Comparison of ERA5-Land reanalysis data with surface observations over Iran

Document Type : Research Article

Authors

1 Assistant Professor Babol Noshirvani University of Technology, Civil Engineering Department, Babol, Mazandaran, Iran

2 Ph.D. in Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran

Abstract

Reanalysis data have been considered as an important source of atmospheric information in a variety of applications such as climate studies, hydrological modeling and numerical weather prediction. Evaluating the effectiveness of the reanalysis products in each area before use is of great importance. With the advent of advanced reanalysis such as ERA5 and ERA5-Land, the interest of many researchers in using these data sources has increased.
    To date, several studies have been conducted in the country to statistically compare the reanalysis products with other meteorological data sources, each of which has its limitations and does not provide a comprehensive evaluation of the reanalysis data across the region. In other words, most of these studies are related to the evaluation of one of the meteorological variables such as precipitation and have been done in a specific location of the country or have used a limited number of ground stations in statistical comparison.
    In this work, the quality of 2m temperature, surface pressure, 10m wind speed and dew point temperature of ERA5-Land are evaluated temporally and spatially over Iran. For this purpose, sub-daily observations of 406 synoptic stations from 1999 to 2019 were used. The bilinear method was used to spatially interpolate the meteorological values obtained from ERA5-Land at the station locations. After preparing the ERA5-Land sub-daily time series and the corresponding actual observations, the error statistics required to evaluate the ERA5-Land data were calculated. Statistical comparisons between ERA5-Land products and ground observations of 2m temperature, surface pressure, 10m wind speed and dew point temperature parameters are done with a 3-hour temporal resolution.
    In the whole region, ERA5-Land products and local measurements of 2m temperature, surface pressure, 10m wind speed and dew point temperature showed agreement about 0.97, 0.98, 0.49 and 0.88, respectively. Also, compared to the actual observations, the mean RMSE for the above ERA5-Land data products achieved 2.87°C, 19.42 hPa, 2.52 m/s and 4.12°C, respectively.
    The study of bias values showed that in the region of Iran, ERA5-Land, on average, estimates all the studied variables less than the observed value. In addition, the study of the relationship between height difference of ERA5-Land grid points and station height with estimated error values showed that with increasing altitude difference, the size of negative bias and root mean square error of 2m temperature and the surface pressure of ERA5-Land increases significantly. Therefore, it is suggested eliminating the existing systematic errors in the area before applying this data.
 
 

