Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China

References

• Aksu, H., & Akgül, M. (2020). Performance evaluation of CHIRPS satellite precipitation estimates over Turkey. Theoretical and Applied Climatology, 142(1–2), 71–19. https://doi.org/10.1007/s00704-020-03301-5
   Web of Science ®Google Scholar
• Ashouri, H., Hsu, K.-L., Sorooshian, S., Braithwaite, D. K., Knapp, K. R., Cecil, L. D., Nelson, B. R., & Prat, O. P. (2015). PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bulletin of the American Meteorological Society, 96(1), 69–83. https://doi.org/10.1175/BAMS-D-13-00068.1
   Web of Science ®Google Scholar
• Ayat, H., Reza Kavianpour, M., Moazami, S., Hong, Y., & Ghaemi, E. (2018). Calibration of weather radar using region probability matching method (RPMM). Theoretical and Applied Climatology, 134(1), 165–176. https://doi.org/10.1007/s00704-017-2266-7
   Web of Science ®Google Scholar
• Baatz, R., Hendricks Franssen, H. J., Euskirchen, E., Sihi, D., Dietze, M., Ciavatta, S., Fennel, K., Beck, H., De Lannoy, G., Pauwels, V. R. N., Raiho, A., Montzka, C., Williams, M., Mishra, U., Poppe, C., Zacharias, S., Lausch, A., Samaniego, L. … Vereecken, H. (2021). Reanalysis in earth system science: Toward terrestrial ecosystem reanalysis. Reviews of Geophysics, 59(3), e2020RG000715. https://doi.org/10.1029/2020RG000715
   Web of Science ®Google Scholar
• Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L., Izsák, B., Szentes, O., Tveito, O. E., & Frei, C. (2022). Evaluation of daily precipitation analyses in E-OBS (v19.0e) and ERA5 by comparison to regional high-resolution datasets in European regions. International Journal of Climatology, 42(2), 727–747. https://doi.org/10.1002/joc.7269
   Web of Science ®Google Scholar
• Belete, M., Deng, J., Wang, K., Zhou, M., Zhu, E., Shifaw, E., & Bayissa, Y. (2020). Evaluation of satellite rainfall products for modeling water yield over the source region of Blue Nile Basin. Science of the Total Environment, 708, 134834. https://doi.org/10.1016/j.scitotenv.2019.134834
   PubMed Web of Science ®Google Scholar
• Chao, L., Zhang, K., Li, Z., Zhu, Y., Wang, J., & Yu, Z. (2018). Geographically weighted regression based methods for merging satellite and gauge precipitation. Journal of Hydrology, 558, 275–289. https://doi.org/10.1016/j.jhydrol.2018.01.042
   Web of Science ®Google Scholar
• Chen, C., Chen, Q., Qin, B., Zhao, S., & Duan, Z. (2020). Comparison of different methods for spatial downscaling of GPM IMERG V06B satellite precipitation product over a typical arid to semi-arid area. Frontiers in Earth Science, 8, 8. https://doi.org/10.3389/feart.2020.536337
   Web of Science ®Google Scholar
• Choi, Y., Cha, K., Back, M., Choi, H., & Jeon, T.(2021). Rain-F: A fusion dataset for rainfall prediction using convolutional neural network. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. https://doi.org/10.1109/igarss47720.2021.9555094
   Google Scholar
• Cooley, S. S., Williams, C. A., Fisher, J. B., Halverson, G. H., Perret, J., & Lee, C. M. (2019). Assessing regional drought impacts on vegetation and evapotranspiration: A case study in Guanacaste, Costa Rica. Ecological Applications, 29(2), e01834. https://doi.org/10.1002/eap.1834
   PubMed Web of Science ®Google Scholar
• Dahri, Z. H., Ludwig, F., Moors, E., Ahmad, S., Ahmad, B., Shoaib, M., Ali, I., Iqbal, M. S., Pomee, M. S., Mangrio, A. G., Ahmad, M. M., & Kabat, P. (2021). Spatio-temporal evaluation of gridded precipitation products for the high-altitude indus basin. International Journal of Climatology: A Journal of the Royal Meteorological Society, 41(8), 4283–4306. https://doi.org/10.1002/joc.7073
   Web of Science ®Google Scholar
• Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M. … Vitart, F. (2011). The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656), 553–597. https://doi.org/10.1002/qj.828
   Web of Science ®Google Scholar
• Dong, J., Crow, W. T., Duan, Z., Wei, L., & Lu, Y. (2019). A double instrumental variable method for geophysical product error estimation. Remote Sensing of Environment, 225, 217–228. https://doi.org/10.1016/j.rse.2019.03.003
   Web of Science ®Google Scholar
• Driscoll, S. J., Harrison, D. L., & Kitchen, M. (2000). Improving precipitation estimates from weather radar using quality control and correction techniques. Meteorological Applications, 7(2), 135–144. https://doi.org/10.1017/S1350482700001468
   Web of Science ®Google Scholar
• Duan, Z., Duggan, E., Chen, C., Gao, H., Dong, J., & Liu, J. (2021). Comparison of Traditional Method and Triple Collocation analysis for Evaluation of multiple gridded precipitation products across Germany. Journal of Hydrometeorology, 22(11), 2983–2999. https://doi.org/10.1175/JHM-D-21-0049.1
   Web of Science ®Google Scholar
• Fang, K., Pan, M., & Shen, C. (2019). The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Transactions on Geoscience and Remote Sensing, 57(4), 2221–2233. https://doi.org/10.1109/TGRS.2018.2872131
   Web of Science ®Google Scholar
• Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66
   PubMed Web of Science ®Google Scholar
• Gan, F., Chen, S., Gao, Y., & Li, Y. (2022). An evaluation of the performance of IMERG hourly precipitation estimates in a geographical sub-region with complex terrain and climate conditions: A case study in the upper Red River Basin. Remote Sensing Letters, 13(3), 301–310. https://doi.org/10.1080/2150704X.2021.2014076
   Web of Science ®Google Scholar
• Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., Silva, A. M. D. … Zhao, B. (2017). The Modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Journal of Climate, 30(14), 5419–5454. https://doi.org/10.1175/JCLI-D-16-0758.1
   Web of Science ®Google Scholar
• Gomis-Cebolla, J., Rattayova, V., Salazar-Galán, S., & Francés, F. (2023). Evaluation of ERA5 and ERA5-land reanalysis precipitation datasets over Spain (1951–2020). Atmospheric Research, 284, 106606. https://doi.org/10.1016/j.atmosres.2023.106606
   Web of Science ®Google Scholar
• Hoang, T., Manomaiphiboon, K., Singhrattna, N., & Assareh, N. (2020). Evaluation of multiple sub-daily satellite precipitation products for Thailand. Journal of Sustainable Energy & Environment, 11(2020), 81–91. https://www.semanticscholar.org/paper/Evaluation-of-multiple-sub-daily-satellite-products-Trang-Manomaiphiboon/aee14b4e1ce4818ad4e226c159b2720c58702f66
   Google Scholar
• Hofstra, N., New, M., & McSweeney, C. (2010). The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data. Climate Dynamics, 35(5), 841–858. https://doi.org/10.1007/s00382-009-0698-1
   Web of Science ®Google Scholar
• Hong, L., Huang, Y., Peng, S., & Hewitt, J. (2020). Monitoring the trends of water-erosion desertification on the Yunnan-Guizhou Plateau, China from 1989 to 2016 using time-series Landsat images. Public Library of Science ONE, 15(2), e0227498. https://doi.org/10.1371/journal.pone.0227498
   Google Scholar
• Hou, A. Y., Kakar, R. K., Neeck, S., Azarbarzin, A. A., Kummerow, C. D., Kojima, M., Oki, R., Nakamura, K., & Iguchi, T. (2014). The global precipitation measurement mission. Bulletin of the American Meteorological Society, 95(5), 701–722. https://doi.org/10.1175/BAMS-D-13-00164.1
   Web of Science ®Google Scholar
