Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (24)

Search Parameters:
Keywords = PDIR

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
28 pages, 62170 KiB  
Article
Comparative Analysis of Satellite-Based Precipitation Products During Extreme Rainfall from Super Typhoon Yagi in Hanoi, Vietnam (September 2024)
by Viet Duc Nguyen, Nazak Rouzegari, Vu Dao, Fahad Almutlaq, Phu Nguyen and Soroosh Sorooshian
Remote Sens. 2025, 17(9), 1598; https://doi.org/10.3390/rs17091598 - 30 Apr 2025
Viewed by 1647
Abstract
This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared [...] Read more.
This study aimed to compare and evaluate three satellite-based precipitation estimation products: Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Early Run (IMERG-Early Run), Climate Prediction Center MORPHing technique Real Time (CMORPH-RT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Dynamic Infrared Rain rate Now (PDIR-Now) to identify the optimal integration strategies to improve the extreme rainfall estimation during Super Typhoon Yagi (September, 2024) in Hanoi, Vietnam, using validation data from 25 ground stations. In-depth analysis of three extreme rainfall series during Typhoon Yagi (6–9 September 2024), examining 93 extreme rainfall events at the 95th percentile precipitation threshold (R95p = 21.78 mm/h), combined with statistics at lower percentile thresholds (R1p, R5p, R10p, and R90p) and upper percentile threshold (R99p), revealed IMERG-Early best captured the peak rainfall, CMORPH-RT achieved highest total rainfall accuracy, while PDIR-Now offered the best spatial analysis. However, limitations included time lags, inability to detect rainfall events above R99p (41.69 mm/hour), and low detection rates (8–12%) in areas first impacted by the typhoon. This study identified that integration strategies combining different satellite products based on their strengths at specific time scales showed potential for improved rainfall estimation: PDIR-Now with IMERG-Early (1–3 h) and IMERG-Early with CMORPH-RT (6–12 h). These integration approaches accounted for each product’s unique capabilities in capturing different aspects of extreme rainfall during super typhoon events. Full article
Show Figures

Figure 1

16 pages, 10679 KiB  
Article
Evaluation of the Artificial Neural Networks—Dynamic Infrared Rain Rate near Real-Time (PDIR-Now) Satellite’s Ability to Monitor Annual Maximum Daily Precipitation in Mainland China
by Yanping Zhu, Gaosong Chang, Wenjiang Zhang, Jingyu Guo and Xiaodong Li
Water 2025, 17(3), 308; https://doi.org/10.3390/w17030308 - 23 Jan 2025
Viewed by 679
Abstract
As one of the countries with the most severe extreme climate disasters in the world, it is of great significance for China to scientifically understand the characteristics of extreme precipitation. The artificial neural network near-real-time dynamic infrared rainfall rate satellite precipitation data (PDIR-Now) [...] Read more.
As one of the countries with the most severe extreme climate disasters in the world, it is of great significance for China to scientifically understand the characteristics of extreme precipitation. The artificial neural network near-real-time dynamic infrared rainfall rate satellite precipitation data (PDIR-Now) is a global, long-term resource with diverse spatial resolutions, rich temporal scales, and broad spatiotemporal coverage, providing an important data source for the study of extreme precipitation. But its applicability and accuracy still need to be evaluated in specific applications. Based on the observation data of 824 surface meteorological stations in China, the correlation coefficient (R), relative deviation (RB), root mean square error (RMSE), and relative root mean square error (RRMSE) of quantitative statistical indicators were used to evaluate the annual maximum daily precipitation of PDIR-Now from 2000 to 2016 in this study, in order to explore the ability of PDIR-Now satellite precipitation products to monitor extreme precipitation in Chinese mainland. The results show that from the perspective of long-term series, the annual maximum daily precipitation of PDIR-Now has a good ability to monitor extreme precipitation across the country, and the R exceeds 0.6 in 65% of the years. The RMSE of different years is generally distributed between 40 and 60 mm, and in terms of time characteristics, the error of each year is relatively stable and does not fluctuate greatly with dry precipitation or abundant years. From the perspective of spatial characteristics, the distribution of RMSE is very regional, with the RMSE in the Qinghai–Tibet Plateau and Northwest China basically in the range of 0~20 mm, the Yunnan–Guizhou Plateau, the Sichuan Basin, Northeast China, and the central part of the study area in the range of 20~50 mm, and the RMSE in a few stations in the southeast coast greater than 80 mm. The RRMSE distribution of most sites is between 0 and 0.6, and the RRMSE distribution of a few sites is between 0.6 and 1.5. Generally, higher RRMSE values and larger errors are observed in the northwest and southeast coastal regions. Overall, PDIR-Now captures the regional characteristics of extreme precipitation in the study area, but it is underestimated in the wet season in humid and semi-humid regions and overestimated in the dry season in arid and semi-arid regions. Full article
(This article belongs to the Section Hydrology)
Show Figures