Keywords


Arsenault, R., Brissette, F., Martel, J. L., Troin, M., Lévesque, G., Davidson-Chaput, J., and Poulin, A., 2020, A comprehensive, multisource database for hydrometeorological modeling of 14,425 North American watersheds: Scientific Data, 7(1), 1-12, https://doi.org/10.1038/s41597-020-00583-2.
Azizi Mobaser, J., Rasoulzadeh, A., Rahmati, A., Shayeghi, A., and Bakhtar, A., 2021, Evaluating the performance of ERA-5 re-analysis data in estimating daily and monthly precipitation, Case study: Ardabil Province: Iranian Journal of Soil and Water Research, 51(11), 2937-2951, https://doi.org/10.22059/IJSWR.2020.302176.668600.
Baker, J. C., Castilho de Souza, D., Kubota, P. Y., Buermann, W., Coelho, C. A., Andrews, M. B., and Spracklen, D. V., 2021, An assessment of land–atmosphere interactions over South America using satellites, reanalysis, and two global climate models: Journal of Hydrometeorology, 22(4), 905-922, https://doi.org/10.1175/JHM-D-20-0132.1.
Cao, B., Gruber, S., Zheng, D., and Li, X., 2020, The ERA5-Land soil temperature bias in permafrost regions: The Cryosphere, 14(8), 2581-2595, https://doi.org/10.5194/tc-14-2581-2020.
Chen, Y., Sharma, S., Zhou, X., Yang, K., Li, X., Niu, X., and Khadka, N., 2021, Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya: Atmospheric Research, 250, 105365, https://doi.org/10.1016/j.atmosres.2020.105365.
Czernecki, B., Taszarek, M., Marosz, M., Półrolniczak, M., Kolendowicz, L., Wyszogrodzki, A., and Szturc, J., 2019, Application of machine learning to large hail prediction - The importance of radar reflectivity, lightning occurrence and convective parameters derived from ERA5: Atmospheric Research, 227, 249-262, https://doi.org/10.1016/j.atmosres.2019.05.010.
Essou, G. R., Sabarly, F., Lucas-Picher, P., Brissette, F., and Poulin, A., 2016, Can precipitation and temperature from meteorological reanalyses be used for hydrological modeling?: Journal of Hydrometeorology, 17, 1929–1950, https://doi.org/10.1175/JHM-D-15-0138.1.
Fallah, A., Rakhshandehroo, G. R., Berg, P. O. S., and Orth, R., 2020, Evaluation of precipitation datasets against local observations in southwestern Iran: International Journal of Climatology, 40(9), 4102-4116, https://doi.org/10.1002/joc.6445.
Fortin, V., Roy, G., Donaldson, N., and Mahidjiba, A., 2015, Assimilation of radar quantitative precipitation estimations in the Canadian Precipitation Analysis (CaPA): Journal of Hydrology, 531, 296–307, https://doi.org/10.1016/j.jhydrol.2015.08.003.
Gao, L., Bernhardt, M., Schulz, K., Chen, X., Chen, Y., and Liu, M., 2016, A first evaluation of ERA-20CM over China: Monthly Weather Review, 144(1), 45-57, https://doi.org/10.1175/MWR-D-15-0195.1.
Hersbach, H., and Dee, D., ERA5 reanalysis is in production, ECMWF Newsletter 147, ECMWF, Reading, UK, available at: https://www.ecmwf.int/en/newsletter/147/news/ era5-reanalysis-production (last aRess: May 2020), 2016 (data available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/ reanalysis-era5-single-levels?tab=form, last aRess: May 2020).
Huai, B., Wang, J., Sun, W., Wang, Y., and Zhang, W., 2021, Evaluation of the near-surface climate of the recent global atmospheric reanalysis for Qilian Mountains, Qinghai-Tibet Plateau: Atmospheric Research, 250, 105401, https://doi.org/10.1016/j.atmosres.2020.105401.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., et al., 2021, ERA5-Land: A state-of-the-art global reanalysis dataset for land applications: Earth System Science Data, 13(9), 4349-4383, https://doi.org/10.5194/essd-2021-82.
Naumann, G., Dutra, E., Barbosa, P., Pappenberger, F., Wetterhall, F., and Vogt, J. V., 2014, Comparison of drought indicators derived from multiple data sets over Africa: Hydrology and Earth System Sciences, 18(5), 1625–1640, https://doi. org/10.5194/hess-18-1625-2014.
Pelosi, A., Terribile, F., D’Urso, G., and Chirico, G. B., 2020, Comparison of ERA5-Land and UERRA MESCAN-SURFEX reanalysis data with spatially interpolated weather observations for the regional assessment of reference evapotranspiration: Water, 12(6), 1669,  https://doi.org/10.3390/w12061669.
Ruffault, J., Moron, V., Trigo, R. M., and Curt, T., 2017, Daily synoptic conditions associated with large fire occurrence in Mediterranean France: evidence for a wind-driven fire regime: International Journal of Climatology, 37(1), 524–533, https://doi.org/10.1002/joc.4680.
Shamshirband, S., Mosavi, A., Nabipour, N., and Chau, K. W., 2020, Application of ERA5 and MENA simulations to predict offshore wind energy potential: arXiv preprint arXiv:2002.10022.
Sheffield, J., Goteti, G., Wood, E.F., 2006,           Development of a 50-year high-resolution             global dataset of meteorological forcings for        land surface modeling. Journal of Climate. 19, 3088–3111.
Singh, V. P., and Woolhiser, D. A., 2002, Mathematical modeling of watershed hydrology: Journal of Hydrologic Engineering, 7, 270–292, https://doi.org/10.1061/(ASCE)1084-0699(2002)7:4(270).
Soci, C., Bazile, E., Besson, F., and Landelius, T., 2016, High-resolution precipitation reanalysis system for climatological purposes: Tellus A: Dynamic Meteorology and Oceanography, 68, 1–19, https://doi.org/10.3402/tellusa.v68.29879.
Sun, G., Hu, Z., Ma, Y., Xie, Z., Yang, S., and Wang, J., 2020, Analysis of local land-atmosphere coupling in rainy season over a typical underlying surface in Tibetan Plateau based on field measurements and ERA5: Atmospheric Research, 243, 105025, https://doi.org/10.1016/j.atmosres.2020.105025.
Tarek, M., Brissette, F. P., and Arsenault, R., 2020, Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America: Hydrology and Earth System Sciences., 24(5), 2527-2544, https://doi.org/10.5194/hess-24-2527-2020.
Urraca, R., Huld, T., Gracia-Amillo, A., Martinez-de-Pison, F. J., Kaspar, F., and Sanz-Garcia, A., 2018, Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalysis using ground and satellite-based data: Solar Energy, 164, 339–354, https://doi.org/10.1016/j.solener.2018.02.059.
Vaghefi, S. A., Keykhai, M., Jahanbakhshi, F., Sheikholeslami, J., Ahmadi, A., Yang, H., and Abbaspour, K. C., 2019, The future of extreme climate in Iran: Scientific Reports, 9, 1464, https://doi.org/10.1038/s41598-018-38071-8.
Xue, C., Wu, H., and Jiang, X., 2019, Temporal and spatial change monitoring of drought grade based on ERA5 analysis data and BFAST method in the belt and road area during 1989–2017: Advances in Meteorology, https://doi.org/10.1155/2019/4053718.
Yang, H., He, C., Wang, Z., and Shao, W. 2019, Reliability Analysis of European ERA5 Water Vapor Content Based on Ground-based GPS in China. In 2019 International Conference on Wireless Communication, Network and Multimedia Engineering (WCNME 2019) (pp. 44-49). Atlantis Press.
Zhang, Y., Cai, C., Chen, B., and Dai, W., 2019, Consistency evaluation of perceptible water vapor derived from ERA5, ERA-Interim, GNSS, and radiosondes over China: Radio Science, 54(7), 561-571, https://doi.org/10.1029/2018RS006789.