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Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring

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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: closed (31 October 2022) | Viewed by 17029

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Special Issue Editors

Dr. Marios Anagnostou

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Guest Editor

National Observatory of Athens, Institute of Enviromental Research and Sustainable Development, Lofos Kofou, 15236 Athens, Greece
Interests: X-band weather radar; dual-polarization; precipitation and microphysical estimation; precipitation retrieval; flash flood; nowcasting
Special Issues, Collections and Topics in MDPI journals

Dr. Petros Katsafados

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Guest Editor

Department of Geography, Harokopio University of Athens, 16122 Athens, Greece
Interests: atmospheric dynamics; air-sea interaction; data assimilation; nowcasting
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Dr. Yagmur Derin

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Guest Editor

Advanced Radar Research Center and School of Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, 73072, USA
Interests: satellites; weather radar; precipitation retrieval; validation

Dr. John Kalogiros

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Guest Editor

Institute of Environmental Research and Sustainable Development, National Observatory of Athens, I. Metaxa and V. Pavlou, P. Penteli, 15236 Athens, Greece
Interests: remote sensing; weather radar; precipitation; flood forecasting; atmospheric turbulence; air–sea interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern flood and flash flood warning systems and the efficient management of water resources call for improved quantitative measurements of precipitation at the temporal scale of minutes and the spatial scale of a few square kilometers.

The use of satellite remote sensing and ground-based weather radar to monitor precipitation at high spatial and temporal scales has generated significant interest and support within the hydrological and meteorological communities.

Over the past two decades, technological advances in satellite and ground-based precipitation products have been developed and used extensively for large-scale hydrological and precipitation studies. Ground-based remote-sensing observations are usually performed individually or by a network of weather radars, which provide high, real-time, spatiotemporal-resolution, precipitation observations. Although the accuracy of satellite and ground-based precipitation products has improved, there remain significant errors associated with the indirect measurement of precipitation.

Advances in modern atmospheric numerical weather prediction and hydrological forecasting models rely on coupling techniques that use Earth observation data acquired from remote sensing data. Despite these advances, the numerical models are associated with various errors related to the numerical methods, resolution, physical parameterizations, and input data. There is room to further increase predictability by improving data assimilation techniques, as well as employing higher quality resolution measurements. The two-way coupling of atmospheric with hydrological, ocean, wave, dust and fire models has the potential to help us reach this goal.

The aim of this Special Issue is to invite contributions from all areas of remote sensing (satellite and ground-based) and various scales of atmospheric dynamics. The focus of the issue is precipitation estimation, error characterization and validation of forecasting, data assimilation and nowcasting applied to precipitation (including extreme events), and flood modeling.

Dr. Marios Anagnostou
Prof. Petros Katsafados
Dr. Yagmur Derin
Dr. John Kalogiros
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

weather satellites
weather radar
atmospheric modeling
nowcasting
flood forecasting
precipitation retrieval
data assimilation
uncertainty reduction
validation

Published Papers (6 papers)

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Research

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17 pages, 14993 KiB

Open AccessArticle

Study on the Quantitative Precipitation Estimation of X-Band Dual-Polarization Phased Array Radar from Specific Differential Phase

by Guo Zhao, Hao Huang, Ye Yu, Kun Zhao, Zhengwei Yang, Gang Chen and Yu Zhang

Remote Sens. 2023, 15(2), 359; https://doi.org/10.3390/rs15020359 - 06 Jan 2023

Cited by 4 | Viewed by 1722

Abstract

In this study, the quantitative precipitation estimation (QPE) capability of three X-band dual-polarization phased array radars (PAR) in Guangzhou, South China, was demonstrated, with an S-band operational dual-polarization radar as the benchmark. Rainfall rate (R) estimators based on the specific differential phase (K_DP) for summer precipitation for both X-band and S-band radars were derived from the raindrop size distributions (DSDs) observed by a 2-dimensional video disdrometer (2DVD) in South China. Rainfall estimates from the radars were evaluated with gauge observations in three events, including pre-summer rainfall, typhoon precipitation, and local severe convective precipitation. Observational results showed that radar echoes from the X-band PARs suffered much more severely from attenuation than those from the S-band radar. Compared to S-band observations, the X-band echoes can disappear when the signal-to-noise ratio drops to a certain level due to severe attenuation, resulting in different estimated rainfall areas for X- and S-band radars. The attenuation corrected by K_DP had good consistency with S-band observations, but the accuracy of attenuation correction was affected by DSD uncertainty and may vary in different types of precipitation. The QPE results demonstrated that the R(K_DP) estimator produced better rainfall accumulations from the X-band PAR observations compared to the S-band observations. For both the X-band and S-band radars, the estimates of hourly accumulated rainfall became more accurate in heavier rainfall, due to the decreases of both the DSD uncertainty and the impact of measurement errors. In the heavy precipitation area, the estimation accuracy of the X-band radar was high, and the overestimation of the S-band radar was obvious. Through the analysis of the Z_H-Z_DR distribution in the three weather events, it was found that the X-band PAR with the capability of high spatiotemporal observations can capture minute-level changes in the microphysical characteristics, which help improve the estimation accuracy of ground rainfall. Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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19 pages, 6898 KiB

