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Advancements in Passive/Active Remote Sensing of Clouds and Precipitation

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

Deadline for manuscript submissions: 18 June 2025 | Viewed by 3485

Special Issue Editors


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Guest Editor
Cooperative Institute for Satellite Earth System Studies (CISESS), Earth System Science and Interdisciplinary Center, University of Maryland, College Park, GA 20742, USA
Interests: remote sensing; data assimilation

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Guest Editor
Cooperative Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison, Madison, WI, USA
Interests: microwave remote sensing; clouds

Special Issue Information

Dear Colleagues,

The global water cycle is crucial to life on Earth and is a complex system that contains many different processes affecting weather and climate. This cycle consists of evaporation, condensation, precipitation, and the terrestrial ecosystem. Since clouds and precipitation are interconnected and fundamental components of the global water cycle, measurements of these components are necessary to understand the complete cycle. Moreover, clouds are radiatively important in the atmosphere while precipitation plays an important role in energy transfer in the atmospheric circulation. However, more accurate and longer-term observations of clouds and precipitation are needed to improve weather forecasts and climate projections. There is currently a wide range of remote sensing methods for clouds and precipitation, including ground-based, airborne, and satellite measurements, active and passive measurements, and observed wavelengths from the visible and infrared to the microwave. However, satellite remote sensing is the only practical means of acquiring long-term widespread observations of clouds and precipitation. With the advancement of remote sensing methods, especially those that combine passive and active sensors (on the same platforms or on different platforms such as satellites, airborne, and ground based), there will be a greater opportunity to provide more accurate and additional cloud/precipitation properties that will benefit weather and climate studies. Inter-comparison between different measurements (on different platforms or between different instruments) would be a crucial part of the synergistic approach.

This Special Issue is focused on recent developments in the remote sensing of clouds and precipitation, in particular those methods that combine passive and active sensors.

We invite papers that cover, but are not limited to, the following areas:

Remote sensing of clouds:

  • Cloud amounts and phase;
  • Vertical profiles of clouds;
  • Microphysical properties such as particle number concentration and size.

Remote sensing of precipitation:

  • Liquid and solid precipitation;
  • Precipitation rate and other properties like polarization.

Dr. Yong-Keun Lee
Dr. Thomas Greenwald
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

  • clouds
  • precipitation
  • active remote sensing
  • ground-based remote sensing
  • satellite remote sensing
  • airborne remote sensing

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Published Papers (3 papers)

