Evaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin

dc.creatorVega-Durán, Jean
dc.creatorEscalante-Castro, Brigitte
dc.creatorCanales, Fausto
dc.creatorAcuña Robles, Guillermo Jesús
dc.creatorKaźmierczak, Bartosz
dc.date2022-03-10T19:26:22Z
dc.date2022-03-10T19:26:22Z
dc.date2021-10-29
dc.date.accessioned2023-10-03T20:11:32Z
dc.date.available2023-10-03T20:11:32Z
dc.identifierVega‐Durán, J.; Escalante‐Castro, B.; Canales, F.A.; Acuña, G.J.; Kaźmierczak, B. Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin. Atmosphere 2021, 12, 1430. https://doi.org/10.3390/atmos12111430
dc.identifierhttps://hdl.handle.net/11323/9067
dc.identifierhttps://doi.org/10.3390/atmos12111430
dc.identifier10.3390/atmos12111430
dc.identifier2073-4433
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174690
dc.descriptionGlobal reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.
dc.format20 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMDPI Multidisciplinary Digital Publishing Institute
dc.publisherSwitzerland
dc.relationAtmosphere
dc.relation1. Bertoni, J.C.; Tucci, C.E.M. Precipitação. In Hidrologia: Ciência e Aplicação; Tucci, C.E.M., Ed.; Editora da UFRGS/ABRH: Porto Alegre, Brazil, 2004; pp. 177–241, ISBN 8570256639.
dc.relation2. Canales, F.A.; Gwoździej‐Mazur, J.; Jadwiszczak, P.; Struk‐Sokołowska, J.; Wartalska, K.; Wdowikowski, M.; Kaźmierczak, B. Long‐Term Trends in 20‐Day Cumulative Precipitation for Residential Rainwater Harvesting in Poland. Water 2020, 12, 1932.
dc.relation3. Nkiaka, E.; Nawaz, N.R.; Lovett, J.C. Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano‐Sahel region: case study of the Logone catchment, Lake Chad Basin. Meteorol. Appl. 2017, 24, 9–18.
dc.relation4. Lozano Sandoval, G.; Monsalve Durango, E.A.; García Reinoso, P.L.; Rodríguez Mejía, C.A.; Gómez Ospina, J.P.; Triviño Loaiza, H.J. Environmental Flow Estimation Using Hydrological and Hydraulic Methods for the Quindío River Basin: WEAP as a Support Tool. Inge CUC 2015, 11, 34–48.
dc.relation5. Dingman, S.L. Physical Hydrology; 3rd ed.; Waveland Press, Inc.: Long Grove, IL, USA, 2015; ISBN 9781478611189.
dc.relation6. World Meteorological Organization Guide to Hydrological Practice. Volume I: Hydrology—From Measurement to Hydrological Information; World Meteorological Organization: Geneva, Switzerland, 2008; ISBN 9789263101686.
dc.relation7. Condom, T.; Martínez, R.; Pabón, J.D.; Costa, F.; Pineda, L.; Nieto, J.J.; López, F.; Villacis, M. Climatological and Hydrological Observations for the South American Andes: In situ Stations, Satellite, and Reanalysis Data Sets. Front. Earth Sci. 2020, 8, 1–20.
dc.relation8. Yu, Z.; Wu, J.; Chen, X. An approach to revising the climate forecast system reanalysis rainfall data in a sparsely‐gauged mountain basin. Atmos. Res. 2019, 220, 194–205.
dc.relation9. Thornton, P.E.; Running, S.W.; White, M.A. Generating surfaces of daily meteorological variables over large regions of complex terrain. J. Hydrol. 1997, 190, 214–251.
dc.relation10. Ramirez Camargo, L.; Gruber, K.; Nitsch, F. Assessing variables of regional reanalysis data sets relevant for modelling small‐ scale renewable energy systems. Renew. Energy 2019, 133, 1468–1478.
dc.relation11. Chawla, I.; Mujumdar, P.P. Evaluating rainfall datasets to reconstruct floods in data‐sparse Himalayan region. J. Hydrol. 2020, 588.
dc.relation12. Sun, S.; Shi, W.; Zhou, S.; Chai, R.; Chen, H.; Wang, G.; Zhou, Y.; Shen, H. Capacity of satellite‐based and reanalysis precipitation products in detecting long‐term trends across Mainland China. Remote Sens. 2020, 12, 2902.
dc.relation13. Blacutt, L.A.; Herdies, D.L.; de Gonçalves, L.G.G.; Vila, D.A.; Andrade, M. Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia. Atmos. Res. 2015, 163, 117–131.
dc.relation14. Bojanowski, J.S.; Vrieling, A.; Skidmore, A.K. A comparison of data sources for creating a long‐term time series of daily gridded solar radiation for Europe. Sol. Energy 2014, 99, 152–171.
dc.relation15. Dee, D.; Fasullo, J.; Shea, D.; Walsh, J. National Center for Atmospheric Research The Climate Data Guide: Atmospheric Reanalysis: Overview & Comparison Tables. Available online: https://climatedataguide.ucar.edu/climate‐data/atmospheric‐ reanalysis‐overview‐comparison‐tables (accessed on 6 June 2021).
dc.relation16. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz‐Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049.
dc.relation17. Springer, A.; Eicker, A.; Bettge, A.; Kusche, J.; Hense, A.; Mahto, S.S.; Pandey, A.C.; Huang, B.; Cubasch, U.; Li, Y.; et al. Evaluation of the Water Cycle in the European COSMO‐REA6 Reanalysis Using GRACE. Water 2017, 9, 289.
dc.relation18. Nguyen, T.H.; Masih, I.; Mohamed, Y.A.; van der Zaag, P. Validating rainfall‐runoff modelling using satellite‐based and reanalysis precipitation products in the sre pok catchment, the mekong river basin. Geosciences 2018, 8, 164.
dc.relation19. Jurasz, J.; Canales, F.A.; Kies, A.; Guezgouz, M.; Beluco, A. A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions. Sol. Energy 2020, 195, 703–724.
dc.relation20. Ramirez Camargo, L.; Schmidt, J. Simulation of multi‐annual time series of solar photovoltaic power: Is the ERA5‐land reanalysis the next big step? Sustain. Energy Technol. Assess. 2020, 42, 100829.
dc.relation21. Canales, F.A.; Jurasz, J.K.; Guezgouz, M.; Beluco, A. Cost‐reliability analysis of hybrid pumped‐battery storage for solar and wind energy integration in an island community. Sustain. Energy Technol. Assess. 2021, 44, 101062.
dc.relation22. Kapica, J.; Canales, F.A.; Jurasz, J. Global atlas of solar and wind resources temporal complementarity. Energy Convers. Manag. 2021, 246, 114692.
dc.relation23. Hurtado‐Montoya, A.F.; Mesa‐Sánchez, Ó.J. Reanalysis of monthly precipitation fields in Colombian territory. Dyna 2014, 81, 251.
dc.relation24. Dinku, T.; Funk, C.; Peterson, P.; Maidment, R.; Tadesse, T.; Gadain, H.; Ceccato, P. Validation of the CHIRPS satellite rainfall estimates over eastern Africa. Q. J. R. Meteorol. Soc. 2018, 144, 292–312.
dc.relation25. Urrea, V.; Ochoa, A.; Mesa, O. Seasonality of Rainfall in Colombia. Water Resour. Res. 2019, 55, 4149–4162.
dc.relation26. Fernandes, K.; Muñoz, A.G.; Ramirez‐Villegas, J.; Agudelo, D.; Llanos‐Herrera, L.; Esquivel, A.; Rodriguez‐Espinoza, J.; Prager, S.D. Improving seasonal precipitation forecasts for agriculture in the orinoquía Region of Colombia. Weather Forecast. 2020, 35, 437–449.
dc.relation27. Urrea, V.; Ochoa, A.; Mesa, O. Validación de la base de datos de precipitación CHIRPS para Colombia a escala diaria, mensual y anual en el período 1981–2014. In Proceedings of the XXVII Congreso Latinoamericano de Hidráulica, Lima, Peru, 28–30 September 2016; p. 11.
dc.relation28. Morales‐Acuña, E.; Linero‐Cueto, J.R.; Canales, F.A. Assessment of Precipitation Variability and Trends Based on Satellite Estimations for a Heterogeneous Colombian Region. Hydrology 2021, 8, 128.
dc.relation29. Funk, C.; Peterson, P.; Landsfeld, M.; Pedreros, D.; Verdin, J.; Shukla, S.; Husak, G.; Rowland, J.; Harrison, L.; Hoell, A.; et al. The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Sci. Data 2015, 2, 1–21.
dc.relation30. Jurasz, J.; Beluco, A.; Canales, F.A. The impact of complementarity on power supply reliability of small scale hybrid energy systems. Energy 2018, 161, 737–743.
dc.relation31. Canales, F.A.; Jurasz, J.; Kies, A.; Beluco, A.; Arrieta‐Castro, M.; Peralta‐Cayón, A. Spatial representation of temporal complementarity between three variable energy sources using correlation coefficients and compromise programming. MethodsX 2020, 7, 100871.
dc.relation32. Canales, F.A.; Jurasz, J.; Beluco, A.; Kies, A. Assessing temporal complementarity between three variable energy sources through correlation and compromise programming. Energy 2020, 192, 116637.
dc.relation33. Copernicus Climate Change Service (C3S) ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate Available online: https://cds.climate.copernicus.eu/cdsapp#!/home (accessed on 25 May 2021).
dc.relation20
dc.relation1
dc.relation11
dc.relation12
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.mdpi.com/2073-4433/12/11/1430
dc.subjectRainfall
dc.subjectReanalysis
dc.subjectERA 5
dc.subjectMERRA 2
dc.subjectThiessen polygons
dc.titleEvaluation of areal monthly average precipitation estimates from MERRA2 and ERA5 reanalysis in a colombian caribbean basin
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.coverageColombia


Este ítem pertenece a la siguiente institución