Upload
landen
View
34
Download
3
Embed Size (px)
DESCRIPTION
CAMELS- uncertainties in data. Bart Kruijt, Isabel van den Wyngaert, Ronald Hutjes, Celso von Randow, Jan Elbers, Eddy Moors. Types of data. vegetation height, LAI, d, z 0 , rooting … heterogeneity, sampling cup anemometer stalling, hygrometers.. calibration, dew on radiation sensor,.. - PowerPoint PPT Presentation
Citation preview
CAMELS- uncertainties in data
Bart Kruijt, Isabel van den Wyngaert, Ronald Hutjes, Celso von Randow, Jan Elbers, Eddy Moors...
Types of data
• Land use, Site parameters•accuracy•representativity
• driving variables (weather)•instrument error/precision•technical/ operational error•siting error
• validation/optimisation data (fluxes)
•stochastic error•technical/ operational error•calculation/conceptual uncertainty•representation of surface
• day • night
vegetation height, LAI, d, z0, rooting …
heterogeneity, sampling
cup anemometer stalling, hygrometers.. calibration, dew on radiation sensor,..Sheltering, shading, …
=(w2c2) *T/ Tscale --> fourth momentscalibration, pump maintenance, window cleaningaveraging time, coordinate rotation, freq. corr
footprint models, heterogeneity, win directioncalm nights drainage, return fluxes
CO2
?
Fc =.w.c
NEE = Fc + z(c/ t)
Eddy correlation
Eddy correlation hopeless?
Raw data
Convert to physical units
Range check and despike
Remove tube delays
High pass filter Rotate, 1, 2, 3
covariances
Frequency correct high low
Convert to area base
Correct calibration
NEE
Pre-rotate?
High pass filter
Rotate, 1, 2, 3 covariances
Storage fluxes
Average T and P
no yes
Each processing step carries uncertainty
Time
Sensitivity to flux calculation methods
Rotation: correction for tilt of mean streamlines
Detrending and averaging:removing non-stationarity
Length scale (m)
1 10 100 1000 10000
Sca
le C
O2
fluxe
s (
mol
/m2 /s
)
-4
-3
-2
-1
0
1
wet seasondry season
CO2 Fluxes (SW Amazon) - Scale contributions
‘Turbulent’ ‘Meso-scale’
Summary effects of rotation and averaging
Relative effects of averaging time and rotation on daily total fluxes, Amazon
Finnigan, Malhi, 2002
Longer averaging times --> better energy closure?
Rio Jaru dry
Time (hours)
0 6 12
Rio Jaru wet
Time (hours)
0 6 12
-10
-5
0
5
Manaus K34 dryManaus K34 wet
Ave
rage
CO
2 flu
x (
mol
m-2
s-1)
-10
-5
0
5
Total uncertainty from rotation and averaging over the day
Frequency correct high low
Convert to area base
Correct calibration
NEE
Assess night time,correct
Fill gaps
Cumulate over time
Rotate, 1, 2, 3 covariances
Filter out poor similarity
Ecosystem physiology
Storage fluxes
Average T and P
Manaus K34 1999-2000 CO2 calibration LiCor
[CO2] CIRAS, ppm
330 340 350 360 370 380 390 400 410 420
[CO
2]
Lic
or,
pp
m
340
360
380
400
420
1999 and 2000 DOY<170 2000 DOY 200-230 2000 DOY 230-263 2000 DOY >283
1999 and 2000 DOY<170 :b[0]=-5.81,b[1]=1.02,r ²=0.86
2000 DOY 200-230:b[0]=121.10, b[1]=0.66, r ²=0.75
2000 DOY 230-263:b[0]=-78.92, b[1]=1.22, r ²=0.91
2000, DOY > 283:b[0]=-79.18, b[1]=1.15, r ²=0.96
Uncertainty in calibration
Calibration a posteriori causes problems and uncertainty
Manaus, K34, Oct 1999-April 2000, Low night-time u*
hour of day
0 5 10 15 20
CO
2 flu
x (
mol
m-2
s-1
)
-30
-20
-10
0
10
20
Eddy flux Storage fluxBiotic flux
Manaus, K34, Oct 1999-April 2000, High night-time u*
hour of day
0 5 10 15 20
CO
2 flu
x (
mo
l m-2
s-1
)
-30
-20
-10
0
10
20
Eddy flux, storage flux and Ecosystem (‘biotic’) flux
Windy nights
Calm nights
Eddy correlation integrates everything but misses advection
0 100 200 300 400 500 600 700 800
15
30
45
60
75
90
[CO2] ppmv
Distance (m)
z (
m)
3D Test - [CO2] along topography (early morning)
300 350 400 450 500 550 600 650 700
Morning
CO2 stored in valleys
CO2 return ?
