Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 3842

Special Issue Editors

Dr. Guangxin He

E-Mail Website
Guest Editor
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: satellite and radar remote sensing; remote sensing data utilization; data assimilation; numerical weather prediction; nowcasting using machine learning methods
Special Issues, Collections and Topics in MDPI journals
Dr. Zhe Zhang

E-Mail Website
Guest Editor
National Center for Atmospheric Research, Boulder, CO 80303, USA
Interests: land surface model; regional climate model; land-atmosphere interaction; agriculture; irrigation; groundwater
Special Issues, Collections and Topics in MDPI journals
Dr. Hongli Wang

E-Mail Website
Guest Editor
National Oceanic and Atmospheric Administration, Washington, DC, USA
Interests: 3/4D variational/ensemble data assimilation techniques; targeted data, radar and satellite observation assimilation; adjoint and ensemble-based study for adaptive observations and forecast
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yu Du

E-Mail Website
Guest Editor
School of Atmospheric Science, Sun Yat-Sen University, Zhuhai 519082, China
Interests: mesoscale meteorology; severe weather; low level jet; gravity waves and cold pool; diurnal cycle of rainfall; sea breeze; mesoscale numerical modelling and forecasting
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lili Lei

E-Mail Website
Guest Editor
School of Atmospheric Sciences, Nanjing University, Nanjing 210063, China
Interests: data assimilation; numerical weather prediction; atmospheric predictability
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Feng

E-Mail Website
Guest Editor
Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai 200438, China
Interests: atmospheric predictability; data assimilation; ensemble forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Synoptic and mesoscale dynamics and forecast is a crucial field in modern meteorology. Synoptic-scale weather systems are large systems that span vast areas, typically thousands of kilometers in horizontal extent, while mesoscale systems are much smaller, typically several hundred kilometers in size. Understanding the behavior of these systems is essential for predicting weather patterns and improving weather forecasts.

Research into synoptic and mesoscale dynamics involves studying how the atmosphere responds to both large-scale and small-scale processes. Scientists use a range of observational tools to study the atmosphere, including radiosondes, radar, satellite imagery, and aircraft measurements. These observations provide valuable data on atmospheric temperature, humidity, wind, and precipitation, which can help to better understand atmospheric processes. Additionally, numerical models are also used to simulate atmospheric processes, using mathematical equations to represent changes in atmospheric variables over time and space. These models enable researchers to gain insights into the behavior of synoptic and mesoscale systems and to test different hypotheses.

Remote sensing works by providing scientists with valuable atmospheric data that can help to understand the weather’s physical properties and phenomena and improve forecasting and warning systems. Remote sensing applications play a significant role in detecting and confirming the onset of extreme weather conditions, such as tornadoes, hurricanes and thunderstorms. Meteorological observations obtained using remote sensing techniques facilitate the real-time tracking and modeling of such weather conditions. These applications provide valuable information that improves forecasting accuracy and early warning system.

This Special Issue encompasses a wide range of topics in synoptic and mesoscale dynamics and forecast. These include, but are not limited to, remote sensing technology, research of synoptic and mesoscale dynamics, numerical weather prediction, data assimilation of radar and satellite data, atmospheric monitoring using remote platforms, analyses and forecasts of weather events utilizing remote sensing data, and artificial intelligence to optimize remote sensing measurements, etc.

Original research papers and/or review papers that cover the developments or applications of remote sensing technology or data for improving the study of synoptic and mesoscale dynamics and forecast are highly encouraged as valuable contributions.

You may choose our Joint Special Issue in Geomatics.

Dr. Guangxin He
Dr. Zhe Zhang
Dr. Hongli Wang
Prof. Dr. Yu Du
Prof. Dr. Lili Lei
Dr. Jie Feng
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • synoptic and mesoscale
  • dynamics
  • numerical weather prediction
  • data assimilation
  • atmospheric predictability
  • machine learning
  • gravity waves and cold pool
  • climate model