Open AccessArticle

Estimating Reservoir Storage Variations by Combining Sentinel-2 and 3 Measurements in the Yliki Reservoir, Greece

by Nikolaos Gourgouletis, Georgios Bariamis, Marios N. Anagnostou and Evangelos Baltas

Remote Sens. 2022, 14(8), 1860; https://doi.org/10.3390/rs14081860 - 12 Apr 2022

Cited by 8 | Viewed by 2701

Abstract

Inland water resources are facing increasing quantitative and qualitative pressures, deriving from anthropogenic causes and the ongoing climate change. The monitoring of reservoirs is essential for sustainable management and preparation against water scarcity and extreme events, such as droughts. This research, relying on the Sentinel-2 and 3 missions, attempts to demonstrate the efficiency of combining remotely sensed water level and water area estimations, in order to estimate the water storage variation of Yliki reservoir. The case study is conducted in one of the few sufficiently monitored reservoirs in Greece, enabling a direct comparison of the proposed methodology results with in situ observations. Moreover, this research work proposes a weekly time interval for pairing level and area estimations, instead of shorter time intervals. The results strongly demonstrate the efficiency of remote sensing in the production of empirical level–area–storage (L–A–S) curves. Correlation to in situ monitored storage- and satellite-derived water level, area stand for 98.81% and 99.27% respectively. Water storage variation is estimated and compared to the observed time series, resulting in an RMSE of 1.28% of the reservoir capacity and a correlation of 96.14%. The empirical L–S relationship underestimates storage, while the A–S relationship overestimates storage when compared to the existing L–A–S curve. Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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24 pages, 11794 KiB

Open AccessArticle

Assessment of a Gauge-Radar-Satellite Merged Hourly Precipitation Product for Accurately Monitoring the Characteristics of the Super-Strong Meiyu Precipitation over the Yangtze River Basin in 2020

by Zihao Pang, Chunxiang Shi, Junxia Gu, Yang Pan and Bin Xu

Remote Sens. 2021, 13(19), 3850; https://doi.org/10.3390/rs13193850 - 26 Sep 2021

Cited by 8 | Viewed by 2059

Abstract

The recently developed gauge-radar-satellite merged hourly precipitation dataset (CMPAS-NRT) offers broad applications in scientific research and operations, such as intelligent grid forecasting, meteorological disaster monitoring and warning, and numerical model testing and evaluation. In this paper, we take a super-long Meiyu precipitation process experienced in the Yangtze River basin in the summer of 2020 as the research object, and evaluate the monitoring capability of the CMPAS-NRT for the process from multiple perspectives, such as error indicators, precipitation characteristics, and daily variability in different rainfall areas, using dense surface rain-gauge observation data as a reference. The results show that the error indicators for CMPAS-NRT are in good agreement with the gauge observations. The CMPAS-NRT can accurately reflect the evolution of precipitation during the whole rainy season, and can accurately capture the spatial distribution of rainbands, but there is an underestimation of extreme precipitation. At the same time, the CMPAS-NRT product features the phenomenon of overestimation of precipitation at the level of light rain. In terms of daily variation of precipitation, the precipitation amount, frequency, and intensity are basically consistent with the observations, except that there is a lag in the peak frequency of precipitation, and the frequency of precipitation at night is less than observed, and the intensity of precipitation is higher than observed. Overall, the CMPAS-NRT product can successfully reflect the precipitation characteristics of this super-heavy Meiyu precipitation event, and has a high potential hydrological utilization value. However, further improvement of the precipitation algorithm is needed to solve the problems of overestimation of light rainfall and underestimation of extreme precipitation in order to provide more accurate hourly precipitation monitoring dataset. Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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17 pages, 6206 KiB

Open AccessArticle

Assessment of Near-Real-Time Satellite Precipitation Products from GSMaP in Monitoring Rainfall Variations over Taiwan

