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Data Assimilation for Predicting Hurricane, Typhoon and Storm

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

Deadline for manuscript submissions: closed (25 March 2024) | Viewed by 10743

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Dr. Feifei Shen

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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
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Dr. Dongmei Xu

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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
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Dear Colleagues,

Many coastal areas suffer hurricane and typhoon damages, with massive economic losses and sudden mortality. The accurate prediction of tropical cyclone (TC) track and intensity is therefore crucial to protect 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 the 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 facilitate 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 skill for the TC structure prediction, TC track, and intensity.

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 on 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 for the future.

Dr. Feifei Shen
Dr. Dongmei Xu
Guest Editors

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Keywords

tropical cyclone
data assimilation
radar data
satellite radiance data
hybrid systems

Published Papers (11 papers)

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Research

17 pages, 7040 KiB

Open AccessArticle

Evaluation of Two Momentum Control Variable Schemes in Radar Data Assimilation and Their Impact on the Analysis and Forecast of a Snowfall Case in Central and Eastern China

by Shen Wan, Feifei Shen, Jiajun Chen, Lin Liu, Debao Dong and Zhixin He

Atmosphere 2024, 15(3), 342; https://doi.org/10.3390/atmos15030342 - 11 Mar 2024

Viewed by 606

Abstract

To evaluate the impact of different momentum control variable (CV) schemes (CV5, the momentum control variable option with ψχ and CV7, the momentum control variable option with UV) on radar data assimilation (DA) in weather research and forecasting model data-assimilation (WRFDA) systems, a heavy snowfall in central and eastern regions of China, which started on 6 February 2022, was taken as a case in this study. The results of the wind-field increments from the single observation tests indicated that the wind-field increments had a larger range of influence when stream function and velocity potential (ψχ) were used as momentum control variables in CV5. Some spurious increments were also generated in the wind-field analysis, since CV5 tended to maintain the integrated value of the wind field. When U-wind and V-wind were used as control variables in CV7, the wind-field increments had a smaller impact range, and there was less dependence among different locations on the wind increments. For the heavy snow case, the CV7 schemes displayed some improvements in simulating the composite reflectivity compared to the other two experiments, since the composite reflectivity in the CV5 and control experiments were overestimated to some level. It was also found that the RMSEs were lower in the CV7 compared to those in the CV5 in the short-term forecasts during the data-assimilation cycles. Results also indicated that the CV7 had a more significant effect on the 6 h accumulated precipitation forecasts. Meanwhile, the experiment Exp_CV7 achieved the best ETS and FSS scores among the three groups of experiments, while Exp_CV5 appeared to be generally superior to the CTRL. In summary, the precipitation of Exp_CV7 yielded the rainfall intensity and location most close to the observation compared to those from both the CTRL and Exp_CV5 experiments. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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21 pages, 14485 KiB

Open AccessArticle

Analysis of Dual-Polarimetric Radar Observations of Precipitation Phase during Snowstorm Events in Jiangsu Province, China

by Lei Wang, Yi Wang, Mei Liu, Wei Chen and Chiqin Li

Atmosphere 2024, 15(3), 321; https://doi.org/10.3390/atmos15030321 - 04 Mar 2024

Viewed by 533

Abstract

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor Z_H ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity Z_DR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase K_DP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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23 pages, 9066 KiB

Open AccessArticle

Sensitive Areas’ Observation Simulation Experiments of Typhoon “Chaba” Based on Ensemble Transform Sensitivity Method

by Yanlong Ao, Yu Zhang, Duanzhou Shao, Yinhui Zhang, Yuan Tang, Jiazheng Hu, Zhifei Zhang, Yuhan Sun, Peining Lyu, Qing Yu and Ziyan He

