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

Gap filling meteorologidal dataGap 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 datafluxes: Integration over time of fluxes, with estimate of uncertainty, needs gaps filled with correct mean and sd distribution needs to be correctMeteo: 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.

Particular to meteo data:Meteo vars often are poorly correlated with other variablesOften, if one variable is missing, most others are as well

Therefore, either use internal variability, autocorrelations, orUse correlations with data measured nearby

Are conditions for grass and forest stations the same?

Neural network (multiple non-linear regressor):Activation functionhidden layer:Input scaled between -1 and 1

Neural network configuration to estimate Lin:NN calibrated on: Lin - T4

Long wave incoming radiation (Validation):Lin clear sky:slope = 1.122 r2 = 0.27

Lin neural netslope = 0.985 r2 = 0.67

Uncertainty and the length of the data gap:

Neural network configuration to estimate F_CO2: Fill missing data AWS Fill missing data latent heat flux Fill missing data CO2 flux

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

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 columnsT, Rad columns are also filledWe 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 sitesFor T, Rad, Rh, P, Precip! the result looks acceptable.We tested this putting in T, Rad or U data in NEE columnCreated artifical gasp in loobos dataCompared with NN gap filling and original data

Hungary Hegygatsal Temperature filled

Hegyhatsal Specific humidity !

Tharandt windspeedSoroe rainfall

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

Results: data, neural network, CE filling: NEE

Compare filled totals (Monthly NEE)

Results: data, neural network, perverse CE fillingTemperatureFive 6-8 day gaps

Results: data, neural network, perverse CE fillingShortwave radiationFive 6-8 day gaps

Results: data, neural network, perverse CE fillingRelative humidityFive 6-8 day gaps

Results: data, neural network, perverse CE fillingWind speedFive 6-8 day gaps

Conclusions:

Also work on filling Meteorology dataFor 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, includingCorrelate 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 rainfallProduce filled series for many towers centrally.

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

Seasonal and interannual variation of net daily carbon fluxesLess seasonalMore seasonal

Total uptake

% data gaps

95% confidence interval

Manaus Jan'00 - Jan '01

6.2 T ha-1

5%-10%

- not specified

Manaus Jul'99 - Jul'00

7.7 T ha-1

17 %

+/- 0.25 T ha-1

Jaru March '99-March '00

5.8 T ha-1

57%

+/- 1.0 T ha-1

Jaru Oct. '99 - Oct '00

6.0 T ha-1

40%

+/- 0.7 T ha-1

U* lmFc=f(C,u*,lm,R,Ps)Advection=f(C)AdvectionConsider the area beneath the sensor a leaky, sloshing vesseland fit both physiological and micrometeorological parametersR, Ps=alpha.PARTo be tested .C=sum(R-Ps-Fc-advection)

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