precipitation observation, known as Climate Prediction Center Morphing Technique, gauge observation and three datasets that were derived from local, Shanghai, and [...] Read more.
By using various skill scores and spatial characteristics of spatial verification methods and traditional techniques of the model evaluation tool, the gridded precipitation observation, known as Climate Prediction Center Morphing Technique, gauge observation and three datasets that were derived from local, Shanghai, and Grapes models, respectively, were conducted to assess the 3 lead day rainfall forecast with 0.5 day intervals during the summer of 2020 over Central East China. Results have shown that the local model generally outperforms the other two for the most skill scores but usually with relatively larger uncertainties than the Shanghai model, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of the Grapes model has been heavily underestimated and is accompanied with a large displacement error. Both the local and Shanghai model can effectively forecast the large-scale convection and rainstorms but over forecast the local convection, while the local model likely over forecasts the local rainstorms. In addition, the Shanghai model slightly favors over forecasting on a broad scale range and a broad threshold range, and the local model slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended when considering reasonable classification of thresholds if the accuracy is highly demanding. In addition, most spatial methods are suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the verification metrics including spatial characteristic difference information should be recommended to emphasize rewarding the severe events forecast under a global warming background. Full article
(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition))
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15 pages, 4050 KiB  
Article
The Assimilation Effect of Multi-New Types Observation Data in the Forecasts of Meiyu-Front Rainstorm
by Hong Zhao, Yu Shu, Yuqing Mao, Yin Liu and Kun Yu
Atmosphere 2023, 14(4), 693; https://doi.org/10.3390/atmos14040693 - 7 Apr 2023
Cited by 6 | Viewed by 1517
Abstract
Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front [...] Read more.
Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front rainstorm on 15 June 2020. The results show that the seven experiments can basically simulate the orientation of rain belt. The comprehensive experiment which assimilates all types of observations performs the best in simulating the location of heavy rainstorm and shows good performance in simulating the precipitation above moderate rain. For the comprehensive experiment, the forecast deviation of rainstorm and heavy rainstorm is small, and the equitable threat score has also been greatly improved compared with other experiments. It is found that the convective available potential energy is enhanced after the assimilation of surface observation data. In addition, the wind convergence and water vapor transportation are modified after assimilating wind profile data. Accordingly, the precipitation efficiency is improved in the comprehensive experiment. The profiles of pseudo-equivalent potential temperature, vorticity and divergence show that, the assimilation of new-types observation data from wind profiler radar and microwave radiometer increases the instability of atmospheric stratification and enhances the ascending motion in the heavy precipitation center. The above results show that the introduction of various some new-type data before the numerical simulation can reduce the forecast deviation. In addition, the combined assimilation of microwave radiometer and sounding data presents better performance than single data assimilation, which indicates that data mutual complementation is essential to improving forecast accuracy. Full article
(This article belongs to the Special Issue Advances in Hazardous Weather Prediction: Data Assimilation, Numerical Model and Tools (2nd Edition))
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