Night CO2 drainage ?
RsRs
Rs
Rs
Manaus, Amazon
Systematic error Random error on half-hourly Fc
Total one-sidederror on annualtotals for Amazon *
Spikes / noise 2% 11% 2%Tube delay errors - 3.5% <0.1%Rotation andaveraging
10% - 25% ** - 10- 25% **
Frequency losscorrections - zeroplane
0.27%(d) - 2.7 %
Frequency losscorrections - flowrate
1%(ft)/(ft*(ncf)) - <0.5%
Convert to area base 0.3 % <0.1%Calibrationcorrection
(0% - 20% for 100days)
- 0% - 6 %
Night-time losses 0% - >100% - 0% - 100%General data gaps Bias to daytimeSimilarity filter gaps Bias to night timeMissing data filling - 0.08 - 1 kg ha-1h-1, or
30 - 50 kg ha-1d-1 /(ndfit)0.25 - 1 t ha-1 y-1, or3% - 20%
(d) = uncertainty in zero-plane displacement; (ft) = uncertainty in tube flow rate ft; ncf
= number of cycles in tube flow rate;* assuming the conditions at the Manaus k34 and Jaru towers as described in this paper,(d)=10, (ft)/ft = 0.5 and ncf=4 y-1.** systematic errors appear to partly compensate between seasons, so average uncertaintymay decrease over time.
Total one-sided error for AMAZON on annual totals is, apart from night-time error, between 12.5% and 32%, or 1-2 t ha-1.
Systematic or random error?
• Error depends on measuerement height, surface type, time of day, weather
•Random error vanishes when the number of independent samples increases.
•BUT: when are atmospheric samples independent?
•Systematic error is persistent. • What if maintenance varies or calibration drifts? • What if low frequencies vary with weather or season?•---> when do systematic errors become random?
Bias. Example from the SW Amazon, with cold periods
PA
R(
mo
l.m-2
.s-1
)
0200400600800
100012001400160018002000
PAR C
O2 (
pp
m)
300
350
400
450
500
CO2 Concentrationq
Day 195 (Friagem)
CO
2 f
lux
(m
ol.m
-2.s
-1)
-30-25-20-15-10
-505
10152025303540
FCO2Tair
PA
R(
mo
l.m-2
.s-1
)
0200400600800100012001400160018002000
Sp
ecifi
c H
um
idity
(g.
kg-1
)
0
5
10
15
20
25
Te
mp
era
ture
(o
C)
1012141618202224262830
Day 192 (normal)
Other bias :
transient periods (morning, early evening) are non-stationary and carry high uncertainty
rainy periods carry high uncertainty
ideal weather associated with specific wind directions
Estimates for CAMELS
NEE day NEE night NEE, systematic night u*<0.1NEE night, u*<0.1NEE night, 0.1<u*<0.2LE LE at RH>95%H flux during rain > 10 mm h-1 additional errorsystematic err on all fluxinstrument accuracy sonic 1 1 0 1 1 1 1 1 0 0instrument accuracy licor 1 1 0 1 1 1 1 0 0 0location and footprint (Rehbmann et al) 20 20 0 20 20 30 30 20 0 0stochastic error in turbulence 20 20 0 20 20 20 20 20 0 0night-time 0 0 100 200 100 0 0 0 0 0Angle of attack 5 5 0 5 5 5 5 5 0 5Webb (density) corr (open path only) (???) 0.025515 0.025515 0 0.025515 0.025515 0.025515 0.025515 0.025515 0 0cleaning instrument 5 5 0 5 5 10 15 10 0 0calibration 5 5 0 5 5 10 0 1 0 0tube delay error ( with closed systems only) 4 10 0 10 10 10 20 0 0 0spikes 10 10 0 10 10 10 10 10 100 0lo and hi frequency errors 10 20 0 20 20 10 40 5 0 0relative uncertainty (no perc!) 0.330606 0.384318 1 2.016804 1.033199 0.377492 0.484768 0.255147 1 0.05
numbers are percentages of measured flux, error per (half-hourly) measurement pointassuming good location choice, maintenance and data treatment
Rebmann et al - CARBOEUROFLUX footprint-quality analysis
Table 4: Land-use classification and quality tests for the fluxes of momentum (includingintegral turbulence characteristics), sensible heat H, latent heat E and carbon dioxide fluxFCO2 (only stationarity) and vertical wind component. Numbers are relative to the totalnumber of investigated cases for each site.
