Articles | Volume 15, issue 11
https://doi.org/10.5194/gmd-15-4569-2022
https://doi.org/10.5194/gmd-15-4569-2022
Model description paper
 | 
14 Jun 2022
Model description paper |  | 14 Jun 2022

CLIMFILL v0.9: a framework for intelligently gap filling Earth observations

Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson

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Cited articles

Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. a
Alemohammad, S. H., Fang, B., Konings, A. G., Aires, F., Green, J. K., Kolassa, J., Miralles, D., Prigent, C., and Gentine, P.: Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence, Biogeosciences, 14, 4101–4124, https://doi.org/10.5194/bg-14-4101-2017, 2017. a
Balsamo, G., Agusti-Panareda, A., Albergel, C., Arduini, G., Beljaars, A., Bidlot, J., Blyth, E., Bousserez, N., Boussetta, S., Brown, A., Buizza, R., Buontempo, C., Chevallier, F., Choulga, M., Cloke, H., Cronin, M. F., Dahoui, M., De Rosnay, P., Dirmeyer, P. A., Drusch, M., Dutra, E., Ek, M. B., Gentine, P., Hewitt, H., Keeley, S. P., Kerr, Y., Kumar, S., Lupu, C., Mahfouf, J.-F., McNorton, J., Mecklenburg, S., Mogensen, K., Muñoz-Sabater, J., Orth, R., Rabier, F., Reichle, R., Ruston, B., Pappenberger, F., Sandu, I., Seneviratne, S. I., Tietsche, S., Trigo, I. F., Uijlenhoet, R., Wedi, N., Woolway, R. I., and Zeng, X.: Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review, Remote Sens., 10, 2038, https://doi.org/10.3390/rs10122038, 2018. a, b
Banerjee, S., Gelfand, A. E., Finley, A. O., and Sang, H.: Gaussian predictive process models for large spatial data sets, J. Roy. Stat. Soc.-B, 70, 825–848, https://doi.org/10.1111/j.1467-9868.2008.00663.x, 2008. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a
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Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
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