Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran

Mosaffa, Hamidreza; Shirvani, Amin; Khalili, Davar; Nguyen, Phu; Sorooshian, Soroosh

doi:10.1080/01431161.2020.1739352

Cited by 30 publications

(19 citation statements)

References 33 publications

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“…Both PERSIANN products were less accurate in tracking the spatial variability of rainfall over the province; this finding was consistent with the results of previous investigations in similar climatic and topographic conditions. The CHIRPS-2.0 and SM2Rain-ASCAT rainfall products could represent the spatial distribution of rainfall, consistent with the findings of Reference [27]. The better performances of the SM2Rain and CHIRPS products in terms of skills to track spatial distribution were also reported by previous studies [27,[39][40][41].…”

Section: Discussionsupporting

confidence: 89%

“…The CHIRPS-2.0 and SM2Rain-ASCAT rainfall products could represent the spatial distribution of rainfall, consistent with the findings of Reference [27]. The better performances of the SM2Rain and CHIRPS products in terms of skills to track spatial distribution were also reported by previous studies [27,[39][40][41]. As evidenced by the low values of correlation coefficients, the linear agreement between the reference observations and the estimates of all the SRPs was poor at the daily scale (<0.50).…”

Section: Discussionsupporting

confidence: 87%

“…This study assessed the accuracy and error characteristics of four SRPs over the Punjab Province of Pakistan using widely used assessment and categorical indices. Previously, the performances of different satellite-based rainfall products (including PERSIANN, CHIRPS-2.0, and SM2Rain-ASCAT products) have been evaluated in different regions of the world [14,27,35,36]. The performances of SRPs are widely known to be highly influenced by regional climatic and topographic conditions.…”

Section: Discussionmentioning

confidence: 99%

“…If the calculated value of CC is 1 and the values of Bias and RMSE are zero, then the SRP is considered as a perfect product. Generally, any SRP is considered to be an acceptable proxy for ground-based data if the estimated value of the rBias is within ±10% and the estimated value of CC is ≥0.70 [27].…”

Section: Methodsmentioning

confidence: 99%

“…Huang et al [25] assessed the error characteristics of PERSIANN family products in the whole of Taiwan and concluded that all products underestimated the rainfall amount over most parts of the country. On the other hand, Mosaffa et al [27] advised that the PERSIANN product could be used to better understand the spatial and temporal variabilities of rainfall in Iran. Hamza et al [14] analyzed the accuracy of IMERG-V06 and TRMM-3B42V7 products and recommended the use of IMERG product for understanding the spatial and temporal variabilities of monthly rainfall over the mountainous regions of Pakistan.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Anjum

Irfan

Waseem

et al. 2022

Water

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This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Anjum

Irfan

Waseem

et al. 2022

Water

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Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran

Rahmati¹,

Bakhtar²,

Shayeghi³

et al. 2021

Preprint

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Access to spatio-temporally consistent precipitation data is a key prerequisite for hydrological studies, especially in data-scarce regions. Different global precipitation products offer an alternative way to estimate precipitation over areas with inadequate gauge distributions. However, before use of the datasets, the accuracy of these global estimations must be carefully studied at local and regional scale. This study evaluated 14 global precipitation products against gauge observations 2003-2012 in Karun and Karkheh basins in southwest Iran. Different categorical and statistical indices, including Kling-Gupta Efficiency (KGE), bias, correlation coefficient, and variability ratio, at varying spatial and temporal resolution were used to evaluate the products. KGE results at both daily and monthly time steps suggested that TMPA-3B42V7.0 and MERRA-2 outperformed all other products, while CMORPH-BLDV1.0 and PERSIANN-CDR was the best-performing product at daily and monthly time steps, respectively. ERA5-Land showed the highest positive bias compared with in-situ observations, particularly for mountainous southeastern parts of Karun basin. Overall, bias-adjusted products obtained by merging ground-based observations in the estimations outperformed the unadjusted versions. The spatial distribution of statistical error metrics indicated that almost all products showed their greatest uncertainties for mountainous regions, due to complex precipitation processes in these regions.These results can significantly contribute to various horological and water resources planning measures in the study region, including early flood warning systems, drought monitoring, and optimal dam operations.

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WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

Maddah,

Mostamandi

2024

Meteorological Applications

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The present study aimed to evaluate performance sensitivity to the Cumulus Parameterization Scheme (CPS) used for the Weather Research and Forecasting (WRF) model to predict the atmospheric river‐related precipitation (ARP) event with 206 and 57 mm, highest and area‐averaged precipitation (AAP) per 24‐h, respectively, that occurred over the central mountainous basins of Iran on 31 March 2019. For this purpose, experiments were designed using the 12 (almost all) CPSs available in WRF v4. To verify the predicted precipitation (from the inner 4‐km domain), both point‐scale and grid‐scale comparisons were performed against gauge‐ and satellite‐based observational data at three accumulation time‐scales (12‐, 18‐, and 24‐h) and in three distinct sub‐regions. All scores obtained from the different statistical metrics used, are in complete agreement with a strongly dependent performance of WRF on the CPS used. In addition, the use of Kain‐Fritsch, KF‐CuP, and Grell‐3 CPSs could provide a realistic picture of impending heavy precipitation for WRF. Contrary, the New SAS, Tiedtke, and Zhang‐McFarlane CPSs did not perform satisfactorily in predicting the ARP event. As a result, CPSs with the “momentum transport” option in their modification mechanism are unlikely to adequately simulate the conversion of incoming low‐level moisture from atmospheric river to precipitation. However, precipitation predictions are more accurate at the 24‐h accumulation time‐scale than at the 12‐ and 18‐h. Also, a dry bias in the predictions is expected as the terrain elevation and accumulation time‐scale decrease and the distance from the core of the precipitation field increases.

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Post and near real-time satellite precipitation products skill over Karkheh River Basin in Iran

Cited by 30 publications

References 33 publications

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran

WRF prediction of an atmospheric river‐related precipitation event: Sensitivity to cumulus parameterization schemes

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