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CC (a,e,i,m,q,u,y,ac,ag), KGE (b,f,j,n,r,v,z,ad,ah), RMSE (c,g,k,o,s,w,aa,ae,ai) and POD (d,h,l,p,t,x,ab,af,aj) from IMERG_E (red line), IMERG_L (dashed line) and IMERG_F (green line) products in different precipitation intensity across nine basins (CB, Continental Basin; HARB, Haihe River Basin; HURB, Huaihe River Basin; PRB, Pearl River Basin; SEB, Southwest Basin; SRB, Songliao River Basin; SWB, Southwest Basin; YARB, Yangtze River Basin; YERB, Yellow River Basin) during 2008 to 2017.
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This study evaluated the performance of the early, late and final runs of IMERG version
06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a grad...
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Water depletion is a growing problem in the world’s arid and semi-arid areas, where groundwater is the primary source of fresh water. Accurate climatic data must be obtained to protect municipal water budgets. Unfortunately, the majority of these arid regions have a sparsely distributed number of rain gauges, which reduces the reliability of the sp...
Citations
... It is crucial to collect precipitation data that has both temporal and spatial resolutions to meet different requirements effectively. Precipitation data serves crucial purposes, including forecasting extreme flooding events, facilitating continuous hydrological simulations to estimate streamflow for dam reservoir management and water supply systems operation, issuing landslide warnings, and providing input for irrigation models, particularly in semiarid environments where agricultural activities are prevalent [1,2]. Through obtaining precise and comprehensive precipitation data, stakeholders can make informed decisions and implement strategies to manage water resources effectively and mitigate potential risks associated with hydrological events. ...
... Principal and commonly used satellite precipitation products for this purpose are Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), the Climate Prediction Center (CPC), the morphing technique product [10], the Tropical Rainfall Measuring Mission (TRMM), multi-sensor precipitation analysis (TMPA) [11], and, more recently, The Integrated Multi-satellitE Retrievals for GPM (IMERG). Assessment of the accuracy of satellite rainfall estimates compared to precipitation observations from gauges has been conducted across various spatial and temporal resolutions in numerous regions and basins worldwide [1]. ...
... The main objectives of this study are (1) to evaluate the precipitation estimation by satellite products in a semiarid mountainous basin, (2) to analyze monthly and annual precipitation trends over 14 years (2005-2018) using the Mann-Kendall test, and (3) to analyze both spatial and temporal variability trends over the 14 years. ...
Accurate observation of precipitation data is crucial for hydrometeorological applications, requiring temporal and spatial precision. Satellite precipitation products offer a promising solution for obtaining precipitation estimates, facilitating long-term observations from global to local scales. However, assessing their accuracy compared to rain gauge observations is essential. This study aims to assess the accuracy and applicability of precipitation data from CMORPH, IMERG, and PERSIANN CCS in the Rio Grande–San Juan Basin in northeast Mexico. The evaluation of estimated precipitation was assessed using the Pearson and Spearman correlations, RMSE, MAE, and BIAS for both monthly and yearly averages. CMORPH showed minimal errors and low underestimation, while IMERG exhibited high correlations with consistent underestimation. PERSIANN CCS had lower correlations, significant overestimation, and higher errors. The Mann–Kendall (MK) test was used to determinate the precipitation trends of observed and estimated data. The observed data showed a significant positive trend in monthly averages, which is not reflected in the annual trend. Furthermore, negative annual trends were found in at least 10 stations across the basin. The application of satellite precipitation data yielded mixed outcomes, with CMORPH showing the highest level of agreement with the trend analysis results from rain gauge data. This demonstrates its reliability for weather and climate studies and suggests the potential for CMORPH to be used as an input in hydrological modeling.
