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Remote Sensing for Precipitation Retrievals

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 July 2022) | Viewed by 32160

Special Issue Editors

Prof. Dr. Kenji Nakamura

E-Mail Website
Guest Editor
Department of International Environmental Economics, Faculty of Economics, Dokkyo University, Soka-shi, Saitama 340-0042, Japan
Interests: satellite remote sensing; precipitation; radar
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Atsushi Hamada

E-Mail Website
Guest Editor
Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, Japan
Interests: precipitation system climatology; tropical meteorology
Special Issues, Collections and Topics in MDPI journals
Dr. Mikiko Fujita

E-Mail Website
Guest Editor
Research Institute for Global Change, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Kanagawa 236-0001, Japan
Interests: precipitation system; water vapor climatology; GPS/GNSS meteorology
Special Issues, Collections and Topics in MDPI journals
Dr. Jungho Kim

E-Mail Website
Guest Editor
Hydrometeorology Modeling and Applications (HMA) Team, Physical Sciences Laboratory (PSL), National Onceanic and Atmospheric Administration (NOAA), Boulder, CO 80521, USA
Interests: remote sensing in hydrology; physical sciences and modeling in hydrology; weather radar hydrology; data sciences in hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent extremes in precipitation are thought to be due to changes in the global environment. Many disastrous events occur due to extreme precipitation. Monitoring and prediction are essential for water disaster prevention. Precipitation data are also essential for studies of current climate change. While many countries have established excellent rain gauge or operational radar networks, many ungauged regions still exist. For the local precipitation observations, many techniques for precipitation retrievals using rain gauge or radar networks have been proposed and applied for obtaining precise rain rate or snow rate. The techniques include not only rain rate or snow rate estimates but also temporal and spatial interpolations. Covering large areas including oceans and satellite observations is essential. The satellite observations use remote sensing techniques. Here, retrieval techniques take important roles.

This Special Issue aims to provide novel techniques of precipitation retrievals and new findings so as to contribute to advancement of precipitation observation techniques. This Special Issue accepts papers related to studies on precipitation retrieval using observations by space-borne or ground-based sensors. This Special Issue also accepts papers on algorithm developments as well as observational studies, data analyses, and numerical simulations aiming to improve precipitation retrievals.

You may choose our Joint Special Issue in Geomatics.

Prof. Dr. Kenji Nakamura
Prof. Dr. Atsushi Hamada
Dr. Mikiko Fujita
Dr. Jungho Kim
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

  • precipitation
  • radar
  • rain gauge
  • microwave radiometer
  • mesoscale model

Published Papers (15 papers)

