Spatio-temporal estimation of climatic variables for gap filling and record extension using Reanalysis data

  • David Morales-Moraga
  • Francisco J. MezaEmail author
  • Marcelo Miranda
  • Jorge Gironás
Original Paper


The availability of reliable meteorological records is crucial for the development of a number of environmental studies. Unfortunately, these records are not always complete, usually show errors and/or have an insufficient length. This paper presents a gap filling and data record extension methodology for minimum temperature, maximum temperature, and precipitation. It uses climatic information from the NCEP-NCAR Reanalysis project, identifying pixels (grid cells) within a Reanalysis domain that have the highest Pearson’s correlation coefficient with the variable of interest. Nine stations in the Maipo River basin (Santiago, Chile) were selected for a reconstruction experiment (from 1950 to 1970) and a subsequent gap filling experiment (from 1970 to 2012). A generalized linear mixed model with a bidirectional stepwise fit procedure was used to model temperature, whereas precipitation occurrence was represented using a generalized linear mixed model with binomial distribution, and precipitation amount used an exponential generalized linear model. The performance of the algorithm was compared with inverse distance weighting and spline interpolation methods and further evaluated using the Standardized Precipitation Evapotranspiration Index, contrasting real versus modeled data. Values of the coefficient of determination averaged 0.76 (0.74–0.84) minimum temperature, 0.73 (0.73–0.81) for maximum temperature, and 0.68 (0.51–0.78) for precipitation. Root-mean-squared error was around 1.5 °C and 5 mm for temperature and precipitation, respectively. The model explains local variation of climatic variables and indicators and can be replicated anywhere, as the Reanalysis data are easily accessible and have a worldwide coverage.


Temperature Precipitation Record extension Gap filling Reanalysis data 



Special thanks to Alvaro Paredes for his help on the algorithm generation and Shaw Lacy for edits. The authors would like to thank two anonymous reviewers for their comments and suggestions on how to improve the robustness of the method and for pointing out its limitations.

Funding information

The authors would like to acknowledge the support from FONDECYT Project Nos. 1120713 and 1170429. This work was partly carried out with the aid of a grant from the Inter-American Institute for Global Change Research (IAI, CRN3056) which is supported by the US National Science Foundation (Grant GEO-1128040).


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Authors and Affiliations

  1. 1.Centro Interdisciplinario de Cambio GlobalPontificia Universidad Católica de ChileSantiagoChile
  2. 2.Departamento de Ecosistemas y Medio AmbientePontificia Universidad Católica de ChileSantiagoChile
  3. 3.Departamento de Ingeniería Hidráulica y AmbientalPontificia Universidad Católica de ChileSantiagoChile
  4. 4.CIGIDEN Centro Nacional de Investigación para la Gestión Integrada de Desastres Naturales, CONICYT/FONDAP/15110017SantiagoChile
  5. 5.CEDEUS Centro de Desarrollo Urbano Sustentable, CONICYT/FONDAP/15110020SantiagoChile

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