by Wan-Ru Huang, Pin-Yi Liu, Jie Hsu, Xiuzhen Li and Liping Deng

Remote Sens. 2021, 13(2), 202; https://doi.org/10.3390/rs13020202 - 08 Jan 2021

Cited by 10 | Viewed by 2624

Abstract

This study assessed four near-real-time satellite precipitation products (NRT SPPs) of Global Satellite Mapping of Precipitation (GSMaP)—NRT v6 (hereafter NRT6), NRT v7 (hereafter NRT7), Gauge-NRT v6 (hereafter GNRT6), and Gauge-NRT v7 (hereafter GNRT7)— in representing the daily and monthly rainfall variations over Taiwan, an island with complex terrain. The GNRT products are the gauge-adjusted version of NRT products. Evaluations for warm (May–October) and cold months (November–April) were conducted from May 2017 to April 2020. By using observations from more than 400 surface gauges in Taiwan as a reference, our evaluations showed that GNRT products had a greater error than NRT products in underestimating the monthly mean rainfall, especially during the warm months. Among SPPs, NRT7 performed best in quantitative monthly mean rainfall estimation; however, when examining the daily scale, GNRT6 and GNRT7 were superior, particularly for monitoring stronger (i.e., more intense) rainfall events during warm and cold months, respectively. Spatially, the major improvement from NRT6 to GNRT6 (from NRT7 to GNRT7) in monitoring stronger rainfall events over southwestern Taiwan was revealed during warm (cold) months. From NRT6 to NRT7, the improvement in daily rainfall estimation primarily occurred over southwestern and northwestern Taiwan during the warm and cold months, respectively. Possible explanations for the differences between the ability of SPPs are attributed to the algorithms used in SPPs. These findings highlight that different NRT SPPs of GSMaP should be used for studying or monitoring the rainfall variations over Taiwan for different purposes (e.g., warning of floods in different seasons, studying monthly or daily precipitation features in different seasons, etc.). Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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21 pages, 11789 KiB

Open AccessArticle

Implementation of a Nowcasting Hydrometeorological System for Studying Flash Flood Events: The Case of Mandra, Greece

by Christos Spyrou, George Varlas, Aikaterini Pappa, Angeliki Mentzafou, Petros Katsafados, Anastasios Papadopoulos, Marios N. Anagnostou and John Kalogiros

Remote Sens. 2020, 12(17), 2784; https://doi.org/10.3390/rs12172784 - 27 Aug 2020

Cited by 33 | Viewed by 4538

Abstract

Severe hydrometeorological hazards such as floods, droughts, and thunderstorms are expected to increase in the future due to climate change. Due to the significant impacts of these phenomena, it is essential to develop new and advanced early warning systems for advance preparation of the population and local authorities (civil protection, government agencies, etc.). Therefore, reliable forecasts of extreme events, with high spatial and temporal resolution and a very short time horizon are needed, due to the very fast development and localized nature of these events. In very short time-periods (up to 6 h), small-scale phenomena can be described accurately by adopting a “nowcasting” approach, providing reliable short-term forecasts and warnings. To this end, a novel nowcasting system was developed and presented in this study, combining a data assimilation system (LAPS), a large amount of observed data, including XPOL radar precipitation measurements, the Chemical Hydrological Atmospheric Ocean wave System (CHAOS), and the WRF-Hydro model. The system was evaluated on the catastrophic flash flood event that occurred in the sub-urban area of Mandra in Western Attica, Greece, on 15 November 2017. The event was one of the most catastrophic flash floods with human fatalities (24 people died) and extensive infrastructure damage. The update of the simulations with assimilated radar data improved the initial precipitation description and led to an improved simulation of the evolution of the phenomenon. Statistical evaluation and comparison with flood data from the FloodHub showed that the nowcasting system could have provided reliable early warning of the flood event 1, 2, and even to 3 h in advance, giving vital time to the local authorities to mobilize and even prevent fatalities and injuries to the local population. Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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Other

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13 pages, 4404 KiB

Open AccessTechnical Note

An Improved Assessment Method and Its Application to the Latest IMERG Rainfall Product in Mainland China

by Xinran Xia, Disong Fu, Ye Fei, Wei Shao and Xiangao Xia

Remote Sens. 2021, 13(24), 5107; https://doi.org/10.3390/rs13245107 - 16 Dec 2021

Cited by 2 | Viewed by 1851

Abstract

Quantification of uncertainties associated with satellite precipitation products is a prior requirement for their better applications in earth science studies. An improved scheme is developed in this study to decompose mean bias error (MBE) and mean square error (MSE) into three components, i.e., MBE and MSE associated hits, missed precipitation, and false alarms, respectively, which are weighted by their relative frequencies of occurrence (RFO). The trend of total MBE or MSE is then naturally decomposed into six components according to the chain rule for derivatives. Quantitative estimation of individual contributions to total MBE and MSE is finally derived. The method is applied to validation of Integrated MultisatellitE Retrievals for GPM (IMERG) in Mainland China. MBE associated with false alarms is an important driver for total MBE, while MSE associated with hits accounts for more than 85% of MSE, except in inland semi-arid area. The RFO of false alarms increases, whereas the RFO of missed precipitation decreases. Both factors lead in part to a growing trend for total MBE. Detection of precipitation should be improved in the IMERG algorithm. More specifically, the priority should be to reduce false alarms. Full article

(This article belongs to the Special Issue Evaluation of Remote Sensing and Radar Based Assimilation and Nowcasting for Precipitation and Flood Monitoring)

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