Site AOI> 80%
flag 1-2
Hstflag 1
Estflag 1
FCO2
stflag 1wm <0.35m s
-1
BE1 79% 90% 84% 54% 83% 100%BE2 60% 83% 82% 76% 76% 100%CZ1 100% 84% 82% 53% 85% 53%FI1 70% 92% 86% 73% 93% 100%FI2 59% 93% 89% 86% 86% 100%FI3 94% 92% 92% 96% 89% 93%FR1 32% 86% 80% 64% 72% 99%FR2 93% 72% 74% 62% 79% 93%FR4 100% 87% 91% 82% 84% 100%GE1 97% 93% 90% 71% 87% 100%GE2 80% 85% 86% 81% 87% 99%GE3 86% 91% 87% 51% 80% 98%IS1 100% 89% 85% 58% 90% 92%IT4 98% 51% 60% 40% 52% 90%IT5 90% 81% 74% 85% 78% 100%IT-ext 99% 95% 79% 79% 41% 98%NL1 96% 94% 87% 49% 87% 100%UK1 100% 91% 86% 63% 92% 97%average 85% 86% 83% 68% 80% 95%
Discussion:
•How to avoid bias when applying uncertainties to model fitting?
Include more processes?Look at daily totals where day-night cross contamination occurs?
•Can we eliminate bias by better matching models and measurements?
•How to fine-tune uncertainties for specific sites or conditions?
U* • lmFc=f(C,u*,lm,R,Ps)
Advection=f(C)Advection
Consider the area beneath the sensor a leaky, sloshing vesseland fit both physiological and micrometeorological parameters
R, Ps=alpha.PAR
To be tested ….
C=sum(R-Ps-Fc-advection)
time19.8.00 10:00
20.8.00 02:00
20.8.00 06:00
20.8.00 10:00
20.8.00 02:00
20.8.00 06:00
20.8.00 10:00
Fcm
eas,
Fc
mod
el
-20
-10
0
10
20
30
leak rate = 6.8e-4 s-1R = 6.6 umol m2 s-1alpha = -0.024 umol umol-1mixing scaling = 3590.78 m
Some early results look good
Raw data
Convert to physical units
Range check and despike
Remove tube delays
High pass filter Rotate, 1, 2, 3
covariances
Pre-rotate?
High pass filter
Rotate, 1, 2, 3 covariances
no yes
CO2 flux (mol m-2 s-1)-40 -20 0 20 40 60
CO
2 f
lux
with
sp
ike
s (m
mol
m-2
s-1
)
-40
-20
0
20
40
60
data with spikes 50 ppm, 1 per 60 sdata with spikes 5 ppm, 1 per 60 s
W
-2-1012
CO
2
380
390
seconds
0 60 120 180 240 300
375
400
425
Manaus K34
GMT=local time + 4 hours
0 6 12 18 24
CO
2 flu
x (
mo
l m-2
s-1)
-25
-20
-15
-10
-5
0
5
10
15
5 ppm spike50 ppm spikeno spike
Manaus k34, July 2000
noise to signal ratio in CO2 concentration
0.1 1 10
Rel
ativ
e un
cert
aint
y in
CO
2 f
lux
0.1
1
10
y = 0.18*x0.72
Effect of spikes in one channel only
5 ppm and 50 ppm spike on CO2.Effect is random relative uncertainty,increasing with spike/signal ratio
Raw data
Convert to physical units
Range check and despike
Remove tube delays
High pass filter Rotate, 1, 2, 3
covariances
Pre-rotate?