... Thus, this study compares the IMERG V07A data with the IMERG V06B data for the same period, specifically until September 2021. Furthermore, this study solely focuses on evaluating the IMERG V06B and V07A Final Run datasets because the correction and calibration process for the IMERG Final Run data has been conducted using more comprehensive datasets, rendering it more suitable for diurnal analysis purposes (Tang et al., 2020;Yu et al., 2021;Zhou et al., 2021). ...
The availability of surface rainfall data with high spatial-temporal resolution is needed to understand the diurnal rainfall characteristics in the Indonesian Maritime Continent (IMC) to improve the accuracy of weather and climate models in this region. This study validates the accuracy of the Final Run product of IMERG data version 06B (V06B) and version 07A (V07A), which have a resolution of 0.1°–30 min for diurnal rainfall analysis over IMC. Validation was conducted for precipitation amount (PA), precipitation frequency (PF), and precipitation intensity (PI), by recording 302 automatic rain gauges (RG) every 10 min from January 2014 to September 2021. IMERG V06B and V07A perform well in observing diurnal PA and PF but struggle in observing diurnal PI compared to RG observations. This has been determined by the correlation coefficient (CC) values of IMERG V06B (V07) data, which are 0.76 (0.72) for PA, 0.77 (0.76) for PF, and 0.21 (0.13) for PI. The IMERG data align to an extent with RG observations for the PA value, having a small relative bias (RB). The results also display a systematic PF and PI values error. The high false alarm ratio (FAR) of IMERG data suggests rain detection errors, leading to overestimated PF values. Additionally, the underestimation of PI values is due to the limitation of IMERG data in observing extreme and small-scale rain events. About 86.10% (78.14%) and 81.45% (81.78) of IMERG V06B (IMERG V07A) data show a peak time difference of <3h for PA and PF when compared with RG observations. Overall, IMERG V06B performs better than V07, possibly due to inaccurate orbits of GPROF data, removal of SAPHIR satellite observations, and inter-calibration issues with CORRA and GPCP data. Continuous monitoring and data improvements are necessary to improve the accuracy and reliability of IMERG data in detecting diurnal rainfall patterns in the IMC region.
... This phenomenon suggested that the calibration of CMFD datasets using CMA station data plays a crucial role in determining their quality. Additionally, the IMERG product was significantly affected by the GPCC monthly precipitation data calibration [49,50], resulting in markedly inferior daily scale performance compared to its monthly scale performance. Figure 6 shows that the CMFD, ERA5-Land, and IMERG overestimate precipitation by 12.29, 165.33, and 38.46 mm, respectively. ...
Due to the scarcity of meteorological stations on the Tibetan Plateau (TP), owing to the high altitude and harsh climate, studies often resort to satellite, reanalysis, and merged multi-source precipitation data. This necessitates an evaluation of TP precipitation data applicability. Here, we assess the following three high-resolution gridded precipitation datasets: the China Meteorological Forcing Dataset (CMFD), the European Center for Medium-Range Weather Forecasts Reanalysis V5-Land (ERA5-Land), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) during TP summers. Using observations from the original 133 China Meteorological Administration stations on the TP as a reference, the evaluation yielded the following conclusions: (1) In summer, from 2000 to 2018, discrepancies among the datasets were largest in the western TP. The CMFD showed the smallest deviation from the observations, and the annual summer precipitation was only overestimated by 12.3 mm. ERA5-Land had the closest trend (0.41 mm/y) to the annual mean summer precipitation, whereas it overestimated the highest precipitation (>150 mm). (2) The reliability of the three datasets at annual and monthly scales was in the following order: CMFD, ERA5-Land, and IMERG. The daily scales exhibited a lower accuracy than the monthly scales (correlation coefficient CC of 0.51, 0.38, and 0.26, respectively). (3) The CMFD assessments, referencing the 114 new stations post-2016, had a notably lower accuracy and precipitation capture capability at the daily scale (CC and critical success index (CSI) decreased by 0.18 and 0.1, respectively). These results can aid in selecting appropriate datasets for refined climate predictions on the TP.