• Irannezhad, M., & Liu, J. (2022). Evaluation of six gauge-based gridded climate products for analyzing long-term historical precipitation patterns across the Lancang-Mekong River Basin. Geography and Sustainability, 3(1), 85–103. https://doi.org/10.1016/j.geosus.2022.03.002
   Google Scholar
• Jackson, G., Kirschbaum, D., Petersen, W., Huffman, G., Kidd, C., Stocker, E., & Kakar, R. (2018). The global precipitation measurement (GPM) mission’s scientific achievements and societal contributions: Reviewing four years of advanced rain and snow observations. Quarterly Journal of the Royal Meteorological Society, 144(S1), 27–48. https://doi.org/10.1002/qj.3313
   PubMedGoogle Scholar
• Jiang, Q., Li, W., Fan, Z., He, X., Sun, W., Chen, S., Wen, J., Gao, J., & Wang, J. (2021). Evaluation of the ERA5 reanalysis precipitation dataset over Chinese mainland. Journal of Hydrology, 595, 125660. https://doi.org/10.1016/j.jhydrol.2020.125660
   Web of Science ®Google Scholar
• Ji, H., Peng, D., Gu, Y., Liang, Y., & Luo, X. (2022). Evaluation of multiple satellite precipitation products and their potential utilities in the Yarlung Zangbo River Basin. Scientific Reports, 12(1), 13334. https://doi.org/10.1038/s41598-022-17551-y
   PubMed Web of Science ®Google Scholar
• Joyce, R. J., Janowiak, J. E., Arkin, P. A., & Xie, P. (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5(3), 487–503. https://doi.org/10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2
   Web of Science ®Google Scholar
• Khan, M. K. U., Iqbal, M. F., Mahmood, I., Shahzad, M. I., Zafar, Q., & Khalid, B. (2023). Evaluation of precipitation products over different climatic zones of Pakistan. Theoretical and Applied Climatology, 151(3), 1301–1321. https://doi.org/10.1007/s00704-022-04355-3
   Web of Science ®Google Scholar
• Kim, S., Sharma, A., Wasko, C., & Nathan, R. (2022). Linking total precipitable water to precipitation extremes globally. Earth’s Future, 10(2), e2021EF002473. https://doi.org/10.1029/2021EF002473
   Web of Science ®Google Scholar
• Kirstetter, P. E. (2022). Evaluation of IMERG over CONUS complex terrain using environmental variables. Geophysical Research Letters, 49(19), e2022GL100186. https://doi.org/10.1029/2022GL100186
   Web of Science ®Google Scholar
• Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., & Takahashi, K. (2015). The JRA-55 reanalysis: General specifications and basic characteristics. Journal of the Meteorological Society of Japan Ser II, 93(1), 5–48. https://doi.org/10.2151/jmsj.2015-001
   Web of Science ®Google Scholar
• Le, X.-H., Nguyen Van, L., Hai Nguyen, D., Nguyen, G. V., Jung, S., & Lee, G. (2023). Comparison of bias-corrected multisatellite precipitation products by deep learning framework. International Journal of Applied Earth Observation and Geoinformation, 116, 103177. https://doi.org/10.1016/j.jag.2022.103177
   Web of Science ®Google Scholar
• Levizzani, V., & Cattani, E. (2019). Satellite remote sensing of precipitation and the Terrestrial Water Cycle in a changing climate. Remote Sensing, 11(19), 2301. https://doi.org/10.3390/rs11192301
   Web of Science ®Google Scholar
• Li, X., Chen, Y., Wang, H., & Zhang, Y. (2020). Assessment of GPM IMERG and radar quantitative precipitation estimation (QPE) products using dense rain gauge observations in the Guangdong-Hong Kong-Macao Greater Bay area, China. Atmospheric Research, 236, 104834. https://doi.org/10.1016/j.atmosres.2019.104834
   Web of Science ®Google Scholar
• Liu, Z., Jiang, L., Shi, C., Zhang, T., Zhou, Z., Liao, J., Yao, S., Liu, J., Wang, M., Wang, H., Liang, X., Zhang, Z., Yao, Y., Zhu, T., Chen, Z., Xu, W., Cao, L., Jiang, H., & Hu, K. (2023). CRA-40/Atmosphere—The first-generation Chinese Atmospheric reanalysis (1979–2018): System description and performance evaluation. Journal of Meteorological Research, 37(1), 1–19. https://doi.org/10.1007/s13351-023-2086-x