High pass filter
Rotate, 1, 2, 3 covariances
no yes
2 October 1999, 8:00
Delay (s)
0 2 4 6 8 10
w't'
co
vari
an
ce (
m s
-1 K
)
-0.005
-0.004
-0.003
-0.002
-0.001
0.000
0.001
0.002
w'c
' co
vari
an
ce (
m s
-1
mo
l mo
l-1)
0.102
0.104
0.106
0.108
0.110
0.112
0.114
0.116
0.118
w'q
' co
vari
an
ce
(m s
-1 m
mo
l mo
l-1)
0.0050
0.0052
0.0054
0.0056
0.0058
0.0060
0.0062
0.0064
w't'w'c'w'q'
2 October 1999, 11:30
w't'
cov
aria
nce
(m s
-1 K
)
0.09
0.10
0.11
0.12
0.13
0.14
0.15
0.16
w'q
' cov
aria
nce
(m
s-1
mm
ol m
ol-1
)
0.075
0.080
0.085
0.090
0.095
0.100
w'c
' cov
aria
nce
(m s
-1
mol
mol
-1)
-0.40
-0.38
-0.36
-0.34
-0.32
-0.30
-0.28
-0.26
2 October 1999, 11:30
w't'
cov
aria
nce
(m s
-1 K
)
-0.0010
-0.0005
0.0000
0.0005
0.0010
0.0015
0.0020
w'q
' cov
aria
nce
(m
s-1
mm
ol m
ol-1
)
-0.0010
-0.0009
-0.0008
-0.0007
-0.0006
-0.0005
-0.0004
w'c
' cov
aria
nce
(m s
-1
mol
mol
-1)
-0.13
-0.12
-0.11
-0.10
-0.09
-0.08
-0.07
-0.06
Uncertainty in tube delay calculations
Manaus k34
Fc(lag0.5) mol m-2s-1
-40 -30 -20 -10 0 10 20 30 40
Fc(
lag3
.5) m
ol m
-2s-1
-40
-20
0
20
40
Manaus K34
GMT = local + 4 hours
0 6 12 18 24
CO
2 flu
x (
mol
m-2
s-1)
-20
-15
-10
-5
0
5
10
Ave
rage
frac
tion
of s
ucce
ssfu
l de
lay
calc
ulat
ions
0.55
0.60
0.65
0.70
Fc(lag3.5)= -0.35+0.78*Fc(lag0.5)
Raw data
Convert to physical units
Range check and despike
Remove tube delays
High pass filter Rotate, 1, 2, 3
covariances
Pre-rotate?
High pass filter
Rotate, 1, 2, 3 covariances
no yes
Cv. Fc
(sd/avg)cv. E(sd/avg)
Abs. Sd Fc
(kg ha-1d-1)Abs. Sd E(MJ m-2d-1)
Total Rnet
(MJ m-2d-1)Manaus K34 average 0.10 1.97Rio Jaru average 0.25 3.47Manaus C14 (wet) 0.10 0.56 13.0 *Manaus K34 wet 0.14 3.29Rio Jaru wet 0.43 0.09 7.56 0.63 12.7Manaus K34 dry 0.15 0.08 2.24 0.61 14.6Rio Jaru dry 0.27 2.83
EffectIncreasing averaging time
30-120 min.True lateral
rotationDetrended
lateral rotationC
on
dit
ion
Ver
tica
lro
tati
on
on
ly
Ver
tica
l an
dla
tera
lro
tati
on
Ver
tica
l an
dd
etre
nd
edla
tera
lro
tati
on
30 m
in
120
min
30 m
in
120
min
Manaus K34 Fc 0.96 0.95 0.95 0.94 0.93 0.97 0.95
Rio Jaru Fc 0.74 0.71 0.85 0.84 0.92 1.00 1.15
Manaus K34 E 0.99 1.08 1.03 0.94 1.01 0.99 1.02
Rio Jaru E 0.98 1.13 1.07 0.88 0.99 0.97 1.04
Manaus C14 E 1.04 1.10 1.09 0.92 0.97 0.93 0.98
Summary effects of rotation and averaging
Variation in sensitivities to treatments
Relative effects of averaging time and rotation
Reference Run1 Run2 Run3 Run4 Run5 Run6 Run7Time constant 200 s * * * * *
800 s * * *Averaging time 30 min * * * * *
120 min * * *Low freq.correction
Yes * * * * *
No * * *Lateral rotation Yes * * * * *
No * * *
reference run1 run2 run3 run4 run5 run6 run7
Car
bon
flux
(g C
m-2
)
-20
-15
-10
-5
0
Frequency correct high low
