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Remote Sensing for High Impact Weather and Extremes

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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: 25 September 2024 | Viewed by 3778

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Prof. Dr. Zhaoxia Pu

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Guest Editor

Department of Atmospheric Sciences, College of Science, University of Utah, Salt Lake City, UT, USA
Interests: high-impact weather and extremes; numerical weather prediction; satellite and radar data assimilation; land-atmosphere interaction and coupled land-atmosphere data assimilation; hurricanes and tropical convection; big data, artificial intelligence, and machine learning

Special Issue Information

Dear Colleagues,

Extreme weather in the form of hurricanes, tornadoes, floods, droughts, heatwaves, and heavy precipitation can result in the destruction of infrastructure, disruption of essential services, and economic losses. Understanding these events and effectively monitoring them are crucial for disaster preparedness, early warning systems, and the development of strategies to mitigate their impacts. Remote sensing plays a vital role in the study of this high-impact weather. It involves the use of satellites, airborne platforms, and ground-based instruments to collect data about the Earth’s atmosphere and surface. Remote sensing data provide valuable information on atmospheric conditions, precipitation patterns, cloud dynamics, and other parameters relevant to high-impact weather phenomena.

To this end, this Special Issue invites submissions of original research, reviews, methodology papers, and case studies that demonstrate the application of remote sensing methods to monitor and predict high-impact weather. The scope of this Special Issue includes, but is not limited to, the following topics:

Advanced remote sensing techniques for detecting and tracking severe weather events, such as tropical cyclones, thunderstorms, and severe precipitation;
Integration of multi-source remote sensing data for improving weather forecasting and warning systems;
Assimilation of remote sensing data for improved numerical prediction of extreme weather;
Use of remote sensing for assessing the impacts of extreme weather on the environment and society;
Development of new models and algorithms for processing large-scale, high-resolution remote sensing data relevant to severe weather;
Evaluation of remote sensing data quality and uncertainty in impact weather studies.

Prof. Dr. Zhaoxia Pu
Guest Editor

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

remote sensing
high impact weather
extreme events
satellite
weather forecasting
natural hazards

Published Papers (6 papers)

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Research

19 pages, 13588 KiB

Open AccessArticle

Advancement of Sea Surface Convective Wind Gust Observation by Different Satellite Sensors and Assessment with In Situ Measurements

by Tran Vu La and Christophe Messager

Remote Sens. 2024, 16(8), 1400; https://doi.org/10.3390/rs16081400 - 16 Apr 2024

Viewed by 324

Abstract

This paper shows the observation and estimation of convective wind gusts by different satellite sensors at the C-band (Sentinel-1 SAR) and L-band (ALOS-1 SAR and SMAP radiometer) over Lake Victoria, the Gulf of Guinea, and the Gulf of Mexico. These areas are significantly impacted by deep convection associated with strong surface winds and heavy rainfall. In particular, the collocation of Sentinel-1 and SMAP images enables the observation of the movement of surface wind gusts corresponding to that of deep convective clouds. The convective wind intensity estimated from Sentinel-1 and SMAP data varies from 10 to 25 m/s. Additionally, we present an agreement in the observation of deep convective clouds, dynamics, and strong surface winds by different satellite sensors, including Meteosat geostationary (GEO), Aeolus Lidar, and Sentinel-1 SAR, respectively. We also evaluate the estimated convective wind gusts by comparison with the in situ measurements of the weather stations installed in the Gulf of Mexico, where deep convection occurs regularly. The result shows an agreement between the two wind sources estimated and measured. Likewise, the peaks of the measured wind gusts correspond to the occurrence of deep convective clouds observed by the GOES-16 GEO satellite. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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19 pages, 6453 KiB

Open AccessArticle

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

by Junkai Liu, Zhaoxia Pu, Wen-Chau Lee and Zhiqiu Gao

Remote Sens. 2024, 16(8), 1351; https://doi.org/10.3390/rs16081351 - 11 Apr 2024

Viewed by 372

Abstract

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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16 pages, 6111 KiB

Open AccessArticle

The Respective Effects of Vapor Pressure Deficit and Soil Moisture on Ecosystem Productivity in Southwest China

by Xupeng Sun, Yao Xiao, Jinghan Wang, Miaohang Zhou, Zengjing Song, Mingguo Ma and Xujun Han

Remote Sens. 2024, 16(8), 1316; https://doi.org/10.3390/rs16081316 - 09 Apr 2024

Viewed by 440

Abstract

This study aims to examine the individual and combined effects of soil moisture (SM) and vapor pressure deficit (VPD) on ecosystem productivity in Southwest China. Utilizing the community land model (CLM) to simulate the regional soil moisture and vapor pressure deficit, we analyzed their impacts on ecosystem productivity through a data binning approach and employed sun-induced chlorophyll fluorescence yield (SIFyield) as a productivity indicator. Our findings highlight a significant coupling effect between SM and VPD, which diminishes with finer temporal data resolution. The data binning analysis indicates that VPD has a predominant influence on SIFyield across 70% of the study area, whereas SM is more influential in the remaining 30%. Notably, the correlation between SIFyield and SM, modulated by VPD, is stronger in forest and shrubland ecosystems, whereas in grasslands, the influence pattern is reversed, with VPD having a more significant impact. The study concludes that in Southwest China, ecosystem productivity is more significantly affected by VPD than by SM. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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15 pages, 11672 KiB