... Something important to note is that PDIR-Now and PERSIANN-CCS are both near-real-time products, which means that they are available with a very short latency. On the other hand, IMERG Final is available about 3.5 months after gathering the data necessary for the algorithm, which means that it is not a near-real-time product [11]. ...
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.
... Regionally, Hamza et al. [16] assessed V06_FR, along with three other SPPs, at varying temporal resolutions in a mountainous area of Pakistan. Similar regional assessments were conducted by Moazami and Najafi [17] in Canada and Yu et al. [18] in China. Beyond overall precipitation rates, precipitation at specific rates has also been evaluated. ...
... The performance of precipitation estimation by IMERG V07 varies under different sub-regions divided by climate features. The changing performance of IMERG products under various basins or climatic regions across mainland China has also been verified by Wang et al. [14] and Yu et al. [18]. Generally, warm and wet sub-regions tend to have lower RB and more accurate detection probability, while dry and high-elevation regions have higher RB and lower detection capability. ...
... The uncertainties in precipitation estimation also apply to regions with similar climate and topographic conditions [43]. The relatively high elevation of this region can also explain the uncertainty of precipitation estimation in the Tibet region [18]. Despite the regional differences, the performance of IMERG V07_FR is superior to V06_FR across mainland China, with higher CSI and lower RMSE. ...
Precipitation is an essential element in earth system research, which greatly benefits from the emergence of Satellite Precipitation Products (SPPs). Therefore, assessment of the accuracy of the SPPs is necessary both scientifically and practically. The Integrated Multi-Satellite Retrievals for GPM (IMERG) is one of the most widely used SPPs in the scientific community. However, there is a lack of comprehensive evaluation for the performance of the newly released IMERG Version 07, which is essential for determining its effectiveness and reliability in precipitation estimation. In this study, we compare the IMERG V07 Final Run (V07_FR) with its predecessor IMERG V06_FR across scales from January 2016 to December 2020 over the globe (cross-compare their similarities and differences) and a focused study on mainland China (validate against 2481 rain gauges). The results show that: (1) Globally, the annual mean precipitation of V07_FR increases 2.2% compared to V06_FR over land but decreases 5.8% over the ocean. The two SPPs further exhibit great differences as indicated by the Critical Success Index (CSI = 0.64) and the Root Mean Squared Difference (RMSD = 3.42 mm/day) as compared to V06_FR to V07_FR. (2) Over mainland China, V06_FR and V07_FR detect comparable precipitation annually. However, the Probability of Detection (POD) improves by 5.0%, and the RMSD decreases by 3.7% when analyzed by grid cells. Further, the POD (+0%~+6.1%) and CSI (+0%~+8.8%) increase and the RMSD (−11.1%~0%) decreases regardless of the sub-regions. (3) Under extreme rainfall rates, V07_FR measures 4.5% lower extreme rainfall rates than V06_FR across mainland China. But V07_FR tends to detect more accurate extreme precipitation at both daily and event scales. These results can be of value for further SPP development, application in climatological and hydrological modeling, and risk analysis.
... LEO-PMW radiometers measure the effect of large liquid and ice particles on the upwelling radiation coming from the Earth's surface to estimate rain rate [1], whereas spaceborne precipitation radars, called LEO-AMW sensors, use the information retrieved by the backscatter signal from rain-sized particles. Moreover, GEO-IR sensors permit the study of a unique part of the world with a high spatiotemporal resolution and can estimate the rain rates derived from cloud-top brightness temperature data by using empirical laws, while LEO-PMW and -AMW sensors, which have better sensing through clouds, have a lower spatiotemporal resolution [16]. ...
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.
