Towards Creating a ESA CCI Root Zone Soil Moisture Product

Transcription

1 NFR grant: , Towards Creating a ESA CCI Root Zone Soil Moisture Product Jostein Blyverket 1,2 Paul Hamer 1 Laurent Bertino 3 William A. Lahoz 1 Alexander Gruber 4 and the ESA CCI soil moisture team 1 Norwegian Institute for Air Research 2 University of Bergen 3 Nansen Environmental and Remote Sensing Center 4 TU Wien Soil moisture user workshop Vienna 18 September 2017 Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

2 Outline Motivation Satellite observations Land surface model Data assimilation Land surface data assimilation system On the expected output product Jostein Blyverket, (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

3 Motivation Create a root zone soil moisture product using the ensemble Kalman Filter (EnKF), to merge the ESA CCI ACTIVE and PASSIVE satellite observations with soil moisture estimates from the SURFEX land surface modelling platform. Potential improvements in: Numerical weather prediction, sub-seasonal prediction [1]. Hydrology, improved runoff estimation [2]. Climate studies, initialization of seasonal predictions [3]. Monitoring of the hydrological cycle. Improve flood and drought prediction [4]. Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

4 Motivation for the L4 soil moisture product Fill temporal and spatial gaps in surface soil moisture and provide estimates of root zone soil moisture that are consistent with the ESA CCI observations. Provide a product that improves over the model-only root zone soil moisture. Create a new data product which can be validated/tested against the Soil Moisture Active/Passive L4 root zone soil moisture product. Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

5 Satellite observations We use the ESA CCI ACTIVE and PASSIVE soil moisture product v03.2, which provide global daily soil moisture from active and passive sensors, separately. Spatial resolution 25 km. Temporal resolution, daily with satellite overpasses as timestamps in the netcdf file (important for sequential data assimilation as we need to match the model forecast at time t i with an observation at time t i.). By assimilating the ACTIVE and PASSIVE product separately we assure that the range is only given by the Land Parameter Retrieval Model (LPRM) for the PASSIVE and the Change Detection Method for the ACTIVE product. Figure: ACTIVE and PASSIVE observations Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

6 Land surface modelling Land surface models have the potential to close the spatio-temporal gaps in soil moisture information, as they describe the vertical transfer of soil moisture from surface to root zone. SURFEX land surface modelling platform, Météo-France [5]. Interaction between Soil Biosphere Atmosphere multilayer diffusion scheme (ISBA-DIF [6]) Heat transfer, 1D Fourier law Water mass transfer, Richards equation Surface energy balance Multilayer snow scheme, runoff schemes, evolving biomass (leaf area index). Limitations: Parametrization, dependent on atmospheric model forcing (error propagation). Figure: ISBA surface soil moisture Jostein Blyverket, (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

7 Land surface modelling, temporal and spatial scales 1D model, the vertical columns are modelled independently. Horizontal scale degree in longitude direction and 0.5 degree in latitude direction. Vertical discretization, 14 layers. 15 min model time step. 12 patches as option on subgrid scale. Figure: Schematic illustration of ISBA, Jostein Blyverket, (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

8 Land surface modelling, atmospheric forcing The system is offline, which means that the land surface model is decoupled from an atmospheric model and only uses atmospheric forcing files as input. No feedback between land and atmosphere. MERRA2 atmospheric forcing from daac-bin/ftpsubset2.pl. Provides hourly precipitation, 2 m temperature, specific humidity and incoming longwave and shortwave radiation. ISBA initialized with same grid as the forcing files. Jostein Blyverket, jb@nilu.no (NILU/UiB) Figure: Examples of forcing files Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

9 Methods: Data assimilation; combining observations and model. Data assimilation (DA) is the optimal way (given some assumptions) to combine observations 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 have chosen to use the ensemble Kalman Filter (EnKF). 1 Ensemble of model states propagated forward by the SURFEX modelling platform (nonlinear, approximates model errors), x f. 2 Stop model propagation when observation d is available, compute sample covariance between ensembles P i. H maps the model to observation space. 3 Update each ensemble member at time t i using the Kalman Filter equations and observation error covariance R i. x a = x f + K(d Hx f ) (1) K = P i H T (HP i H T + R i ) 1 (2) Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

10 Land data assimilation system, how it works External atmospheric forcing drives land surface model (LSM). Fields such as land cover, topography, sand and clay fraction provided by ancillary data. Nonlinear state variables propagated forward in time by LSM. Errors in the model represented by the ensemble, weighted analysis depending on model and observation errors. Results in optimal land surface state, superior to satellite or land model data alone [7]. Jostein Blyverket, (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

11 Work in progress Ongoing work: Converting MERRA2 forcing files to SURFEX readable. Perturbation of forcing files using an AR(1) model and cross-correlations between the perturbations. (Increase in precipitation is related to decrease in shortwave radiation). Tuning of model and observation errors to obtain a consistent data assimilation system. Convert ESA CCI ACTIVE and PASSIVE to normalized anomalies using an ensemble open loop run from ISBA. Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

12 Expected output The expected outputs of this project are: 3h global fields of soil moisture (state variable) and surface temperature, from surface to root zone (with uncertainties given from the ensemble). Analyzed states every 6th hour. Covering the period (as a start). The output will be validated against in situ stations from the ISMN database and internal diagnostics such as observation-minus-forecast vs observation-minus-analysis statistics. Coupling to the Model of Emissions of Gases and Aerosols from Nature (MEGAN) model [8], using the improved soil moisture estimates. Figure: Example of ensemble output fields Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13

13 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: 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 hydrologic simulation in the Murray Darling Basin, Australia, Remote Sensing of Environment, 2015, Entekhabi, D. et al. The Soil Moisture Active Passive (SMAP) Mission, Proceedings of the IEEE 98.5 (2010) Sheffield, J. et al. Global Trends and Variability in Soil Moisture and Drought Characteristics, , from Observation-Driven Simulations of the Terrestrial Hydrologic Cycle, Journal of Climate, volume 21, 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.,, Noilhan, J., and J.-F. Mahfouf, The ISBA land surface parameterisation scheme., Global Planet. Change, 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, 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., Jostein Blyverket, jb@nilu.no (NILU/UiB) Towards Creating a ESA CCI Root Zone Soil Moisture Product Vienna 18 September / 13