A Novel Method for Interpolating Daily Station Rainfall Data Using a Stochastic Lattice Model

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dc.contributor.authorKhouider, B.
dc.contributor.authorSabeerali, C. T.
dc.contributor.authorAjayamohan, R. S.
dc.contributor.authorPraveen, V.
dc.contributor.authorMajda, A. J.
dc.contributor.authorPai, D. S.
dc.contributor.authorRajeevan, M.
dc.date.accessioned2021-12-09T23:37:33Z
dc.date.available2021-12-09T23:37:33Z
dc.date.copyright2020en_US
dc.date.issued2020
dc.description.abstractRain gauge data are routinely recorded and used around the world. However, their sparsity and inhomogeneity make them inadequate for climate model calibration and many other climate change studies. Various algorithms and interpolation techniques have been developed over the years to obtain adequately distributed datasets. Objective interpolation methods such as inverse distance weighting (IDW) are the most widely used and have been employed to produce some of the most popular gridded daily rainfall datasets (e.g., India Meteorological Department gridded daily rainfall). Unfortunately, the skill of these techniques becomes very limited to nonexistent in areas located far away from existing recording stations. This is problematic as many areas of the world lack adequate rain gauge coverage throughout the recording history. Here, we introduce a new probabilistic interpolation method in an attempt to address this issue. The new algorithm employs a multitype particle interacting stochastic lattice model that assigns a binned rainfall value, from a given number of bins to each lattice site or grid cell, with a certain probability according to the rainfall amounts observed in neighboring sites and a background climatological rain rate distribution, drawn from the available data. Grid cells containing recording stations are not affected and are being used as “boundary” input conditions by the stochastic model. The new stochastic model is successfully tested and compared against two widely used gridded daily rainfall datasets over the Indian landmass for data from the summer monsoon seasons (June–September) for 1951–70.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.identifier.citationKhouider, B., Sabeerali, C. T., Ajayamohan, R. S., Praveen, V., Majda, A. J., Pai, D. S., … Rajeevan, M. (2020). A Novel Method for Interpolating Daily Station Rainfall Data Using a Stochastic Lattice Model. Journal of Hydrometeorology, 21(5), 909- 933. https://doi.org/10.1175/JHM-D-19-0143.1en_US
dc.identifier.urihttps://doi.org/10.1175/JHM-D-19-0143.1
dc.identifier.urihttp://hdl.handle.net/1828/13582
dc.language.isoenen_US
dc.publisherJournal of Hydrometeorologyen_US
dc.subjectHydrometeorology
dc.subjectData processing
dc.subjectIn situ atmospheric observations
dc.subjectMeasurements
dc.subjectInterpolation schemes
dc.subjectStatistical techniques
dc.subject.departmentDepartment of Mathematics and Statistics
dc.titleA Novel Method for Interpolating Daily Station Rainfall Data Using a Stochastic Lattice Modelen_US
dc.typeArticleen_US

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