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

Deadline for manuscript submissions: 15 May 2025 | Viewed by 15431

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Special Issue Editors

Dr. Ehsan Sharifi

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Guest Editor

Helmholtz Centre for Environmental Research (UFZ), Leipzig, Germany
Interests: satellite-based earth observation; precipitation; groundwater; drought monitoring; data assimilation; downscaling
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Silas Michaelides

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Guest Editor

The Cyprus Institute, 20 Konstantinou Kavafi Street 2121, Aglantzia, Nicosia, Cyprus
Interests: meteorology; atmospheric remote sensing
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Prof. Dr. Vincenzo Levizzani

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Guest Editor

Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, via Gobetti 101, 40129 Bologna, Italy
Interests: clouds and precipitation structure; passive and active remote sensing of precipitation from satellite; climatology of precipitation
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Special Issue Information

Dear Colleagues,

Extreme rainfall and snowfall are key parameters for studying and monitoring hydro-meteorological events also from a climatological perspective. Extreme events are likely to increase in frequency and severity in the near future due to climate change. Therefore, extreme rainfall monitoring through remote- sensing sensors (satellite and radar) is of paramount importance. Although some countries have established dense rain gauge and/or operational radar networks, extensive areas remain ungauged. Therefore, there is a growing necessity to strengthen our ability to monitor precipitation intensity, duration, frequency, phase, etc. and analyze its spatial characteristics in a timely manner, and consequently to enhance our capacity to manage water resources. Recent developments in satellite-based precipitation products (i.e., high spatio-temporal resolution, quasi-global coverage, and free near-real-time data availability) open new doors for further development in water-related applications. The use of satellite-based precipitation data offers an efficient and effective tool to cope with some of the known challenges of in situ observations, particularly for monitoring extreme events.

The aim of this Special Issue is to present advances and new findings in satellite-based precipitation products for extreme rainfall monitoring and analysis. We solicit contributions focusing on various aspects, including, but not limited to:

Development of new observation strategies and algorithms for precipitation monitoring;
Characterization of extreme precipitation events;
Use of satellite-based precipitation estimates to predict floods and droughts;
Near-real-time and post-real-time rainfall monitoring;
Downscaling and bias correction of satellite-based precipitation products;
Assessment and analysis of extreme rainfall events at different time scales (e.g., sub-daily, daily, monthly) and spatial scales (local, regional and global);
Development and implementation of machine learning techniques for monitoring extreme precipitation events.

Dr. Ehsan Sharifi
Prof. Dr. Silas Michaelides
Prof. Dr. Vincenzo Levizzani
Guest Editors

Manuscript Submission Information

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Keywords

satellite
extreme precipitation
algorithms for precipitation monitoring
flood and drought prediction
flash floods
near-real-time precipitation monitoring
convective and orographic precipitation events
severe storms
downscaling
bias correction
precipitation intensity, amount, and duration

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Published Papers (8 papers)

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Research

19 pages, 12761 KiB

Open AccessArticle

Comparison of Different Quantitative Precipitation Estimation Methods Based on a Severe Rainfall Event in Tuscany, Italy, November 2023

by Alessio Biondi, Luca Facheris, Fabrizio Argenti and Fabrizio Cuccoli

Remote Sens. 2024, 16(21), 3985; https://doi.org/10.3390/rs16213985 - 26 Oct 2024

Viewed by 730

Abstract

Accurate quantitative precipitation estimation (QPE) is fundamental for a large number of hydrometeorological applications, especially when addressing extreme rainfall phenomena. This paper presents a comprehensive comparison of various rainfall estimation methods, specifically those relying on weather radar data, rain gauge data, and their fusion. The study evaluates the accuracy and reliability of each method in estimating rainfall for a severe event that occurred in Tuscany, Italy. The results obtained confirm that merging radar and rain gauge data outperforms both individual approaches by reducing errors and improving the overall reliability of precipitation estimates. This study highlights the importance of data fusion in enhancing the accuracy of QPE and also supports its application in operational contexts, providing further evidence for the greater reliability of merging methods. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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30 pages, 12891 KiB

Open AccessArticle

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

by Mohammad Sadegh Keikhosravi-Kiany and Robert C. Balling, Jr.

