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Long term weather and flux data: treatment of discontinuous data.

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

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Long term weather and flux data: treatment of discontinuous data.

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  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 Lin: NN calibrated on: Lin - T4

  7. Long wave incoming radiation (Validation): • Lin clear sky: slope = 1.122 r2 = 0.27 • Lin neural net slope = 0.985 r2 = 0.67

  8. Uncertainty and the length of the data gap:

  9. Neural network configuration to estimate F_CO2: • Fill missing data AWS • Fill missing data latent heat flux • Fill missing data CO2 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. Soroe rainfall Tharandt windspeed

  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

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

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

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