{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:30:02Z","timestamp":1768869002415,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,7]],"date-time":"2022-01-07T00:00:00Z","timestamp":1641513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004811","name":"World Meteorological Organization","doi-asserted-by":"publisher","award":["20170888"],"award-info":[{"award-number":["20170888"]}],"id":[{"id":"10.13039\/501100004811","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Satellites offer a way of estimating rainfall away from rain gauges which can be utilised to overcome the limitations imposed by gauge density on traditional rain gauge analyses. In this study, Australian station data along with the Japan Aerospace Exploration Agency\u2019s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) and the Bureau of Meteorology\u2019s (BOM) Australian Gridded Climate Dataset (AGCD) rainfall analysis are combined to develop an improved satellite-gauge rainfall analysis over Australia that uses the strengths of the respective data sources. We investigated a variety of correction and blending methods with the aim of identifying the optimal blended dataset. The correction methods investigated were linear corrections to totals and anomalies, in addition to quantile-to-quantile matching. The blending methods tested used weights based on the error variance to MSWEP (Multi-Source Weighted Ensemble Product), distance to the closest gauge, and the error from a triple collocation analysis to ERA5 and Soil Moisture to Rain. A trade-off between away-from- and at-station performances was found, meaning there was a complementary nature between specific correction and blending methods. The most high-performance dataset was one corrected linearly to totals and subsequently blended to AGCD using an inverse error variance technique. This dataset demonstrated improved accuracy over its previous version, largely rectifying erroneous patches of excessive rainfall. Its modular use of individual datasets leads to potential applicability in other regions of the world.<\/jats:p>","DOI":"10.3390\/rs14020261","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Comparison of Various Correction and Blending Techniques for Creating an Improved Satellite-Gauge Rainfall Dataset over Australia"],"prefix":"10.3390","volume":"14","author":[{"given":"Zhi-Weng","family":"Chua","sequence":"first","affiliation":[{"name":"Bureau of Meteorology, Melbourne, VIC 3008, Australia"}]},{"given":"Yuriy","family":"Kuleshov","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Melbourne, VIC 3008, Australia"},{"name":"School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia"}]},{"given":"Andrew B.","family":"Watkins","sequence":"additional","affiliation":[{"name":"Bureau of Meteorology, Melbourne, VIC 3008, Australia"}]},{"given":"Suelynn","family":"Choy","sequence":"additional","affiliation":[{"name":"School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5421-5838","authenticated-orcid":false,"given":"Chayn","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC 3000, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mukabutera, A., Thomson, D., Murray, M., Basinga, P., Nyirazinyoye, L., Atwood, S., Savage, K.P., Ngirimana, A., and Hedt-Gauthier, B.L. (2016). Rainfall variation and child health: Effect of rainfall on diarrhea among under 5 children in Rwanda, 2010. BMC Public Health, 16.","DOI":"10.1186\/s12889-016-3435-9"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-021-04575-2","article-title":"Building capacity for a user-centred Integrated Early Warning System (I-EWS) for drought in the Northern Murray-Darling Basin","volume":"107","author":"Bhardwaj","year":"2021","journal-title":"Nat. Hazards"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"984","DOI":"10.1061\/(ASCE)HE.1943-5584.0000150","article-title":"Association between Uncertainties in Meteorological Variables and Water-Resources Planning for the State of Texas","volume":"16","author":"Mishra","year":"2011","journal-title":"J. Hydrol. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/TGRS.2012.2196282","article-title":"Understanding the dependence of satellite rainfall uncertainty on topography and climate for hydrologic model simulation","volume":"51","author":"Gebregiorgis","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/nature04312","article-title":"Global pattern of trends in streamflow and water availability in a changing climate","volume":"438","author":"Milly","year":"2005","journal-title":"Nature"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1175\/BAMS-D-17-0138.1","article-title":"MSWep v2 Global 3-hourly 0.1\u00b0 precipitation: Methodology and quantitative assessment","volume":"100","author":"Beck","year":"2019","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.jhydrol.2003.10.005","article-title":"Systematic correction of precipitation gauge observations using analyzed meteorological variables","volume":"290","author":"Michelson","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"325718","DOI":"10.1155\/2015\/325718","article-title":"How Well Do Gridded Datasets of Observed Daily Precipitation Compare over Australia?","volume":"2015","author":"Contractor","year":"2015","journal-title":"Adv. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1007\/s00382-009-0698-1","article-title":"The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data","volume":"35","author":"Hofstra","year":"2010","journal-title":"Clim. Dyn."