   Web of Science ®Google Scholar
• Li, X., Yang, Y., Mi, J., Bi, X., Zhao, Y., Huang, Z., Liu, C., Zong, L., & Li, W. (2021). Leveraging machine learning for quantitative precipitation estimation from fengyun-4 geostationary observations and ground meteorological measurements. Atmospheric Measurement Techniques, 14(11), 7007–7023. https://doi.org/10.5194/amt-14-7007-2021
   Web of Science ®Google Scholar
• Loo, Y. Y., Billa, L., & Singh, A. (2015). Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geoscience Frontiers, 6(6), 817–823. https://doi.org/10.1016/j.gsf.2014.02.009
   Web of Science ®Google Scholar
• Lu, H., Ding, L., Ma, Z., Li, H., Lu, T., Su, M., & Xu, J. (2020). Spatiotemporal assessments on the satellite-based precipitation products from Fengyun and GPM over the Yunnan-Kweichow Plateau, China. Earth and Space Science, 7(1), e2019EA000857. https://doi.org/10.1029/2019EA000857
   Web of Science ®Google Scholar
• Lu, H., Huang, Z., Ding, L., Lu, T., & Yuan, Y. (2021). Calibrating FY4A QPE using CMPA over Yunnan–Kweichow Plateau in summer 2019. European Journal of Remote Sensing, 54(1), 476–486. https://doi.org/10.1080/22797254.2021.1960202
   Web of Science ®Google Scholar
• Luo, X., Fan, X., Ji, X., & Li, Y. (2020). Evaluation of corrected APHRODITE estimates for hydrological simulation in the Yarlung Tsangpo–Brahmaputra River Basin. International Journal of Climatology, 40(9), 4158–4170. https://doi.org/10.1002/joc.6449
   Web of Science ®Google Scholar
• Mannig, B., Müller, M., Starke, E., Merkenschlager, C., Mao, W., Zhi, X., Podzun, R., Jacob, D., & Paeth, H. (2013). Dynamical downscaling of climate change in central asia. Global and Planetary Change, 110, 26–39. https://doi.org/10.1016/j.gloplacha.2013.05.008
   Web of Science ®Google Scholar
• Ma, Z., Xu, J., Ma, Y., Zhu, S., He, K., Zhang, S., Ma, W., & Xu, X. (2022). AERA5-Asia: A long-term Asian precipitation dataset (0.1°, 1-hourly, 1951–2015, Asia) anchoring the ERA5-land under the total volume control by APHRODITE. Bulletin of the American Meteorological Society, 103(4), E1146–E1171. https://doi.org/10.1175/BAMS-D-20-0328.1
   Web of Science ®Google Scholar
• Ma, Z., Xu, J., Zhu, S., Yang, J., Tang, G., Yang, Y., Shi, Z., & Hong, Y. (2020). AIMERG: A new Asian precipitation dataset (0.1°/half-hourly, 2000–2015) by calibrating the GPM-era IMERG at a daily scale using APHRODITE. Earth System Science Data, 12(3), 1525–1544. https://doi.org/10.5194/essd-12-1525-2020
   Web of Science ®Google Scholar
• Miao, Q., Yuan, S., Shi, L., & Gu, X. (2007). Study on the spring drought rule in the karst region of Yunnan and Guizhou Plateau in China. Proceedings of SPIE - The International Society for Optical Engineering, 6790. https://doi.org/10.1117/12.746860
   Google Scholar
• Michaelides, S., Levizzani, V., Anagnostou, E., Bauer, P., Kasparis, T., & Lane, J. E. (2009). Precipitation: Measurement, remote sensing, climatology and modeling. Atmospheric Research, 94(4), 512–533. https://doi.org/10.1016/j.atmosres.2009.08.017
   Web of Science ®Google Scholar
• Mo, C., Meng, X., Ruan, Y., Wang, Y., Lei, X., Xing, Z., & Lai, S. (2022). Drought assessment based on fused satellite and station precipitation data: An example from the Chengbi River Basin, China. ISPRS International Journal of Geo-Information, 11(1), 48. https://doi.org/10.3390/ijgi11010048
   Web of Science ®Google Scholar
• Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., & Thépaut, J.-N. (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-13-4349-2021
   Web of Science ®Google Scholar
• Nan, L., Yang, M., Wang, H., Xiang, Z., & Hao, S. (2021). Comprehensive Evaluation of Global Precipitation Measurement Mission (GPM) IMERG Precipitation products over Mainland China. Water, 13(23), 3381. https://doi.org/10.3390/w13233381
   Web of Science ®Google Scholar
• Nawaz, Z., Li, X., Chen, Y., Nawaz, N., Gull, R., & Elnashar, A. (2020). Spatio-temporal assessment of Global precipitation products over the largest agriculture region in Pakistan. Remote Sensing, 12(21), 3650. https://doi.org/10.3390/rs12213650
   Web of Science ®Google Scholar
• Ning, S., Song, F., Udmale, P., Jin, J., Thapa, B., & Ishidaira, H. (2017). Error analysis and evaluation of the latest GSMap and IMERG precipitation products over Eastern China. Advances in Meteorology, 2017, 1–16. https://doi.org/10.1155/2017/1803492
   Web of Science ®Google Scholar
• Özerdem, M. S., Acar, E., & Ekinci, R. (2017). Soil moisture estimation over vegetated agricultural areas: Tigris Basin, Turkey from radarsat-2 data by Polarimetric Decomposition Models and a Generalized Regression Neural Network. Remote Sensing, 9(4), 395. https://doi.org/10.3390/rs9040395
   Web of Science ®Google Scholar
• Parker, W. S. (2016). Reanalyses and observations: What’s the difference? Bulletin of the American Meteorological Society, 97(9), 1565–1572. https://doi.org/10.1175/BAMS-D-14-00226.1
   Web of Science ®Google Scholar
• Peng, F., Zhao, S., Chen, C., Cong, D., Wang, Y., & Ouyang, H. (2020). Evaluation and comparison of the precipitation detection ability of multiple satellite products in a typical agriculture area of China. Atmospheric Research, 236, 104814. https://doi.org/10.1016/j.atmosres.2019.104814
   Web of Science ®Google Scholar
• Prakash, S., Mitra, A. K., Momin, I. M., Rajagopal, E. N., Basu, S., Collins, M., Turner, A. G., Achuta Rao, K., & Ashok, K. (2015). Seasonal intercomparison of observational rainfall datasets over India during the southwest monsoon season. International Journal of Climatology, 35(9), 2326–2338. https://doi.org/10.1002/joc.4129
   Web of Science ®Google Scholar
• Prein, A. F., & Gobiet, A. (2017). Impacts of uncertainties in European gridded precipitation observations on regional climate analysis. International Journal of Climatology, 37(1), 305–327. https://doi.org/10.1002/joc.4706
   PubMed Web of Science ®Google Scholar
• Qiu, S., Peng, J., Zheng, H., Xu, Z., & Meersmans, J. (2022). How can massive ecological restoration programs interplay with social-ecological systems? A review of research in the South China karst region. Science of the Total Environment, 807, 150723. https://doi.org/10.1016/j.scitotenv.2021.150723
   PubMed Web of Science ®Google Scholar
• Reda, K., Liu, X., Haile, G., Sun, S., & Tang, Q. (2021). Hydrological evaluation of satellite and Reanalysis-based Rainfall Estimates Over the Upper Tekeze Basin, Ethiopia. https://doi.org/10.22541/au.161494395.50246119/v1
   Google Scholar
• Ren, P., Li, J., Feng, P., Guo, Y., & Ma, Q. (2018). Evaluation of multiple satellite precipitation products and their use in hydrological modelling over the Luanhe River Basin, China. Water, 10(6), 677. https://doi.org/10.3390/w10060677
   Web of Science ®Google Scholar
• Rong, G., & Zhang, J.(2023). Risk assessment of extreme precipitation-geological hazard chains in the Yunnan-Guizhou Plateau. Advances in Economics, Business and Management Research, 18–25. https://doi.org/10.2991/978-94-6463-194-4_4
   Google Scholar
• Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., & Rudolf, B. (2014). GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoretical and Applied Climatology, 115(1–2), 15–40. https://doi.org/10.1007/s00704-013-0860-x
   Web of Science ®Google Scholar