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18 pages, 5265 KiB  
Article
Diurnal Variations in Different Precipitation Duration Events over the Yangtze River Delta Urban Agglomeration
by Rui Yao, Shuliang Zhang, Peng Sun, Yaojin Bian, Qiqi Yang, Zongkui Guan and Yaru Zhang
Remote Sens. 2022, 14(20), 5244; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14205244 - 20 Oct 2022
Cited by 2 | Viewed by 1447
Abstract
Studying the characteristics of precipitation diurnal variation is beneficial for understanding precipitation formation and underlying mechanisms. In this study, using hourly rain gauge data from 108 stations in the Yangtze River Delta Urban Agglomeration (YRDUA) from 1980–2021, the diurnal variations of the precipitation [...] Read more.
Studying the characteristics of precipitation diurnal variation is beneficial for understanding precipitation formation and underlying mechanisms. In this study, using hourly rain gauge data from 108 stations in the Yangtze River Delta Urban Agglomeration (YRDUA) from 1980–2021, the diurnal variations of the precipitation amount (PA), precipitation frequency (PF), precipitation duration (PD), and precipitation intensity (PI) were analyzed. The effects of elevation, distance of the station from the east coastline, and urbanization on the characteristics of different precipitation duration events were determined. The results indicated that (1) the spatial distributions of PA, PD, and PF were similar in short-duration (SD), long-duration (LD), and ultra-long-duration (ULD), with high values in the south and low values in the north. Most of PA, PD, and PF showed an increasing trend after breakpoint in LD and ULD, but precipitation characteristics in SD showed a decreasing trend before and after breakpoint; (2) the diurnal cycles of PA presented two comparable peaks in the late afternoon and early morning, which occurred SD and ULD precipitation events, respectively. A single peak in the late afternoon (15:00 local solar time [LST]) occurred during the diurnal cycle of PI. The start and peak times occurred mainly in the afternoon for SD and LD. In contrast, the peak time of ULD mainly occurred in the early morning, accounting for 63% of the stations. The start and peak times of LD and ULD occurred in the early morning mainly along the Yangtze River; (3) from the plains to the mountains, the diurnal peaks of PA and PI had gradual variations from noon to afternoon. In addition, dominant diurnal peak values of PA and PI, which are affected by the distance from the east coast, were observed in the early morning in ULD. The effect of urbanization on the difference between urban and rural areas changed from negative to positive after 2000. In addition, urbanization had a significant impact on SD. After 2000, the increase of PA in urban areas was mainly due to the obvious increase of PD and PF in SD, while the increasing trend of LD and ULD in urban areas was smaller than that in rural areas. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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15 pages, 2308 KiB  
Article
Statistical Characteristics of Warm Season Raindrop Size Distribution in the Beibu Gulf, South China
by Xiaoyu Li, Sheng Chen, Zhi Li, Chaoying Huang and Junjun Hu
Remote Sens. 2022, 14(19), 4752; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194752 - 23 Sep 2022
Viewed by 1548
Abstract
Raindrop size distribution (DSD) can be used to improve the accuracy of radar quantitative precipitation estimation (QPE) and further understand the microphysical process of precipitation; however, its spatio-temporal characteristics vary with different climates, rain types, and geographical locations. Due to the lack of [...] Read more.
Raindrop size distribution (DSD) can be used to improve the accuracy of radar quantitative precipitation estimation (QPE) and further understand the microphysical process of precipitation; however, its spatio-temporal characteristics vary with different climates, rain types, and geographical locations. Due to the lack of observations, the DSD characteristics in the Beibu Gulf, especially at the rainfall center of Guangxi in South China, is poorly understood. In this paper, these regional DSD characteristics were analyzed during the warm season with an upgraded version of the OTT Particle Size Velocity (Parsivel) (OTT2) disdrometer. The DSD datasets from June to October 2020 and March to May 2021 were grouped into convective and stratiform precipitation by rain rate (R). The rainfall parameters were calculated from DSDs to further understand the rain characteristics. The results showed that: (1) the regional DSDs feature the lowest concentration of largest-sized drops when compared with the statistical results for other areas such as Zhuhai in South China, Nanjing in East China, Hubei province in Central China and Beijing in North China; (2) the raindrop spectra have an excellent fit with the three-parameter gamma distribution, particularly in regard to the medium-size raindrops; (3) the μ–Λ relation is closer to the coastal regions than the inland area of South China; (4) the localized Z−R relations differ greatly for convective rainfall (Z = 202.542 R1.553) and stratiform rainfall (Z = 328.793 R1.363). This study is the first study on DSDs in the Beibu Gulf region. The above findings will provide a better understanding of the microphysical nature of surface precipitation for different rain types along the Beibu Gulf in southern China, which may improve precipitation retrievals from remote sensing observations. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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22 pages, 7497 KiB  
