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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 3 J_Indu

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5.3 J Indu

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IUKWC Workshop Nov16: Developing Hydro-climatic Services for Water Security – Session 5 – Item 3 J_Indu

  1. 1. LSM-RTM Coupling for Microwave Tb Assimilation over India By Dr. J. Indu Assistant Professor Department of Civil Engineering I.I.T Bombay
  2. 2. Land Surface Models (LSM) Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India Configuration of the CLM subgrid hierarchy emphasizing the vegetation landunit (Source: Adapted from NCAR Technical Note, 2010) 11/30/2016 2
  3. 3. The RTM is used as forward operator for assimilating the brightness temperature in LSM The community Microwave Emission Model(CMEM)has been developed by European Centre for Medium-Range Weather Forecasts (ECMWF) as the forward operator for low frequency microwave brightness temperature from 1GHz to 20 GHz TBtoa,p = TBau,p + exp(-τatm,p). TBtov,p TBtov,p = TBsoil,p .exp (-τveg,p) + TBveg,p (1+ rr,p. exp(-τveg,p)) + TBad,p . rr,p .exp(-2.τveg,p) Tbsoil,p = Teff .er,p Tbveg,p = Tc . (1-ωp ). (1- exp(τveg,p )) Community Microwave Emission Model (CMEM) Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 3
  4. 4. Ensemble Kalman Filter • Bayesian filtering process where an ensemble of model states are propagated forward in time. • The updated land surface states are also known as analyzed estimate which is given as: Where i is the grid number, j is the number of ensembles and U i,jt F i,jt and O i jt are updated states, forecast states and observation state vectors respectively, H is the observation operator (relating model states to observation states and K is the Kalman gain matrix. Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 4 , = , + ( , − , ) = +
  5. 5. Data Assimilation Framework in LSM • The propagation is made through the state equation xi+1 = f t (xt , ut , wt) • where, xt is the state vector of dimension n at time t, ft (.) is a dynamic function, ut, and wt are the forcing variables and the process noise at time t, respectively • The state ensemble is propagated to the time step where a new measurement becomes available. • The measurement is related to the state via the measurement equation, yt = ht (x t) + vt • where, yt is the measurement vector of dimension m at time t, ht () is a measurement transformation matrix and vt is independent measurement noise. [Alemohammad, 2015]The conceptual diagram of the EnKF (Source: Adapted from Alemohammad,2015) Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 5
  6. 6. a). Whether satellite observed Tb circumvents the need for processing soil moisture retrievals? b). Relationship between the assimilation of Tb observations and the Radiative Transfer Model (RTM)? Motivation Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 6
  7. 7. Data Used Variable Spatial Resolution Temporal Resolution Source Forcing Data Near surface air temperature 0.47° × 0.47° 3 Hourly GDAS Near surface specific humidity Total incident shortwave radiation Incident Longwave Radiation Eastward wind Northward wind Surface pressure Rainfall rate Convective rainfall rate LSM Parameters Landcover 0.01 × 0.01 - AVHRR/UMD Soil Texture 0.25 × 0.25 - FAO Soil Fraction(clay,sand,silt) 0.25 × 0.25 - FAO Slope type 0.01 × 0.01 - NCEP_LIS Elevation SRTM Albedo 0.01 × 0.01 Monthly NCEP_LIS Greenness fraction 0.01 × 0.01 - NCEP_LIS RTM Parameters Soil fraction 0.25 × 0.25 - Ecoclimap/FAO Geopotential 0.25 × 0.25 - NCEP Vegetation fraction 0.25 × 0.25 - Ecoclimap Vegetation Type 0.25 × 0.25 - Ecoclimap [* LIS framework developed by Hydrological Sciences Laboratory at NASA’s Goddard Space Flight Center used for assimilating the SMOPS soil moisture in Noah LSM v 3.6] [* The Noah LSM for the present study is spun up by cycling seven times (2 years) through the period from 1 January 2008 to 31 December 2010 using the meteorological forcing from GDAS.]Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 7
  8. 8. Taylor diagram for monthly mean surface soil moisture for each of JJAS months from: (a,c,e,g) 2010; and (b,d,f,h) 2011 (+=Data Assimilated SSM; • = GLDAS SSM) Cumulative Bias between DA and Openloop simulation: (a,c,e,g) June,July,August,September (JJAS)2010; and (b,d,f,h) JJAS 2011. [ Akhilesh and Indu (2016) ] 11/30/2016 Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 8
  9. 9. Error Variance from TC: (a) DA; and (b) GLDAS [ Akhilesh and Indu (2016) ] Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 9
  10. 10. Figure 1:TB simulation for ascending pass for parameterization 1: (a–d) AMSR-E H-polarized TB; (e–h) simulated H-polarized TB; (i–l) AMSR-E V-polarized TB ; and (m–p) simulated V- polarized TB. . Figure 2.TB simulation for ascending pass for parameterization 2: (a–d) AMSR-E H- polarized TB; (e–h) simulated H-polarized TB; (i–l) AMSR-E V-polarized TB; and (m–p) simulated V- polarized TB. [ Akhilesh and Indu (2016) ] Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 10
  11. 11. Importance of Microwave Tb  Direct assimilation of Tb observations in time-constrained forecasting applications e.g. of hydrologic events, as it circumvents the need for soil moisture retrieval data that are generally provided with longer time-lag. [ Lievens et al. (2015) ] Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 11 [ Lievens et al. (2015) ]
  12. 12. Issues? For assimilation of Tb directly into an LSM, the observations need to be unbiased with respect to the model simulations (Reichle et al. 2004). Strong seasonal cycle of Tb. Complexity of Radiative Transfer processes involved [ De Lannoy et al. 2013). Estimation of RTM Parameters? Differing climatology between SM of LSM and satellite. Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 12
  13. 13. Physics based model or statistical models? Abramowitz et al. (2008) found that statistical models outperform physics-based models at estimating land surface states and fluxes.  Gong et al. (2013) provided a theoretical explanation for this result It was shown that the extent to which the information available from forcing data was unable to resolve the total uncertainty about the predicted phenomena. Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 13
  14. 14. Way Forward? The amount of information contained in the forcing data = Total amount of information actually made available to translate into predictions/simulations [ Upper bound of Uncertainty ]. Our ability to resolve prediction problems will, to a large extent, be dependent on our ability to collect and make use of observational data, Model benchmarking scheme is required!!! Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 14
  15. 15. THANK YOU FOR YOUR ATTENTION Indo-UK Workshop on 'Developing Hydro-Climatic Services for Water Security', Nov 29 - Dec 1, 2016, Indian Institute of Tropical Meteorology Pune, India 11/30/2016 15 [Akhilesh S. Nair and J. Indu [2016], Enhancing Noah Land Surface Model Prediction Skill over Indian Subcontinent by Assimilating SMOPS Blended Soil Moisture, Remote Sensing, 2016, 8, 976, doi:10.3390/rs8120976 (IF=3.03) ]

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