Figure 1

31 pages, 1004 KiB  
Article
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
by Matteo Bodini
Signals 2024, 5(4), 659-689; https://doi.org/10.3390/signals5040037 - 10 Oct 2024
Cited by 2 | Viewed by 2358
Abstract
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning [...] Read more.
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article
(This article belongs to the Special Issue Rainfall Estimation Using Signals)
Show Figures

Figure 1

20 pages, 10409 KiB  
Article
Assessment of Surface Water Availability in the Riyadh Region Using Integrated Satellite Data and Field Measurements (2001 to 2024)
by Raied Saad Alharbi
Water 2024, 16(19), 2743; https://doi.org/10.3390/w16192743 - 26 Sep 2024
Viewed by 2103
Abstract
Surface water availability in arid regions like the Riyadh region of Saudi Arabia is a significant concern due to its low and highly variable rainfall. This study represents the first comprehensive attempt to estimate surface runoff in the Riyadh region by integrating satellite [...] Read more.
Surface water availability in arid regions like the Riyadh region of Saudi Arabia is a significant concern due to its low and highly variable rainfall. This study represents the first comprehensive attempt to estimate surface runoff in the Riyadh region by integrating satellite data with field measurements, including dam observations, for enhanced accuracy. Utilizing the advanced Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) dataset, the study covers a 23-year period from 2001 to 2023. The research aimed to determine runoff coefficients, which are critical for predicting how much rainfall contributes to surface runoff. Analysis of annual runoff volumes and rainfall data from 39 dams, divided into calibration and validation sets, led to a runoff coefficient of 0.059, indicating that 5.9% of rainfall contributes to runoff. The calibration process, validated by statistical measures such as mean bias (0.23 mm) and RMSE (0.94 mm), showed reasonable model accuracy but also highlighted areas for refinement. With an average annual rainfall of 89.6 mm, resulting in 1733.1 million cubic meters (mil. m3) of runoff, the study underscores the importance of localized calibration and ongoing model refinement to ensure sustainable water management in the face of environmental and climatic challenges. Full article
Show Figures

Figure 1

23 pages, 10000 KiB  
Article
Assessment of PERSIANN Satellite Products over the Tulijá River Basin, Mexico
by Lorenza Ceferino-Hernández, Francisco Magaña-Hernández, Enrique Campos-Campos, Gabriela Adina Morosanu, Carlos E. Torres-Aguilar, René Sebastián Mora-Ortiz and Sergio A. Díaz
Remote Sens. 2024, 16(14), 2596; https://doi.org/10.3390/rs16142596 - 16 Jul 2024
Cited by 1 | Viewed by 1533
Abstract
Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial [...] Read more.
Precipitation is a fundamental component of the Earth’s hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error. Full article
Show Figures