Article
Comparison of Satellite Precipitation Products: IMERG and GSMaP with Rain Gauge Observations in Northern China
by Huiqin Zhu, Sheng Chen, Zhi Li, Liang Gao and Xiaoyu Li
Remote Sens. 2022, 14(19), 4748; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194748 - 22 Sep 2022
Cited by 6 | Viewed by 1856
Abstract
Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a [...] Read more.
Extreme precipitation events have increasingly happened at global and regional scales as the global climate has changed in recent decades. Accurate quantitative precipitation estimation (QPE) plays an important role in the warning of extreme precipitation events. With hourly rain gauge observations as a reference, this study compares the performance of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) quantitative precipitation estimation (QPE) products over Northern China in 2021. The Probability of Detection (POD), Relative Bias (RB), Root-Mean-Squared Error (RMSE), and Fractional Standard Error (FSE) are among the assessment metrics, as are the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI). We examined the spatial distribution of cumulative precipitation and the temporal distribution of hourly average precipitation for three severe precipitation occurrences using these assessment metrics. The IMERG products capture strong precipitation centers that are compatible with the gauge observations, especially in extreme precipitation events in areas with relatively flat terrain and low-altitude (≤1000 m). Both IMERG (National Aeronautics and Space Administration, NASA) and GSMaP (Japan Aerospace Exploration Agency, JAXA) satellite-based QPE products have precipitation peaks in advance (2–4 h) and generally underestimate (overestimate) precipitation when the actual precipitation is heavy (light). The satellite-based QPE products generally overestimate the heavy rainfall caused by non-typhoons and underestimate the heavy rainfall caused by typhoons. The GSMaP products may have the capacity to detect short-term rainstorm events. The accuracy of satellite-based QPE products may be influenced by precipitation intensity, sensors, terrain, and other variables. Therefore, in accordance with our recommendations, more ground rainfall stations should be used to collect actual precipitation data in regions with high levels of spatial heterogeneity and complex topography. The data programmers should strengthen the weights computation retrieval technique and fully utilize infrared (IR)-based data. Furthermore, this study is expected to give helpful feedback to the algorithm developers of IMERG and GSMaP products, as well as those researchers into the use of IMERG and GSMaP satellite-based QPE products in applications. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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20 pages, 4114 KiB  
Article
Evaluation of Three Gridded Precipitation Products in Characterizing Extreme Precipitation over the Hengduan Mountains Region in China
by Wenchang Dong, Genxu Wang, Li Guo, Juying Sun and Xiangyang Sun
Remote Sens. 2022, 14(17), 4408; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174408 - 05 Sep 2022
Cited by 4 | Viewed by 1558
Abstract
Extreme precipitation events can lead to severe mountain hazards, and they have therefore received widespread attention. The study of extreme precipitation can be hindered by the insufficient number and uneven distribution of rain gauge stations, especially in mountainous areas with complex terrain. In [...] Read more.
Extreme precipitation events can lead to severe mountain hazards, and they have therefore received widespread attention. The study of extreme precipitation can be hindered by the insufficient number and uneven distribution of rain gauge stations, especially in mountainous areas with complex terrain. In this study, the daily precipitation data of three gridded precipitation products (Integrated Multi-satellite Retrievals for GPM, IMERG; Multi-Source Weighted-Ensemble Precipitation, MSWEP; and Tropical Rainfall Measuring Mission, TRMM) were compared with rain gauge observations at 62 ground stations from 2001 to 2016 over the Hengduan Mountain region in China. Deviations between the gridded and ground precipitation datasets were compared using four daily heavy rainfall sequences. Various extreme precipitation indices were used to evaluate the performance of selected precipitation products. The results show that IMERG and TRMM are better than MSWEP in characterizing extreme precipitation. The accuracy of these three products in detecting heavy precipitation varied with altitude gradient. All products provided more accurate estimates of heavy precipitation in higher-altitude areas than in lower-altitude areas. Notably, they are more applicable for heavy precipitation detection in subalpine or alpine regions, and there are still uncertainties in capturing the accurate characterization of extreme precipitation at low (<1000 m) altitudes in the Hengduan Mountain region. These precipitation products should be used with caution in future applications when analyzing extreme precipitation at low elevations. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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20 pages, 7383 KiB  
Article
Hail Climatology in the Mediterranean Basin Using the GPM Constellation (1999–2021)