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

19 pages, 2894 KiB  
Article
The Retrieval Relationship between Lightning and Maximum Proxy Reflectivity Based on Random Forest
by Junhong Yin, Liqing Tian, Kuo Zhou, Weiguang Zhang and Lingkun Ran
Remote Sens. 2024, 16(4), 719; https://doi.org/10.3390/rs16040719 - 18 Feb 2024
Viewed by 602
Abstract
Using the SWAN (Severe Weather Automatic Nowcasting) maximum reflectivity mosaic product and the lightning positioning observations (LPOs) from the ADTD (Advanced Direction and Time of Arrival Detection) system obtained during the 2018–2020 warm season (May to September), adding multi-characteristic LPO parameters in addition [...] Read more.
Using the SWAN (Severe Weather Automatic Nowcasting) maximum reflectivity mosaic product and the lightning positioning observations (LPOs) from the ADTD (Advanced Direction and Time of Arrival Detection) system obtained during the 2018–2020 warm season (May to September), adding multi-characteristic LPO parameters in addition to lightning density, the retrieval relationship between lightning and maximum proxy reflectivity, deemed FRST, is constructed by using random forest. The FRST is compared with two empirical relationships from the GSI (Gridpoint Statistical Interpolation) assimilation system, and the results show that the FRST retrieved result better reflects the frequency distribution structure and peak interval of maximum reflectivity. The correlation coefficient between the FRST retrieved result and the observed maximum reflectivity is 0.7037, which is 3.38 (3.12) times greater than that of empirical GSI relationships. The root mean square error and the mean absolute error are 50.85% (28.05%) and 57.15% (35.19%) lower than those for the empirical GSI relationships, respectively. The equitable threat score (ETS) and bias score (BIAS) for FRST are better than those of the empirical GSI relationships in all three maximum reflectivity intervals. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast)
►▼ Show Figures

Figure 1

15 pages, 5106 KiB  
Article
An Long Short-Term Memory Model with Multi-Scale Context Fusion and Attention for Radar Echo Extrapolation
by Guangxin He, Haifeng Qu, Jingjia Luo, Yong Cheng, Jun Wang and Ping Zhang
Remote Sens. 2024, 16(2), 376; https://doi.org/10.3390/rs16020376 - 17 Jan 2024
Viewed by 721
Abstract
Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the [...] Read more.
Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the effective improvement in forecasting quality, there are still problems with blurred and distorted prediction images, as well as difficulties in effectively forecasting high echo regions. To solve the above problems, this article presents a spatio-temporal long–short-term memory network model in view of multi-scale context fusion and attention mechanisms. This method fully extracts the short-term context information of different scales of radar image through the multi-scale context fusion module. The attention module broadens the time perception domain of the prediction unit so that the model perceives more historical time dynamics. Using the Hong Kong region weather radar data as a sample, the results of the experimental comparative analysis show that the spatio-temporal long and short-term memory network in view of multi-scale context fusion and attention mechanism achieves better prediction performance. Our model is effective in improving both image quality and meteorological assessment metrics with higher accuracy and more details. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast)
►▼ Show Figures

Figure 1

18 pages, 26251 KiB  
Article
Simulation and Analysis of the Mesoscale Vortex Affecting the “21·7” Extreme Rainstorm in Henan
by Lan Xu, Tao Chen, Juanjuan Liu, Shenming Fu, Wei Cheng, Hongbo Liu, Bing Lu, Yujun He, Shujun Zhu, Yiran Liu, Xiao Shen and Bin Wang
Remote Sens. 2024, 16(2), 280; https://doi.org/10.3390/rs16020280 - 10 Jan 2024
Viewed by 659
Abstract
From 17 to 22 July 2021, the “21·7” extreme rainfall event (“21·7” ERE) hit Henan Province, breaking the record for mainland China with a maximum hourly rainfall of 201.9 mm at the Zhengzhou station. The long-lived (20 h) mesoscale Huang-Huai vortex (HHV) was [...] Read more.
From 17 to 22 July 2021, the “21·7” extreme rainfall event (“21·7” ERE) hit Henan Province, breaking the record for mainland China with a maximum hourly rainfall of 201.9 mm at the Zhengzhou station. The long-lived (20 h) mesoscale Huang-Huai vortex (HHV) was an important system that directly affected the major rainfall stage, including the extreme hourly rainfall. This study investigates the formation and development mechanism of the HHV, as well as its association with the simulation of extreme hourly rainfall through numerical simulations. The simulated rainfall and radar composite reflectivity were in good agreement with the observations, thus effectively reproducing the generation and developmental process of the HHV. The analysis results showed that the HHV initially formed at 850 hPa on 19 July at 1800 UTC and eventually developed to 550 hPa. The positive feedback formed by the horizontal convergence and vertical vorticity transport was the main mechanism leading to the generation and deepening of the HHV. The stretching effect (STR) term played an absolutely dominant role in the increase in the vorticity tendency, and it primarily originated from the coupling effect of boundary layer jets (BLJs) and synoptic-weather-system-related low-level jets (SLLJs). The accurate simulation of the HHV allowed the early rainfall to reasonably reproduce the surface cold pool near the Zhengzhou station, and then the cooperation of the SLLJs, the BLJs, and the cold pool made the simulated extreme hourly rainfall exactly close to the Zhengzhou station, but with a weaker intensity, due to the fact that the HHV moved northeastward after its formation, resulting in a narrow range of southerly flow in southern Henan, which is not conducive to convective triggering in the southerly flow. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast)
►▼ Show Figures