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1002\/joc.680","article-title":"Precipitation measurements and trends in the twentieth century","volume":"21","author":"New","year":"2001","journal-title":"Int. J. Climatol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1175\/1525-7541(2001)002<0621:EORIC>2.0.CO;2","article-title":"Estimation of Rainfall Interstation Correlation","volume":"2","author":"Habib","year":"2001","journal-title":"J. Hydrometeorol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1175\/BAMS-D-14-00283.1","article-title":"So, how much of the Earth\u2019s surface is covered by rain gauges?","volume":"98","author":"Kidd","year":"2017","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1175\/2008JAMC1757.1","article-title":"Statistical characteristics of daily precipitation: Comparisons of gridded and point datasets","volume":"47","author":"Ensor","year":"2008","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1175\/1520-0477(1997)078<2539:GPAYMA>2.0.CO;2","article-title":"Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs","volume":"78","author":"Xie","year":"1997","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3389\/frwa.2020.00001","article-title":"Triple Collocation Based Multi-Source Precipitation Merging","volume":"2","author":"Dong","year":"2020","journal-title":"Front. Water"},{"key":"ref_16","unstructured":"Chua, Z.-W., Kuleshov, Y., Watkins, A., Choy, S., and Sun, C. (J. Hydrometeorol., 2022). Developing a blended satellite-gauge rainfall dataset over Australia, J. Hydrometeorol., under review."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Toward the true near-surface wind speed: Error modeling and calibration using triple collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res. Ocean"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6229","DOI":"10.1002\/2014GL061322","article-title":"Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target","volume":"41","author":"McColl","year":"2014","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1175\/JHM-D-11-089.1","article-title":"Triple collocation of summer precipitation retrievals from SEVIRI over europe with gridded rain gauge and weather radar data","volume":"13","author":"Roebeling","year":"2012","journal-title":"J. Hydrometeorol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3489","DOI":"10.5194\/hess-19-3489-2015","article-title":"Characterization of precipitation product errors across the United States using multiplicative triple collocation","volume":"19","author":"Alemohammad","year":"2015","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4347","DOI":"10.5194\/hess-21-4347-2017","article-title":"An assessment of the performance of global rainfall estimates without ground-based observations","volume":"21","author":"Massari","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1109\/TGRS.2018.2870199","article-title":"Gauge-Adjusted Global Satellite Mapping of Precipitation","volume":"57","author":"Mega","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1175\/BAMS-88-1-47","article-title":"Comparison of near-real-time precipitation estimates from satellite observations and numerical models","volume":"88","author":"Ebert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1322","DOI":"10.1175\/2010JAMC2281.1","article-title":"Evaluating detection skills of satellite rainfall estimates over desert locust recession regions","volume":"49","author":"Dinku","year":"2010","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1002\/qj.3803","article-title":"The ERA5 global reanalysis","volume":"146","author":"Hersbach","year":"2020","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2279","DOI":"10.1175\/2008JAMC1921.1","article-title":"Evaluation of global precipitation in reanalyses","volume":"47","author":"Bosilovich","year":"2008","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_27","unstructured":"Evans, A., Jones, D., Smalley, R., and Lellyett, S. (2020). An Enhanced Gridded Rainfall Dataset Scheme for Australia, Bureau of Meteorology."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.5194\/essd-11-1583-2019","article-title":"SM2RAIN-ASCAT (2007\u20132018): Global daily satellite rainfall data from ASCAT soil moisture observations","volume":"11","author":"Brocca","year":"2019","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1127\/0941-2948\/2013\/0399","article-title":"The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications","volume":"22","author":"Wagner","year":"2013","journal-title":"Meteorol. Zeitschrift"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1007\/978-3-030-24568-9_19","article-title":"Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG)","volume":"Volume 67","author":"Huffman","year":"2020","journal-title":"Advances in Global Change Research"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rudolf, B., Hauschild, H., Rueth, W., and Schneider, U. (1994). Terrestrial Precipitation Analysis: Operational Method and Required Density of Point Measurements. Global Precipitations and Climate Change, Springer.","DOI":"10.1007\/978-3-642-79268-7_10"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1175\/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2","article-title":"Global land precipitation: A 50-yr monthly analysis based on gauge observations","volume":"3","author":"Chen","year":"2002","journal-title":"J. Hydrometeorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"D21106","DOI":"10.1029\/2011JD016118","article-title":"A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses","volume":"116","author":"Xie","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1175\/JHM-D-18-0190.1","article-title":"Evaluating