... • Due to the satisfactory results reported when using the TRMM and GPM missions products, comparable with the CMORPH and PERSIANN products as described by Gebregiorgis & Hossain (2011) and Yu et al. (2021), respectively, for this paper the rainfall estimated with the IMERG algorithm was considered. Raster files for the dates and geographic location of interest are products of the NASA Giovanni tool, a web application developed by the Goddard Earth Sciences Data and Information Services Center (GES DISC). ...
The analysis of the spatial and temporal distribution of storm events contributes to a better use of water resources, for example, the supply of drinking water, irrigation practices, electricity generation and management of extreme events to control floods and mitigate droughts, among others. The traditional observation of rainfall fields in Mexico has been carried out using rain gauge network data, but their spatial representativeness is unsatisfactory. Therefore, this study reviewed the possibility of obtaining better estimates of the spatial distribution of daily rainfall considering information from three different databases, which include rain gauge measurements and remotely sensed precipitation products of satellite systems and weather radars. In order to determine a two-dimensional rainfall distribution, the information has been merged with a sequential data assimilation scheme up to the diagnostic stage, paying attention to the benefit that the rain gauge network density has on the estimation. With the application of the Barnes method, historical events in the Mexican territory were analyzed using statistical parameters for the validation of the estimates, with satisfactory results because the assimilated rainfalls turned out to be better approximations than the values calculated with the individual databases, even for a not very low density of surface observations.
... Finally, our findings show that PPs, such as MSWXv100 and MERRA-2, which incorporate daily gauge observations from different global networks and public streams (Gelaro et al. 2017;Beck et al. 2022), generally perform better than those PPs (CFSR, SM2RAIN-ASCAT, and PDIR-Now) whose temporal validation entirely depends on either satellite or reanalysis (Table 1). These results support previous findings on PP consistency analysis (Derin and Yilmaz 2014;Amjad et al. 2020;Uysal and Şorman 2021;Yu et al. 2021). However, the outcomes also indicate that CFSR displays relatively higher performance compared to MERRA-2 over some climate zones, especially for the daily time step. ...
Precipitation with high spatio-temporal resolution is essential for hydro-meteorological studies on global and regional scales.
In recent decades, the scarcity and infrequent distribution of gauge networks have been a great challenge for precipitation
observation over different climate zones in Turkey. Conversely, precipitation products (PPs) from various sources can be an
alternative to fulfill this shortcoming. In this study, the spatio-temporal consistency of five precipitation products, including
MSWXv100, MERRA-2, CFSR, SM2RAIN-ASCAT, and PDIR-Now, is evaluated over the variable climate of Turkey,
considering two distinct temporal windows as daily and monthly. The Kling-Gupta efficiency (KGE), including its three
components (Pearson correlation coefficient, ratio of bias, and variability ratio), is exploited to assess the consistency of PPs
over seven climate zones. Moreover, the True Skill Statistic (TSS) is utilized to evaluate the detectability strength of each PP
for different precipitation intensities during five hydrologic years (2015–2019). The results indicate that five PPs differed in
their behavior over the variable climate of Turkey and different time steps, while showing higher performance in monthly
terms compared to daily. Overall, MSWXv100 is able to present a reliable precipitation estimate over time and space and
display the highest performance (median KGE; 0.75) for the entire region, where MERRA-2 comes with the second-best
performance (median KGE; 0.64) followed by CFSR (median KGE; 0.61), SM2RAIN-ASCAT (median KGE; 56), and
PDIR-Now (median KGE; 53). All PPs show diverse performance over each climate zone, and their best performance (except
CFSR) is obtained over Southeastern Anatolia (SAN) for the monthly time step, while CFSR showed its best performance
(median KGE; 0.70) over the Mediterranean (MED) climate zone.
... The root mean square error (RMSE; ranges from 0 to infinity, and ideal score is 0), the Kling Gupta efficiency (KGE; ranges from −∞ to 0, and ideal score is 0), probability of detection (POD; ranges from 0 to 1, and ideal score is 1), and probability of false alarm (POFA; ranges from 0 to 1, and ideal score is 0) are used to evaluate the performance of precipitation data, consistent with previous precipitation studies [18,32,33]. KGE measures the correlation between estimated and ground truth precipitation as well as the ability of precipitation estimates to capture the mean and standard deviation of the ground truth. ...
