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

Ghajarnia, Navid; Akbari, Mahdi; Saemian, Peyman; Ehsani, Mohammad Reza; Hosseini‐Moghari, Seyed‐Mohammad; Azizian, Asghar; Kalantari, Zahra; Behrangi, Ali; Tourian, Mohammad J.; Kløve, Bjørn; Haghighi, Ali Torabi

doi:10.1029/2022ea002352

Cited by 7 publications

(5 citation statements)

References 75 publications

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“…In the PERSIANN-CDR datasets, the GPCP (version 2.2) was used to correct the biases of the PERSIANN rain rate estimates [16]. The GPCP monthly dataset (version 2.2) was corrected based on the CPCC dataset [69] and the GPCC dataset was also compiled by the IRIMO [70]. In this research, to have an independent evaluation, only the precipitation data provided by the TAMAB were used.…”

Section: Meteorological Datasets Precipitationmentioning

confidence: 99%

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Salehi,

Gharechelou,

Golian

et al. 2024

Water

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Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets.

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Section: Meteorological Datasets Precipitationmentioning

confidence: 99%

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Salehi,

Gharechelou,

Golian

et al. 2024

Water

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show abstract

“…Reanalysis models originate from weather forecasting and are widely used datasets in studying weather and climate (Edwards, (2010); Doddy et al, (2021); Jiao et al, (2021)). Unlike pure simulations, these models simulate the weather and blend the results with actual weather observations to produce fully global, uniform data (Gleixner et al, (2020); Ghajarnia et al, (2022)). Reanalyses are valuable datasets for monitoring and comparing past and present climate conditions, testing the accuracy of past forecasts, driving numerical weather prediction (NWP) models, and identifying climate variations and change (Hersbach et al, (2020) ; Jiao et al, (2021)).…”

Section: Introductionmentioning

confidence: 99%

“…One of the tools using such reanalysis models, is ERA5: in 2010, the European Center for Medium-Range Weather Forecasts (ECMWF) developed it as the fifth-generation Re-Analysis dataset and replaced the ERA-Interim dataset in 2019 (Hoffmann et al, (2019); Jiao et al, (2021); Ghajarnia et al, (2022)). ERA5 is a weather forecasting system that employs advanced techniques like four-dimensional variational data assimilation (4D-Var) and a high-resolution numerical weather model to provide more precise and accurate spatial and temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

“…ERA5 is a weather forecasting system that employs advanced techniques like four-dimensional variational data assimilation (4D-Var) and a high-resolution numerical weather model to provide more precise and accurate spatial and temporal resolution. Compared to its predecessors, ERA-Interim, ERA5 has a much higher resolution with 31km and hourly against 79km every 3 hours, making it more reliable (Hersbach et al, (2020); McNicholl et al, (2021); Ghajarnia et al, (2022)). ERA5 uses a sophisticated numerical weather model that assimilates a diverse set of observational data to produce a comprehensive and high-quality representation of global atmospheric conditions (Jiao et al, (2021); Yu et al, (2021)).…”

Section: Introductionmentioning

confidence: 99%

“…ERA5 combines observations from different sources such as weather stations, satellites, and ocean buoys, with a numerical weather model to generate a detailed and consistent representation of the Earth's atmosphere (Cucchi et al, (2020)). This process is known as data assimilation, which involves adjusting the initial conditions of the weather model using observations to create a more accurate representation of the atmospheric state (Cucchi et al, (2020); Ghajarnia et al, (2022)). Over the past few decades, advancements in data assimilation techniques have significantly improved the accuracy of NWP forecasts (Kalnay, (2003) ;Parker, (2016)).…”

Section: Introductionmentioning

confidence: 99%

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Detecting Location Errors with ERA5 Pseudo Stations

Bezeau,

Geissel,

Hayes

et al. 2024

Preprint

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Significant efforts are made to eliminate biases from models and observations, especially at operational centres. However, these biases still significantly impact the quality of assimilated data products. In the case of numerical weather prediction, residual biases can result in suboptimal utilization of available data or even render them unusable. In climate research based on re-analyzed datasets, it can be difficult to distinguish between accurate signals and trends from inaccurate ones caused by biases in models and data.This study used a detection algorithm written in the R language to perform statistical computing and data analysis. The algorithm was applied to a synthetic study utilizing pseudo-stations based on ERA5 to simulate and detect instrumental effects. Rather than using observational data from real-world sources, the study generated artificial scenarios to guarantee the quality of the data assessment.ERA5 is a well-known atmospheric reanalysis product that was used to create simulated or pseudo-weather stations. These stations were designed to mimic actual stations but were generated computationally to enable controlled experimentation. The study constructed twenty-five pseudo-stations in Frankfurt, Germany, within the latitude 49–50° and longitude 8–9° in the Northern Hemisphere. The study utilized the ERA5 land surface dataset of hourly 2-m air temperature of September in 2013 and 2014. The study tool significantly improves data quality assessment by evaluating the synthetic dataset’s precision, dependability, and general robustness. It introduces a range of factors to assess the degree to which the data quality can be enhanced and maintained, including station movements, errors, and noise.To determine the likelihood of the threshold correlation occurring at our confirmed noise threshold, the correlation values occurring at 1.53 for each locational trial were extracted. Our threshold correlation was evaluated to see if it occurred within a likely range of correlations occurring at 1.53 degrees of noise, where 0.9744052 is less than 0.9744667 but greater than 0.9781093. This process helps improve detection methods for data anomalies, contributing to advancements in data quality assessment.

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Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa

et al. 2023

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Access to spatiotemporal distribution of precipitation is needed in many hydrological applications. However, gauges often have spatiotemporal gaps. To mitigate this, we considered three main approaches: (i) using remotely sensing and reanalysis precipitation products; (ii) machine learning-based approaches; and (iii) a gap-filling software explicitly developed for filling the gaps of daily precipitation records. This study evaluated all approaches over a sparsely gauged basin in East Africa. Among the examined precipitation products, PERSIANN-CDR outperformed other satellite products in terms of root mean squared error (7.3 mm), and correlation coefficient (0.46) while having a large bias (50%) compared to the available in situ precipitation records. PERSIANN-CDR also demonstrates the highest skill in distinguishing rainy and non-rainy days. On the other hand, Random Forest outperformed all other approaches (including PERSIANN-CDR) with the least relative bias (-2%), root mean squared error (6.9 mm), and highest correlation coefficient (0.53).

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

Cited by 7 publications

References 75 publications

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran

Detecting Location Errors with ERA5 Pseudo Stations

Application of Machine Learning and Remote Sensing for Gap-filling Daily Precipitation Data of a Sparsely Gauged Basin in East Africa

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