Comprehensive evaluation of precipitation datasets over Iran

Saemian, Peyman; Hosseini‐Moghari, Seyed‐Mohammad; Fatehi, Iman; Shoarinezhad, Vahid; Modiri, Ehsan; Tourian, Mohammad J.; Tang, Qiuhong; Nowak, Wolfgang; Bàrdossy, András; Sneeuw, Nico

doi:10.1016/j.jhydrol.2021.127054

Cited by 57 publications

(25 citation statements)

References 116 publications

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“…This observation indicates that the choice of point-grid or grid-grid approaches and the aforementioned scale issue, can influence the statistical calculations and would marginally change the final results, but it does not alter the ranking of products. This is exactly in line with previous findings of Saemian et al, 2021 in the evaluation of 44 precipitation estimation products across…”

Section: The Issue Of Scalesupporting

confidence: 93%

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Ghajarnia¹,

Akbari²,

Saemian³

et al. 2022

Preprint

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ECMWF Reanalysis (ERA), one of the most widely used precipitation products evolved over time from ERA-40 to ERA-20CM, ERA-20C, ERA-Interim, and ERA5. Studies evaluating the performance of individual ERA products cannot adequately assess the evolution in the products. We compared the performance of all ERA precipitation products using daily, monthly, and annual data (1980-2018) from more than 2100 Iran precipitation gauges. Results indicated that ERA-40 performed worst, followed by ERA-20CM, which showed only minor improvements over ERA-40. ERA-20C considerably outperformed its predecessors, benefiting from assimilation of observational data. Although several previous studies have reported full superiority of ERA5 over ERA-Interim, our results revealed several shortcomings compared with ERA-Interim in ERA5 estimates. Both ERA-Interim and ERA5 performed best overall, with ERA-Interim showing better statistical and categorical skill scores, and ERA5 performing better in estimating extreme precipitations. These results suggest that the accuracy of ERA precipitation products improved from ERA-40 to ERA-Interim, but not consistently from ERA-Interim to ERA5. This study employed a grid-grid comparison approach by firstly creating a gridded reference dataset through spatial aggregation of point source observations, however, the results from a point-grid approach show no change in the overall ranking of products (despite the existence of slight changes in the error index values). These findings are useful for model development at global scale and for hydrological applications in Iran.

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Section: The Issue Of Scalesupporting

confidence: 93%

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Ghajarnia¹,

Akbari²,

Saemian³

et al. 2022

Preprint

Self Cite

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“…Afterwards, the monthly accumulations of precipitation estimates are bias corrected at the monthly scale using Global Precipitation Climatology Project (GPCP) v2.3 monthly gridded gauge data 60 , following the homogeneity-focused methodology of other PERSIANN-based CDRs 38 , 39 . Though experimental in nature, continued evaluation of PDIR-Now shows generally skillful performance across temporal and spatial scales 61 , 62 . For more information on PERSIANN data sets, refer to Nguyen et al 63 , 64 .…”

Section: Methodsmentioning

confidence: 99%

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Shearer

Gorooh

Nguyen

et al. 2022

Sci Rep

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Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15%/year, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

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“…This study showed that PCP outputs of ERA5 PCP dataset were unsatisfactory at most stations. In contrast, some researchers have reported acceptable performance for ERA5PCP dataset (Khoshchehreh et al 2020;Saemian et al 2021;Taghizadeh et al 2021).…”

Section: Discussionmentioning

confidence: 92%

“…Howerer, Eini et al (2019) showed that CRU and NCEP CFSR PCP datasets have better performance in comparison with APHRODITE PCP dataset. Saemian et al 2021 evaluated PCP datasets in Iran. They observed that the R 2 values of PCP outputs of PCP datasets are more than 0.83 (similar to results of this study).…”

Section: Discussionmentioning

confidence: 99%

“…They include studies performed in Central and Western Europe (Lockhoff et al 2019), China (An et al 2020;Bai et al 2020;Li et al 2018), Central Asia (Lu et al 2021), Australia and Africa (Awange et al 2019), India (Bhattacharyya et al 2022;Kolluru et al 2020), Thailand (Gunathilake et al 2021), Iraq and Eastern Africa (Salman et al 2019), Kenya, Uganda, and Tanzania (Garibay et al 2021). In addition, various research works in Iran have addressed global precipitation datasets for arid and semiarid regions (Darand and Khandu 2020;Eini et al 2019;Fallah et al 2020;Ghajarnia et al 2015;Hosseini-Moghari et al 2018;Izadi et al 2021;Keikhosravi-Kiany et al 2021;Nasseri et al 2021;Saemian et al 2021;Shayeghi et al 2020;Taghizadeh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

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Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

2022

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This study evaluated the outputs of five precipitation (PCP) datasets. These models are ECMWF reanalysis 5th generation (ERA5), precipitation estimation from remotely sensed information using artificial neural networks-climate data record (PERSIANN-CDR), Asian precipitation-highly resolved observational data integration toward evaluation (APHRODITE), The national centers for environmental prediction climate forecast system reanalysis (NCEP CFSR) and climatic research unit (CRU). The PCP outputs of these models were compared with data of nine synoptic stations in the Khuzestan province. The results indicated a better match between the APHRODITE outputs and the PCP data at most stations (R2 > 0.85, root-mean-square error (RMSE) < 17.049 mm and − 4.25 < Bias < 2.633 mm). However, CRU model has the highest critical success index (more than 0.711) and the lowest false alarm ratio (less than 0.2) and ERA5 has the highest probability of detection (more than 0.967) at most stations. Then, PCP outputs of five reanalysis (ERA5), interpolated (APHRODITE, NCEP CFSR and CRU) and satellite (PERSIANN-CDR) PCP datasets were combined to reduce the PCP estimation error. The multivariate adaptive regression splines models were employed for this purpose. The results show that the RMSE of all the stations, except Ahvaz station, decreased and the BIAS decreased too. Given the results, using ensemble data methods is a suitable way for reducing the error and increasing the accuracy of these models.

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Comprehensive evaluation of precipitation datasets over Iran

Cited by 57 publications

References 116 publications

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Evaluating the Evolution of ECMWF Precipitation Products Using Observational Data for Iran: From ERA40 to ERA5

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Improving the accuracy of satellite and reanalysis precipitation data by their ensemble usage

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