SENSIBLE HEAT FLUX ESTIMATION USING
SURFACE ENERGY BALANCE SYSTEM (SEBS),
MODIS PRODUCTS, AND NCEP REANALYSIS DATA
Yuanyuan Wanga, Xiang Lia,b
a, National Satellite Meteorological Center, China Meteorological Administration
b, Nanjing University of Information Science & Technology
OUTLINE:
1. INTRODUCTION
2. METHODOLOGY
3. DATA
4. RESULTS AND ANALYSIS
5. DISCUSSION AND CONCLUSION
1. INTRODUCTION
•
•
•
This paper used the Surface Energy Balance System (SEBS) to
estimate the regional sensible heat flux, fully using the advantages of
temporal and spatial resolutions of MODIS data and the height of the
Planetary Boundary Layer of NCEP reanalysis data. In addition, a new
approach which deriving the value of roughness length of momentum
transport was being used in this paper to improve the accuracy of
calculation. Based on the LAS continuously measurements at Arou site
and sensible heat predictions data provided by NCEP, regional sensible
heat flux in Arou area estimated by MODIS data and NCEP data
extended over several months were validated.
The results demonstrated that running SEBS model with NCEP
meteorological data was feasible.
And a sensitivity analysis for  T ,z0m and u _ pbl has been performed in
order to investigate how the variable affect the result of regional
sensible heat flux calculated by SEBS.
1. INTRODUCTION
To estimate the components of energy balance
• Conventional
techniques : point measurements ( point scales )
• Recently : Remote sensing ( regional scales )
Validation of estimated sensible heat flux
• Conventional
techniques : point measurements ( point scales )
i.e. Bowen ratio system, Eddy correlation system
• Recently
: Large Aperture Scintillometers (LAS) ( regional scales )
1. INTRODUCTION
• The roughness length of momentum z0m can be derived from
wind and temperature profiles in the past.
• disadvantage :
a. sensitive to measurement errors ;
b. rejecting a large number of valuable datasets under non-neutral
conditions .
• Since z0m is physically related to the underlying surface and not
sensitive to the diurnal variation of atmospheric stability, a new
approach to derive the value of proposed by Kun Yang(2003).
•
z0m can
be considered constant over a short period, In this
paper, the short periods of time means 30 days (1 month).
1. INTRODUCTION
Fig.2 The structure SEBS
(Wang et al., 2008)
2. METHODOLOGY
• Surface energy balance equation in its instantaneous form is
expressed as :
Rn  G0  H   E
Where: Rn is the net radiation, G0 is the soil heat flux, H is the turbulent sensible
heat flux, and  E is the turbulent latent heat flux.
the soil heat flux is estimated as:
G0  Rn  c  1  f c     s   c  
Where :‘c’ and ‘ s’ are the proportions of G0 / Rn for full cover and bare soil, fixed as 0.05
and 0.315 respectively. The fractional vegetation cover ‘ fc’ weights between limiting
cases.
2. METHODOLOGY
Innovations of SEBS:
(1)Following the full canopy only model of Choudhury and Monteith (1988), a bare soil
surface of Brutsaert (1982), SEBS describes the parameterization method to interaction
between vegetation and bare soil surface.
kC d
kB 1 
4Ct
u*
(1  e nec / 2 )
u ( h)
k
fc  2 fc f s
2
z
u*
 0m
u ( h) h
1 2

kB
fs
s
*
Ct
Then, the roughness length for heat transfer can be derived by:
z0h  z0m / exp(kB 1 )
2. METHODOLOGY
(2) In order to derive the actual sensible heat flux H , use is made of the similarity
theory.
u
u*
k
 z  d0
z  d0
z0m 
ln(
)


(
)


(
)

m
m
z0m
L
L 

0  a 
L
H
ku* C p
 z  d0
z  d0
z0h 
ln(
)


(
)