Remote Sens. 2024, 16(15), 2779; https://doi.org/10.3390/rs16152779 - 30 Jul 2024

Cited by 3 | Viewed by 812

Abstract

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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21 pages, 9541 KiB

Open AccessArticle

Rainfall Differences and Possible Causes of Similar-Track Tropical Cyclones Affected and Unaffected by Binary Tropical Cyclones (BTCs) in China

by Mingyang Wang, Fumin Ren, Guanghua Chen and Xiaohong Lin

Remote Sens. 2024, 16(10), 1692; https://doi.org/10.3390/rs16101692 - 9 May 2024

Viewed by 1308

Abstract

Binary tropical cyclones (BTCs) typically refer to the coexistence of two tropical cyclones (TCs) within a specific distance range, often resulting in disastrous rainstorms in coastal areas of China. However, the differences in rainfall and underlying causes between BTC-influenced typhoons and general typhoons remain unclear. In this article, the TC closer to the rainfall center in the BTC is referred to as the target typhoon (tTC), while the other is termed the accompanying typhoon (cmp_TC). This study compares and analyzes the rainfall differences and potential causes of tTCs and similar typhoons (sim_TC) with a comparable track but which are unaffected by BTCs from 1981 to 2020. The results show that: (1) On average, tTCs and cmp_TCs experience 18.79% heavier maximum daily rainfall compared to general TCs, with a significantly increased likelihood of rainfall ≥250 mm. (2) Given similar tracks, the average rainfall for tTCs (212.62 mm) is 30.2% heavier than that for sim_TCs (163.30 mm). (3) The analysis of potential impact factors on rainfall (translation speed, intensity, direction change) reveals that sim_TCs move at an average of 21.38 km/h, which is about 19.66% faster than the 17.87 km/h of tTCs, potentially accounting for the observed differences in rainfall. (4) Further investigation into the causes of west–east oriented BTC rainfall in the Northern Fujian (N_Fujian) region suggests that water vapor transport and slowing down of the translation speed are the possible mechanisms of BTC influence. Specifically, 80% of tTCs receive water vapor from the direction of their cmp_TC, and the steering flow for tTC is only 59.88% of that for sim_TC. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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22 pages, 3743 KiB

Open AccessArticle

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at the Daily and Sub-Daily Scales in the Western Mediterranean

by Eric Peinó, Joan Bech, Mireia Udina and Francesc Polls

Remote Sens. 2024, 16(3), 457; https://doi.org/10.3390/rs16030457 - 24 Jan 2024

Cited by 3 | Viewed by 2392

Abstract

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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25 pages, 12876 KiB

Open AccessArticle

Evaluation of Five Satellite-Based Precipitation Products for Extreme Rainfall Estimations over the Qinghai-Tibet Plateau

by Wenjuan Zhang, Zhenhua Di, Jianguo Liu, Shenglei Zhang, Zhenwei Liu, Xueyan Wang and Huiying Sun

Remote Sens. 2023, 15(22), 5379; https://doi.org/10.3390/rs15225379 - 16 Nov 2023

Cited by 7 | Viewed by 1711

Abstract

The potential of satellite precipitation products (SPPs) in monitoring and mitigating hydrometeorological disasters caused by extreme rainfall events has been extensively demonstrated. However, there is a lack of comprehensive assessment regarding the performance of SPPs over the Qinghai-Tibet Plateau (QTP). Therefore, this research aimed to evaluate the effectiveness of five SPPs, including CMORPH, IMERG-Final, PERSIANN-CDR, TRMM-3B42V7, and TRMM-3B42RT, in identifying variations in the occurrence and distribution of intense precipitation occurrences across the QTP during the period from 2001 to 2015. To evaluate the effectiveness of the SPPs, a reference dataset was generated by utilizing rainfall measurements collected from 104 rainfall stations distributed across the QTP. Ten standard extreme precipitation indices (SEPIs) were the main focus of the evaluation, which encompassed parameters such as precipitation duration, amount, frequency, and intensity. The findings revealed the following: (1) Geographically, the SPPs exhibited better retrieval capability in the eastern and southern areas over the QTP, while displaying lower detection accuracy in high-altitude and arid areas. Among the five SPPs, IMERG-Final outperformed the others, demonstrating the smallest inversion error and the highest correlation. (2) In terms of capturing annual and seasonal time series, IMERG-Final performs better than other products, followed by TRMM-3B42V7. All products performed better during summer and autumn compared to spring and winter. (3) The statistical analysis revealed that IMERG-Final demonstrates exceptional performance, especially concerning indices related to precipitation amount and precipitation intensity. Moreover, it demonstrates a slight advantage in detecting the daily rainfall occurrences and occurrences of intense precipitation. On the whole, IMERG-Final’s ability to accurately detect extreme precipitation events on annual, seasonal, and daily scales is superior to other products for the QTP. It was also noted that all products overestimate precipitation events to some extent, with TRMM-3B42RT being the most overestimated. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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25 pages, 9616 KiB

