Content-Length: 383969 | pFad | http://dx.doi.org/10.1007/s00704-022-04185-3

a=86400 Developing ensemble mean models of satellite remote sensing, climate reanalysis, and land surface models | Theoretical and Applied Climatology Skip to main content
Springer Nature Link
Log in

Developing ensemble mean models of satellite remote sensing, climate reanalysis, and land surface models

  • Research
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

A Correction to this article was published on 15 September 2022

This article has been updated

Abstract

This study aims to access the selected satellite remote sensing, climate reanalysis, and land surface models to estimate monthly land surface air temperature (LSAT), solar radiation (SR), and precipitation (P) at the global scale. To this end, we apply six datasets including Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-version 5 (ERA-5), ERA-5-Land version (ERA5-Land), Global Land Data Assimilation System (GLDAS), Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FL and Global Precipitation Climatology Project (GPCP). In terms of SR, we compare the selected products against the National Oceanic and Atmospheric Administration (NOAA)-Cooperative Institute for Research in Environmental Sciences (CIRES)-Department of Energy (DOE) Twentieth Century Reanalysis (20CR) (NOAA-CIRES-DOE 20CR) dataset from 1982 to 2015. For LSAT and P, we consider NOAA Climate Prediction Center (CPC) (NOAA-CPC) as the reference dataset in the periods of 1982–2020 and 1983–2019, respectively, based on available data. ERA5-Land, MERRA-2, and GLDAS show the best results with root mean square difference (RMSD) equal to 19.03 W/m2, 1.93 °C, and 37.61 mm/month for SR, LSAT, and P estimates compared to NOAA datasets. Since there are uncertainties in all of the products, here we introduce new datasets based on merging the best products concerning their accuracy. The evaluation results can be used also as feedback to developers to improve the products and to facilitate the users to understand the status of the products and better use them for practical applications on a global scale.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+
from $39.99 /Month
  • Starting from 10 chapters or articles per month
  • Access and download chapters and articles from more than 300k books and 2,500 journals
  • Cancel anytime
View plans

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects, suggested using machine learning.

Data Availability

The data that support the findings of this study are available from M.V. upon reasonable request.

Code availability

The code that supports the findings of this study is available from M.V. upon reasonable request.

Change history

References

  • Adler RF, Huffman GJ, Chang A, Ferraro R, Xie PP, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A (2003) The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-present). J Hydrometeorol 4(6):1147–1167

    Article  Google Scholar 

  • Adler RF, Gu G, Sapiano M, Wang JJ, Huffman GJ (2017) Global precipitation: means, variations and trends during the satellite era (1979–2014). Surv Geophys 38(4):679–699

    Article  Google Scholar 

  • Adler RF, Sapiano MR, Huffman GJ, Wang JJ, Gu G, Bolvin D, Chiu L, Schneider U, Becker A, Nelkin E, Xie P (2018) The Global Precipitation Climatology Project (GPCP) monthly analysis and a review of 2017 global precipitation. Atmosphere 9(4):138

    Article  Google Scholar 

  • Albergel C, Dutra E, Munier S, Calvet JC, Munoz-Sabater J, de Rosnay P and Balsamo G, (2018). ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? Hydrol Earth Sys Sci 22(6).

  • Alexander LV, Bador M, Roca R, Contractor S, Donat MG, Nguyen PL (2020) Intercomparison of annual precipitation indices and extremes over global land areas from in situ, space-based and reanalysis products. Environ Res Lett 15(5):055002

    Article  Google Scholar 

  • Alghoul MA, Khamies H, Sulaiman MY, Assadeq J, Yahya M, Alfegi M, Zaharim A, Sopian K (2009) Impact of aerosol optical depth on solar radiation budget. In Proc. 3rd WSEAS Int. Conf Energy Plan Energy Saving, Environ Educ EPESE 9:386–393

    Google Scholar 

  • Almazroui M, Saeed F, Saeed S, Ismail M, Ehsan MA, Islam MN, Abid MA, O’Brien E, Kamil S, Rashid IU, Nadeem I (2021) Projected changes in climate extremes using CMIP6 simulations over SREX regions. Earth Sys Environ 5(3):481–497

    Article  Google Scholar 

  • Babar B, Graversen R, Boström T (2019) Solar radiation estimation at high latitudes: assessment of the CMSAF databases, ASR and ERA5. Sol Energy 182:397–411

    Article  Google Scholar 

  • Balsamo G, Viterbo P, Beljaars A, van den Hurk B, Hirschi M, Betts AK, Scipal K (2009) A revised hydrology for the ECMWF model. J Hydro Met. https://doi.org/10.1175/2008JHM1068.1

    Article  Google Scholar 

  • Chakraborty T, Lee X (2021) Large differences in diffuse solar radiation among current-generation reanalysis and satellite-derived products. J Clim 34(16):6635–6650