Convert to area base
Correct calibration
NEE
Assess night time,correct
Fill gaps
Cumulate over time
Rotate, 1, 2, 3 covariances
Filter out poor similarity
Ecosystem physiology
Storage fluxes
Average T and P
K34, 2-12 October 1999
GMT = local time+4 h
0 6 12 18 24
Frequen
cy correc
tion ( m
ol m
-2s-1
)
-2
-1
0
1
2
Only high frequency correctionsOnly high frequency corrections, low flow rateAll frequency correctionsAll frequency corrections, d=30 m
Frequency corrections
Zero-plane, tube NOT important. Low frequencies ARE important.
Frequency correct high low
Convert to area base
Correct calibration
NEE
Assess night time,correct
Fill gaps
Cumulate over time
Rotate, 1, 2, 3 covariances
Filter out poor similarity
Ecosystem physiology
Storage fluxes
Average T and P
Manaus k34
GMT= local + 4 hours
0 6 12 18 24
Ave
rag
e ai
r m
ola
r de
nsity
(m
ol m
-3)
40.0
40.2
40.4
40.6
40.8
41.0
Atm
osp
he
ric P
ress
ure
(h
Pa)
1006
1007
1008
1009
1010
1011
1012
1013
1014
Conversion ppm m s-1 to area based fluxes
Small potential errors average out over days
Frequency correct high low
Convert to area base
Correct calibration
NEE
Assess night time,correct
Fill gaps
Cumulate over time
Rotate, 1, 2, 3 covariances
Filter out poor similarity
Ecosystem physiology
Storage fluxes
Average T and P
Similarity relations - representativity for surface
Filtering for poor similarity will discard important periods such as early morning
Jaru, 1999-2000
u*
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
w
)
0.0
0.5
1.0
1.5
2.0
b[0]=0.05b[1]=1.16r ²=0.91
(z-d)/L (stable)
0.1 1 10 100
1
10
-(z-d)/L (unstable)
0.1110100
(w
)/u*
1
10
-0.1 < z/L < 0.1
Jaru 50%-100%
Ave
rag
e d
aily
car
bo
n fl
ux
(T h
a-1 d
-1)
-0.04
-0.02
0.00
0.0210-day average daily total fluxfit to only even 10-day periodsfit to only odd 10-periods
Col 61 vs fit tots
Jaru 25%
Jan 99 Jul 99 Jan 00 Jul 00 Jan 01
Ave
rage
dai
ly c
arbo
n flu
x (T
ha-1
d-1)
-0.04
-0.02
0.00
0.02 10-day average daily total fluxfit to every 2nd in 4 10-day periodsfit to every 4th in 4 10-day periodsfit to every 1st in 4 10-day periodsfit to every 3rd in 4 10-day periods
Jaru 12.5%
Jan 99 Jul 99 Jan 00 Jul 00 Jan 01
10-day average daily total fluxfit to every 1st in 8 10-day periodsfit to every 2nd in 8 10-day periodsfit to every 3rd in 8 10-day periodsfit to every 4th in 8 10-day periods
Uncertainty as a function of the percentage good data - Rebio Jaru
Percentage annual data coverage
0 10 20 30 40 50 60 70 80 90 100
Cum
ula
tive
sta
nda
rd e
rror
of e
stim
ate
(T h
a-1y-1
)
0.0
0.5
1.0
1.5
2.0
2.5
Number of full data days per year
0 50 100 150 200 250 300 350
JaruManaus K34
Uncertainty on annual totals from (well distributed) data gaps
And finally….