Atmosphere 2024, 15(3), 269; https://doi.org/10.3390/atmos15030269 - 23 Feb 2024

Viewed by 557

Abstract

High-impact weather (HIW) events, such as typhoons, usually have sensitive regions where additional observations can be deployed and sensitive observations assimilated, which can improve forecasting accuracy. The ensemble transform sensitivity (ETS) method was employed to estimate the sensitive regions in the “Chaba” case in order to explore the impact of observation data in sensitive areas on typhoon forecasting during the rapid intensification phase. A set of observation system simulation experiments were conducted, with assimilations of sensitive observations (SEN), randomly selected observations (RAN), whole domain observations (ALL), and no assimilation (CTRL). The results show that (1) the sensitive areas of Typhoon “Chaba” are primarily located in the southwest of the typhoon center and are associated with the distribution of the wind field structure; (2) the typhoon intensity and tracks simulated by the SEN and RAN experiments are closer to the truth than the CTRL; (3) the SEN experiment, with only 3.6% of assimilated data observations, is comparable with the ALL experiment during the rapid intensification phase of the typhoon; (4) the uncertainty of the mesoscale model can be improved by capturing large-scale vertical wind shear and vorticity features from the GEFS data and then using the data assimilation method, which makes the vertical shear and vorticity field more reasonable. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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15 pages, 6190 KiB

Open AccessArticle

Application of GOES-16 Atmospheric Temperature-Profile Data Assimilation in a Hurricane Forecast

by Zhiying Qian, Yansong Bao, Zirui Liu, Qifeng Lu, Fu Wang and Weiyao Tang

Atmosphere 2023, 14(12), 1757; https://doi.org/10.3390/atmos14121757 - 29 Nov 2023

Viewed by 661

Abstract

This paper selects the case of the Atlantic hurricane “Michael” in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious “flow-dependent” characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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17 pages, 14846 KiB

Open AccessArticle

Impacts of Radar Data Assimilation on the Forecast of “12.8” Extreme Rainstorm in Central China (2021)

by Zhixin He, Jinyin Ye, Zhijia Li, Chunze Lin and Lixin Song

Atmosphere 2023, 14(12), 1722; https://doi.org/10.3390/atmos14121722 - 23 Nov 2023

Viewed by 576

Abstract

Dual-polarization radar data are useful for numerical models to improve precipitation forecasts. For an extremely heavy precipitation event that occurred in Central China on 11 August 2021, the hydrometeor concentration and water vapor content used in the initial field of the Weather Research and Forecasting (version 4.1) model are retrieved by the statistical relationship of relative humidity with dual-polarization radar reflectivity in Suizhou City of Central China. Three experiments are conducted, and the simulation results are compared after assimilating the radar data. The results indicate that the multiple factors contributing to this extreme heavy precipitation event included the divergence of upper-level airflows, the middle- and low-level low vortex/shear, the easterly jet stream in front of the low vortex, and the continuous intrusion of cold air on the ground. In addition, with the retrieval of the hydrometeor concentration and water vapor content, the composite reflectivity forecast results are more similar to the observations. Also, the location and intensity of the short-term extremely heavy precipitation event are less different from the observations. In addition, by cyclically adjusting the hydrometeor concentration and water vapor content in the initial field, we can obtain better forecasts of the reflectivity and short-term extremely heavy precipitation, and this improvement can be maintained for approximately 3 h. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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14 pages, 4120 KiB

Open AccessArticle

Study of the Sea Temperature Backgrounds to Tropical Cyclones Affecting Hainan Province in the Dry Season

by Jingjing Zhu, Xiaoping Zhao, Hui Wu, Shengan Wu, Deqiang Hu and Caiying Xing

Atmosphere 2023, 14(11), 1663; https://doi.org/10.3390/atmos14111663 - 09 Nov 2023

Viewed by 707

Abstract

Using the best path data set of tropical cyclones from the China Meteorological Administration, NCEP/NCAR (National Centers for Environmental Prediction–National Center for Atmospheric Research) reanalysis data, JMA (Japan Meteorological Agency) SST (surface sea temperature) data and NCEP subsurface sea temperature data, the sea temperature background of tropical cyclones affecting Hainan Province in the dry season was analyzed using typical cases from November to December. The results show that there was a significant positive correlation between the number of tropical cyclones affecting Hainan Province and the sea surface temperature in the South China Sea and the Western Pacific. The high SST in the South China Sea and Northwest Pacific were favorable for the occurrence and development of tropical cyclones. The circulation situation also had a significant impact on tropical cyclones during the concurrent period. The wind field convergence was conducive to the occurrence and development of tropical cyclones. The high subsurface sea temperature in the Western Pacific at the depth of 5–50 m was conducive to the strengthening of convection in the Pacific warm pool and the occurrence of the tropical cyclones. The typical cases of 1993 and 2017 have effectively verified the impact of the sea surface temperature background and circulation situation on tropical cyclones affecting the Hainan Province from November to December. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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22 pages, 30686 KiB

