Evaporative Fraction and Bulk Transfer Coefficients Estimate through Radiometric Surface Temperature Assimilation

Transcription

1 Evaporative Fraction and Bulk Transfer Coefficients Estimate through Radiometric Surface Temperature Assimilation Francesca Sini, Giorgio Boni CIMA Centro di ricerca Interuniversitario in Monitoraggio Ambientale Dara Entekhabi Massachusetts Institute of Technology 1

2 Regional estimates of ET Surface water balance hydrology meteorology Eco-hydrology 2

3 Applications Severe Weather Forecast Future NCEP 10 km NWP Domains With Analyzed Soil Moisture Observed Rainfall 0000Z to 0400Z 13/7/96 (Chen et al., 2001) 24-Hours Ahead Atmospheric Model Forecasts With Climate Soil Moisture 3

4 Applications Hydrological modelling With satellite SM estimates (RMSE=1.2) With discharge measurements only (RMSE=1.5) (After Campo et al, 2004) 4

5 Problems Surface heterogeneity: need of spatially distributed ancillary data Surface roughness fractional cover f c LAI... Remote sensing 5

6 ET prediction from remote sensing Classical approach: diagnostic Solve surface energy balance R n = G F +H+LE U wind velocity ρ air density T s =LST T a =air temperature Net radiation Ground flux Sensible heat flux R n G F = c g exp[-κlai/(2cosθ s ) 0.5 ] Norman et al. 1995, Anderson et al c g =0.6, κ=0.35 (Kustas et al. 1999) H= ρ c p C H U (T s -T a ) Latent heat flux LE= R n -G F -H 6

7 ET prediction from remote sensing Diagnostic approach: two source model (Norman et al. 1995, Kustas et al 1996) Radiometric temperature estimates mix radiometric contribution of bare soil and vegetation T s = [f c Tv 4 +(1-f c )Tb 4 ] 0.25 Caparrini et al,

8 Limits of the diagnostic approach Flux estimated only when T s available No memory of past thermal states Many parameters, values to be assumed Many ancillary data 8

9 Dynamic approach (Castelli e al, 1999, Boni et al, 2001) The evolution of LST, as a response to the surface energy balance, is governed by the law of heat diffusion Force-restore approximation: Soil uniform thermal properties with depth Periodic temperature variations at the surface Assumptions: H= ρ c p C H U (T s -T a ) LE=H/(1-EF) (Caparrini et al, 2003, 2004) daytime EF variations negligible (Crago & Brutsaert, 1996) EF FIFE 87 dataset 9

10 Dynamic approach Advantages Continuous estimates (Castelli et al, 1999) Interpolation between sparse observations (Boni et al, 2001) No Need for a-priori estimate of surface roughness 10

11 Data assimilation Variational data assimilation: minimizing cost function dependent on T obs -T model Goal: find EF (diurnal average) and (C H ) N that minimize RMSE of T model Bouttier & Courtier, (1999) 11

12 Details : variational scheme (Caparrini et al. 2003, 2004) Minimize J... assuming X=T s, Y=[(C H ) N, EF] and (C H ) N =C B =exp(r) 12

13 Performance of the model Both forward and backwards equation depend on C B /(1-EF) The problem tends to be ill posed: there are combinations of EF and C B that leads to almost same results in terms of Ts RMSE The physical interpretation of the retrieval is sometimes compromised Performance is good for sites with large and highly variable Ts-Ta (dry - sparsely vegetated sites) Performance can be enhanced defining a-priori bounds for EF or using TWO Source Models Crow and Kustas (2004) After Crow and Kustas (2004) 13

14 Including effects of soil moisture on energy balance Kustas et al. (93): -Daily EF correlated to Ts and vegetation cover -correlation dependent on near-surface soil moisture Equation simulating soil surface moisture dynamics added: Antecedent Precipitation Index (API, indicated as S here) as nearsurface soil moisture proxy: γ I model parameter rainfall intensity Same form of the heat diffusion equation

15 Coupling of dynamic equations Daylight values of EF almost constant: Assimilation done in the time window 9 a.m. 4 p.m. Coupling of two dynamic eqs. done with EF-S relation Daily S value: τ + T 1 S( t) = S( τ ') dτ ' T τ EF = a + b arctan( K S) π a, b evaluated from FIFE experiment, K model parameter

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18 Observation Data input Assimilated data Land surface temperature LST Micrometeorological forcing: Air Temperature T a Wind speed U Net Radiation R n Precipitation December July Estimated variables Data output LE (W/m 2 ) Daily EF = f (γ, K ) hour Hourly latent and heat fluxes = f ([C b ] N ) 19

19 VALIDATION: Southern Great Plains 1997 hydrology field experiment data Field site:16000 km 2 Kansas- Oklahoma-USA Model grid: 4km*4 km Period: 18th June-18th July 1997 Forcing Data: LST Data: AVHRR resolution data: 1km*1km GOES resolution data: 50km*50km SSM/I resolution data: 25 km*25km R s : GOES derived products Micrometeorological data (ρ v, U, T a ) Ground network Precipitation fields: WSR-88D Nexrad radar precipitation estimates and raingauge reports Validation data of soil moisture and energy fluxes available!! 20

20 Precipitation input Improvement of EF and LST pattern with a better distributed precipitation field WSR-88D Nexrad radar precipitation estimates and raingauge reports National Weather Service Arkansas-Red Basin River Forecast Center Thiessen interpolation raingauges measurements 21

21 Half hour time step 6-18 local time Data in SGP domain for each time step LST from satellite platform LST by model Wind velocity Air temperature Interpolated ground data Incoming net radiation Meteo Radar precipitation field 22

22 NDVI vs. C B and K 23

23 DEM vs C B 24

24 EL RENO - SGP97 Heat fluxes (W/m 2 ) LE (W/m 2 ) Dots : model estimates Bars : observed variability inside site pixel H (W/m 2 ) 25

25 FLUXES: the time sequences of diurnal averages in the period of analysis El reno site-sgp97 LE latent heat flux H sensible heat flux 26

26 SGP97 Estar soil moisture data and Evaporative fraction: pattern comparison Rescaled variables Average over the domain Julian day daily EF EF Daily values Correlation coefficient ρ Available daily data, for the same domain, inside Julian days period Daily average over the domain 0.85 All period (available days)- all pixels 0.52 % ESTAR soil moisture 27

27 DATA: Tanaro MSG, MODIS and AVHRR data LST and cloud mask products Regional micrometeorological ground net measurements Basilicata Application to mediterranean region Basilicata region (~10000 km 2 ) Summer 2004 Tanaro basin (~8500 km 2 ) Fall 2004 Model grid: 3km*3 km 28

28 C B, K vs. NDVI 29

29 ρ (NDVI and log(k) )= 0.67 ρ (NDVI and log(cb N ))=

30 Conclusions Consistent relations between parameters and land use Physical interpretation of model parameters acceptable Energy fluxes consistent with measurements Regional estimates Further development: use of meteorological analyses instead of micrometeorological ground observations for R n, T a and U 31