Evaluation of Daily Precipitation from the ERA5 Global Reanalysis against GHCN Observations in the Northeastern United States

Crossett, Caitlin C.; Betts, Alan K.; Dupigny-Giroux, L.-A.; Bomblies, Arne

doi:10.3390/cli8120148

Cited by 28 publications

(12 citation statements)

References 24 publications

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“…Comparisons between ERA5 and GHCN measurements have been made in the past for precipitation in the Northeastern United States. This study found that distance from the coast and elevation affected the precipitation relationship between the two data sets [8]. These results suggest that differences between air temperature measurements will also be found between the data sets.…”

Section: Introductionmentioning

confidence: 60%

Evaluating the Reliability of Air Temperature From ERA5 Reanalysis Data

McNicholl

Lee

Campbell

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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The reliability of ERA5 satellite-based air temperature data is under investigation in this paper. To evaluate this, the ERA5 data will be compared with land-based data obtained from weather stations on the Global Historical Climatology Network. Two climate regions are taken into consideration, temperate and tropical. Five years worth of data is collected and compared through box plots, regression models and statistical metrics. The results show that the satellite temperature performs better in the temperate region than the tropical region. This suggests that the time of year and climate region have an impact on the accuracy of the satellite data as milder temperatures produce better approximations.

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Section: Introductionmentioning

confidence: 60%

Evaluating the Reliability of Air Temperature From ERA5 Reanalysis Data

McNicholl

Lee

Campbell

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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“…The reduction (see the follow‐up discussion on Figure 3a1 for level of reduction) in both wet and dry bias of ERA5 rainfall as compared to ERA‐Interim over EA indicates the improvement of the model in handling the sensitivity to orography. The wet bias in ERA‐Interim is reduced in ERA5 as noted by different researches over different areas (Karypidou & Katragkou, 2018; Crossett et al ., 2020; Gleixner et al ., 2020; Hersbach et al ., 2020). Most parts of EA represent the region where the wet bias in ERA5 relative to each reference rainfall is significantly reduced in comparison to the wet bias in ERA‐Interim relative to the same reference rainfall particularly during SOND and DJF.…”

Section: Resultsmentioning

confidence: 99%

“…(2020) and Crossett et al . (2020) over North America, Gleixner et al . (2020) and Karypidou & Katragkou (2018) over Africa, and Xu et al .…”

Section: Introductionmentioning

confidence: 98%

“…Following the release of ERA5, comparisons with other rainfall dataset and reanalysis were carried out for different parts of the world. For example, assessments by Fallah et al (2020) over Iran, Tarek et al (2020) and Crossett et al (2020) over North America, Gleixner et al (2020) and Karypidou &Katragkou (2018) over Africa, andXu et al (2019) over Northern Great Plains are some of the comparisons. Gleixner et al (2020) in particular evaluated the performance of ERA5 to ERA-Interim and Climatic Research Unit (CRU) Time-Series (TS) version 4.02 (CRU-TS4.02) for temperature and Climate Hazards Group Infra-Red Precipitation with Stations version 2 (CHIRPS V2.0) for precipitation.…”

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confidence: 99%

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Assessing improvement in the fifth‐generation ECMWF atmospheric reanalysis precipitation over East Africa