Although rain gauges provide valuable point-based precipitation observations, gauge data is globally sparse, necessitating interpolation between often-distant measurement locations. Interpolated gauge data is subject to uncertainty just as other precipitation data sources. Previous studies have focused either on the effect of decreasing gauge density on interpolated gauge estimate performance or on the ability of gauge data to accurately assess satellite multi-sensor precipitation data as a function of gauge density. No previous work has directly compared the performance of interpolated gauge estimates and satellite precipitation data as a function of gauge density to identify the gauge density at which satellite precipitation data and interpolated estimates have similar accuracy. This study seeks to provide insight into interpolated gauge product accuracy at low gage densities using a Monte Carlo interpolation scheme at locations across the continental U.S. and Brazil. We hypothesize that the error in interpolated precipitation estimates increases drastically at low rain gauge densities and at high distances to the nearest gauge. Results show that the multisatellite precipitation product, IMERG, has comparable performance in precipitation detection to interpolated gauge data at very low gauge densities (i.e., less than 2 gauges/10,000 km2) and that IMERG often outperforms interpolated data when the distance to the nearest gauge used during interpolation is greater than 80–100 km. However, there does not appear to be a consistent relationship between this performance ‘break point’ and the geographical variables of elevation, distance to coast, and annual precipitation.
... The different parameters that were studied in this research are: In ANSYS, the material models for studying the effect of different concrete and steel material properties are listed in Tables 5 and 6. Because of missing test information for the steel material, the ultimate strength f u was determined using Equation (1) or Equation (2) obtained from [34]. ...
... 10 −3 C c = 0.568 + 0.00612F c (33) l K /D ≤ 4 sN C = sA. sf C = sA × F/sV (34) l K /D > 12 and sλ ≤ Λ cN C = sA 1 − 0.4(sλ/Λ) 2 × F/sV (35) sλ > Λ cN C = sA (0.6 F)/(sλ/Λ) 2 × sV (36) ...
Concrete-filled steel tube (CFST) columns are used in tall buildings and bridges, and they provide more rigidity and higher bearing capacity, but buckling affects their behavior. There is an exceptional
need to study the behavior of these columns under various conditions. The numerical method is beneficial in supplementing the experimental works and is used to explore the effects of various parameters
because of the limitations in cost, apparatus, and time of the experimental program. The various parameters, such as the different slenderness ratios, i.e., column-height-to-cross-section-dimension (H/D),
different steel-tube-thickness-to-column-dimension (D/t), and different compressive strength of
concrete to yield strength of steel tube ratio (fc/fy) under concentric axial loading are considered
in this current study. Firstly, a finite element model used the “ANSYS” software program and was
constructed to validate the results of the experimental works. The extensive numerical models were
carried out to extensively widen the study in this field. The numerical work was conducted on sixty-four specimens. Moreover, the analytical calculations from the different international codes/standards
were compared with the numerical results to test their reliability in predicting the ultimate carrying
loads. The study provided results that show the improvement effect of CFST columns with the high
compressive strength of infilled concrete, while no remarkable enhancement effect with the high yield
strength of steel tube was observed. Increasing the columns’ diameter is more effective in enhancing
the load capacity (about three times more) than increasing the tube thickness (about 1.3 times). Ring
stiffeners for long CFST columns (H/D > 12) do not lead to any enhancement of the column behavior
due to yielding occurring firstly at the location of the rings. ECP205-2007 is the most conservative
design code in predicting the load capacity of CFST columns, while the AIJ design code is good at
predicting the ultimate load failure compared to the other codes/standards. Eurocode 4 provides
underestimation values of the load-carrying capacity of CFST columns.
