Towards Creating a ESA CCI Root Zone Soil Moisture Product

NFR grant: 239947, 2015-2018

Towards Creating a ESA CCI Root Zone Soil Moisture Product

Jostein Blyverket1,2 Paul Hamer1 Laurent Bertino3 William A. Lahoz1 AlexanderGruber4 and the ESA CCI soil moisture team

1Norwegian Institute for Air Research

2University of Bergen

3Nansen Environmental and Remote Sensing Center

4TU Wien

Soil moisture user workshop Vienna 18 September 2017

Jostein Blyverket, [email protected] (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September 2017 1 / 13

Outline

MotivationSatellite observationsLand surface modelData assimilationLand surface data assimilation systemOn the expected output product


Motivation

Create a root zone soil moisture product using the ensemble Kalman Filter (EnKF), tomerge the ESA CCI ACTIVE and PASSIVE satellite observations with soil moistureestimates from the SURFEX land surface modelling platform.

Potential improvements in:

Numerical weather prediction,sub-seasonal prediction [1].

Hydrology, improved runoffestimation [2].

Climate studies, initialization ofseasonal predictions [3].

Monitoring of the hydrological cycle.Improve flood and droughtprediction [4].


Motivation for the L4 soil moisture product

Fill temporal and spatial gaps in surfacesoil moisture and provide estimates ofroot zone soil moisture that areconsistent with the ESA CCIobservations.

Provide a product that improves overthe model-only root zone soil moisture.

Create a new data product which canbe validated/tested against the SoilMoisture Active/Passive L4 root zonesoil moisture product.


Satellite observations

We use the ESA CCI ACTIVE and PASSIVE soil moisture product v03.2, which provideglobal daily soil moisture from active and passive sensors, separately.

Spatial resolution 25 km.

Temporal resolution, daily with satelliteoverpasses as timestamps in thenetCDF file (important for sequentialdata assimilation as we need to matchthe model forecast at time ti with anobservation at time ti .).

By assimilating the ACTIVE andPASSIVE product separately we assurethat the range is only given by the LandParameter Retrieval Model (LPRM) forthe PASSIVE and the Change DetectionMethod for the ACTIVE product. Figure: ACTIVE and PASSIVE observations


Land surface modelling

Land surface models have the potential to close the spatio-temporal gaps in soil moistureinformation, as they describe the vertical transfer of soil moisture from surface to rootzone.

SURFEX land surface modellingplatform, Meteo-France [5].

Interaction between Soil BiosphereAtmosphere multilayer diffusionscheme (ISBA-DIF [6])

Heat transfer, 1D Fourier lawWater mass transfer, RichardsequationSurface energy balance

Multilayer snow scheme, runoffschemes, evolving biomass (leaf areaindex).

Limitations: Parametrization,dependent on atmospheric modelforcing (error propagation).

Figure: ISBA surface soil moisture


Land surface modelling, temporal and spatial scales

1D model, the vertical columns aremodelled independently.

Horizontal scale 0.625 degree inlongitude direction and 0.5 degree inlatitude direction.

Vertical discretization, 14 layers.

15 min model time step.

12 patches as option on subgrid scale.Figure: Schematic illustration of ISBA,http://www.umr-cnrm.fr/surfex


http://www.umr-cnrm.fr/surfex

Land surface modelling, atmospheric forcing

The system is offline, which means that the land surface model is decoupled from anatmospheric model and only uses atmospheric forcing files as input. No feedback betweenland and atmosphere.

MERRA2 atmospheric forcing fromhttps://disc.sci.gsfc.nasa.gov/

daac-bin/FTPSubset2.pl.

Provides hourly precipitation, 2 mtemperature, specific humidity andincoming longwave and shortwaveradiation.

ISBA initialized with same grid as theforcing files.

Figure: Examples of forcing files


https://disc.sci.gsfc.nasa.gov/daac-bin/FTPSubset2.pl

https://disc.sci.gsfc.nasa.gov/daac-bin/FTPSubset2.pl

Methods: Data assimilation; combining observations and model.