• Shen, Y., Zhao, P., Pan, Y., & Yu, J. (2014). A high spatiotemporal gauge‐satellite merged precipitation analysis over China. Journal of Geophysical Research: Atmospheres, 119(6), 3063–3075. https://doi.org/10.1002/2013JD020686
   Web of Science ®Google Scholar
• Song, X., Zhang, G., Liu, F., Li, D., Zhao, Y., & Yang, J. (2016). Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 8(5), 734–748. https://doi.org/10.1007/s40333-016-0049-0
   Web of Science ®Google Scholar
• Soo, E. Z. X., Wan Jaafar, W. Z., Lai, S. H., Othman, F., Elshafie, A., Islam, T., Srivastava, P., & Othman Hadi, H. S. (2020). Precision of raw and bias-adjusted satellite precipitation estimations (TRMM, IMERG, CMORPH, and PERSIANN) over extreme flood events: Case study in Langat river basin, Malaysia. Journal of Water and Climate Change, 11(S1), 322–342. https://doi.org/10.2166/wcc.2020.180
   Google Scholar
• Sun, H., Yang, Y., Wu, R., Gui, D., Xue, J., Liu, Y., & Yan, D. (2019). Improving estimation of cropland evapotranspiration by the bayesian Model averaging method with surface energy balance models. Atmosphere, 10(4), 188. https://doi.org/10.3390/atmos10040188
   Web of Science ®Google Scholar
• Tang, X., Li, H., Qin, G., Huang, Y., & Qi, Y. (2023). Evaluation of satellite-based precipitation products over complex topography in Mountainous Southwestern China. Remote Sensing, 15(2), 473. https://doi.org/10.3390/rs15020473
   Web of Science ®Google Scholar
• Tanim, A. H., Mullick, M. R. A., & Sikdar, M. S. (2021). Evaluation of spatial rainfall products in sparsely gauged region using copula uncertainty modeling with triple collocation. Journal of Hydrologic Engineering, 26(4), 04021004. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002071
   Web of Science ®Google Scholar
• Tian, Y., Peters-Lidard, C. D., Eylander, J. B., Joyce, R. J., Huffman, G. J., Adler, R. F., Hsu, K.-L., Turk, F. J., Garcia, M., & Zeng, J. (2009). Component analysis of errors in satellite-based precipitation estimates. Journal of Geophysical Research: Atmospheres, 114(D24). https://doi.org/10.1029/2009JD011949
   Google Scholar
• Tong, K., Su, F., Yang, D., & Hao, Z. (2014). Evaluation of satellite precipitation retrievals and their potential utilities in hydrologic modeling over the Tibetan Plateau. Journal of Hydrology, 519, 423–437. https://doi.org/10.1016/j.jhydrol.2014.07.044
   Web of Science ®Google Scholar
• Torsri, K., Lin, Z., Dike, V., Thodsan, T., & Wongsaming, P. (2022). Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand inferred from different gridded datasets. Water, 14(9), 1359. https://doi.org/10.3390/w14091359
   Web of Science ®Google Scholar
• Wang, R., Chen, J., & Wang, X. (2017). Comparison of IMERG level-3 and TMPA 3B42V7 in estimating typhoon-related heavy rain. Water, 9(4), 276. https://doi.org/10.3390/w9040276
   Web of Science ®Google Scholar
• Wedajo, G. K., Muleta, M. K., & Awoke, B. G. (2021). Performance evaluation of multiple satellite rainfall products for Dhidhessa River Basin (DRB), Ethiopia. Atmospheric Measurement Techniques, 14(3), 2299–2316. https://doi.org/10.5194/amt-14-2299-2021
   Web of Science ®Google Scholar
• Wu, X., & Zhao, N. (2023). Evaluation and comparison of six high-resolution daily precipitation products in Mainland China. Remote Sensing, 15(1), 223. https://doi.org/10.3390/rs15010223
   Web of Science ®Google Scholar
• Xiao, X., Liu, X., Gao, Y., Zhang, S., & Xu, W. (2023). Evaluation of Regional Drought in Yunnan–Guizhou Plateau of China. Proceedings of the nternational Conference on Computational & Experimental Engineering and Sciences (pp. 345–359). https://doi.org/10.1007/978-3-031-42515-8_23.