the benefits of merging near-real-time satellite precipitation products: A case study in the Kinu basin region, Japan","volume":"20","author":"Mastrantonas","year":"2019","journal-title":"J. Hydrometeorol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s00704-009-0134-9","article-title":"Statistical bias correction for daily precipitation in regional climate models over Europe","volume":"99","author":"Piani","year":"2010","journal-title":"Theor. Appl. Climatol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.agrformet.2006.03.009","article-title":"Bias correction of daily GCM rainfall for crop simulation studies","volume":"138","author":"Ines","year":"2006","journal-title":"Agric. For. Meteorol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1016\/j.jhydrol.2015.02.014","article-title":"Evaluation of extreme precipitation estimates from TRMM in Angola","volume":"523","author":"Pombo","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Alam, M.A., Emura, K., Farnham, C., and Yuan, J. (2018). Best-fit probability distributions and return periods for maximum monthly rainfall in Bangladesh. Climate, 6.","DOI":"10.3390\/cli6010009"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1623\/hysj.52.5.863","article-title":"Probability distribution of annual, seasonal and monthly precipitation in Japan","volume":"52","author":"Yue","year":"2007","journal-title":"Hydrol. Sci. J."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11069-016-2687-0","article-title":"Selection of the best fit probability distribution in rainfall frequency analysis for Qatar","volume":"86","author":"Mamoon","year":"2017","journal-title":"Nat. Hazards"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"401","DOI":"10.2166\/wcc.2020.261","article-title":"Bias correction capabilities of quantile mapping methods for rainfall and temperature variables","volume":"12","author":"Enayati","year":"2021","journal-title":"J. Water Clim. Chang."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5194\/essd-5-71-2013","article-title":"A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present","volume":"5","author":"Becker","year":"2013","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wackernagel, H. (1995). Multivariate Geostatistics: An Introduction with Applications, Springer. Multivar. geostatistics an Introd. with Appl.","DOI":"10.1007\/978-3-662-03098-1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6935","DOI":"10.1038\/s41598-021-86412-x","article-title":"Spatial\u2013temporal characterization of rainfall in Pakistan during the past half-century (1961\u20132020)","volume":"11","author":"Ali","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1459","DOI":"10.1002\/joc.4437","article-title":"Comparison of geostatistical approaches to spatially interpolate month-year rainfall for the Hawaiian Islands","volume":"36","author":"Frazier","year":"2016","journal-title":"Int. J. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1002\/hyp.11163","article-title":"Cokriging for enhanced spatial interpolation of rainfall in two Australian catchments","volume":"31","author":"Adhikary","year":"2017","journal-title":"Hydrol. Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"137290","DOI":"10.1016\/j.scitotenv.2020.137290","article-title":"Empirical Bayesian kriging implementation and usage","volume":"722","author":"Gribov","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1007\/s12665-017-6814-3","article-title":"Comparison of ordinary and Bayesian kriging techniques in depicting rainfall variability in arid and semi-arid regions of north-west India","volume":"76","author":"Gupta","year":"2017","journal-title":"Environ. Earth Sci."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Vald\u00e9s-Pineda, R., Demar\u00eda, E., Vald\u00e9s, J., Wi, S., and Serrat-Capdevilla, A. (2016). Bias correction of daily satellite-based rainfall estimates for hydrologic forecasting in the Upper Zambezi, Africa. Hydrol. Earth Syst. Sci. Discuss., 1\u201328.","DOI":"10.5194\/hess-2016-473"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Katiraie-Boroujerdy, P.S., Naeini, M.R., Asanjan, A.A., Chavoshian, A., Hsu, K.L., and Sorooshian, S. (2020). Bias correction of satellite-based precipitation estimations using quantile mapping approach in different climate regions of Iran. Remote Sens., 12.","DOI":"10.3390\/rs12132102"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2915","DOI":"10.5194\/hess-23-2915-2019","article-title":"Performance of bias-correction schemes for CMORPH rainfall estimates in the Zambezi River basin","volume":"23","author":"Gumindoga","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"379","DOI":"10.2307\/2344931","article-title":"Introduction to Statistical Time Series","volume":"140","author":"Chatfield","year":"1977","journal-title":"J. R. Stat. Soc. Ser. A"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1080\/01621459.1951.10500769","article-title":"The Kolmogorov-Smirnov Test for Goodness of Fit","volume":"46","author":"Massey","year":"1951","journal-title":"J. Am. Stat. Assoc."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:27:01Z","timestamp":1760362021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/2\/261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,7]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14020261"],"URL":"https:\/\/doi.org\/10.3390\/rs14020261","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,7]]}}}