by Sante Laviola, Giulio Monte, Elsa Cattani and Vincenzo Levizzani
Remote Sens. 2022, 14(17), 4320; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174320 - 01 Sep 2022
Cited by 5 | Viewed by 3862
Abstract
The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored [...] Read more.
The impacts of hailstorms on human beings and structures and the associated high economic costs have raised significant interest in studying storm mechanisms and climatology, thus producing a substantial amount of literature in the field. To contribute to this effort, we have explored the hail frequency in the Mediterranean basin during the last two decades (1999–2021) on the basis of hail occurrences derived from the observations of the microwave radiometers on board satellites of the Global Precipitation Measurement Constellation (GPM-C) from 2014 (date of GPM Core Observatory launch) onwards and merging multiple other satellite platforms prior to 2014. According to the MWCC-H method, two hail event categories (hail and super hail) are identified, and their spatiotemporal distributions are evaluated to identify the hail development areas in the Mediterranean and the corresponding monthly climatology of hail occurrences. Our results show that the northern sectors of the domain (France, Alpine Region, Po Valley, and Central-Eastern Europe) tend to be hit by hailstorms from June to August, while the central sectors (from Spain to Turkey) are more affected as autumn approaches. The trend analysis shows that the mean number of hail events over the entire domain tends to substantially increase, showing a higher increment during 2010–2021 than during 1999–2010. This behavior was particularly enhanced over Southern Italy and the Balkans. Our findings point to the existence of “sub-hotspots”, i.e., Mediterranean regions most susceptible to hail events and thus possibly more vulnerable to climate change effects. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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18 pages, 13127 KiB  
Article
Generation of Combined Daily Satellite-Based Precipitation Products over Bolivia
by Oliver Saavedra and Jhonatan Ureña
Remote Sens. 2022, 14(17), 4195; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174195 - 26 Aug 2022
Cited by 1 | Viewed by 1676
Abstract
This study proposes using Satellite-Based Precipitation (SBP) products and local rain gauge data to generate information on the daily precipitation product over Bolivia. The selected SBP products used were the Global Satellite Mapping of Precipitation Gauge, v6 (GSMaP_Gauge v6) and the Climate Hazards [...] Read more.
This study proposes using Satellite-Based Precipitation (SBP) products and local rain gauge data to generate information on the daily precipitation product over Bolivia. The selected SBP products used were the Global Satellite Mapping of Precipitation Gauge, v6 (GSMaP_Gauge v6) and the Climate Hazards Group Infrared Precipitations with Stations (CHIRPS). The Gridded Meteorological Ensemble Tool (GMET) is a generated precipitation product that was used as a control for the newly generated products. The correlation coefficients for raw data from SBP products were found to be between 0.58 and 0.60 when using a daily temporal scale. The applied methodology iterates correction factors for each sub-basin, taking advantage of surface measurements from the national rain gauge network. Five iterations showed stability in the convergence of data values. The generated daily products showed correlation coefficients between 0.87 and 0.98 when using rain gauge data as a control, while GMET showed correlation coefficients of around 0.89 and 0.95. The best results were found in the Altiplano and La Plata sub-basins. The database generated in this study can be used for several daily hydrological applications for Bolivia, including storm analysis and extreme event analysis. Finally, a case study in the Rocha River basin was carried out using the daily generated precipitation product. This was used to force a hydrological model to establish the outcome of simulated daily river discharge. Finally, we recommend the usage of these daily generated precipitation products for a wide spectrum of hydrological applications, using different models to support decision-making. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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23 pages, 15088 KiB  
Article
Mapping the Distribution of Summer Precipitation Types over China Based on Radar Observations
by Jing Tang, Sheng Chen, Zhi Li and Liang Gao
Remote Sens. 2022, 14(14), 3437; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143437 - 17 Jul 2022
Cited by 3 | Viewed by 1447
Abstract
In this study, the spatiotemporal distribution and characteristics of different precipitation types (stratiform, convective, and snow) over China are analyzed using the radar mosaic images during the summer season over 4 years (from 2018 to 2021). The convective precipitation occurs most frequently along [...] Read more.
