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Land Data Assimilation

Land Data Assimilation. Tristan Quaife , Emily Lines, Ian Davenport, Jon Styles, Philip Lewis Robert Gurney. Rationale . Satellite data one of the most powerful observational constraints on land surface models Synoptic spatial and temporal coverage

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Land Data Assimilation

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  1. Land Data Assimilation Tristan Quaife, Emily Lines, Ian Davenport, Jon Styles, Philip Lewis Robert Gurney.

  2. Rationale • Satellite data one of the most powerful observational constraints on land surface models • Synoptic spatial and temporal coverage • Direct measure of energy leaving the system

  3. Rationale • Highly derived satellite ‘products’ often physically inconsistent with assumptions in LSMs and difficult to quantify uncertainty • Hence want to use low-level data (e.g. radiance) • Most LSMs lack appropriate physical representation required for this • For example typically 1D turbid medium canopies

  4. Land components of earth system models • Developed for NWP – extended for other studies • Fluxes added for carbon, nutrients, aerosols Evapo- transpiration Radiation interactions with atm, veg. and soil Precipitation & evaporation Groundwater and Channel flows Developed to calculate the exchange of energy and water between the land surface and atmosphere

  5. Processes and timescales • Diurnal • Radiation and water balance • Seasonal • Phenology • Snow • Centennial • Vegetation dynamics • Soil turnover

  6. EO data • Assimilation of EO data (cfstate estimation) is relevant at diurnal and seasonal timescales… • … for forecasting, seasonal forecasting, crop monitoring, carbon cycle … • Models not developed with EO in mind • Canopy models simple • Limited handling of canopy structure – Can’t simulate BRFs

  7. International landscape • UK Community model – JULES / TRIFFID • US Community model – CLM • Meteo-France– ISBA • ECMWF – [C/H]-TESSEL • EC-Earth community model also uses TESSEL scheme

  8. Concept • Build parsimonious land surface scheme • Water, energy and carbon fluxes • Invest complexity in the necessary physics to represent satellite observations correctly • Optical, thermal and passive microwave • Embed in DA scheme as early as possible • EOLDAS/Particle Filter

  9. Model concept Interception& Evaporation Photosynthesis& Allocation Vegetationprocesses Soil column Soil “skin” layer Soilprocesses Water SW Carbon LW

  10. Force restore water/heat Soil temperature Soil & vegetation water e.g. Noilhan and Planton (1989)

  11. Force restore model • The force restore approach predicts surface temperature & moisture for a small, finite depth • Depth can be tuned to match the response of thermal and passive microwave sensors • In this phase of project no plans to implement surface flows and routing

  12. Heat fluxes Niwot Ridge, 2002, day of year 151

  13. Heat fluxes Niwot Ridge, 2002, day of year 170

  14. Problems with the 1D operator • For any canopy that departs from 1D: • Cannot correctly describe the variation of path length with viewing and illumination geometry • Does not predict viewed amount of bare soil or how this varies with viewing geometry • Both of these are critical for correct modelling of satellite signals from large parts of the Earth’s surface

  15. Observation operator - GORT Illuminated crown Illuminated soil Shaded crown Shaded soil

  16. Geometric Observation Operator Shaded crown Illuminated crown Shaded soil Illuminated soil

  17. Short-wave partitioning

  18. Short-wave partitioning

  19. Short-wave partitioning

  20. Short-wave partitioning

  21. Metropolis-Hastings α = min 1, u*=u + random proposition P(B|u*)P(A) P(B|u)P(A) Draw z from U(0,1) u* if z≤α u if z> α u+=

  22. MCMC calibration (Oregon) PDF Leaf area index Soil brightness H/B ratio PDF Projected Crown Cover Crown shape Leaf chlorophyll

  23. PDF – LAI vs. chlorophyll Leaf chlorophyll Leaf area index

  24. PDF – Crown cover vs. shape Crown cover Crown shape

  25. PDF – faparvs. albedo fapar Albedo

  26. Forward modelled BRF

  27. Forward modelled BRF

  28. Next steps - immediate • Improve model integration • Currently using Euler integration… • Couple GORT fully • Sun angle effects • Diffuse/direct • Interception of precipitation

  29. Next steps – short term • Data Assimilation framework • Particle Filter • Assimilate optical data • MODIS, GlobAlbedo • Comprehensive testing • Flux tower sites • Neon sites

  30. Next steps – medium term • Add photosynthesis model • Farquhar based • Add allocation model • DALEC type • More testing… • Implement on large scale…

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