The study focuses on the accuracy of reanalysis and satellite rainfall data by comparing them with ground rain gauges. Using different statistical techniques, it is shown that the global data struggles to estimate rainfall at both daily and monthly intervals. The study divided the analysis into annual and seasonal scales, applied to daily and monthly data. The results show that the global datasets vary with time and space, which means that the performance of all datasets is unstable. Also, the study shows that most of the datasets underestimated the rainfall in all time scales except IMERG 0.2 & 0.5, which overestimated the rainfall. Furthermore, the correlation coefficient shows that the datasets struggled with the local convection during the summertime. However, they show a good performance during springtime, which is associated with torrential and widespread rainfall. The satellite data overestimated the rainfall by 4 mm to 20 mm at the best performing locations, where the reanalysis underestimated the rainfall by 0.2 mm to 4 mm in general, which is applied to all reanalysis datasets.
References
[1]
Li, H., Haugen, J.E. and Xu, C. (2018) Precipitation Pattern in the Western Himalayas Revealed by Four Datasets. Hydrology and Earth System Sciences, 22, 5097-5110. https://doi.org/10.5194/hess-22-5097-2018
[2]
Bližňák, V., Pokorná, L. and Rulfová, Z. (2022) Assessment of the Capability of Modern Reanalyses to Simulate Precipitation in Warm Months Using Adjusted Radar Precipitation. Journal of Hydrology: Regional Studies, 42, Article ID: 101121. https://doi.org/10.1016/j.ejrh.2022.101121
[3]
Hassler, B. and Lauer, A. (2021) Comparison of Reanalysis and Observational Precipitation Datasets Including ERA5 and WFDE5. Atmosphere, 12, Article 1462. https://doi.org/10.3390/atmos12111462
[4]
Irvem, A. and Ozbuldu, M. (2019) Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey. Advances in Meteorology, 2019, Article ID: 4820136. https://doi.org/10.1155/2019/4820136
[5]
Nkiaka, E., Nawaz, N.R. and Lovett, J.C. (2016) Evaluating Global Reanalysis Precipitation Datasets with Rain Gauge Measurements in the Sudano-Sahel Region: Case Study of the Logone Catchment, Lake Chad Basin. Meteorological Applications, 24, 9-18. https://doi.org/10.1002/met.1600
[6]
Morales-Velázquez, M.I., Herrera, G.D.S., Aparicio, J., Rafieeinasab, A. and Lobato-Sánchez, R. (2021) Evaluating Reanalysis and Satellite-Based Precipitation at Regional Scale: A Case Study in Southern Mexico. Atmósfera, 34, 189-206. https://doi.org/10.20937/atm.52789
[7]
Lemma, E., Upadhyaya, S. and Ramsankaran, R. (2019) Investigating the Performance of Satellite and Reanalysis Rainfall Products at Monthly Timescales across Different Rainfall Regimes of Ethiopia. International Journal of Remote Sensing, 40, 4019-4042. https://doi.org/10.1080/01431161.2018.1558373
[8]
Goodarzi, M.R., Pooladi, R. and Niazkar, M. (2022) Evaluation of Satellite-Based and Reanalysis Precipitation Datasets with Gauge-Observed Data over Haraz-Gharehsoo Basin, Iran. Sustainability, 14, Article 13051. https://doi.org/10.3390/su142013051
[9]
Dubache, G., Asmerom, B., Ullah, W., Ogwang, B.A., Amiraslani, F., Weijun, Z., et al. (2021) Testing the Accuracy of High-Resolution Satellite-Based and Numerical Model Output Precipitation Products over Ethiopia. Theoretical and Applied Climatology, 146, 1127-1142. https://doi.org/10.1007/s00704-021-03783-x
[10]
Serrat-Capdevila, A., Merino, M., Valdes, J. and Durcik, M. (2016) Evaluation of the Performance of Three Satellite Precipitation Products over Africa. Remote Sensing, 8, Article 836. https://doi.org/10.3390/rs8100836
[11]
Nguyen, P., Shearer, E.J., Tran, H., Ombadi, M., Hayatbini, N., Palacios, T., et al. (2019) The CHRS Data Portal, an Easily Accessible Public Repository for PERSIANN Global Satellite Precipitation Data. Scientific Data, 6, Article No. 180296. https://doi.org/10.1038/sdata.2018.296
[12]
Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., Molod, A., Takacs, L., et al. (2017) The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Journal of Climate, 30, 5419-5454. https://doi.org/10.1175/jcli-d-16-0758.1
[13]
Huffman, G.J., Bolvin, D.T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., et al. (2019) NASA Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals for GPM (IMERG). NASA. https://pps.gsfc.nasa.gov/Documents/IMERG_ATBD_V06.pdf
[14]
NOAA Climate Data Record Program (2017) Precipitation—Global Precipitation Climatology Project (GPCP) Monthly (01B-34), NOAA. Precipitation—Global Precipitation Climatology Project (GPCP) Monthly (01B-34).