Figure 1

15 pages, 3579 KiB  
Article
Woody Plant Structural Diversity Changes across an Inverse Elevation-Dependent Warming Gradient in a Subtropical Mountain Forest
by Yuqiao Su, Xianhua Gan, Weiqiang Zhang, Guozhang Wu and Fangfang Huang
Forests 2024, 15(6), 1051; https://doi.org/10.3390/f15061051 - 18 Jun 2024
Viewed by 1178
Abstract
Examining the changes in woody plant structural diversity along an inverse elevation-dependent warming gradient will enhance our mechanistic understanding of how warming affects forest communities because such an inverse elevational gradient reflects a warming trend in a mountain landscape. Here, we investigated the [...] Read more.
Examining the changes in woody plant structural diversity along an inverse elevation-dependent warming gradient will enhance our mechanistic understanding of how warming affects forest communities because such an inverse elevational gradient reflects a warming trend in a mountain landscape. Here, we investigated the effects of warming on the patterns of species composition and structural diversity in a subtropical broadleaved forest. We calculated a warming index based on elevational difference and modeled the aspect-related potential incident radiation (PDIR) using nonparametric multiplicative regression. We tested the changes in structural diversity of three communities for significant differences along the warming gradient. We associated both the warming index and PDIR with the principal components and tested their relationships for significant differences. We found that trees of different sizes varied in their response to the warming gradient. While a significant decreasing trend was exhibited in both species diversity and size diversity for trees of all sizes and for adult trees along the warming gradient, no significant changes in seedlings were found, and the average basal area value was the highest for the warmest community. Our findings demonstrated that a short-range elevational gradient was adequate to separate the communities in species composition and structural diversity. Patterns of structural diversity along the warming gradient varied in size classes. The community at a higher elevation had more indicator species that were unique in separating the community from others. Principal component analysis showed that the first two principal components were negatively correlated with the warming index, indicating that warming destabilized species composition and community structure. Our study suggests that warming is the major driver of changes in structural diversity and species composition of woody plant communities in a subtropical broadleaved forest and that warming may promote tree productivity at the community level but reduce structural diversity at the quadrat level. Full article
Show Figures

Figure 1

16 pages, 12435 KiB  
Technical Note
Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia
by Raied Saad Alharbi, Vu Dao, Claudia Jimenez Arellano and Phu Nguyen
Remote Sens. 2024, 16(4), 703; https://doi.org/10.3390/rs16040703 - 17 Feb 2024
Cited by 4 | Viewed by 2883
Abstract
In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, [...] Read more.
In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies. Full article
Show Figures

Figure 1

15 pages, 1749 KiB  
Article
Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices
by Claudia Jimenez Arellano, Vu Dao, Vesta Afzali Gorooh, Raied Saad Alharbi and Phu Nguyen
Atmosphere 2023, 14(12), 1832; https://doi.org/10.3390/atmos14121832 - 16 Dec 2023
Cited by 3 | Viewed by 1396
Abstract
Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information [...] Read more.
Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1 by 0.1 and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
Show Figures

Figure 1

36 pages, 28783 KiB  
Article
Evaluation of Rain Estimates from Several Ground-Based Radar Networks and Satellite Products for Two Cases Observed over France in 2022
by Antoine Causse, Céline Planche, Emmanuel Buisson and Jean-Luc Baray
Atmosphere 2023, 14(12), 1726; https://doi.org/10.3390/atmos14121726 - 24 Nov 2023
Cited by 2 | Viewed by 2114
Abstract
The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT [...] Read more.
The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. Full article
Show Figures