In this study, the spatiotemporal distribution and characteristics of different precipitation types (stratiform, convective, and snow) over China are analyzed using the radar mosaic images during the summer season over 4 years (from 2018 to 2021). The convective precipitation occurs most frequently along the eastern coast regions. In June, the strong convection center is located in Southern China and moves northward to Eastern China in July, while the lowest frequency occurs in August. Stratiform precipitation dominates summer precipitation over China and mainly distributes in inland regions, with the highest frequency in August. Snowfall primarily presents in the mountains and plateau regions of Western China with the frequency of occurrence around 20%. The snowfall area in July is significantly smaller than that in June and August. The convective, stratiform, and snowfall show strong diurnal variation in terms of solar standard time (LST) especially for snowfall. The convective precipitation demonstrates a bimodal pattern, with the highest peak in the afternoon (15–16 LST) and the secondary peak in the early morning (04–07 LST). Stratiform precipitation is mainly active from the afternoon to the next morning (14–05 LST). Snowfall is significantly more common in the nighttime (around 12%) than in the daytime (around 4%). The occurrence ratio of snowfall at midnight in July is significantly higher than that in June and August. It is expected that this study on summer precipitation over China can be used as a reference to hydrometeorological research and also to improve the understanding of radar precipitation research over China. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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22 pages, 11504 KiB  
Article
Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links
by Yingcheng Zhao, Xichuan Liu, Kang Pu, Jin Ye and Minghao Xian
Remote Sens. 2022, 14(9), 2220; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14092220 - 06 May 2022
Cited by 5 | Viewed by 1327
Abstract
High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) [...] Read more.
High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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24 pages, 5627 KiB  
Article
A Two-Step Approach to Blending GSMaP Satellite Rainfall Estimates with Gauge Observations over Australia
by Zhi-Weng Chua, Yuriy Kuleshov, Andrew B. Watkins, Suelynn Choy and Chayn Sun
Remote Sens. 2022, 14(8), 1903; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14081903 - 14 Apr 2022
Cited by 10 | Viewed by 1913
Abstract
An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station [...] Read more.
An approach to developing a blended satellite-rainfall dataset over Australia that could be suitable for operational use is presented. In this study, Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates were blended with station-based rain gauge data over Australia, using operational station data that has not been harnessed by other blended products. A two-step method was utilized. First, GSMaP satellite precipitation estimates were adjusted using rain gauge data through multiplicative ratios that were gridded using ordinary kriging. This step resulted in reducing dry biases, especially over topography. The adjusted GSMaP data was then blended with the Australian Gridded Climate Dataset (AGCD) rainfall analysis, an operational station-based gridded rain gauge dataset, using an inverse error variance weighting method to further remove biases. A validation that was performed using a 20-year range (2001 to 2020) showed the proposed approach was successful; the resulting blended dataset displayed superior performance compared to other non-gauge-based datasets with respect to stations as well as displaying more realistic patterns of rainfall than the AGCD in areas with no rain gauges. The average mean absolute error (MAE) against station data was reduced from 0.89 to 0.31. The greatest bias reductions were obtained for extreme precipitation totals and over mountainous regions, provided sufficient rain gauge availability. The newly produced dataset supported the identification of a general positive bias in the AGCD over the north-west interior of Australia. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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21 pages, 7929 KiB  
Article
Response of Precipitation in Tianshan to Global Climate Change Based on the Berkeley Earth and ERA5 Reanalysis Products
by Mengtian Fan, Jianhua Xu, Dahui Li and Yaning Chen
Remote Sens. 2022, 14(3), 519; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030519 - 21 Jan 2022
Cited by 12 | Viewed by 2407
Abstract
Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 [...] Read more.
Global climate change has readjusted a global-scale precipitation distribution in magnitude and timing. In mountainous areas, meteorological stations and observation data are very limited, making it difficult to accurately understand the response of precipitation to global climate change. Based on ECMWF Reanalysis v5 precipitation products, Berkeley Earth global temperature, and typical atmospheric circulation indexes, we integrated a gradient descent-nonlinear regression downscaling model, cross wavelet transform, and wavelet correlation method to analyze the precipitation response in Tianshan to global climate change. This study provides a high-resolution (90 m × 90 m) precipitation dataset in Tianshan and confirms that global warming, the North Pacific Pattern (NP), the Western Hemisphere Warm Pool (WHWP), and the Atlantic Multidecadal Oscillation (AMO) are related to the humidification of Tianshan over the past 40 years. The precipitation in Tianshan and global temperature have a resonance period of 8–15 months, and the correlation coefficient is above 0.9. In Tianshan, spring precipitation is determined mainly by AMO, North Tropical Atlantic Sea Level Temperature, Pacific Interdecadal Oscillation (PDO), Tropical North Atlantic Index, WHWP, NP, summer by NP, North Atlantic Oscillation, and PDO, autumn by AMO, and winter by Arctic Oscillation. This research can serve the precipitation forecast of Tianshan and help in the understanding of the regional response to global climate change. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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22 pages, 7003 KiB  
