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Gap-filling: What, why, how? - an Introduction

Biogeochemical Model-Data Integration Group. Gap-filling Comparison Workshop, September 18-20, 2006 Max Planck Institute for Biogeochemistry. Gap-filling: What, why, how? - an Introduction. M. Reichstein

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Gap-filling: What, why, how? - an Introduction

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  1. Biogeochemical Model-Data Integration Group Gap-filling Comparison Workshop, September 18-20, 2006 Max Planck Institute for Biogeochemistry Gap-filling: What, why, how? -an Introduction M. Reichstein (Biogeochemical Model-Data Integration Group, Max-Planck Intstitute for Biogeochemistry, Jena)

  2. CARBOEUROPE FLUXNET 2004: Eddy QC/QA/GF/FP workshop MIND 2003: Boost of gap-filling methods FLUXNET (AMERIFLUX, EUROFLUX) 2002: MDS online gap-filling tool 2001: Falge et al. Why are we here – a short historical perspective ? Today: Comp.of 15 methods + spin-offs from gap-filling

  3. What is a gap ? • “Gap is a synonym for any hole or opening; a chasm. Many uses of the word are either literally or figuratively based on this meaning.” (wikipedia.org) • “A gap is a series of missing data of eddy-covariance flux data (and/or meteorology) caused by instruments failure, unfavorable measurement conditions or removal of data point during the quality control.” • Univariate gaps (e.g. only NEE) • Multivariate flux gaps (e.g. all fluxes missing  sonic failure) • Flux and meteo gaps (e.g. all missing  storm or Xmas) • Length of a gap?

  4. Gap-percentage varies CE database ~30% (without ½ year gaps) Falge et al. 2001

  5. Frequency [log(year-1)] Frequency [log(year-1)] Length of gap [log(day)] Length of gap [days] Gaps abundance

  6. Cumulative percentage affected Length of gap [days] Days affected by gaps Total days affected [days/year] Length of gap [days]

  7. Why ? Statisticaltime-series analysis Data analysts Syntheses at monthly time scale Uncertainty estimation ‘Annual sums’ Model parameterizationat hourly to daily scale Model validation at daily to monthly scale Modellers

  8. Available data at daily and monthly scale before and after gap-filling

  9. How? • Gap-filling requirements • Conservation of annual sums • Conservation of fluxes at other time integrals • Minimum of a-priori theoretical assumptions • Usage of a much as possible information from data • Applicability with available data • Conversation of statistical time-series properties • Availability of conditional error estimate

  10. Development of gap-filling methods

  11. Classification of gap-filling methods • With vs. without meteorological drivers • Data-oriented versus process-oriented approaches • Incorporation vs. ignorance of autocorrelation • Smooth versus non-smooth methods • Look-up tables vs. regressions vs. neural networks

  12. Gap-filling methods characterized

  13. Conclusions • Gap-filling is important from different perspectives • Annual NEE is not the only target • Existence of vast majority of methods with different characteristics •  need for a characterization and cross-comparison

  14. Conclusions II Open questions • Can we transfer methods established for NEE also to energy fluxes and meteorological data ? • How can discontinuous systems be gap-filled ? • How critically do gap-filling methods affect the statistical properties of the time-series? • How can gap-filling be used for uncertainty estimation of flux data ? • And for flux-partitioning?

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