(
)

h
h
z
L
L
0h


C p u*3 v
kgH
Where,  0 is the potential temperature at the surface， a is the potential temperature at
PBL .
Definding the reference height:
hst  max(0.12  z _ pbl,125  z0m )
If the reference height z_pbl≥hst(the height of Atmospheric Surface Layer)，BAS set of
equation applied；otherwise z_pbl＜hst，MOS does.
2. METHODOLOGY
(3) Considering energy balance at limiting cases, then the derived ‘H’ is further subjected
to constraints in the range set by the sensible heat flux at the wet limit Hwet, and at dry
limit Hdry in SEBS.
Under the dry-limit,
●
the latent heat becomes zero due to the limitation of soil moisture, and the sensible
heat flux is at its maximum value.
 Edry  Rn  G0  Hdry  0
or
Hdry  Rn  G0
●Under the wet-limit,
where the evaporation takes place at potential rate,
(i.e. wet the evaporation is only limited by the available energy under the given surface
and atmospheric conditions), the sensible heat flux takes its minimum value.
 Ewet  Rn  G0  H wet
3. DATA
LAS measurements :
• Arou County, east of Qinghai province
• covered with grasslands and with an altitude about 3000 m
• The LAS made measurements along a path between transmitter
(38°03′24.3″N, 100°28′16.4″E) and receiver (38°02′18.1″N,
100°27′25.9″E) with distance of 2390 m.
T
R
Fig.3 The location of LAS on MODIS pixels;
• Where, T is the transmitter of LAS located on MODIS pixel;
R is the receiver of LAS located on MODIS pixel.
3. DATA
MODIS products and preprocessing
· ALBEDO
albedo  0.2  wsa _ sw  0.8  bsa _ sw
· EMISSIVITY
emissivity  0.4587   31  0.5414   32
NCEP data and preprocessing
The meteorological parameters of NCEP data
The name of variables
planetary boundary layer height （h_pbl）
HPBL (m)
Temperature（t_pbl）
TMP (K)
Pressure（p_pbl）
PRES (Pa)
Speed of wind（u_pbl）
UGRD /VGRD (m/s)
Relative humidity （hr_pbl）
RH (%)
Downward shortwave radiation flux （swgclr） DSWRF (w/m2)
3. DATA
●
Previous research using empirical relationship :
z0 m  0.005  0.5  (
NDVI
)2.5
max( NDVI )
●a
new approach to derive the value of proposed by Kun
Yang(2003) is being used in this paper.
According to this, z0m can be considered constant over a short period, since is physically
related to the underlying surface and not sensitive to the diurnal variation of atmospheric
stability.
● the specific values of from May to September in 2011 are as follows (Table 1. ):
(m)
MAY.
z0m
0.0247
Table.2
JUN.
JULY.
AUG.
SEPT.
0.04187 0.04438 0.05299 0.04048
values being used in this paper
4. RESULTS AND ANALYSIS
4.1 Comparison between Sensible heat from SEBS and LAS
Sensible Heat From SEBS(W/m 2 )
300
Cor.=0.71
RMSE = 64.27
relative RMSE
= 0.43
250
200
150
100
50
0
0
50
100
150
200
250
300
Sensible Heat From LAS Observation(W/m 2 )
Fig.2 Comparsion between SEBS-predicted sensible heat flux and LAS observation from
Jul. to Sept.
4. RESULTS AND ANALYSIS
4.1 Comparison between Sensible heat from SEBS and LAS
Periods
SEBS estimated
LAS measured
Jul.-Sept.
May-Sept.
mean
167.6097
194.03
s.d.
89.63643
128.90
mean
147.3592
147.36
s.d.
24.82358
25.89
Table.3 Comparsion between SEBS-predicted sensible heat flux and LAS observation
from Jul. to Sept.
4. RESULTS AND ANALYSIS
4.2 Comparison between Sensible heat from SEBS and NCEP
As for means and standard deviation, SEBS outputs showed higher values, suggesting
SEBS overestimated sensible heat with more fluctuations compared to LAS
measurements (Table.4).
Periods
SEBS estimated
LAS measured
NCEP data
Jul.-Sept. May-Sept.
mean
167.6097 194.03
s.d.
89.63643 128.90
mean
147.3592 147.36
s.d.
24.82358 25.89
mean
158.5419 164.78
s.d.
55.09799 62.69
