SENSIBLE HEAT FLUX ESTIMATION USING SURFACE ENERGY BALANCE SYSTEM (SEBS), MODIS PRODUCTS, AND NCEP REANALYSIS DATA. Yuanyuan Wang a , Xiang Li a,b a , National Satellite Meteorological Center, China Meteorological Administration b , Nanjing University of Information Science & Technology.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
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
a. sensitive to measurement errors ;
b. rejecting a large number of valuable datasets under non-neutral conditions .
1. INTRODUCTION
Fig.2 The structure SEBS (Wang et al., 2008)
Where: is the net radiation, is the soil heat flux, is the turbulent sensible heat flux, and is the turbulent latent heat flux.
the soil heat flux is estimated as:
Where :‘ ’ and ‘ ’ are the proportions of for full cover and bare soil, fixed as 0.05 and 0.315 respectively. The fractional vegetation cover ‘ ’ 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.
Then, the roughness length for heat transfer can be derived by:
2. METHODOLOGY
(2) In order to derive the actual sensible heat flux H , use is made of the similarity
theory.
Where, is the potential temperature at the surface， is the potential temperature at PBL .
Definding the reference height:
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.
or
●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.
3. DATA
LAS measurements :
Fig.3 The location of LAS on MODIS pixels;
R is the receiver of LAS located on MODIS pixel.
3. DATA
MODIS products and preprocessing
· ALBEDO
· EMISSIVITY
NCEP data and preprocessing
3. DATA
4. RESULTS AND ANALYSIS
4.1 Comparison between Sensible heat from SEBS and LAS
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
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).
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
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
So we performed a sensitivity analysis on three variables, which are temperature difference between ground surface and reference height ( ), wind speed at PBL( ), and surface roughness for momentum transport ( ).
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
Fig 3. Sensitivity of sensible heat flux(H) when varying from 0.01m to 0.4m
4. SENSITIVITY ANALYSIS
Fig 4. Sensitivity of sensible heat flux(H) when varying from 2k to 34k
4. SENSITIVITY ANALYSIS
Fig 5. Sensitivity of sensible heat flux(H) when varying u_pbl from 1 m/s to 20 m/s
4. SENSITIVITY ANALYSIS
●Sensitivity analysis showed , and 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 heat flux was linear, while for other two parameters, the relationship was non-linear.
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.
Thank You !