1. 1 Welcome to the Gap Filling Comparison Workshop September 18-20, 2006 Antje Moffat

2. 2 Welcome 12 of the 14 members from the gap filling comparison Over 30 participants - from all over Europe (Germany, Italy, Netherlands, Switzerland), US, Canada, Russia, and Australia

3. 3 Goals of the Workshop Review of Gap Filling Techniques Completion of the Gap Filling Comparison –Discussion of the Results –Review of Paper –Evaluation of the Techniques Work Sessions and Plenary Debates to Exchange our Experiences and Expertise Generate Ideas for Further Improvement of the Gap Filling New Insights into the Eddy Flux Data

4. 4 Outline Workshop Program GFC Analysis Performance of Techniques Error on Annual Sum

5. 5 Program: This Morning (Monday) 9:00 Registration of the Participants If you need any kind of help, please contact Ulli or Silvana! 9:30 Setting the Stage of the Workshop Antje Moffat: “Welcome” Martin Heimann: “Biogeochemistry Research at the MPI in Jena” Dario Papale: “The CarboeuropeIP Ecosystem Component Database: data processing and availability” Markus Reichstein: "Gap filling: Why and how?” 11:00 COFFEE BREAK (Foyer) 11:30 Review of Gap Filling Techniques: SPM and ANNs Vanessa Stauch: “Semi-parametric models” Dario Papale: “Gap filling of eddy fluxes with artificial neural networks” Rob Braswell: "Gap filling by iterative regression using a regularized neural network” 12:30 LUNCH (Campus Cafeteria)

6. 6 This Afternoon 14:00 Review of Gap Filling Techniques (cont.): NLRs and UKF Ankur Desai: “Towards a robust, generalizable non-linear regression gap filling algorithm” Asko Noormets: “NLR_AM - AQRTa-Model” (10 min recording) Andrew Richardson: “Maximum likelihood non-linear regression model” David Hollinger: “Data assimilation for eddy flux filling: The unscented Kalman filter” 15:30 COFFEE BREAK 16:00 Review of Gap Filling Techniques (cont.): Models and comparison Zisheng Xing: “A gap-filling model for tower-based NEP measurements” Jens Kattge: “Model parameter inversion against Eddy Covariance Data using a Monte Carlo Technique” Bart Kruijt: “Comparing gap filling using neural networks and the CarboEurope tool, for Fluxes and Meteo data” 19:30 Dinner Suggestion: Restaurant Papiermuehle (Please sign up!)

7. 7 Tuesday Morning 9:00 Eddy and Component Flux Measurements Corinna Rebmann: “Eddy covariance measurements and their shortcomings for the determination of NEE” David Hollinger: “Uncertainty in eddy flux data and its relevance to gap filling” Eva van Gorsel: “Nocturnal Carbon Efflux: Can eddy covariance and chamber measurements be reconciled?” Pasi Kolari: “Gapfilling submodel selection based on measured component fluxes” 10:30 COFFEE BREAK 11:00 Gap Filling of Grassland and Agricultural Sites Christof Ammann: “Gap-Filling of CO2 Fluxes of Frequently Cut Grassland” Mauro Colavincenzo: “A gap filling methodology used at a agricultural site in Southern Italy” Irene Lehner: “Carbon balance of a maize canopy: comparison of different gap filling strategies” 12:00 LUNCH END of OPEN SESSIONS!

8. 8 Tuesday Afternoon 13:00-15:00 Parallel Work Sessions Part 1 Group 1: “Analysis of the partitioned GPP/ER comparison results” (Ankur Desai) Group 2: “Gap filling of meteorological data and water and energy fluxes” (Dario Papale) 15:00 COFFEE BREAK 15:30-17:30 Parallel Work Sessions Part 2 Group 3: "Gap filling of sites with non-steady time series, e.g. cut grassland, cropland" (Christof Ammann) Group 4: "Using gap-filling techniques for estimating random errors in eddy covariance data" (Andrew Richardson) Please sign up for the work sessions! 19:30 WORKSHOP DINNER (Restaurant: Weinbauernhaus “Im Sack”)

9. 9 Wednesday Roundtable on the Gap Filling Comparison 8:30 Review of Gap Filling Comparison Paper, Antje Moffat - Interpretation of the comparison results - Derivation of key findings - Evaluation of techniques 12:30 LUNCH 13:30 Minutes from the four Work Sessions 14:30 COFFEE BREAK 15:00 Plenary Debates - Site dependency of gap filling technique performances - Filling of long gaps using previous years - Conception of a public domain code library with filling routines - Extended gap filling comparison for urban and crop - Workshop resume and outlook 17:00 End of Workshop

10. 10 Handling of Presentations 20 min presentations: 15 min talk plus 5 min discussion Please transfer your presentation onto common laptop during coffee or lunch break (Ulli or Silvana) Publicized on GFC webpage after workshop Questions?