   Google Scholar
• Xie, W., Yi, S., Leng, C., Xia, D., Li, M., Zhong, Z., & Ye, J. (2022). The evaluation of IMERG and ERA5-land daily precipitation over China with considering the influence of gauge data bias. Scientific Reports, 12(1), 8085. https://doi.org/10.1038/s41598-022-12307-0
   PubMed Web of Science ®Google Scholar
• Xin, Y., Lu, N., Jiang, H., Liu, Y., & Yao, L. (2021). Performance of ERA5 reanalysis precipitation products in the Guangdong-Hong Kong-Macao greater Bay area, China. Journal of Hydrology, 602, 126791. https://doi.org/10.1016/j.jhydrol.2021.126791
   Web of Science ®Google Scholar
• Xin, Y., Yang, Y., Chen, X., Yue, X., Liu, Y., & Yin, C. (2022). Evaluation of IMERG and ERA5 precipitation products over the Mongolian Plateau. Scientific Reports, 12(1), 21776. https://doi.org/10.1038/s41598-022-26047-8
   PubMed Web of Science ®Google Scholar
• Xu, J., Ma, Z., Yan, S., & Peng, J. (2022). Do ERA5 and ERA5-land precipitation estimates outperform satellite-based precipitation products? A comprehensive comparison between state-of-the-art model-based and satellite-based precipitation products over mainland China. Journal of Hydrology, 605, 127353. https://doi.org/10.1016/j.jhydrol.2021.127353
   Web of Science ®Google Scholar
• Yao, J., Chen, Y., Yu, X., Zhao, Y., Guan, X., & Yang, L. (2020). Evaluation of multiple gridded precipitation datasets for the arid region of northwestern China. Atmospheric Research, 236, 104818. https://doi.org/10.1016/j.atmosres.2019.104818
   Web of Science ®Google Scholar
• Yu, C., Hu, D., Liu, M., Wang, S., & Di, Y. (2020). Spatio-temporal accuracy evaluation of three high-resolution satellite precipitation products in China area. Atmospheric Research, 241, 104952. https://doi.org/10.1016/j.atmosres.2020.104952
   Web of Science ®Google Scholar
• Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2021). New insights into error decomposition for precipitation products. Geophysical Research Letters, 48(17), e2021GL094092. https://doi.org/10.1029/2021GL094092
   Web of Science ®Google Scholar
• Zhang, Y., Ye, A., Nguyen, P., Analui, B., Sorooshian, S., & Hsu, K. (2022). QRF4P-NRT: Probabilistic Post-Processing of Near-Real-Time Satellite Precipitation Estimates Using Quantile Regression Forests. Water Resources Research, 58(5), e2022WR032117. https://doi.org/10.1029/2022WR032117
   Web of Science ®Google Scholar
• Zhao, Y., Xu, K., Dong, N., & Wang, H. (2022). Optimally integrating multi-source products for improving long series precipitation precision by using machine learning methods. Journal of Hydrology, 609, 127707. https://doi.org/10.1016/j.jhydrol.2022.127707
   Web of Science ®Google Scholar
• Zhou, Z., Guo, B., Xing, W., Zhou, J., Xu, F., & Xu, Y. (2020). Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China. Atmospheric Research, 246, 105132. https://doi.org/10.1016/j.atmosres.2020.105132
   Web of Science ®Google Scholar
• Zhu, S., & Ma, Z. (2022). PECA-FY4A: Precipitation estimation using Chromatographic analysis methodology for full-disc multispectral observations from FengYun-4A/AGRI. Remote Sensing of Environment, 282, 113234. https://doi.org/10.1016/j.rse.2022.113234
   Web of Science ®Google Scholar
• Zhu, D., Yang, Q., Xiong, K., & Xiao, H. (2022). Spatiotemporal variations in daytime and night-time precipitation on the Yunnan–Guizhou Plateau from 1960 to 2017. Atmosphere, 13(3), 415. https://doi.org/10.3390/atmos13030415
   Web of Science ®Google Scholar