Article
Retrieving Rain Drop Size Distribution Moments from GPM Dual-Frequency Precipitation Radar
by Merhala Thurai, Viswanathan Bringi, David Wolff, David A. Marks, Patrick N. Gatlin and Matthew T. Wingo
Remote Sens. 2021, 13(22), 4690; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13224690 - 20 Nov 2021
Cited by 1 | Viewed by 1979
Abstract
A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, [...] Read more.
A novel method for retrieving the moments of rain drop size distribution (DSD) from the dual-frequency precipitation radar (DPR) onboard the global precipitation mission satellite (GPM) is presented. The method involves the estimation of two chosen reference moments from two specific DPR products, namely the attenuation-corrected Ku-band radar reflectivity and (if made available) the specific attenuation at Ka-band. The reference moments are then combined with a function representing the underlying shape of the DSD based on the generalized gamma model. Simulations are performed to quantify the algorithm errors. The performance of methodology is assessed with two GPM-DPR overpass cases over disdrometer sites, one in Huntsville, Alabama and one in Delmarva peninsula, Virginia, both in the US. Results are promising and indicate that it is feasible to estimate DSD moments directly from DPR-based quantities. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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17 pages, 3857 KiB  
Article
The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation
by Kingsley K. Kumah, Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao and Bob Z. Su
Remote Sens. 2021, 13(16), 3274; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163274 - 19 Aug 2021
Cited by 6 | Viewed by 3181
Abstract
Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation [...] Read more.
Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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16 pages, 4871 KiB  
Article
Physical Retrieval of Rain Rate from Ground-Based Microwave Radiometry
by Wenyue Wang, Klemens Hocke and Christian Mätzler
Remote Sens. 2021, 13(11), 2217; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112217 - 05 Jun 2021
Cited by 11 | Viewed by 2422
Abstract
Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval [...] Read more.
Because of its clear physical meaning, physical methods are more often used for space-borne microwave radiometers to retrieve the rain rate, but they are rarely used for ground-based microwave radiometers that are very sensitive to rainfall. In this article, an opacity physical retrieval method is implemented to retrieve the rain rate (denoted as Opa-RR) using ground-based microwave radiometer data (21.4 and 31.5 GHz) of the tropospheric water radiometer (TROWARA) at Bern, Switzerland from 2005 to 2019. The Opa-RR firstly establishes a direct connection between the rain rate and the enhanced atmospheric opacity during rain, then iteratively adjusts the rain effective temperature to determine the rain opacity, based on the radiative transfer equation, and finally estimates the rain rate. These estimations are compared with the available simultaneous rain rate derived from rain gauge data and reanalysis data (ERA5). The results and the intercomparison demonstrate that during moderate rains and at the 31 GHz channel, the Opa-RR method was close to the actual situation and capable of the rain rate estimation. In addition, the Opa-RR method can well derive the changes in cumulative rain over time (day, month, and year), and the monthly rain rate estimation is superior, with the rain gauge validated R2 and the root-mean-square error value of 0.77 and 22.46 mm/month, respectively. Compared with ERA5, Opa-RR at 31GHz achieves a competitive performance. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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14 pages, 3471 KiB  
Article
Precipitation Retrievals from Passive Microwave Cross-Track Sensors: The Precipitation Retrieval and Profiling Scheme
by Chris Kidd, Toshi Matsui and Sarah Ringerud
Remote Sens. 2021, 13(5), 947; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13050947 - 03 Mar 2021
Cited by 15 | Viewed by 2182
Abstract
The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and for many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the [...] Read more.
The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and for many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is, therefore, necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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11 pages, 2882 KiB  
Technical Note
Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders
by Munehisa K. Yamamoto and Takuji Kubota
Remote Sens. 2022, 14(18), 4445; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184445 - 06 Sep 2022
Cited by 1 | Viewed by 1543
Abstract
This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among [...] Read more.
This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals. Full article
(This article belongs to the Special Issue Remote Sensing for Precipitation Retrievals)
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