11. 11 Gap Filling Comparison Analysis

12. 12  Keyfile: Artificial Gap Flags  Golden File - fragmented:  Superimposition  Comparison of Observed NEP and Predicted NEP: Basic Principle

13. 13 Statistical Metrics Bias Error Root Mean Square Error Correlation Coefficient p - predicted NEP o - observed NEP

14. 14 Analysis Half-hourly basis Daily sum basis for full day artificial gaps Daytime/Nighttime data Weighted ALL data Predicted versus Observed

15. 15 Challenge of the Analysis 5 artificial gap length scenarios (single hh - 12 days) *10 permutations *3 subsets: day, night, all *12 golden sites *19 submissions *15 statistical metrics: RMS, R2, Bias, Daily Sum, normalized, benchmarked, … 513,000 comparison results! (see selection on posters in foyer)

16. 16

17. 17 Some Comparison Findings

18. 18 RMSE and R2: Half-hourly Basis Performance of gap filling techniques from bottom MIM, MDV, UKF_LM, NLR & Others 3 ANNs leading Correlation Coefficient R2 Root Mean Square Error (gCm -2 ) Daytime Nighttime

19. 19 Root Mean Square Error (gCm -2 ) Daily sum basis Daytime R2: 0.8 - 0.95 RMSE: 1.0 - 1.8 gCm -2 Nighttime R2: 0.75 - 0.9 RMSE: 0.5 - 1.0 gCm -2  Very good filling performance for daytime and nighttime data Techniques: MIM, Others, ANNs leading Half-hourly basis Daytime R2: 0.6 - 0.8 RMSE: 2.5 - 4.0 gCm -2 Nighttime R2: 0.2 - 0.4 RMSE: 1.5 - 2.5 gCm -2  Good filling performance for daytime but not for nighttime Techniques: MIM, MDV, UKF_LM, NLR & Others, 3 ANNs leading RMSE and R2: Half-hourly & Daily Sums Correlation Coefficient R2 Daytime Nighttime Daytime Root Mean Square Error (gCm -2 )

20. 20 DailySum Bias per Site Year: Medium Gaplength, ALL Bias Techniques Medium gap length (1.5 days): Bias of <0.07 gCm -2 per filled day

21. 21 DailySum Bias per Site Year: Long Gaplength, ALL Bias Techniques Long gap length (12 days): Bias of <0.2 gCm -2 per filled day

22. 22 Annual Sum Error Estimate Assumption: representative choice of golden sites good technique (red stars)  Error estimate on the annual sum Annual Sum Error Small to med gaps: <0.07 gCm -2 per filled day equivalent Periods of longer gaps: <0.2 gCm -2 per filled day equivalent  Quality of long gap filling critical

23. 23 Calculation Example Example for average file with 35% gaps: 18% small to medium gaps 18% periods of longer gaps of 5-10 days 18% = 66 filled days Error estimation 66 x 0.07 gCm -2 : 5 gCm -2 66 x 0.2 gCm -2 : 13 gCm -2  Total error induced by filling of the gaps on the annual sum: ±18 gCm -2 ^

24. 24 Test using Real Gap Filling Results Standard deviation between techniques of filling the real dataset with  35% gaps ≤ 16 gCm -2 1) no soil temperature 2) 30 day system failure Are 18gCm -2 an appropriate estimate of the error on the annual sum prediction?

25. 25 Questions?

26. 26 Let’s fill our “knowledge gaps” and have a fun and productive workshop! Let’s fill our “knowledge gaps” and have a fun and productive workshop!

27. 27 Separation of Daytime and Nighttime Data Keyfile: 10% artificial gaps Fragmented Golden File: 80% daytime NEP data, 35% nighttime NEP data Real gap filling: 20% real day gaps, 65% real night gaps 1:3 Artificial gap filling: 8% artificial day gaps, 3.5% artificial night gaps 2:1  Important to consider daytime and nighttime data separately

28. 28 Bias on daily sums Daytime data Distribution of bias error of the individual daily sums Bias Error ( gCm -2 ) Daytime data Daily Bias Error: - up to 4 gCm -2 ANNs leading ANN_BR

29. 29 Bias on daily sums: Nighttime data Distribution of bias error of the individual daily sums Bias Error ( gCm -2 ) Daytime data Daily Bias Error: - up to 2 gCm -2 ANN_PS leading