1. 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
2. 1. INTRODUCTION 2. METHODOLOGY 3. DATA 4. RESULTS AND ANALYSIS 5. DISCUSSION AND CONCLUSION OUTLINE:
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6. SEBS (Surface Energy Balance System) was developed by Z.Su to estimate land surface fluxes using remotely sensed data and available meteorological observations. 2. METHODOLOGY Fig.1 Single-source Model Reference Height Land Surface
9. 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:
10. 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.
11. 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.
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13. MODIS products and preprocessing · ALBEDO · EMISSIVITY NCEP data and preprocessing 3. DATA The name of variables The meteorological parameters of NCEP data RH (%) Relative humidity （ hr_pbl ） UGRD /VGRD (m/s) Speed of wind （ u_pbl ） DSWRF (w/m 2 ) Downward shortwave radiation flux （ swgclr ） PRES (Pa) Pressure （ p_pbl ） TMP (K) Temperature （ t_pbl ） HPBL (m) planetary boundary layer height （ h_pbl ）
14. ● Previous research using empirical relationship : ● a new approach to derive the value of proposed by Kun Yang(2003) is being used in this paper. According to this, 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. ): Table.2 values being used in this paper 3. DATA 0.05299 AUG. 0.04048 0.04438 0.04187 0.0247 SEPT. JULY. JUN. MAY. (m)
15. 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.
16. 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. 25.89 24.82358 s.d. 147.36 147.3592 mean LAS measured 128.90 89.63643 s.d. 194.03 167.6097 mean SEBS estimated May-Sept. Jul.-Sept. Periods
17. 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 62.69 55.09799 s.d. 164.78 158.5419 mean NCEP data 25.89 24.82358 s.d. 147.36 147.3592 mean LAS measured 128.90 89.63643 s.d. 194.03 167.6097 mean SEBS estimated May-Sept. Jul.-Sept. Periods
18. 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 0.55 83.38 0.74 Sept. 0.38 56.91 0.54 Aug. 0.77 77.78 0.81 Jul. 0.65 110.78 0.93 Jun. 0.38 88.54 0.63 May RRMSE RMSE(w/m2) R Month
19. 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.
20. 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.
21. Fig 3. Sensitivity of sensible heat flux(H) when varying from 0.01m to 0.4m 4. SENSITIVITY ANALYSIS
22. Fig 4. Sensitivity of sensible heat flux(H) when varying from 2k to 34k 4. SENSITIVITY ANALYSIS
23. Fig 5. Sensitivity of sensible heat flux(H) when varying u_pbl from 1 m/s to 20 m/s 4. SENSITIVITY ANALYSIS
24. ● 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. 4. SENSITIVITY ANALYSIS
25. 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.