Table.4 Statistics of sensible heat (w/m2) from LAS observation, SEBS-predicted and
NCEP sensible heat flux data
4. RESULTS AND ANALYSIS
4.2 Comparison between Sensible heat from SEBS and NCEP
Month
R
RMSE(w/m2)
RRMSE
May
0.63
88.54
0.38
Jun.
0.93
110.78
0.65
Jul.
0.81
77.78
0.77
Aug.
0.54
56.91
0.38
Sept.
0.74
83.38
0.55
Table.5 The Root Mean Square Error (RMSE), Relative Root Mean Square Error
(RRMSE) and Correlation Coefficient (r) of SEBS-predicted sensible heat flux and NCEP
sensible heat flux data
4. RESULTS AND ANALYSIS
From July to September, the disparity between SEBS results and LAS
measurements was smaller. When results from May and June were taken into
account, the disparity increased. This was probably related to the vegetation
condition.
· Before July, the surfaces are nearly bare soil with sparse vegetation;
· From July to the end of August, the surfaces are partially covered by growing
grasses.
· After September the surfaces are covered by mature grasses .
The better Hs estimation from July to September suggests SEBS
is more applicable for dense vegetation.
4. SENSITIVITY ANALYSIS
4.1 SENSITIVITY ANALYSIS
According to the sensible heat flux defined by equation in SEBS
H  C p
ra 
Ts  Ta
ra
z
1
zd
zd
[ln
 h (
)  h ( 0 h )]
ku *
z0h
L
L
So we performed a sensitivity analysis on three variables, which are temperature
difference between ground surface and reference height ( T ), wind speed at PBL(u _ pbl ),
and surface roughness for momentum transport (z0m ).
Three typical dates (respectively are 21,June, 11,Aug. and 22,Sept.) were chosen for
sensitive analysis. For each date, one parameter was varied and others were fixed.
Fig 3-5 showed the results.
4. SENSITIVITY ANALYSIS
500
21,June
Sensible heat flux H(w/m2 )
450
400
350
z0m=0.111m
300
250
200
22,Sept.
150
11,Aug.
100
50
0
0
0.1
0.2
0.3
0.4
0.5
z0m(m)
Fig 3. Sensitivity of sensible heat flux(H) when varying from 0.01m to 0.4m
4. SENSITIVITY ANALYSIS
500
21,June
Sensible heat flux H(w/m2 )
450
400
350
△T=14.0k
300
250
200
22,Sept.
150
11,Aug.
100
50
0
0
5
10
15
20
25
30
Temperature difference △T(k)
35
40
Fig 4. Sensitivity of sensible heat flux(H) when varying from 2k to 34k
4. SENSITIVITY ANALYSIS
500
21,June
Sensible heat fulx H(w/m2 )
450
400
350
300
250
200
22,Sept.
150
11,Aug.
100
50
0
0
5
10
15
Windspeed at PBL u_pbl(m/s)
20
25
Fig 5. Sensitivity of sensible heat flux(H) when varying u_pbl from 1 m/s to 20 m/s
4. SENSITIVITY ANALYSIS
analysis showed z0m , u _ pbl and T all
influenced sensible heat strongly. However, the influence
disappeared when sensible heat reached the maximum
value under dry limit. Besides, the relationship between
and sensible
 T heat flux was linear, while for other two
parameters, the relationship was non-linear.
●Sensitivity
5. DISCUSSION AND CONCLUSION
● Although NCEP meteorological data is on 1x1 degree grids, it can still be used with
meso-scale remote sensing data to get high-quality sensible heat results given the
strong correlation between NCEP and SEBS.
● NCEP data may not be appropriate for geostationary satellite data to calculate
sensible heat at morning or night time when the height of PBL is small.
● However, LAS measurements were line-averaged over 3km and integrated over 30
minutes. SEBS model outputs were instantaneous and pixel-averaged. The mismatch
could be another source of error.
● To get more accurate sensible heat with SEBS model, local parameterization scheme
on roughness length of momentum, and higher resolution meteorological information
maybe needed.
● More in-depth researches are forthcoming in the future.