Data assimilation (DA) is the optimal way (given some assumptions) to combineobservations and model data. There are several ways to perform data assimilation,3D-VAR, 4D-VAR, Kalman Filter, Particle Filter and varieties of these methods. We havechosen to use the ensemble Kalman Filter (EnKF).

1 Ensemble of model states propagated forward by the SURFEX modelling platform(nonlinear, approximates model errors), xf .

2 Stop model propagation when observation d is available, compute sample covariancebetween ensembles Pi . H maps the model to observation space.

3 Update each ensemble member at time ti using the Kalman Filter equations andobservation error covariance Ri .

xa = xf + K(d−Hxf ) (1)

K = PiHT (HPiH

T + Ri )−1 (2)


Land data assimilation system, how it works

External atmospheric forcing drives landsurface model (LSM).

Fields such as land cover, topography,sand and clay fraction provided byancillary data.

Nonlinear state variables propagatedforward in time by LSM.

Errors in the model represented by theensemble, weighted analysis dependingon model and observation errors.

Results in ”optimal” land surface state,superior to satellite or land model dataalone [7].


Work in progress

Ongoing work:

Converting MERRA2 forcing files to SURFEX readable.

Perturbation of forcing files using an AR(1) model and cross-correlations betweenthe perturbations. (Increase in precipitation is related to decrease in shortwaveradiation).

Tuning of model and observation errors to obtain a consistent data assimilationsystem.

Convert ESA CCI ACTIVE and PASSIVE to normalized anomalies using an ensembleopen loop run from ISBA.


Expected output

The expected outputs of this project are:

3h global fields of soil moisture (statevariable) and surface temperature, fromsurface to root zone (with uncertaintiesgiven from the ensemble).

Analyzed states every 6th hour.

Covering the period 2010 - 2016 (as astart).

The output will be validated against insitu stations from the ISMN databaseand internal diagnostics such asobservation-minus-forecast vsobservation-minus-analysis statistics.

Coupling to the Model of Emissions ofGases and Aerosols from Nature(MEGAN) model [8], using theimproved soil moisture estimates.

Figure: Example of ensemble output fields


References I

Koster, RD. et al. The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill, J

Hydrometeorol 12:805822

H. Lievens a, S.K. Tomer, A. Al Bitar b , G.J.M. De Lannoy, M. Drusch, G. Dumedah, H.-J. Hendricks Franssen, Y.H. Kerr, B. Martens, M. Pan,

J.K. Roundy, H. Vereecken, J.P. Walker, E.F. Wood, N.E.C. Verhoest, V.R.N. Pauwels SMOS soil moisture assimilation for improved hydrologicsimulation in the Murray Darling Basin, Australia, Remote Sensing of Environment, 2015, 146-162.

Entekhabi, D. et al. The Soil Moisture Active Passive (SMAP) Mission, Proceedings of the IEEE 98.5 (2010) 704-716.

Sheffield, J. et al. Global Trends and Variability in Soil Moisture and Drought Characteristics, 1950-2000, from Observation-Driven Simulations of

the Terrestrial Hydrologic Cycle, Journal of Climate, volume 21, 2008.

Masson, V et al. The SURFEX v7.2 land and ocean surface platform for coupled or offline simulations of earth surface variables and fluxes., Geosci.

Model Dev.,, 2013.

Noilhan, J., and J.-F. Mahfouf, The ISBA land surface parameterisation scheme., Global Planet. Change, 1996.

Reichle, R., Koster, R., De Lannoy, G., Crow, W., Kimball, J. Level 4 Surface and Root Zone Soil Moisture (L4˙SM) Data Product, Algorithm and

Theoretical Basis Document, 2014.

Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P.I., Geron, C. Estimates of global terrestrial isoprene emissions using MEGAN (Model

of Emission of Gases and Aerosols from Nature), Atmos.Chem. Phys., 2006.


Towards Creating a ESA CCI Root Zone Soil Moisture Product

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