Figure 1

22 pages, 13170 KiB  
Article
High-Frequency Microbarograph-Observed Pressure Variations Associated with Gust Fronts during an Extreme Rainfall Event
by Jingjing Zhang, Lanqiang Bai, Zhaoming Li, Yu Du and Shushi Zhang
Remote Sens. 2024, 16(1), 101; https://doi.org/10.3390/rs16010101 - 26 Dec 2023
Viewed by 677
Abstract
This study aims to explore the roles of multiple gust fronts (i.e., outflow boundaries) during a short-lived extreme rainfall that occurred in the Greater Bay Area of South China in the afternoon of 1 August 2021. Through the use of microbarographs and Doppler [...] Read more.
This study aims to explore the roles of multiple gust fronts (i.e., outflow boundaries) during a short-lived extreme rainfall that occurred in the Greater Bay Area of South China in the afternoon of 1 August 2021. Through the use of microbarographs and Doppler weather radars, the research highlights how the interactions of five gust fronts, approaching the region from different directions, have contributed to the high precipitation efficiency and damaging surface winds during the event. The close convergence of these gust fronts funneled unstable air masses into the region of interest, priming the mesoscale convective environment. Some isolated convection initiated before the gust fronts’ arrival. Preceding the arrival of these gust fronts, subtle wave-like pressure jumps were identified from the high-frequency (1 Hz) microbarograph observations. The amplitude of the pressure jump is approximately 40 Pa with minimal changes in air temperature. During the early stage of the gust front passages, very high-frequency oscillations in surface pressure are recognized, indicating interaction between the density currents and the low-level troposphere. As suggested through numerical simulations, the subtle pressure jumps are associated with upward displacements of isentropic surfaces aloft, deepening the moist layer and enhancing the lapse rate that are conducive to convective development. The simulated vertical profiles show no evident capping inversion above the dry neutral boundary layer, suggesting that the pressure jumps are likely to be dynamically induced through the collision of the outflows and environmental air masses. The findings of this study suggest the potential application of microbarographs in the nowcasting of the convective development associated with gust fronts. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast)
►▼ Show Figures

Figure 1

Review

Jump to: Research

22 pages, 14370 KiB  
Review
Radar Characteristics and Causal Analysis of Two Consecutive Tornado Events Associated with Heavy Precipitation during the Mei-Yu Season
by Shuya Cao, Yi Wang, Guangxin He, Peifeng Shen, Yan He and Yue Wu
Remote Sens. 2023, 15(23), 5470; https://doi.org/10.3390/rs15235470 - 23 Nov 2023
Viewed by 674
Abstract
This paper comprehensively analyzed two consecutive tornado events associated with heavy precipitation during the Mei-yu season (a period of continuous rainy weather that occurs in the middle and lower reaches of the Yangtze River in China from mid-June to mid-July each year) and [...] Read more.
This paper comprehensively analyzed two consecutive tornado events associated with heavy precipitation during the Mei-yu season (a period of continuous rainy weather that occurs in the middle and lower reaches of the Yangtze River in China from mid-June to mid-July each year) and detailed the formation and development process of the tornadoes using Doppler weather radar, wind profiler radar, ERA5 reanalysis data, ground automatic station data and other multi-source data. The results showed that: (1) Small-scale vortices were triggered and developed during the eastward movement of the low vortex, forming two tornadoes successively on the eastern section of the Mei-yu front. (2) The presence of a gap on the front side of the reflectivity factor profile indicated that strong incoming airflow entered the updraft. Mesocyclones were detected with decreasing heights and increasing shear strengths. The bottom height of the tornado vortex signature (TVS) dropped to 0.7 km, and the shear value increased to 55.4 × 10−3 s−1. Tornado debris signatures (TDSs) could be seen with a low cross-correlation coefficient (CC) value area of 0.85–0.9 in the mesocyclone. The difference between the lowest-level difference velocity (LLDV) and the maximum difference velocity (MXDV) reached the largest value when a tornado occurred. (3) The continuously enhanced low-level jet propagated downward to form a super-low-level jet, and the strong wind direction and wind speed convergence in the boundary layer created a warm, moist and unstable atmosphere in Suzhou. With the entrainment of dry air, the northwest dry jet and the southeast moist jet stimulated the formation of a miniature supercell. (4) The low-level vertical wind shear of 0–1 km increased significantly upon tornado occurrence, which was more conducive to the formation and intensification of horizontal vorticity tubes. Encountering updrafts and downdrafts, the vorticity tubes might have been stretched and intensified. The first lightning jumps appeared 15 min and 66 min earlier than the Kunshan Bacheng tornado and the Taicang Liuhe tornado. The Liuhe tornado occurred during the stage when the lightning frequency reached its peak and then fell back. Full article
(This article belongs to the Special Issue Remote Sensing Applications for Synoptic and Mesoscale Dynamics and Forecast)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop