LBA Flux Tower Workshop Software Intercomparison Celso von Randow Ganabathula Prasad CPTEC/INPE Celso von Randow Ganabathula Prasad CPTEC/INPE December 2001

Quality control – Vickers & Mahrt (1997) Flux sampling problems – Mahrt (1998)  Surface heterogeneity, complex terrains  Nonstationarity  Random Flux Errors  Systematic underestimation of low frequency fluxes Sensitivity of flux calculations Results of intercomparison among LBA flux measurements Software intercomparison

Quality control for Sonic Data Spike Removal Insufficient Amplitude Resolution Drop outs Absolute Limits Skewness and Kurtosis Discontinues- Haar mean, Haar var. Lag Correlation – Not used. Vertical Profile – Not used.

Spike Removal Electronic spikes to have a max. width of 3 consecutive points in the time series and more than 3.5 standard deviations from the window mean (L=3000 points The point is replaced using linear interpolation between data points. The record is flagged when the total number of spikes replaced exceeds 1% of the total number of data points

Insufficient Amplitude Resolution Compute a series of discrete frequency distributions for half-overlapping windows of length 1,000 data points The window move one-half the window width at a time through the series the number of bins is 100 and the interval for the distribution is min(range, 7  ). Flagged if number of empty bins in the discrete frequency distribution >70%

Amplitude Resolution

Drop outs Consecutive points that fall into the same bin are tentatively identified as dropouts Max no of consecutive dropouts as % of total window points same window and frequency distributions used for the resolution problem if bin is within 10% and 90% tentiles of distribution, compare with threshold.

Drop out

Absolute Limits |u|> 30 m/s |v| > 30m/s |w| > 5 m/s T 323 K (50 C) H2O 40 g/kg

Skewness and Kurtosis Detrend record Skewness  [-2 2] (Empirical value) Kurtosis  [1 8] (Empirical value)

Discontinuites Haar transform of window Normalize with the smallest S.D. Discontinuity if Haar mean >3

Flux Sampling Errors Systematic Error: failure to capture all the largest transporting eddies– underestimation of flux. Random Error: Inadequate sampling of main transporting eddies, inadequate record size. Mesoscale variability: inhomogeneity of flow. Dependence of flux on choice of scale.

Systematic Error Relative Systematic Error: L2 - L1 < Th L1

Random Error Partition record into non overlapping subrecords (i=1,2,…) Average Flux F i = +F tr + F * i F tr = a 0 + a 1 t using a Least Squares fit. RFE =  F* | | -1 N -0.5 RN =  Ftr | | -1 N -0.5

Flux Event Measure of Isolated flux event: Max(F i ) | | -1 F i is the aver sub-record flux is the record mean value of F i

Preliminary Results on Santarem km67 Day 267 No of Records 44 RFE RN EVT RSE FSR WU WV WT WH2O

Sensitivity of flux calculations Averaging time scales Rotations Block averaging / Linear detrending / Recursive digital filter Low frequency corrections Uncertainties

LBA tower sites Rondônia  Rebio Jaru forest – Primary forest  Fazenda Nossa Senhora) - Pasture Manaus  Tower K34 – Primary forest  Tower C14 – Primary forest Santarém  Tower Km 67 – Primary forest  Tower km 83 – Logged forest (primary)  Tower km 77 – Pasture site

LBA tower sites Caxiuanã  Primary forest Brasília  Cerrado  Campo Sujo (Biennial fire regime)  Campo Sujo (Quadriennial fire regime) Mato Grosso  Sinop Forest Bragança  Mangrove Venezuela – Savanna site

Venezuela

Softwares used in intercomparison Rondônia, Manaus – EddyWSC v.2 (Alterra)  3 rotations;  Digital recursive filter (800 s time constant);  Low frequency corrections Vickers & Mahrt Software (Oregon St. Univ.)  2 rotations;  Block averaging  Discard records with high random flux errors and nonstationarity + Softwares used in LBA flux sites

Santarém, km 83 – EddyWSC v.1 (Alterra)  3 rotations;  Digital recursive filter (200 s time constant); Santarém, km 67 – CD-10 Program (Harvard Univ.) Santarém, Pasture – CD-03 Program (ASRC, Albany) Caxiuanã – Edisol (Univ. of Edinburgh) Brasília – EddySoft package (MPI)  3 rotations;  Linear detrending; Venezuela – Edisol (Univ. of Edinburgh)

Edisol x EddyWSC

EddyWSC x Vickers & Mahrt Software

CD-10 x Vickers & Mahrt Software ~ dry season

CD-04 x EddyWSC 200 s x 800 s time constant

CD-03 x Vickers & Mahrt Software

CD-03 x EddyWSC

Eddysoft package x EddyWSC ~ dry season

Venezuela (Edisol) x EddyWSC

In summary... Fluxes calculated by different LBA groups might give quite different values specially considering different parameters (averaging time scale; corrections; etc) As fluxes calculations in complex terrains are very sensitive to parameters like rotations and averaging time scales, LBA groups should be VERY CAREFUL when integrating or comparing measurements from different groups. Softwares used by a few groups (Rondonia/Manaus, Santarém km 67, Caxiuanã/Bragança) agree within + 6 %. Softwares used by groups CD-03, CD-04, Brasilia and Venezuela calculated substantially lower fluxes than other programs (averaging time scales ? corrections ? )

Acknowledgements Jair F. Maia Maria Betânia L. de Oliveira Paulo Kubota

Suggestions for (near) future Continue software intercomparison  Put together a “golden” data set that can be run by each group on their own program; Integration of measurements on large scale  Standardize software parameters ? How do the differences are reflected in long term budgets ?  Are the differences the same in positive (respiration) and negative (assimilation) fluxes ?

To be continued...