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Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition)
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Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition)

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 4382

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. Dongmei Xu

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University of Information Science & Technology, Nanjing 211544, China
Interests: satellite remote sensing observation data assimilation; radiance data application for cloud retrievals; ensemble–variational data assimilation; radar data assimilation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume in a series of publications dedicated to “Data Assimilation for Predicting Hurricanes, Typhoons, and Storms” (https://www.mdpi.com/journal/atmosphere/special_issues/RE3826D2OM).

Many coastal areas suffer hurricane and typhoon damage, resulting in massive economic losses and sudden mortality. The accurate prediction of tropical cyclone (TC) track and intensity is therefore crucial to protecting life and property in coastal areas. The numerical estimation of tropical cyclones’ intensity, frequency, and track is an active research area. Improvements to TC forecasting can be attributed mainly to improvements in numerical weather prediction (NWP) models but also to more effective data assimilation (DA) approaches that can be optimized based on both the forecast background and observations. It is important to develop data assimilation technologies to enhance the application of multi-source observations. In addition, evaluating the performance of new types of observation facilitates the design of observation networks for regional- and storm-scale numerical models.

We are interested in submissions on any of the topics listed below. Improvements and innovations may cover the NWP of TCs as well as the improvements obtained by applying existing or new types of remote sensing observations. Possible topics include (but are not limited to) ground-based radar, all-sky radiances, atmospheric motion vectors, and airborne reconnaissance mission-collected observations. Manuscripts should clearly illustrate applications and results for the improvement of forecast skills for TC structure prediction, TC track, and intensity. This Special Issue should include the following topics:

  • Advancements in remote sensing data assimilation technologies;
  • Development of high-spatial-resolution models for TC structure and intensity (RI/RW);
  • Development of probabilistic prediction methods for TC;
  • Development of verification methods for TC;
  • Application of artificial intelligence for numerical models in TC prediction;
  • Investigation of new types of observation in numerical models for TC prediction.

Manuscripts may present original research or reviews of the state-of-the-art of the science, thereby providing context for the current research as well as the direction in which modeling and data assimilation for TCs should be moving in the future.

Dr. Feifei Shen
Dr. Dongmei Xu
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

  • tropical cyclone
  • data assimilation
  • radar data
  • satellite radiance data
  • hybrid systems

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Published Papers (4 papers)