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25 pages, 10198 KiB  
Article
Estimating Rainfall Anomalies with IMERG Satellite Data: Access via the IPE Web Application
by Kenneth Okechukwu Ekpetere, Amita V. Mehta, James Matthew Coll, Chen Liang, Sandra Ogugua Onochie and Michael Chinedu Ekpetere
Remote Sens. 2024, 16(22), 4137; https://doi.org/10.3390/rs16224137 - 6 Nov 2024
Cited by 2 | Viewed by 1529
Abstract
This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data [...] Read more.
This study assesses the possibilities of the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-GPM) to estimate extreme rainfall anomalies. A web application, the IMERG Precipitation Extractor (IPE), was developed which allows for the querying, visualization, and downloading of time-series satellite precipitation data for points, watersheds, country extents, and digitized areas. The tool supports different temporal resolutions ranging from 30 min to 1 week and facilitates advanced analyses such as anomaly detection and storm tracking, an important component for climate change study. To validate the IMERG precipitation data for anomaly estimation over a 22-year period (2001 to 2022), the Rainfall Anomaly Index (RAI) was calculated and compared with RAI data from 2360 NOAA stations across the conterminous United States (CONUS), considering both dry and wet climate regions. In the dry region, the results showed an average correlation coefficient (CC) of 0.94, a percentage relative bias (PRB) of −22.32%, a root mean square error (RMSE) of 0.96, a mean bias ratio (MBR) of 0.74, a Nash–Sutcliffe Efficiency (NSE) of 0.80, and a Kling–Gupta Efficiency (KGE) of 0.52. In the wet region, the average CC of 0.93, PRB of 24.82%, RMSE of 0.96, MBR of 0.79, NSE of 0.80, and KGE of 0.18 were computed. Median RAI indices from both the IMERG and NOAA indicated an increase in rainfall intensity and frequency since 2010, highlighting growing concerns about climate change. The study suggests that IMERG data can serve as a valuable alternative for modeling extreme rainfall anomalies in data-scarce areas, noting its possibilities, limitations, and uncertainties. The IPE web application also offers a platform for extending research beyond CONUS and advocating for further global climate change studies. Full article
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19 pages, 3354 KiB  
Article
The Characteristics of Precipitation with and without Bright Band in Summer Tibetan Plateau and Central-Eastern China
by Liu Yang, Nan Sun, Ming Ma, Chunguang Cui, Bin Wang, Xiaofang Wang and Yunfei Fu
Remote Sens. 2024, 16(19), 3703; https://doi.org/10.3390/rs16193703 - 5 Oct 2024
Viewed by 1144
Abstract
The bright band (BB) is an important symbol of the ice–water transition zone in stratiform precipitation, and the presence or absence of BB will lead to different microphysical processes. In this paper, the characteristics of BB and precipitation characteristics with and without BB [...] Read more.
The bright band (BB) is an important symbol of the ice–water transition zone in stratiform precipitation, and the presence or absence of BB will lead to different microphysical processes. In this paper, the characteristics of BB and precipitation characteristics with and without BB in summer at Tibetan Plateau (TP) as well as Central-eastern China (CEC) are analyzed by using Global Precipitation Measurement (GPM) and the fifth generation ECMWF atmospheric reanalysis of the global climates (ERA5) datasets. The results show the freezing level height and BB height in TP are 0.5 km higher than those in CEC. With the increase in rain rate, the BB height decreases in TP but increases in CEC. The BB width becomes wider with the increase in maximum radar reflectivity. Secondly, the maximum reflectivity factor and particle diameter of stratiform precipitation with BB appear at 5 km, while the maximum reflectivity factor of stratiform precipitation without BB and convective precipitation appear near the ground. The particle diameter first decreases and then increases from the cloud top to the ground. Thirdly, the land surface temperature of convective precipitation is about 2.5 °C higher than stratiform precipitation with BB, indicating higher land surface temperatures are more likely to trigger convection. Lastly, BB can lead to a decrease in brightness temperature and an increase in polarized difference at 89 GHZ and 166 GHZ in CEC, likely due to the increasing ice particles in stratiform precipitation with BB. Full article
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17 pages, 6987 KiB  
Technical Note
Comparison of the Reflectivities from Precipitation Measurement Radar Onboard the FY-3G Satellite and Ground-Based S-Band Dual-Polarization Radars
by Rui He, Hong Li, Jingyao Luo, Hao Huang and Yijie Zhu
Remote Sens. 2025, 17(7), 1117; https://doi.org/10.3390/rs17071117 - 21 Mar 2025
Viewed by 329
Abstract
Fengyun-3G (FY-3G), successfully launched on 16 April 2023, is China’s first and the third in the world satellite dedicated to precipitation measurement. In this study, the reflectivity factors of the FY-3G satellite Precipitation Measurement Radar (PMR) are analyzed and compared with ground-based S-band [...] Read more.
Fengyun-3G (FY-3G), successfully launched on 16 April 2023, is China’s first and the third in the world satellite dedicated to precipitation measurement. In this study, the reflectivity factors of the FY-3G satellite Precipitation Measurement Radar (PMR) are analyzed and compared with ground-based S-band dual-polarized radar (GR) data for typical precipitation events in parts of southern China during April–August 2024. By performing preprocessing and spatiotemporal matching, 169,657 matched pairs of FY-3G PMR and GR datasets are obtained, from which the agreement of reflectivity between FY-3G PMR and GR and the sensitivities to different precipitation types and phase states are evaluated. The results show that the reflectivity factors of FY-3G PMR and GR have a strong positive correlation, with an overall correlation coefficient of 0.82, especially in the stratiform precipitation. In addition, FY-3G PMR agrees with GR well in moderate precipitation, but systematically underestimates reflectivity in heavy rain rates and overestimates in light rain rates. Furthermore, FY-3G PMR has high accuracy in detecting liquid precipitation below the bright band, although with some underestimation of reflectivity for ice-phase precipitation above the bright band. Nevertheless, FY-3G PMR still provides valuable information on ice-phase precipitation. Overall, PMR has great potential for application in the monitoring of stratiform and liquid precipitation, but more complete processing is needed when applying PMR observations to heavy precipitation and complex meteorological conditions. Full article
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