1. Long term weather and flux data: treatment of discontinuous data. Bart Kruijt, Wilma Jans, Cor Jacobs, Eddy Moors Loobos

2. Gap filling – meteorologidal data Gap filling is a grey area between measurement, statistics and modelling. We should be careful not to ‘double model’: use filled data for calibration, validation, etc. Should we not go for just modelling? There is a need for continuous data fluxes: –Integration over time of fluxes, with estimate of uncertainty, needs gaps filled with correct mean and sd  distribution needs to be correct Meteo: –Models need updating of state variables (soil moisture, biomass) –Total radiation, rainfall, means of T, Rh, U etc need to be correct EU – GEOLAND project required gap-filled meteo data for 2003, to test- run 1-D surface-atmosphere models.

3. Particular to meteo data: –Meteo vars often are poorly correlated with other variables –Often, if one variable is missing, most others are as well Therefore, either use internal variability, autocorrelations, or Use correlations with data measured nearby

4. Are conditions for grass and forest stations the same?

5. Neural network (multiple non-linear regressor): Activation function hidden layer: Input scaled between -1 and 1

6. Neural network configuration to estimate L in : NN calibrated on: L in -  T 4

7. Long wave incoming radiation (Validation): L in clear sky: slope = 1.122 r 2 = 0.27 L in neural net slope = 0.985 r 2 = 0.67

8. Uncertainty and the length of the data gap:

9. Neural network configuration to estimate F_CO 2 : Fill missing data AWS Fill missing data latent heat flux Fill missing data CO 2 flux

10. Neural networks are useful as they can combine correlations with any internal or external data, and make few assumptoins However, setting up NN for individual sites can be time consuming (Moors method) and using external data also (convert, standardise, link )

11. ‘perverted’ CE method (CE= web-based tool Reichstein&Papale) We are usually in a hurry and needed only ‘reasonable’ results We discovered: CE method accepts any data series as input in any of the filling columns! –NEE (and other fux) columns are correlated with T, Rad columns –T, Rad columns are also filled We thought we might use this as an easy, lazy way to fill gaps in meteo data! –Assumes the methis is a purely statistical tool We applied the method to create continuous data for GEOLAND, for several FLUXNET sites –For T, Rad, Rh, P, Precip! – the result looks acceptable. We tested this putting in T, Rad or U data in NEE column –Created artifical gasp in loobos data –Compared with NN gap filling and original data

12. Hungary – Hegygatsal – Temperature filled

13. Hegyhatsal – Specific humidity !

14. Tharandt windspeed Soroe rainfall

15. Results Loobos test: data, neural network, CE filling: LE

16. Results: data, neural network, CE filling: NEE

17. Compare filled totals (Monthly NEE)

18. Results: data, neural network, perverse CE filling -Temperature -Five 6-8 day gaps

19. Results: data, neural network, perverse CE filling -Shortwave radiation -Five 6-8 day gaps

20. Results: data, neural network, perverse CE filling -Relative humidity -Five 6-8 day gaps

21. Results: data, neural network, perverse CE filling -Wind speed -Five 6-8 day gaps

22. Conclusions: Also work on filling Meteorology data –For Meteo data the Perverse CE does not perform very well after all (in representing variability and pattern. –Filling in winter is more difficult than in summer –NN is good at representing pattern and variability, but mean can be biased Future: develop NN methods, including –Correlate with ECMWF reanaysis data. Partly with the reanalysis product, partly with the forecast product (rainfall). 3- to 6 hourly data. –Possibly use measured data for rainfall –Produce filled series for many towers centrally. ………… 

23. Uncertainty as a function of the percentage good data - Rebio Jaru

25. Seasonal and interannual variation of net daily carbon fluxes Less seasonal More seasonal

26. U* lm Fc=f(C,u*,lm,R,Ps) Advection=f(C) Advection Consider the area beneath the sensor a leaky, sloshing vessel and fit both physiological and micrometeorological parameters R, Ps=alpha.PAR To be tested …. C=sum(R-Ps-Fc-advection)