Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

Asif, Muhammad; Nadeem, Muhammad Umer; Anjum, Muhammad Naveed; Ahmad, Bashir; Gulakhmadov, Manuchekhr; Umer, Muhammad; Hamza, Muhammad; Javaid, Muhammad Mashood; Liu, Tie

doi:10.3390/atmos14010008

Cited by 4 publications

(5 citation statements)

References 43 publications

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“…The ground validation demonstrates that, at a monthly scale, all SBPPs, SBTPs, and TRPs exhibit better agreement with in-situ gauge estimations, corroborating the outcomes of numerous previous studies. For example, Anjum et al (2016) found that CHIRPS-2.0 and SM2Rain-ASCAT precipitation products effectively represent the spatial distribution of precipitation over most parts of Pakistan, a result also supported by Asif et al (2023) and Nadeem et al (2022) in their investigations of spatial abilities. At the daily scale, the linear agreement between the reference data and the estimations of all SBPPs was found to be poor, as indicated by low correlation coe cient values.…”

Section: Discussionmentioning

confidence: 92%

“…The in uence of local topography and meteorological factors on the performance of these products has been widely recognized. Previous studies by Asif et al (2023) and Nadeem et al (2022bNadeem et al ( , 2022a examined various satellite and reanalysis products and found that regional climatology and precipitation retrieval techniques signi cantly affect the accuracy of precipitation products. Similarly, Hamza et al (2020) compared the performance of SM2Rain and PERSIANN products and noted that evaluation indices such as RMSE and Bias were highly dependent on in-situ climatic conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 3 illustrates the use of the Thiessen polygon method to calculate airborne precipitation, where the gauge weight for each meteorological station was determined using ArcGIS 10.7.1 and multiplied by the corresponding precipitation amount to estimate the airborne precipitation (Asif et al, 2023). The capability of each examined precipitation product was further assessed according to the probability of detection (POD) criteria, following the ground assessment protocol recommended by the World Meteorological Organization (WMO).…”

Section: 3: Methodsmentioning

confidence: 99%

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WITHDRAWN: Performance assessment of multiple precipitation and temperature products for hydro- climatic applications over Pakistan

Munir

Chen

et al. 2023

Preprint

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Due to limited gauge network, Pakistan presents a challenge for cryosphere, hydrological, and ecological studies. Thus, before using precipitation and temperature products for hydro-climatic applications, they must be properly assessed. This study compared six satellite-based precipitation products (SBPPs), one satellite-based temperature product (SBTP), and three temperature reanalysis products (TRPs) to in-situ gauge data to assess their accuracy using ground gauge-based rainfall measurements for the period (2000–2020). The evaluation investigated point-to-pixel data on daily, monthly, seasonal (winter, spring, summer, and fall), and yearly timescales. All products were assessed using four continuous indices (RMSE, CC, bias, and rBias) and four categorical indices (false alarm ratio, probability of detection, success ratio, and crucial success index). According to the evaluation findings, CHIRPS and IMERG outperformed soil moisture family products in daily spatial-temporal capabilities. In terms of accuracy, ERA5 outperformed other temperature products. Monthly satellite-based temperature and precipitation data and temperature reanalysis products performed better than daily estimates (CC < 0.7 and rBIAS within ± 10). On a seasonal scale, IMERG precipitation estimates and ERA5 temperature estimates agreed well with in-situ gauge estimates. In areas with moderate topography, SM2Rain-GPM and ASCAT performed effectively. The evaluation of temperature reanalysis products on the ground demonstrated higher capabilities than AMSR2 LPRM. In terms of detection probability, the ground validation of IMERG beat other SBPPs, whereas ERA5 fared best among SBT and TRPs. The probability density function (PDF) showed that all satellite-based precipitation products captured light precipitation occurrences (> 2mm/day). In general, MEERA-2 and GEOS-5 FP demonstrated moderate performance in low elevated regions, whereas ERA5 and AMSR2 LPRM demonstrated performance comparable to that of gauge estimations across the entire country. The ground evaluation suggests using IMERG's daily and monthly precipitation estimates and ERA5 monthly temperature estimates for hydroclimatic applications in Pakistan's subtropical climates.

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Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: 3: Methodsmentioning

confidence: 99%

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WITHDRAWN: Performance assessment of multiple precipitation and temperature products for hydro- climatic applications over Pakistan

Munir

Chen

et al. 2023

Preprint

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“…According to the low correlation coefficient values, the performance of all GPDS was very poor in comparison to gauge estimations, indicating the better agreement between the GPDS and the reference datasets on a monthly scale as compare to daily. According to [33,37], the estimations of GPM and CHIRPS calculations with the monthly data of the in situ stations were >0.70, showing the superior linear connections of their monthly observations. Moreover, the algorithm used, the data sources used as input, and the spatial and temporal resolution can all affect how well a precipitation estimator performs.…”

Section: Discussionmentioning

confidence: 98%

“…As a result, only that region is affected by the amount of precipitation recorded at pixel I. Each pixel's weight depends on the region of the Thiessen polygon to which it belongs, following the methodology of [37]. For the performance analysis of GPDS in accordance with the WMO (World Meteorological Organization conventions), the standards for daily precipitation scales for low (2 mm/day), medium (>10 mm/day), and heavy precipitation were (>10 mm/day) used [27].…”

Section: Methodsmentioning

confidence: 99%

Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region

Gulakhmadov,

Chen,

Gulakhmadov

et al. 2023

Remote Sensing

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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.

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Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

Jazem Ghanim,

Anjum,

Alharbi

et al. 2024

AIP Advances

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In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (<2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

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Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

Cited by 4 publications

References 43 publications

WITHDRAWN: Performance assessment of multiple precipitation and temperature products for hydro- climatic applications over Pakistan

WITHDRAWN: Performance assessment of multiple precipitation and temperature products for hydro- climatic applications over Pakistan

Multiscale Evaluation of Gridded Precipitation Datasets across Varied Elevation Zones in Central Asia’s Hilly Region

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

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