Figure 1

24 pages, 11557 KiB  
Article
Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region
by Manuchekhr Gulakhmadov, Xi Chen, Aminjon Gulakhmadov, Muhammad Umar Nadeem, Nekruz Gulahmadov and Tie Liu
Remote Sens. 2023, 15(20), 4990; https://doi.org/10.3390/rs15204990 - 17 Oct 2023
Cited by 2 | Viewed by 1598
Abstract
The lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia’s hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground [...] Read more.
The lack of observed data makes research on the cryosphere and ecology extremely difficult, especially in Central Asia’s hilly regions. Before their direct hydroclimatic uses, the performance study of gridded precipitation datasets (GPDS) is of utmost importance. This study assessed the multiscale ground evaluation of three reanalysis datasets (ERA5, MEERA2, and APHRO) and five satellite datasets (PERSIANN-PDIR, CHIRPS, GPM-SM2Rain, SM2Rain-ASCAT, and SM2Rain-CCI). Several temporal scales (daily, monthly, seasonal (winter, spring, summer, autumn), and annual) of all the GPDS were analyzed across the complete spatial domain and point-to-pixel scale from January 2000 to December 2013. The validation of GPDS was evaluated using evaluation indices (Root Mean Square Error, correlation coefficient, bias, and relative bias) and categorical indices (False Alarm Ratio, Probability of Detection, success ratio, and Critical Success Index). The performance of all GPDS was also analyzed based on different elevation zones (≤1500, ≤2500, >2500 m). According to the results, the daily estimations of the spatiotemporal tracking abilities of CHIRPS, APHRO, and GPM-SM2Rain are superior to those of the other datasets. All GPDS performed better on a monthly scale than they performed on a daily scale when the ranges were adequate (CC > 0.7 and r-BIAS (10)). Apart from the winter season, the CHIRPS beat all the other GPDS in standings of POD on a daily and seasonal scale. In the summer, all GPDS showed underestimations, but GPM showed the biggest underestimation (−70). Additionally, the CHIRPS indicated the best overall performance across all seasons. As shown by the probability density function (PDF %), all GPDS demonstrated more adequate performance in catching the light precipitation (>2 mm/day) events. APHRO and SM2Rain-CCI typically function moderately at low elevations, whereas all GPDS showed underestimation across the highest elevation >2500 m. As an outcome, we strongly suggest employing the CHIRPS precipitation product’s daily, and monthly estimates for hydroclimatic applications over the hilly region of Tajikistan. Full article
Show Figures

Figure 1

21 pages, 9471 KiB  
Article
Assessment and Data Fusion of Satellite-Based Precipitation Estimation Products over Ungauged Areas Based on Triple Collocation without In Situ Observations
by Xiaoqing Wu, Jialiang Zhu and Chengguang Lai
Remote Sens. 2023, 15(17), 4210; https://doi.org/10.3390/rs15174210 - 27 Aug 2023
Cited by 4 | Viewed by 1922
Abstract
Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a [...] Read more.
Reliable assessment of satellite-based precipitation estimation (SPE) and production of more accurate precipitation data by data fusion is typically challenging in sparsely gauged and ungauged areas. Triple collocation (TC) is a novel assessment approach that does not require gauge observations; it provides a feasible solution for this problem. This study comprehensively validates the TC performance for assessing SPEs and performs data fusion of multiple SPEs using the TC-based merging (TCM) approach. The study area is the Tibetan Plateau (TP), a typical area lacking gauge observations. Three widely used SPEs are used: the integrated multi-satellite retrievals for global precipitation measurement (IMERG) “early run” product (IMERG-E), the precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN) dynamic infrared (PDIR), and the Climate Prediction Center (CPC) morphing technique (CMORPH). Validation of the TC assessment approach shows that TC can effectively assess the SPEs’ accuracy, derive the spatial accuracy pattern of the SPEs, and reveal the accuracy ranking of the SPEs. TC can also detect the SPEs’ accuracy patterns, which are difficult to obtain from a traditional approach. The data fusion results of the SPEs show that TCM incorporates the regional advantages of the individual SPEs, providing more accurate precipitation data than the original SPEs, revealing that data fusion is reasonable and reliable in ungauged areas. In general, the TC approach performs well for the assessment and data fusion of SPEs, showing reasonable applicability in the TP and other areas lacking gauge data than other methods because it does not rely on gauge observations. Full article
(This article belongs to the Special Issue Multi-Source Data with Remote Sensing Techniques)
Show Figures