Open AccessArticle

A Comprehensive Assessment of Multiple High-Resolution Precipitation Grid Products for Monitoring Heavy Rainfall during the “7.20” Extreme Rainstorm Event in China

by Zihao Pang, Yu Zhang, Chunxiang Shi, Junxia Gu, Qingjun Yang, Yang Pan, Zheng Wang and Bin Xu

Remote Sens. 2023, 15(21), 5255; https://doi.org/10.3390/rs15215255 - 6 Nov 2023

Cited by 2 | Viewed by 1562

Abstract

Precipitation products play an important role in monitoring rainstorm processes. This study takes a rare historical event of extreme, heavy precipitation that occurred in Henan Province, China, in July 2021 as a research case. By analyzing the distribution of the spatial and temporal characteristics of precipitation errors, using a probability density function of the occurrence of precipitation and the daily variation pattern, we assess the capability of a radar precipitation estimation product (RADAR), satellite precipitation products (IMERG and GSMAP), a reanalysis product (ERA5) and a precipitation fusion product (the CMPAS) to monitor an extreme rainstorm in the Henan region. The CMPAS has the best fit with the gauge observations in terms of the precipitation area, precipitation maximum and the evolution of the whole process, with a low spatial variability of errors. However, the CMPAS slightly underestimated the precipitation extremum at the peak moment (06:00–08:00). The RADAR product was prone to a spurious overestimation of the originally small rainfall, especially during peak precipitation times, with deviations concentrated in the core precipitation area. The IMERG, GSMAP and ERA5 products have similar performances, all of which failed to effectively capture heavy precipitation in excess of 60 mm/h, with negative deviations in precipitation at mountainfront locations west of northern Henan Province. There is still a need for terrain-specific error revisions for areas with large topographic relief. By merging and processing precipitation data from multiple sources, the accuracy of the CMPAS is better than any single-source precipitation product. The CMPAS has the characteristic advantage of high spatial and temporal resolutions (0.01° × 0.01°/1 h), which play a positive role in precipitation dynamic monitoring, providing early warnings of heavy rainfall processes and hydrological application research. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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27 pages, 5984 KiB

Open AccessArticle

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

by Rahat Ullah, Jahangir Khan, Irfan Ullah, Faheem Khan and Youngmoon Lee

Remote Sens. 2023, 15(6), 1680; https://doi.org/10.3390/rs15061680 - 20 Mar 2023

Cited by 5 | Viewed by 2792

Abstract

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r= 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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22 pages, 5193 KiB

Open AccessArticle

Investigating the Influence of Water Vapor on Heavy Rainfall Events in the Southern Korean Peninsula

by Yoo-Jun Kim, Joon-Bum Jee and Byunghwan Lim

Remote Sens. 2023, 15(2), 340; https://doi.org/10.3390/rs15020340 - 6 Jan 2023

Cited by 6 | Viewed by 2293

Abstract

In this study, we examined the influence of water vapor on heavy rainfall events over the complex mountainous terrain of the southern Korean Peninsula using rawinsonde and global navigation satellite system (GNSS) datasets from a mobile observation vehicle (MOVE). Results demonstrated that the prevailing southeasterly winds enhanced precipitation on the leeward side of the mountainous region. The probability of severe rainfall increased in the highest precipitable water vapor (PWV) bin (>60 mm). A lead–lag analysis demonstrated that the atmosphere remained moist for 1 h before and after heavy rainfall. The temporal behavior of PWV retrieved from the MOVE-GNSS data demonstrated that during Changma (the summer monsoon) (Case 1), heavy rainfall events experience a steep decrease after a long increasing trend in PWV. However, the most intense rainfall events occurred after a rapid increase in PWV along with a strong southwesterly water vapor flow during convective instability (Case 2), and they had consistently higher moisture and greater instability than those in Case 1 over the entire period. The results of this study can provide some insights to improve the predictability of heavy rainfall in the southern Korean Peninsula. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation Extremes)

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