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Research

16 pages, 5064 KiB  
Article
Impacts of Forecast Time and Verification Area Setting on the Targeted Observation of Typhoon
by Jiaqi Kang, Jianxia Guo, Jia Wang and Chao Zhang
Atmosphere 2024, 15(11), 1335; https://doi.org/10.3390/atmos15111335 - 7 Nov 2024
Viewed by 713
Abstract
The results of the identification of sensitive areas are affected by the forecast time and verification area settings in targeted observations. Understanding this setting issue is important for improving the effectiveness of the identification of sensitive areas in real-time field campaigns. To determine [...] Read more.
The results of the identification of sensitive areas are affected by the forecast time and verification area settings in targeted observations. Understanding this setting issue is important for improving the effectiveness of the identification of sensitive areas in real-time field campaigns. To determine this, a series of experiments were carried out based on the Ensemble Transform Sensitivity (ETS) method, and the results are as follows: (1) First, Observation System Simulation Experiments (OSSEs) were conducted to assimilate simulated dropsondes in sensitive areas (SENS) or non-sensitive areas (OTHR). The results showed that the SENS experiment improved forecasts of typhoon intensity, track, precipitation score, and RMSE of forecast elements. However, the OTHR experiment only improved the forecast in some aspects and even had negative effects on other aspects. This indicates that the sensitive areas identified by the ETS method are effective. (2) Different forecast time experiments were carried out. There were significant differences between the sensitive areas of fixed verification times and variable targeted observation times, indicating that the sensitive areas changed greatly with time. In the field campaign, it was necessary to calculate the sensitive area for multiple times in advance and to design or adjust the observation scheme according to the time. (3) Finally, comparative experiments of position deviation and size change in the verification area were carried out. It was found that for a big deviation, too large or too small a verification area will result in significant differences in the sensitive areas. Based on the study in this article, a verification area size of about 6° × 6° is recommended; this can not only accommodate the position deviation of the verification area from the typhoon center caused by forecast errors, but also does not contain too much noise unrelated to typhoons, which may affect the accuracy of identification of sensitive areas. Full article
(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition))
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16 pages, 5066 KiB  
Article
Analysis of a Rainstorm Process in Nanjing Based on Multi-Source Observational Data and Lagrangian Method
by Yuqing Mao, Youshan Jiang, Cong Li, Yi Shi and Daili Qian
Atmosphere 2024, 15(8), 904; https://doi.org/10.3390/atmos15080904 - 29 Jul 2024
Viewed by 999
Abstract
Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process [...] Read more.
Using multi-source observation data including automatic stations, radar, satellite, new detection equipment, and the Fifth Generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-5) data, along with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) platform, an analysis was conducted on a rainstorm process that occurred in Nanjing on 15 June 2020, with the aim of providing reference for future urban flood control planning and heavy rainfall forecasting and early warning. The results showed that this rainstorm process was generated under the background of an eastward-moving northeast cold vortex and a southward retreat of the Western Pacific Subtropical High. Intense precipitation occurred near the region of large top brightness temperature (TBB) gradient values or the center of low TBB values on the northern side of the convective cloud cluster. During the heavy precipitation period, the differential propagation phase shift rate (KDP), differential reflectivity factor (ZDR), and zero-lag correlation coefficient (ρHV) detected by the S-band dual-polarization radar all increased significantly. The vertical structure of the wind field detected by the wind profile radar provided a good indication of changes in precipitation intensity, showing a strong correspondence between the timing of maximum precipitation and the intrusion of upper-level cold air. The abrupt increase in the integrated liquid water content observed by the microwave radiometer can serve as an important indicator of the onset of stronger precipitation. During the Meiyu season in Nanjing, convective precipitation was mainly composed of small to medium raindrops with diameters less than 3 mm, with falling velocities of raindrops mainly clustering between 2 and 6 m·s−1. The rainstorm process featured four water vapor transport channels: the mid-latitude westerly channel, the Indian Ocean channel, the South China Sea channel, and the Pacific Ocean channel. During heavy rainfall, the Pacific Ocean water vapor channel was the main channel at the middle and lower levels, while the South China Sea water vapor channel was the main channel at the upper level, both accounting for a trajectory proportion of 34.2%. Full article
(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition))
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23 pages, 8912 KiB  
Article
A New Post-Processing Method for Improving Track and Rainfall Ensemble Forecasts for Typhoons over Eastern China
by Chun Liu, Hanqing Deng, Xuexing Qiu, Yanyu Lu and Jiayun Li
Atmosphere 2024, 15(8), 874; https://doi.org/10.3390/atmos15080874 - 23 Jul 2024
Viewed by 1055
Abstract
This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The [...] Read more.
This paper proposes a new post-processing method for model data in order to improve typhoon track and rainfall forecasts. The model data used in the article include low-resolution ensemble forecasts and high-resolution forecasts. The entire improvement method contains the following three steps. The first step is to correct the typhoon track forecast: three ensemble member optimization methods are applied to the low-resolution ensemble forecasts, and then the best optimization method is selected with the principle of the smallest average distance error. The results of rainfall forecasts show that the corrected rainfall forecast performs better than the original forecasts. The second step is to derive the high-resolution probability rainfall forecast: the neighborhood method is applied to the deterministic high-resolution rainfall forecast. The last step is to correct the typhoon rainfall forecast: the low- and high-resolution forecasts are blended using the probability-matching method with two different schemes. The results show that the forecasts of the two schemes perform better than the original forecast under all rainfall thresholds and all forecast lead times. In terms of bias score, a rain forecast from one scheme corrects the rainfall deviation from observation better for light and moderate rainfall, whereas a rain forecast from another scheme corrects the rainfall deviation better for heavy and torrential rainfall. The better performance of corrected rain forecasts in the case of Typhoon Lekima and Rumbia over eastern China is demonstrated. Full article
(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition))
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18 pages, 11359 KiB  
Article
Study of Quality Control Methods Utilizing IRMCD for HY-2B Data Assimilation Application
by Jiazheng Hu, Yu Zhang, Jianjun Xu, Jiajing Li, Duanzhou Shao, Qichang Tan and Junjie Feng
Atmosphere 2024, 15(6), 728; https://doi.org/10.3390/atmos15060728 - 18 Jun 2024
Viewed by 964
Abstract
Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions [...] Read more.
Quality control (QC) of HaiYang-2B (HY-2B) satellite data is mainly based on the observation process, which remains uncertain for data assimilation (DA). The data in operation have not been widely used in numerical weather prediction. To ensure HY-2B data meet the theoretical assumptions for DA applications, the iterated reweighted minimum covariance determinant (IRMCD) QC method was studied in HY-2B data based on the typhoon “Chanba”. The statistical results showed that most of the outliers were eliminated, and the observation increment distribution of the HY-2B data after QC (QCed) was closer to a Gaussian distribution than the raw data. The kurtosis and skewness of the QCed data were much closer to zero. The QCed track demonstrated the lowest accumulated error and the best intensity in typhoon assimilation, and the QCed intensity was closest to the observation during the nearshore enhancement, exhibiting the strongest intensity among the experiment. Further analysis revealed that the improvement was accompanied by a significant reduction in vertical wind shear during the nearshore enhancement of the typhoon. The QCed moisture flux divergence and vertical velocity in the upper layer increased significantly, which promoted the upward transport of momentum in the lower layers and contributed to the maintenance of the typhoon’s barotropic structure. Compared with the assimilation of raw data, the effective removal of outliers using the IRMCD algorithm significantly improved the simulation results for typhoons. Full article
(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm (2nd Edition))
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