Figure 1

16 pages, 4868 KiB  
Article
The Effect of the Iridium Alloying and Hydrogen Sorption on the Physicochemical and Electrochemical Properties of Palladium
by Katarzyna Hubkowska, Małgorzata Pająk and Andrzej Czerwiński
Materials 2023, 16(13), 4556; https://doi.org/10.3390/ma16134556 - 24 Jun 2023
Cited by 2 | Viewed by 1477
Abstract
Thin layers (up to 1 µm) of Pd-Ir alloys were electrodeposited from aqueous, galvanic baths of PdCl2 and IrCl3 mixtures. The morphology of the electrodeposits was examined by means of scanning electron microscopy. The composition of alloys was determined with the [...] Read more.
Thin layers (up to 1 µm) of Pd-Ir alloys were electrodeposited from aqueous, galvanic baths of PdCl2 and IrCl3 mixtures. The morphology of the electrodeposits was examined by means of scanning electron microscopy. The composition of alloys was determined with the use of energy-dispersive spectroscopy, atomic absorption spectrometry, X-ray photoelectron spectroscopy, and Auger electron spectroscopy. For the studies of the electrochemical properties of alloys, cyclic voltammetry, chronoamperometry, and chronopotentiometry were used. It was found that Pd-Ir alloy electrodes were surface-enriched with Pd. Pd-Ir alloys subjected to different electrochemical treatment involving hydrogen sorption changed their surface state. The continuous hydrogen sorption enhanced the Ir ions’ dissolution. The values of thermodynamic functions of hydrogen sorption in strong alkaline electrolytes were comparable with those in acidic electrolytes, whereas the kinetics of the process in alkaline medium was hindered. The miscibility gap in the Pd-Ir-H system vanished for the electrode containing ca. 93.7 at.% Pd. Full article
(This article belongs to the Special Issue Hydrogen Storage in Metal Hydrides and Related Materials)
Show Figures

Figure 1

24 pages, 7424 KiB  
Article
Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region
by Faisal Baig, Muhammad Abrar, Haonan Chen and Mohsen Sherif
Remote Sens. 2023, 15(4), 1078; https://doi.org/10.3390/rs15041078 - 16 Feb 2023
Cited by 22 | Viewed by 2876
Abstract
Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity [...] Read more.
Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions. Full article
(This article belongs to the Special Issue Synergetic Remote Sensing of Clouds and Precipitation)
Show Figures

Figure 1

16 pages, 6501 KiB  
Article
Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon
by Jie Hsu, Wan-Ru Huang and Pin-Yi Liu
Remote Sens. 2022, 14(22), 5900; https://doi.org/10.3390/rs14225900 - 21 Nov 2022
Cited by 9 | Viewed by 2452
Abstract
This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and [...] Read more.
This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful. Full article
(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)
Show Figures

Graphical abstract

21 pages, 3384 KiB  
Article
Comparison of High-Resolution Satellite Precipitation Products in Sub-Saharan Morocco
by Mariame Rachdane, El Mahdi El Khalki, Mohamed Elmehdi Saidi, Mohamed Nehmadou, Abdellatif Ahbari and Yves Tramblay
Water 2022, 14(20), 3336; https://doi.org/10.3390/w14203336 - 21 Oct 2022
Cited by 26 | Viewed by 4009
Abstract
Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six [...] Read more.
Precipitation is a crucial source of data in hydrological applications for water resources management. However, several regions suffer from limited data from a ground measurement network. Remotely sensed data may provide a viable alternative for these regions. This study aimed to evaluate six satellite products (GPM-F, CHIRPS, PERSIANN-CCS-CDR, GPM-L, GPM-E and PDIR-Now), with high spatio-temporal resolution, in the sub-Saharan regions of Morocco. Precipitation observation data from 33 rain-gauge stations were collected and used over the period from September 2000 to August 2020. The assessment was performed on three temporal scales (daily, monthly and annually) and two spatial scales (pixel and basin scales), using different quantitative and qualitative statistical indices. The results showed that the GPM-F product performed the best, according to the different evaluation metrics, up to events with 40 mm/day, while the GPM near real-time products (GPM-E and GPM-L) were better at detecting more intense rainfall events. At the daily time scale, GPM-E and GPM-L and, on monthly and annual scales, CHIRPS and PERSIANN-CCS-CDR, provided satisfactory precipitation estimates. Moreover, the altitude-based analysis revealed a bias increasing from low to high altitudes. The continental and mountainous basins showed the lowest performance compared to the other locations closer to the Atlantic Ocean. The evaluation based on the latitudes of rain gauges showed a decrease of bias towards the most arid zones. These results provide valuable information in a scarcely gauged and arid region, showing that GPM-F could be a valuable alternative to rain gauges. Full article
(This article belongs to the Section Hydrology)
Show Figures

Figure 1

Back to TopTop