the FAO/ ESA/ GWSP Workshop on Earth Observations and the Water-Energy-Food Nexus 25-27 March 2014 in Rome, Italy Ju Hyoung, Lee, B. Candy, R. Renshaw Met Office, UK 1
1. Agriculture needs, current methods, limitation, request, challenge case study of SMOS datasets over West Africa 2. Climate & Meteorology needs, issues and limitations case study over GAME-Tibet dataset 3. Weather forecast DA needs and limitations Met Office case study of ASCAT/SMOS datasets 4. Epilogue 5. Reference 2
soil properties required for accurate flooding/drought monitoring : - van Genuchten from ISLSCPII (Schaap et al., 1998) - Cosby methods from ECOCLIMAP/HWSD (Cosby et al., 1984) - SWI neglecting ET, constant soil properties (Wagner, 1998) (Minasny et al., 2004, Gutmann and Small, 2007) - soil maps-based PTFs are empirical & site-specific - NNs are limited to their soil database inversion from satellite (Gutmann and Small, 2007) 3
Satellite Hydrol. model Sampling depth changing fixed Moisture source No ET ET (canopy) External forcing Rainfall errors - layer Only surface subsurface range 0-1 WP, FC, θ sat.. 4
Objective function: min θ g,ref θ g,sim (C 1, θ geq ) Subject to 0.6 θ rz < θ geq < 0.9 θ rz 0 < C 1 and θ geq < θ wp if θ g < θ wp 0 < C 1 and θ geq < θ sat if θ g > θ wp Fig. Surface soil moisture in Niger Fig. Surface soil moisture in Benin Reference Lee, J.H., Pellarin, T., Kerr, Y.H. 2014. Inversion of soil hydraulic properties from the DEnKF analysis of SMOS soil moisture over West Africa, Agri. & Fore. Meteo. 5
Latitude Latitude Latitude Latitude soil input variables Inverted C 1 original C 1 by PTFs 16 0.45 16 4 14 0.4 0.35 14 3.5 3 12 0.3 12 2.5 10 0.25 0.2 10 2 8 0.15 0.1 8 1.5 1 6 0.05 6 0.5-10 -5 0 5 10 Longitude 0-10 -5 0 5 10 Longitude 0 16 Inverted θ geq 0.45 16 original θ geq by PTFs 0.45 14 0.4 0.35 14 0.4 0.35 12 0.3 0.25 12 0.3 0.25 10 0.2 10 0.2 8 0.15 0.1 8 0.15 0.1 6 0.05 6 0.05-10 -5 0 5 10 Longitude -10-5 0 5 10 Longitude 6
Latitude Latitude Latitude Surface soil moisture estimations using inverted soil variables 16 14 12 10 0.45 0.4 0.35 0.3 0.25 0.2 16 14 12 10 0.45 0.4 0.35 0.3 0.25 0.2 8 0.15 0.1 8 0.15 0.1 6 0.05 6 0.05-10 -5 0 5 10 Longitude Calibrated SVAT 16 SMOS L3(DoY 186-188) -10-5 0 5 10 Longitude Un-calibrated SVAT 0.45 14 0.4 0.35 (b) 12 0.3 0.25 10 0.2 8 0.15 0.1 6 0.05 SMOS L3-10 -5 0 5 10 Longitude 7
A lack of vertical characterization land surface information: e.g. z om optical depth etc A lack of subsurface/root zone characteristics e.g. soil texture map etc.. 8
Aerodynamic roughness (m) 10-1 10-2 10-3 120 140 160 180 200 220 240 260 Julian day Reference Lee JH, Timmermans J, Su Z, Mancini M. 2012. Calibration of aerodynamic roughness over the Tibetan Plateau with Ensemble Kalman Filter analysed heat flux. Hydrology and Earth System Sciences. 9
A quality of Land surface is important for weather forecast Satellite observation is a backbone of DA system Linear operator for analyzing most of state variables Current satellite bias correction methods: e.g. CDF matching Satellite observation error specification is missing in a current DA system 10
Take three collocated independent estimates of the soil moisture variances. In situ & SMOS Level 2 products used for: determination of the background error (Met office short range forecasts of soil moisture)/ascat observation error: State variables Error (m 3 /m 3 ) UM Background level 1 0.030 SMOS (v500) 0.052 ASCAT (version 1) 0.041 11
O-B standard deviation (m3/m3) Control mean innovation Control EKF 0.06 0.05 0.04 EKF exp mean innovation 0.03 1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 Cycle Number Consistent drying signal at surface over US and Eastern Australia 12
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Cosby BJ, Hornberger GM, Clapp RB, Ginn TR. 1984. A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils. Water Resources Research 20(6):682 690, doi:10.1029/wr020i006p00682. Gutmann ED, Small EE. 2007. A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions. Water Resources Research 43, W05418, doi:10.1029/2006wr005135. Minasny, B., Hopmans, J.W., Harter. T.H., Tuli. A.M., Eching, S.O., Denton. D.A.. 2004. Neural network prediction of soil hydraulic functions for alluvial soils using multi-step outflow data. Soil Science Soc. Amer. J. 68, 417-429. Schaap MG, Leij FJ, van Genuchten MTh. 1998. Neural Network Analysis for Hierarchical prediction of Soil Hydraulic Properties. Soil Sciences Society of America Journal 62:847-855. Wagner, W. 1998. Soil Moisture Retrieval from ERS Scatterometer Data, PhD dissertation, Vienna University of Technology, Austria 15
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