Ju Hyoung, Lee, B. Candy, R. Renshaw Met Office, UK

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1 the FAO/ ESA/ GWSP Workshop on Earth Observations and the Water-Energy-Food Nexus March 2014 in Rome, Italy Ju Hyoung, Lee, B. Candy, R. Renshaw Met Office, UK 1

2 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

, 1984) - SWI neglecting ET, constant soil properties (Wagner, 1998) (Minasny et al.

3 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

4 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

9 θ rz 0 < C 1 and θ geq < θ wp if θ g < θ wp 0 < C

5 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 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

45 16 4 14 0.4 0.35 14 3.5 3 12 0.3 12 2.5 10 0.25 0.

5-10 -5 0 5 10 Longitude 0-10 -5 0 5 10 Longitude 0 16

45 14 0.4 0.35 14 0.4 0.35 12 0.3 0.25 12 0.3 0.25 10 0.

6 Latitude Latitude Latitude Latitude soil input variables Inverted C 1 original C 1 by PTFs Longitude Longitude 0 16 Inverted θ geq original θ geq by PTFs Longitude Longitude 6

Latitude Latitude Latitude Surface soil moisture estimations using

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.

05-10 -5 0 5 10 Longitude Calibrated SVAT 16 SMOS L3(DoY 186-188)

7 Latitude Latitude Latitude Surface soil moisture estimations using inverted soil variables Longitude Calibrated SVAT 16 SMOS L3(DoY ) Longitude Un-calibrated SVAT (b) SMOS L Longitude 7

8 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

9 Aerodynamic roughness (m) Julian day Reference Lee JH, Timmermans J, Su Z, Mancini M 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

10 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

11 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 SMOS (v500) ASCAT (version 1)

O-B standard deviation (m3/m3) Control mean innovation Control EKF 0.06 0.05 0.04 EKF exp mean innovation 0.

12 O-B standard deviation (m3/m3) Control mean innovation Control EKF EKF exp mean innovation Cycle Number Consistent drying signal at surface over US and Eastern Australia 12

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15 Cosby BJ, Hornberger GM, Clapp RB, Ginn TR A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils. Water Resources Research 20(6): , doi: /wr020i006p Gutmann ED, Small EE A comparison of land surface model soil hydraulic properties estimated by inverse modeling and pedotransfer functions. Water Resources Research 43, W05418, doi: /2006wr Minasny, B., Hopmans, J.W., Harter. T.H., Tuli. A.M., Eching, S.O., Denton. D.A Neural network prediction of soil hydraulic functions for alluvial soils using multi-step outflow data. Soil Science Soc. Amer. J. 68, Schaap MG, Leij FJ, van Genuchten MTh Neural Network Analysis for Hierarchical prediction of Soil Hydraulic Properties. Soil Sciences Society of America Journal 62: Wagner, W Soil Moisture Retrieval from ERS Scatterometer Data, PhD dissertation, Vienna University of Technology, Austria 15

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Land Data Assimilation for operational weather forecasting

Land Data Assimilation for operational weather forecasting Brett Candy Richard Renshaw, JuHyoung Lee & Imtiaz Dharssi * *Centre Australian Weather and Climate Research Contents An overview of the Current