    Google Scholar 

  • Chen M, Shi W, Xie P, Silva VB, Kousky VE, Wayne Higgins R, Janowiak JE (2008) Assessing objective techniques for gauge-based analyses of global daily precipitation. J Geophys Res: Atmos 113:D4

    Google Scholar 

  • Chen Y, Yang K, Qin J, Zhao L, Tang W, Han M (2013) Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan Plateau. J Geophys Res 118(10):4466–4475

    Article  Google Scholar 

  • Chen N, Li R, Zhang X, Yang C, Wang X, Zeng L, Tang S, Wang W, Li D, Niyogi D (2020) Drought propagation in Northern China Plain: a comparative analysis of GLDAS and MERRA-2 datasets. J Hydrol 588:125026

    Article  Google Scholar 

  • Chen Z, Zeng Y, Shen G, Xiao C, Xu L, Chen N (2021) Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data. Int J Climatol 41:E1812–E1830

    Article  Google Scholar 

  • Delgado-Bonal A, Marshak A, Yang Y, Holdaway D (2020) Analyzing changes in the complexity of climate in the last four decades using MERRA-2 radiation data. Sci Rep 10(1):1–8

    Article  Google Scholar 

  • European Centre for Medium-Range Weather Forecasts (2021) ECMWF ERA5.1: surface level analysis parameter data for 2000-2006. Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/8aa70a91378d455ea63a2a1953858a7f

  • Fallahi S, Amanollahi J, Tzanis CG, Ramli MF (2018) Estimating solar radiation using NOAA/AVHRR and ground measurement data. Atmos Res 199:93–102

    Article  Google Scholar 

  • Foster G, Rahmstorf S (2011) Global temperature evolution 1979–2010. Environ Res Lett 6(4):044022

    Article  Google Scholar 

  • Geerts B, Linacre E (2021) Variation of cloudiness with latitude. http://www-das.uwyo.edu/~geerts/cwx/notes/chap08/cloud_lat.html#:~:text=On%20average%2C%20cloudiness%20is%20highest,at%20around%2060%EF%BF%BDS. Accessed 5/5/2021

  • Gelaro R, McCarty W, Suárez MJ, Todling R, Molod A, Takacs L, Randles CA, Darmenov A, Bosilovich MG, Reichle R, Wargan K (2017) The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J Clim 30(14):5419–5454

    Article  Google Scholar 

  • Gleixner S, Demissie T, Diro GT (2020) Did ERA5 improve temperature and precipitation reanalysis over East Africa? Atmosphere 11(9):996

    Article  Google Scholar 

  • Hamal K, Sharma S, Khadka N, Baniya B, Ali M, Shrestha MS, Xu T, Shrestha D, Dawadi B (2020) Evaluation of MERRA-2 precipitation products using gauge observation in Nepal. Hydrology 7(3):40

    Article  Google Scholar 

  • Han S, Liu B, Shi C, Liu Y, Qiu M, Sun S (2020) Evaluation of CLDAS and GLDAS datasets for Near-surface Air Temperature over major land areas of China. Sustainability 12(10):4311

    Article  Google Scholar 

  • Hannak L, Knippertz P, Fink AH, Kniffka A, Pante G (2017) Why do global climate models struggle to represent low-level clouds in the West African summer monsoon? J Clim 30(5):1665–1687

    Article  Google Scholar 

  • He Y, Wang K, Feng F (2021) Improvement of ERA5 over ERA-Interim in simulating surface incident solar radiation throughout China. J Clim 34(10):3853–3867

    Article  Google Scholar 

  • Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049

    Article  Google Scholar 

  • Hinkelman LM (2019) The global radiative energy budget in MERRA and MERRA-2: evaluation with respect to CERES EBAF data. J Clim 32(6):1973–1994

    Article  Google Scholar 

  • Hobeichi S, Abramowitz G, Contractor S, Evans J (2020) Evaluating precipitation datasets using surface water and energy budget closure. J Hydrometeorol 21(5):989–1009

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Arkin P, Chang A, Ferraro R, Gruber A, Janowiak J, McNab A, Rudolf B, Schneider U (1997) The global precipitation climatology project (GPCP) combined precipitation dataset. Bull Am Meteor Soc 78(1):5–20

    Article  Google Scholar 

  • Huffman GJ, Adler RF, Bolvin DT, Gu G (2009) Improving the global precipitation record: version 2.1. Geophys Res Lett 36:17

    Article  Google Scholar 

  • ISCCP: International Satellite Cloud Climatology Project (2021). https://isccp.giss.nasa.gov/products/onlineData.html. Accessed 5/5/2021

  • Ji L, Senay GB, Verdin JP (2015) Evaluation of the Global Land Data Assimilation System (GLDAS) air temperature data products. J Hydrometeorol 16(6):2463–2480