[15]
Finger, P. (2022) GPCC Precipitation Climatology Version 2022. http://doi.org/10.5676/DWD_GPCC/CLIM_M_V2022_025
[16]
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., et al. (2014) Global Gridded Precipitation over Land: A Description of the New GPCC First Guess Daily Product. Earth System Science Data, 6, 49-60. https://doi.org/10.5194/essd-6-49-2014
[17]
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., et al. (2020) The ERA5 Global Reanalysis. Quarterly Journal of the Royal Meteorological Society, 146, 1999-2049. https://doi.org/10.1002/qj.3803
[18]
Harris, I., Osborn, T.J., Jones, P. and Lister, D. (2020) Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset. Scientific Data, 7, Article No. 109. https://doi.org/10.1038/s41597-020-0453-3
[19]
Chen, M., Shi, W., Xie, P., Silva, V.B.S., Kousky, V.E., Wayne Higgins, R., et al. (2008) Assessing Objective Techniques for Gauge-Based Analyses of Global Daily Precipitation. Journal of Geophysical Research: Atmospheres, 113, D04110. https://doi.org/10.1029/2007jd009132
[20]
Xie, P., Joyce, R., Wu, S., Yoo, S., Yarosh, Y., Sun, F., et al. (2017) Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation Estimates from 1998. Journal of Hydrometeorology, 18, 1617-1641. https://doi.org/10.1175/jhm-d-16-0168.1
[21]
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., et al. (1996) The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437-471. https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2
[22]
Yang, Y., Ji, W., Niu, L., Zheng, Z., Huang, W., Zhang, C., et al. (2024) Assessing Satellite and Reanalysis-Based Precipitation Products in Cold and Arid Mountainous Regions. Journal of Hydrology: Regional Studies, 51, Article ID: 101612. https://doi.org/10.1016/j.ejrh.2023.101612
[23]
Hou, C., Huang, D., Xu, H. and Xu, Z. (2022) Evaluation of ERA5 Reanalysis over the Deserts in Northern China. Theoretical and Applied Climatology, 151, 801-816. https://doi.org/10.1007/s00704-022-04306-y
[24]
Hamal, K., Sharma, S., Khadka, N., Baniya, B., Ali, M., Shrestha, M.S., et al. (2020) Evaluation of MERRA-2 Precipitation Products Using Gauge Observation in Nepal. Hydrology, 7, Article 40. https://doi.org/10.3390/hydrology7030040
[25]
Giordani, A., Cerenzia, I.M.L., Paccagnella, T. and Di Sabatino, S. (2023) SPHERA, a New Convection-Permitting Regional Reanalysis over Italy: Improving the Description of Heavy Rainfall. Quarterly Journal of the Royal Meteorological Society, 149, 781-808. https://doi.org/10.1002/qj.4428
