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Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 4130

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Special Issue Editors

Dr. Hao Huang

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Guest Editor

School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
Interests: weather radar; cloud microphysics; severe weather

Dr. Xiantong Liu

E-Mail Website
Guest Editor

Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510640, China
Interests: cloud precipitation physics; monsoon and typhoon precipitation microphysical characteristics; polarimetric radar data application; quantitative precipitation estimation; numerical forecast of precipitation
Special Issues, Collections and Topics in MDPI journals

Dr. Silke Trömel

E-Mail Website
Guest Editor

Institute of Geosciences, Department of Meteorology, University of Bonn, 53121 Bonn, Germany
Interests: radar polarimetry; precipitation estimation and prediction; fusion of radar polarimetry

Special Issue Information

Dear Colleagues,

Severe weather events such as tornadoes, hurricanes, and thunderstorms pose a significant threat to human life and property. Accurate observations of these events and the ability to forecast them with precision are critical for reducing their impact. Remote sensing technology, including satellite and radar systems, has considerably advanced in recent years, as has our ability to exploit them for process understanding and data assimilation. Their measurements can provide information about the location, intensity, movement, and kinetic and microphysical structures of severe storms, which has greatly improved our ability to understand them. In addition, advances in meteorological modeling have allowed for more accurate and detailed predictions of severe weather events. By combining remote sensing data with meteorological models, researchers can develop highly accurate predictions of severe weather events and provide warnings to those in the path of the storm, which allows a faster response to severe weather events, potentially saving countless lives and reducing the economic impact of these events.

This Special Issue aims to publish studies related to the observation of severe weather using remote sensing, the modeling of severe weather, and particularly the in-depth evaluation, improvement, and development of models based on remote sensing observations such as radars and satellites.

Topics may cover novel instruments and methods for the remote sensing of severe weather, new datasets for severe weather based on observations or modeling, new findings about thermodynamics and/or microphysics, and the application of remote sensing data for the development/improvement of numerical models. Articles may address, but are not limited to, the following topics:

New radar and satellite technologies;
Retrieval methods;
Dynamics;
Microphysics;
Numerical model;
Data assimilation;
Parameterization.

Dr. Hao Huang
Dr. Xiantong Liu
Dr. Silke Trömel
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

radar and satellite
retrieval method
dynamics
microphysics
numerical model
parameterization

Published Papers (4 papers)

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Research

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24 pages, 20771 KiB

Open AccessArticle

Overestimated Fog-Top Entrainment in WRF Simulation Leading to Unrealistic Dissipation of Sea Fog: A Case Study

by Li Zhang, Hao Shi, Shanhong Gao and Shun Li

Remote Sens. 2024, 16(10), 1656; https://doi.org/10.3390/rs16101656 - 7 May 2024

Viewed by 478

Abstract

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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22 pages, 14050 KiB

Open AccessArticle

An Evaluation and Improvement of Microphysical Parameterization for a Heavy Rainfall Process during the Meiyu Season

by Zhimin Zhou, Muyun Du, Yang Hu, Zhaoping Kang, Rong Yu and Yinglian Guo

Remote Sens. 2024, 16(9), 1636; https://doi.org/10.3390/rs16091636 - 3 May 2024

Viewed by 562

Abstract

The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (Z_DR), specific differential phase (K_DP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, Z_DR, K_DP and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) is reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even if the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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22 pages, 11197 KiB

Open AccessArticle

Case Study on the Evolution and Precipitation Characteristics of Southwest Vortex in China: Insights from FY-4A and GPM Observations

by Jie Xiang, Hao Wang, Zhi Li, Zhichao Bu, Rong Yang and Zhihao Liu

Remote Sens. 2023, 15(16), 4114; https://doi.org/10.3390/rs15164114 - 21 Aug 2023

Cited by 1 | Viewed by 871

Abstract

This research investigates Southwest Vortex (SWV) events in China’s Sichuan Basin using Fengyun-4A (FY-4A) and Global Precipitation Mission (GPM) observations. We selected representative cloud systems and precipitation cases, divided into developing, mature, and dissipating stages. Detailed analysis revealed critical characteristics of precipitation cloud systems at each stage. Our findings reveal that (1) during the SWV’s developing and mature stages, a high concentration of water particles and ice crystals stimulates precipitation. In contrast, the dissipating stage is marked by fewer mixed-phase and ice particles, reducing precipitation area and intensity. (2) Near-surface precipitation in all stages is predominantly liquid, with a bright band of around 5.5 km. At the same time, stratiform precipitation is dominant in each life stage. Stratiform precipitation remains dominant throughout the life stages of the SWV, with localized convective activity evident in the developing and mature stages. (3) Mature stage particles, characterized by a configuration of 1.0–1.2 mm Dm and 31–35 dBN_W (dBN_W = 10log₁₀N_W), contribute significantly to near-surface precipitation. The Cloud Top Height (CTH) serves as an indicator of convective intensity and assists in characterizing raindrop concentration. These findings considerably enhance routine observations, advance our understanding of SWV events, and propose a novel approach for conducting refined observational experiments. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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Review

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30 pages, 3076 KiB

Open AccessReview

Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives

by Peng Zhang, Xichuan Liu and Kang Pu

Remote Sens. 2023, 15(19), 4821; https://doi.org/10.3390/rs15194821 - 4 Oct 2023

Cited by 2 | Viewed by 1317

Abstract

As rainfall exhibits high spatiotemporal variability, accurate and real-time rainfall monitoring is vitally important in fields such as hydrometeorological research, agriculture and disaster prevention and control. Nevertheless, the current dedicated rain sensors cannot fulfill the requirement for comprehensive precipitation observation, owing to their respective limitations. Within the last two decades, the utilization of commercial microwave links (CMLs) for rainfall estimation, as an opportunistic sensing method, has generated considerable attention. Relying on CML networks deployed and maintained by mobile network operators can provide near-surface precipitation information over large areas at a low cost. Although scholars have developed several algorithms for obtaining rainfall estimates from CML data, the rainfall estimation technique based on CMLs remains challenging due to the complex effect in the microwave radiation transmission process. In this paper, we provide a comprehensive review of the technical principles, developments and workflows for this technology, alongside its application in environmental monitoring and hydrological modeling. Furthermore, this paper outlines the current challenges and future research directions, which will hopefully draw the attention of researchers and provide valuable guidance. Full article

(This article belongs to the Special Issue Severe Weather Observations and Meteorology Modeling Development Using Remote Sensing)

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