Open AccessArticle

Dynamical Analyses of a Supercell Tornado in Eastern China Based on a Real-Data Simulation

by Shiqi Wang and Jinzhong Min

Atmosphere 2023, 14(5), 884; https://doi.org/10.3390/atmos14050884 - 18 May 2023

Viewed by 1290

Abstract

Tornadoes are extremely destructive natural disasters, and East China has become a high-incidence area for tornadoes in China in recent years. On 7 July 2013, an EF2-intensity tornado occurred in Gaoyou County, Jiangsu Province in eastern China, within a supercell storm near a Meiyu frontal system. To investigate the dynamical process of the tornado, a numerical simulation was performed using four one-way nested grids within the Advanced Regional Prediction System (ARPS). Data from a nearby operational S-band Doppler radar are assimilated using a 4D ensemble Kalman filter (4DEnKF) at 5 min intervals. Forecasts are run with a nested 50 m grid, capturing the tornado embedded within the supercell storm with a reasonable agreement with observations. The tornadogenesis processes within the simulation results are analyzed in detail, including the three-dimensional evolution of the tornado vortex. It is found that a cold surge within the rear flank downdraft region plays a key role in instigating tornadogenesis when the leading edge of the cold surge approaches a near-ground convergence center located underneath the main updraft, and the enhancement of the convergence center caused by the descending of the low-level mesocyclone is the direct cause of the rapid increase in tornado vorticity. Backward trajectories are calculated based on model output, and the origins of the parcels feeding the intensifying tornado vortex are identified. It is found that parcels from the mid-level of the rear flank downdraft region follow the cold surge, descending to the ground under the influence of the downdraft in the cold surge, and then entering the convergence center, merging into the core of the tornado and being lifted up. Vertical profiles of the mass and vorticity fluxes into the core of the tornado vortex are examined, and it is found that the near-ground airflow contributes significantly to the growth of the tornado vorticity, with the contribution increasing as it gets closer to the ground. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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23 pages, 37622 KiB

Open AccessArticle

A Hurricane Initialization Scheme with 4DEnVAR Satellite Ozone and Bogus Data Assimilation (SOBDA) and Its Application: Case Study

by Yin Liu

Atmosphere 2023, 14(5), 866; https://doi.org/10.3390/atmos14050866 - 12 May 2023

Cited by 1 | Viewed by 1113

Abstract

The aim of this study is to joint assimilate the ozone product from the satellite Atmospheric Infrared Sounder (AIRS) and bogus data using the four-dimensional ensemble-variational (4DEnVar) method, and demonstrate the potential benefits of this initialization technique in improving hurricane forecasting through a case study. Firstly, the quality control scheme is employed to enhance the ozone product quality from the satellite AIRS; a bogus sea level pressure (SLP) at the hurricane center is constructed simultaneously based on Fujita’s mathematical model for subsequent assimilation. Secondly, a 4DEnVar satellite ozone and bogus data assimilation (SOBDA) model is established, incorporating an observation operator of satellite ozone that utilizes the relationship between satellite ozone and potential vorticity (PV) from the lower level of 400 hPa to the upper level of 50 hPa. Finally, several comparative experiments are performed to assess the influence of assimilating satellite ozone and/or bogus data, the 4DEnVAR method and four-dimensional variational (4D-Var) method, and ensemble size on hurricane prediction. It is found that assimilating satellite ozone and bogus data with the 4DEnVar method concurrently brings about significant alterations to the initial conditions (ICs) of the hurricane vortex, resulting in a more homogeneous and deeper vortex with a larger, warmer, and more humid core as opposed to assimilating only one type of data. As the duration of integration increases, the initial perturbations in the upper levels gradually propagate downwards, giving rise to significant disparities in the hurricane prediction when satellite ozone and/or bogus information is incorporated. The results demonstrate that utilizing the 4DEnVar approach to assimilate both satellite ozone and bogus data leads to the maximum enhancement in reducing track error and central SLP error of hurricane simulation throughout the entire 72 h forecasting period, compared to assimilating a single dataset. Furthermore, comparative experiments have indicated that the performance of 4DEnVar SOBDA in hurricane forecasting is influenced by the ensemble size. Generally, selecting an appropriate number of ensemble members can not only effectively improve the accuracy of hurricane prediction but can also significantly reduce the demand for computational resources relative to the 4D-Var method. This study can also serve as an advantageous technical reference for numerical applications of ozone products from other satellites and hurricane initialization. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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14 pages, 5963 KiB

Open AccessArticle

Interdecadal Variation of Summer Extreme Heat Events in the Beijing–Tianjin–Hebei Region

by Yanan Liang, Junzhi Zhang, Ji Wang and Tiejun Xie

Atmosphere 2023, 14(5), 854; https://doi.org/10.3390/atmos14050854 - 11 May 2023

Viewed by 1096

Abstract

Extreme heat events are frequent in the Beijing–Tianjin–Hebei (BTH) region due to global warming and accelerated urbanization. While previous studies have analyzed the trend of extreme heat events in the Beijing–Tianjin–Hebei (BTH) region, the interdecadal changes of these events remain unclear. Therefore, this study aims to analyze the interdecadal temporal and spatial characteristics of summer extreme heat events in the BTH region using daily mean and maximum temperature datasets from 174 stations over the period 1979–2020. The results are shown as follows: (1) From 1979 to 2020, extreme heat events showed an overall upward trend in the BTH region. There were similarities in the changes in the extreme maximum temperature (TXx) and the number of high-temperature days (Htd) between different generations, and both were low until the mid-1990s. (2) In terms of the spatial pattern, TXx and Htd both showed the spatial distribution characteristics of being high in the south and low in the north. Extreme heat events in the BTH region were mainly concentrated in Beijing City, Tianjin City, and the eastern region of Hebei, and the TXx increase in most areas reached 1.5–2.0 °C. (3) The number of high-temperature days (Htd) increased significantly in the background of global warming, especially in Beijing, Tianjin, and Shijiazhuang Cities. (4) Extreme heat events in the BTH region mainly occurred in June and July, and the interdecadal changes showed a decreasing trend in June and an increasing trend in July. A high proportion of Htd was concentrated in Northern Hebei Province in July. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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15 pages, 8129 KiB

Open AccessArticle

Application of Radar Radial Velocity Data Assimilation in the Forecasts of Typhoon Linfa Based on Different Horizontal Length Scale Factors

by Huimin Bian, Jinzhong Min and Feifei Shen

Atmosphere 2023, 14(3), 582; https://doi.org/10.3390/atmos14030582 - 17 Mar 2023

Viewed by 1339

Abstract

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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14 pages, 5402 KiB

Open AccessArticle

Assessment of FY-3D SST Data on Typhoon In-Fa Simulation

by Chun Yang and Jingyu Li

Atmosphere 2023, 14(1), 101; https://doi.org/10.3390/atmos14010101 - 03 Jan 2023

Viewed by 1150

Abstract

Sea surface temperature (SST) plays an important role in the typhoon forecast. By comparison, satellite retrieval products can provide more accurate SST data than reanalysis data in typhoon simulations. However, the effect of SST data from Chinese Fengyun-3 (FY-3) satellites on typhoon simulation hasn’t been evaluated yet. In this paper, the impact of FY-3D SST retrieval data with ascending and descending orbits on the forecast of typhoon In-Fa, 2021, is investigated with the Weather Research Forecast (WRF) model. Compared to the control experiments with SST data from Global Data Assimilation System (GDAS) data, the replacement experiments with FY-3D SST data significantly improve the forecast of typhoon central sea level pressure, track, and precipitation. Especially, the SST from the descending orbit satellite data provides the optimal track and intensity forecast, which are verified against the best track data from the Japan Meteorological Agency. Full article

(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)

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