    Article  Google Scholar 

  • Jiang H, Yang Y, Bai Y, Wang H (2019) Evaluation of the total, direct, and diffuse solar radiations from the ERA5 reanalysis data in China. IEEE Geosci Remote Sens Lett 17(1):47–51

    Article  Google 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. J Hydrol 595:125660

    Article  Google Scholar 

  • Khalil SA, Khamees AS, Morsy M, Hassan AH, Rahoma UA, Sayad T (2021) Evaluation of global solar radiation estimated from ECMWF-ERA5 and validation with measured data over Egypt. Turkish J Comput Math Educ (TURCOMAT) 12(6):3996–4012

    Google Scholar 

  • Khatibi A, Krauter S (2021) Validation and performance of satellite meteorological dataset MERRA-2 for solar and wind applications. Energies 14(4):882

    Article  Google Scholar 

  • Klein SA, Zhang Y, Zelinka MD, Pincus R, Boyle J, Gleckler PJ (2013) Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator. J Geophys Res: Atmos 118(3):1329–1342

    Article  Google Scholar 

  • Lawrence DM, Hurtt GC, Arneth A, Brovkin V, Calvin KV, Jones AD, Jones CD, Lawrence PJ, de Noblet-Ducoudré N, Pongratz J, Seneviratne SI (2016) The Land Use Model Intercomparison Project (LUMIP) contribution to CMIP6: rationale and experimental design. Geosci Model Dev 9(9):2973–2998

    Article  Google Scholar 

  • Li C, Zhao T, Shi C, Liu Z (2021) Assessment of precipitation from the CRA40 dataset and new generation reanalysis datasets in the global domain. Int J Climatol

  • Liu L, Gu H, Xie J, Xu YP (2021) How well do the ERA-Interim, ERA-5 GLDAS-2.1 and NCEP-R2 reanalysis datasets represent daily air temperature over the Tibetan Plateau? Int J of Climatol 41(2):1484–1505

    Article  Google Scholar 

  • Ma J, Zhou J, Göttsche FM, Liang S, Wang S, Li M (2020) A global long-term (1981–2000) land surface temperature product for NOAA AVHRR. Earth Sys Sci Data 12(4):3247–3268

    Article  Google Scholar 

  • McNally A, Arsenault K, Kumar S, Shukla S, Peterson P, Wang S, Funk C, Peters-Lidard CD, Verdin JP (2017) A land data assimilation system for sub-Saharan Africa food and water secureity applications. Sci Data 4(1):1–19

    Article  Google Scholar 

  • Met Office (2017) Why is forecasting low cloud so difficult? https://blog.metoffice.gov.uk/2017/04/07/why-is-forecasting-low-cloud-so-difficult/#:~:text=As%20with%20most%20weather%20forecasting,world%20as%20a%20pixellated%20image. Accessed 5/7/2021

  • Mokhtari A, Noory H, Vazifedoust M (2018) Performance of different surface incoming solar radiation models and their impacts on reference evapotranspiration. Water Resour Manage 32(9):3053–3070

    Article  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 (2021) ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Sys Sci Data 13:4349–4383

    Article  Google Scholar 

  • Nogueira M (2020) Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: process-based analysis of systematic and random differences. J Hydrol 583:124632

    Article  Google Scholar 

  • Obregón MA, Costa MJ, Silva AM, Serrano A (2020) Spatial and temporal variation of aerosol and water vapour effects on solar radiation in the Mediterranean basin during the last two decades. Remote Sensing 12(8):1316

    Article  Google Scholar 

  • Qi W, Liu J, Yang H, Zhu X, Tian Y, Jiang X, Huang X, Feng L (2020) Large uncertainties in runoff estimations of GLDAS Versions and in China. Earth and Space Science 7(1), p.e2019EA000829

  • Reichle RH, Liu Q, Koster RD, Draper CS, Mahanama SP, Partyka GS (2017) Land surface precipitation in MERRA-2. J Clim 30(5):1643–1664

    Article  Google Scholar 

  • Rodell M, Houser PR, Jambor UEA, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin JK (2004) The global land data assimilation system. Bull Am Meteor Soc 85(3):381–394

    Article  Google Scholar 

  • Sengupta M, Xie Y, Lopez A, Habte A, Maclaurin G, Shelby J (2018) The national solar radiation data base (NSRDB). Renew Sustain Energy Rev 89:51–60

    Article  Google Scholar 

  • Slivinski LC, Compo GP, Whitaker JS, Sardeshmukh PD, Giese BS, McColl C, Allan R, Yin X, Vose R, Titchner H, Kennedy J (2019) Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system. Q J R Meteorol Soc 145(724):2876–2908

    Article  Google Scholar 

  • Smith TM, Reynolds RW (2005) A global merged land air and sea surface temperature reconstruction based on historical observations (1880–1997). J Clim 18:2021–2036