Assamnew

Tsidu

2022

Intl Journal of Climatology

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This study aims to assess the performance of European Center for Medium-Range Weather Forecasting (ECMWF) Reanalysis fifth generation (ERA5) in terms of the progress made over its predecessor, ERA-Interim, over East Africa (EA) in reproducing observed rainfall. The observed rainfall used as reference rainfall are Global Precipitation Climatology Center (GPCC) and Climate Research Unit Time Series (CRU-TS). The performance of ERA5 to its predecessor is evaluated using root-mean-square error (RMSE), correlation, and bias. The reductions of wet bias from ERA-Interim to ERA5 are 16.81-6.94% from June to September (JJAS), 31.99-19.33% from September to December (SOND), and 29.24-17.69% from December to February (DJF) over most of EA relative to CRU-TS. Similar reductions in the wet bias relative to GPCC are also noted. Spatially, notable reductions are observed over western Ethiopia, Sudan Republic, South Sudan, and Uganda. The decreasing trends revealed in the bias of seasonal ERA5 rainfall relative to CRU-TS and GPCC are attributed to an increase in the number and quality of assimilated observations in ERA5 over its predecessors. RMSE of ERA5 rainfall relative to the two reference rainfall and correlations with the two reference rainfall are consistent with spatial and temporal features captured in the bias. Specifically, the correlation of the monthly time series of ERA5 ranges from 0.90 with CRU-TS over equatorial EA (EEA) to 0.98 with GPCC over southern EA (SEA) in contrast to that of ERA-Interim which ranges from 0.84 with CRU-TS over EEA to 0.93 with CRU-TS over SEA. However, the wet bias in ERA5 along the Great Rift Valley remained the same as in ERA-Interim whereas spatial correlation of ERA5 with the two reference rainfall is relatively weaker than that of ERA-Interim.

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“…The wind profile data is used to estimate “near‐surface” wind components. Note that ERA5 resolves the mean orography at 0.25° scale, but also has sub‐grid scale orography to better represent the momentum transfers due to small‐scale variations in orography (ECMWF 2019; Crossett et al., 2020). This needs to be considered in the comparison of ERA 5 precipitation with the other products.…”

Section: Datamentioning

confidence: 99%

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

Adhikari

Behrangi

2022

Earth and Space Science

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Satellite-based precipitation estimates have been widely used in many research and application areas, including short-term weather and long-term climate prediction (Arkin & Ardanuy, 1989;Huffman et al., 1995). Precipitation estimates from satellite measurements are unique due to their global coverage, including oceanic and high terrain mountainous regions where ground-based observations are not always possible (Kidd et al., 2017). It is difficult to get broad spatial coverage in mountainous areas using rain gauges, and ground-based radar observation has limitations (Sapiano & Arkin, 2009). Despite the great coverage of satellite products, their precipitation retrieval accuracy in mountain regions is still a challenge (Mei et al., 2014;Shige et al., 2013). The primary types of sensors used to estimate precipitation from satellite measurements are radar, passive microwave imager/ sounder, and Infrared radiometers. The space-borne radar onboard satellites such as the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) and the Global Precipitation Measurement (GPM; Hou et al., 2014;Skofronick-Jackson et al., 2018) have shown success in measuring precipitations globally, but they still suffer from uncertainties especially in mountainous regions (Adhikari et al., 2019). One major source of uncertainty associated with space-borne radars is the contamination of near-surface reflectivity profiles due to ground-clutter (Arulraj & Barros, 2019;Liao et al., 2014). Passive microwave sensors (PMW) are popular and have been widely used in estimating precipitation for decades (Prigent, 2010;Wilheit, 1986). The benefit of using PMW sensors compared to radar is their higher sampling rate, although they can misclassify rainfall over mountainous regions (Yamamoto & Shige, 2015). For example, snow and ice-covered surfaces over the mountains could be classified wrongly as precipitating clouds resulting in an overestimation of precipitation. The PMW precipitation estimates mainly rely on the cumulative signal from the vertical column of cloud, hydrometeors and their emission or scattering properties. The heavy rainfall associated with mountainous regions can be generated from clouds with no or little ice aloft (You & Liu, 2012), resulting in an underestimation in PMW estimates (Dinku et al., 2010;Houze, 2012;Shige et al., 2013). Studies have shown that satellite-based precipitation products underestimate precipitation rate over several mountainous regions of the globe such as Japan (Kubota et al., 2009;Shige et al., 2013), Africa

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Evaluation of Daily Precipitation from the ERA5 Global Reanalysis against GHCN Observations in the Northeastern United States

Cited by 28 publications

References 24 publications

Evaluating the Reliability of Air Temperature From ERA5 Reanalysis Data

Evaluating the Reliability of Air Temperature From ERA5 Reanalysis Data

Assessing improvement in the fifth‐generation ECMWF atmospheric reanalysis precipitation over East Africa

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

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