Open AccessCommunication

Precipitation and Soil Moisture Variation over the Tibetan Plateau to the Anomaly of Indian Summer Monsoon from 1979 to 2019

by Tianyu Liu, Jinghua Chen, Yuanjie Zhang and Zhiqiu Gao

Remote Sens. 2024, 16(6), 1014; https://doi.org/10.3390/rs16061014 - 13 Mar 2024

Viewed by 634

Abstract

The Indian Summer Monsoon (ISM) can profoundly influence the summer precipitation patterns of the Tibetan Plateau (TP) and indirectly affect the TP’s soil humidity. This study investigates the responses of TP’s precipitation and soil moisture to the ISM in the monsoon season (June to September, JJAS) from 1979 to 2019. Precipitation in the TP and the ISM intensity generally exhibit a positive correlation in the west and a negative correlation in the east. The response of TP soil moisture to the ISM generally aligns with precipitation patterns, albeit with noted inconsistencies in certain TP regions. A region exhibiting these inconsistencies (30°–32°N, 80°–90°E) is selected as the study area, hereafter referred to as IRR. In periods of strong ISM, precipitation in IRR increases, yet soil moisture decreases. Conversely, in years with a weak ISM, the pattern is reversed. During strong ISM years, the rainfall increase in IRR is modest, and the soil remains drier compared to other TP regions. Under the combined effects of a marginal increase in precipitation and relatively rapid evaporation, soil moisture in the IRR decreased during years of strong ISM. During weak ISM years, the surface temperature in the IRR is higher compared to strong ISM years, potentially accelerating the melting of surface permafrost and snow in this region. Additionally, glacier meltwater, resulting from warmer temperatures in the northwest edge of the TP, may also result in the humidification of the soil in the IRR. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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20 pages, 7429 KiB

Open AccessArticle

Application of WRF-LES on the Simulation of Seasonal Characteristics of Atmospheric Boundary Layer Structure in Taklamakan Desert

by Xiaoyi Xu, Xin Li, Yuanjie Zhang, Zhiqiu Gao and Jingxi Sun

Remote Sens. 2024, 16(3), 558; https://doi.org/10.3390/rs16030558 - 31 Jan 2024

Viewed by 622

Abstract

The lack of observational data in Taklamakan Desert makes it very difficult to study its unique boundary layer structure. As a common means of supplementing observational data, the mesoscale boundary layer parameterization scheme in the numerical model method is difficult to capture small-scale turbulent processes, which may lead to large deviations in simulation. In order to obtain more accurate simulation data of desert atmospheric boundary layer, nested LES into WRF (WRF-LES) was configured to simulate the seasonal variations in Taklamakan Desert. By comparing LES with the conventional boundary layer parameterization scheme, the error characteristics between the two schemes are analyzed. The results show that LES exhibits superior performance in solving key atmospheric features such as small-scale processes and low-level jet streams. The simulation results in winter and summer have great uncertainty due to the boundary condition errors, respectively. LES also shows the maximum and minimum optimization degree in summer and winter, respectively, while the simulation results in spring and autumn are relatively stable. In the analysis of turbulence parameters, there are clear seasonal differences in turbulence characteristics, and the intensity of turbulence in summer is significantly higher than that in other seasons. When turbulent activity is strong, the difference in potential temperature and horizontal wind speed simulated between the two schemes is closely related to intense turbulent kinetic energy in LES. More accurate turbulence reproduced in LES leads to the better potential temperature and horizontal wind speed simulations in summer. In addition, large-scale cloud systems can lead to considerable simulation bias. Neither scheme can accurately simulate the cloud emergence process, and large differences between the two schemes occur at this point. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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34 pages, 48390 KiB

Open AccessArticle

Assessing CYGNSS Satellite Soil Moisture Data for Drought Monitoring with Multiple Datasets and Indicators

by Zhaolu Hou and Zhaoxia Pu

Remote Sens. 2024, 16(1), 116; https://doi.org/10.3390/rs16010116 - 27 Dec 2023

Viewed by 849

Abstract

Drought monitoring is crucial for various sectors, and soil moisture data play a pivotal role, especially in agricultural contexts. This study focuses on the recent CYGNSS Level 3 soil moisture data derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS), notable for its wide coverage and rapid revisit times, yet underexplored in drought research. Spanning from 1 January 2018 to 31 December 2022, this research analyzed daily CYGNSS soil moisture data, comparing them with the ERA5, SMAP, and GLDAS-NOAH datasets. It was found that the average and standard deviation (std) of CYGNSS soil moisture exhibited spatial patterns largely similar to other datasets, although some regions showed discrepancies (std differences reached up to 0.05 in some regions). The correlation coefficients and RMSE values between CYGNSS and other datasets depended on climate and land cover types. Four drought indicators from different soil moisture datasets were compared with the improved monthly Standardized Precipitation Evapotranspiration Index (SPEI). The drought indicators based on CYGNSS data demonstrate the capacity to describe drought extent and intensity. The correlation coefficients between certain drought indicators obtained from CYGNSS and SPEI reached 0.27 for drought percentage and 0.16 for drought intensity. Further investigations with selected extreme drought cases revealed that the indicator from CYGNSS data is relatively weak, influenced by the selected regions, times, and drought indicators. The results of this study provide insights into the potential application of CYGNSS soil moisture data in drought monitoring, offering a foundation for future research and practical implementation with current and future improved products. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes)

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