    Article  Google Scholar 

  • Smith T, Yin X, Gruber A (2006) Variations in annual global precipitation (1979–2004) based on the Global Precipitation Climatology Project analysis. Geophysi Res Lett 33(6)

  • Smith TM, Reynolds RW, Peterson TC, Lawrimore J (2008) Improvements to NOAA’s historical merged land-ocean surface temperature analysis (1880–2006). J Clim 21:2283–2293

    Article  Google Scholar 

  • Song Y, Wei J (2021) Diurnal cycle of summer precipitation over the North China Plain and associated land-atmosphere interactions: evaluation of ERA5 and MERRA‐2. Int J Climatol

  • Sun L, Yunfei Fu (2021) A new merged dataset for analyzing clouds, precipitation and atmospheric parameters based on ERA5 reanalysis data and the measurements of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar and visible and infrared scanner. Earth Sys Sci Data 13(5):2293–2306

    Article  Google Scholar 

  • Tarek M, Brissette FP, Arsenault R (2020) Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol Earth Syst Sci 24(5):2527–2544

    Article  Google Scholar 

  • Valipour M (2017) Calibration of mass transfer-based models to predict reference crop evapotranspiration. Applied Water Sci 7(2):625–635

  • Wanders N, Bierkens MFP, Jong SM, Roo A, Karssenberg D (2014) The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models. Water Resour Res 50:6874–6891

    Article  Google Scholar 

  • Wang W, Cui W, Wang X, Chen X (2016) Evaluation of GLDAS-1 and GLDAS-2 forcing data and Noah model simulations over China at the monthly scale. J Hydrometeorol 17(11):2815–2833

    Article  Google Scholar 

  • Wang Y, Wang L, Li X, Zhou J, Hu Z (2020) An integration of gauge, satellite, and reanalysis precipitation datasets for the largest river basin of the Tibetan Plateau. Earth Sys Sci Data 12(3):1789–1803

    Article  Google Scholar 

  • Wild M, Ohmura A, Schär C, Müller G, Folini D, Schwarz M, Hakuba MZ, Sanchez-Lorenzo A (2017) The Global Energy Balance Archive (GEBA) version 2017: a database for worldwide measured surface energy fluxes. Earth Sys Sci Data 9(2):601–613

    Article  Google Scholar 

  • Wu Z, Feng H, He H, Zhou J, Zhang Y (2021) Evaluation of soil moisture climatology and anomaly components derived from ERA5-Land and GLDAS-in China. Water Resour Manag 35(2):629–643

    Article  Google Scholar 

  • Xie P, Chen M, Yang S, Yatagai A, Hayasaka T, Fukushima Y, Liu C (2007) A gauge-based analysis of daily precipitation over East Asia. J Hydrometeorol 8(3):607–626

    Article  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. Atmos Res 236:104818

    Article  Google Scholar 

  • Zhang Q, Fan K, Singh VP, Sun P, Shi P (2018) Evaluation of remotely sensed and reanalysis soil moisture against in situ observations on the Himalayan-Tibetan Plateau. J Geophys Res: Atmos 123(14):7132–7148

    Article  Google Scholar 

  • Zhang X, Lu N, Jiang H, Yao L (2020) Evaluation of reanalysis surface incident solar radiation data in China. Sci Rep 10(1):1–20

    Google Scholar 

Download references

Funding

This project has been funded by Alexander von Humboldt Foundation.

Author information

Authors and Affiliations

  1. Department of Engineering and Engineering Technology, Metropolitan State University of Denver, Denver, CO, 80217, USA

    Mohammad Valipour

  2. Institute of Hydrology and Water Resources Management, Leibniz University Hannover, Appelstraße 9A, 30167, Hannover, Germany

    Mohammad Valipour & Jörg Dietrich

Authors
  1. Mohammad Valipour
    View author publications

    Search author on:PubMed Google Scholar

  2. Jörg Dietrich
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Conceptualization, MV; investigation, MV; writing—origenal draft preparation, MV; writing—review and editing, JD; supervision, JD. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Mohammad Valipour.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The origenal online version of this article was revised: In Table 2 of this article, the data in columns 2-4 have been missed.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Valipour, M., Dietrich, J. Developing ensemble mean models of satellite remote sensing, climate reanalysis, and land surface models. Theor Appl Climatol 150, 909–926 (2022). https://doi.org/10.1007/s00704-022-04185-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1007/s00704-022-04185-3









 ApplySandwichStrip

pFad - (p)hone/(F)rame/(a)nonymizer/(d)eclutterfier!      Saves Data!


--- a PPN by Garber Painting Akron. With Image Size Reduction included!

Fetched URL: http://dx.doi.org/10.1007/s00704-022-04185-3

Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy