Spatio-temporal estimation of climatic variables for gap filling and record extension using Reanalysis data
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.
KeywordsTemperature 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.
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).
- Bates D, Maechler M, Bolker B (2012) lme4: linear mixed-effects models using S4 classes. R package version 0.999999–0Google Scholar
- Beguería S, Vicente-Serrano SM (2013) SPEI: calculation of the Standardized Precipitation-Evapotranspiration Index. R package version 1.3Google Scholar
- Casanueva A, Herrera S, Fernandez J, Frias MD, Gutierrez JM (2012) Comparison of statistical and dynamical downscaling methods in representing temperature extremes. 12th Annual Meeting of the European Meteorological Society (EMS) and the 9th European Conference on Applied Climatology (ECAC), Poland, 10-14 September 2012Google Scholar
- Flannigan MD, Wotton BM (2001) Climate, weather, and area burned. In: Johnson E, Miyanishi K (eds) Forest fires, behavior and ecological effects. EE.UU. Academic Press, New York, pp 351–373Google Scholar
- Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year Reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
- Kemp MU, Kemp MMU (2012) Package ‘RNCEP’Google Scholar
- Kubik M, Brayshaw D, Coker P (2012) Reanalysis: an improved data set for simulating wind generation?. In: WREF 2012. Denver, CO. http://tinyurl.com/c4ge72x
- Kuznetsova A, Brockhoff PB, Bojesen RH (2013) lmerTest: tests for random and fixed effects for linear mixed effect models (lmer objects of lme4 package). R package version 1.2–0Google Scholar
- Laurikkala J, Juhola M, Kentala E, Lavrac N, Miksch S, Kavsek B (2000) Informal identification of outliers in medical data. In Fifth international workshop on intelligent data analysis in medicine and pharmacology (pp. 20–24)Google Scholar
- Nagata K (2011) Quantitative precipitation estimation and quantitative precipitation forecasting by the Japan Meteorological Agency. RSMC Tokyo –Typhoon Center Technical Review 13:37–50Google Scholar
- Pierce D (2011) ncdf: Interface to Unidata netCDF data files. R package version 16.6Google Scholar
- Pinheiro J, Bates D, DebRoy S, Sarkar D, the R Development Core Team. (2012). Nlme: linear and nonlinear mixed effects models. R package version 3.1–104Google Scholar
- Rienecker MM, Suarez MJ, Gelaro R, Todling R, Bacmeister J, Liu E, Bosilovich MG, Schubert SD, Takacs L, Kim GK, Bloom S, Chen J, Collins D, Conaty A, da Silva A, Gu W, Joiner J, Koster RD, Lucchesi R, Molod A, Owens T, Pawson S, Pegion P, Redder CR, Reichle R, Robertson FR, Ruddick AG, Sienkiewicz M, Woollen J (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648CrossRefGoogle Scholar
- Saha S, Moorthi S, Pan HL, Wu X, Wang J, Nadiga S, Tripp P, Kistler R, Woollen J, Behringer D, Liu H, Stokes D, Grumbine R, Gayno G, Wang J, Hou YT, Chuang HY, Juang HMH, Sela J, Iredell M, Treadon R, Kleist D, van Delst P, Keyser D, Derber J, Ek M, Meng J, Wei H, Yang R, Lord S, van den Dool H, Kumar A, Wang W, Long C, Chelliah M, Xue Y, Huang B, Schemm JK, Ebisuzaki W, Lin R, Xie P, Chen M, Zhou S, Higgins W, Zou CZ, Liu Q, Chen Y, Han Y, Cucurull L, Reynolds RW, Rutledge G, Goldberg M (2010) The NCEP climate forecast system Reanalysis. Bull Am Meteorol Soc 91:1015–1057CrossRefGoogle Scholar
- Simmons A, Uppala S, Dee D, Kobayashi S (2007) ERA-Interim: new ECMWF Reanalysis products from 1989 onwards. ECMWF Newsl 110:25–35Google Scholar
- Vrac M, Naveau P (2007) Stochastic downscaling of precipitation: from dry events to heavy rainfalls. Water Resour Res 43(7). https://doi.org/10.1029/2006WR005308
- Wang D, Murphy M (2004) Estimating optimal transformations for multiple regression using the ACE algorithm. J Data Sci 2(4):329–346Google Scholar
- Wilks DS (2006) Statistical methods in the atmospheric sciences, 2nd edn. Academic Press/Elsevier, New York 627 ppGoogle Scholar
- Zavala MA (2004) Estructura, dinámica y modelos de ensamblaje del bosque mediterráneo: entre la necesidad y la contingencia. Ecología del bosque mediterráneo en un mundo cambiante. Organismo Autónomo de Parques Nacionales. Ministerio de Medio Ambiente, Madrid, pp 249–280Google Scholar