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Global Modeling and Assimilation Office NASA Goddard Space Flight Center Phone : 301-614-5693

JCSDA Science Meeting College Park, MD 24 May 2011 Land assimilation activities in the NASA Global Modeling and Assimilation Office. Rolf Reichle, Gabrielle De Lannoy, Clara Draper, Bart Forman, Randy Koster, Qing Liu & Ally Toure. Global Modeling and Assimilation Office

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Global Modeling and Assimilation Office NASA Goddard Space Flight Center Phone : 301-614-5693

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  1. JCSDA Science Meeting College Park, MD 24 May 2011 Land assimilation activities in the NASA Global Modeling and Assimilation Office Rolf Reichle, Gabrielle De Lannoy, Clara Draper, Bart Forman, Randy Koster, Qing Liu & Ally Toure Global Modeling and Assimilation Office NASA Goddard Space Flight Center Phone: 301-614-5693 Email: Rolf.Reichle@nasa.gov

  2. Outline • Soil moisture • AMSR-E • ASCAT • SMOS & SMAP • Land surface temperature • ISCCP • LaRC • Snow and terrestrial water storage • MODIS • AMSR-E • GRACE

  3. NASA/GMAO re-analysis MERRA: Recently completed GEOS-5 re-analysis • 1979-present (updated w/ ~1 month latency), global, • Lat=0.5º, Lon=0.67º, 72 vertical levels • MERRA-Land: Enhanced product for land surface hydrological applications (Reichle et al., J. Clim., 2011) Gauge & satellite estimates Latest GEOS-5 model version

  4. Soil moisture validation (2002-2009) Skill (pentad anomaly R) vs. SCAN in situ observations • MERRA-Land has • better soil moisture anomalies than MERRA (attributed to GPCP corrections; not shown), and • is comparable to ERA-Interim. Improvements also in canopy interception, latent heat flux, and runoff (not shown). Anomalies≡ mean seasonal cycle removed Skill metric: Anom. time series corr. coeff. R Reichle et al. JCLIM (2011) submitted.

  5. Precipitation corrections v. retrieval assimilation Skill v. SCAN in situ obs Anomalies≡ mean seasonal cycle removed Skill metric: Anom. time series corr. coeff. R • Soil moisture skill increases with • precipitation corrections and • assimilation of surface soil moisture retrievals. Improved root zone soil moisture! See poster for details. Different precipitation forcing inputs Liu et al. JHM (2011) doi:10.1175/JHM-D-10-05000.

  6. Observation error estimation via Triple Co-location Estimated anomaly error std-dev of surface soil moisture (2007-10) • Obtain error std-dev from 3 independent estimates: • active MW • passive MW • modeling ASCAT (active MW) GEOS-5 (model) Active retrievals better in more vegetated areas. Passive retrievals better in more arid conditions. AMSR-E (passive MW) AMSR-E (passive MW) • LPRM retrievals better than NSIDC. • Work in progress! LPRM-X NSIDC Units: Normalized to local anom. soil moisture variability. small errors large errors

  7. SMOS and SMAP SMOS (ESA) SMAP (NASA) Launched Nov 2009 L-band passive 40 km resolution Launch ~2015 L-band active/passive 3-40 km resolution Use SMOS data to prepare for SMAP Level 4 Surface and Root Zone Soil Moistureproduct.

  8. SMOS and SMAP radiometers SMOS: Each location seen from multiple incidence angles during overpass; less accurate (σ = 4 K). SMAP: Each location seen once per overpass at 40°; more accurate (σ = 1.3 K). SMOS soil moisture retrievals based on Tb angular signature.

  9. H-pol Tb at 42.5°: SMOS vs. GEOS-5 Mean (4/2010 – 4/2011) K GEOS-5 colder than SMOS. Similar for v-pol and other incidence angles. In progress: Calibrate parameters of L-band radiative transfer model. SMOS Time series correlation coeff. GEOS-5 Reasonable agreement where expected.

  10. Outline • Soil moisture • AMSR-E • ASCAT • SMOS & SMAP • Land surface temperature • ISCCP • LaRC • Snow and terrestrial water storage • MODIS • AMSR-E • GRACE

  11. Land surface temperature (LST) assimilation Validate against in situ observations RMSE of LST [K] Assimilate ISCCP LST retrievals into off-line land models LST estimates from model runs without data assimilation are comparable to each other and superior to ISCCP retrievals. Assimilation of ISCCP observations provides modest, yet statistically significant RMSE improvements (up to 0.7 K). The impact on flux estimates is small (not shown). Assimilation: s0: Without a priori scaling. b0, b8: Without and with dynamic bias correction. ISCCP = International Satellite Cloud Climatology Project Reichle et al. JHM (2010) doi:10.1175/2010JHM1262.1

  12. Evaluation of near-real time Tskin from NASA/LaRC Tskin bias [21z] JFM 2011 GEOS-5 – LaRC 1 Implement afore-mentioned Tskin assimilation method for near-real time, global, geostationary LaRC retrievals (Minnis et al.).

  13. Outline • Soil moisture • AMSR-E • ASCAT • SMOS & SMAP • Land surface temperature • ISCCP • LaRC • Snow and terrestrial water storage • MODIS • AMSR-E • GRACE

  14. Assimilation of MODIS and AMSR-E snow observations Noah land surface model (1 km resolution) 100 km X 75 km domain in northern Colorado AMSR-E MODIS • Multi-scale assimilation of • AMSR-E snow water equivalent (SWE) and • MODIS snow cover fraction (SCF). Validation against in situ obs from COOP (Δ) and Snotel (▪) sites for 2002-2010.

  15. Assimilation of MODIS snow cover fraction (SCF) MODIS Noah SCF assim. 3 Nov 09 25 Dec 09 28 Jan 10 14 Feb 10 25 Mar 10 10 Apr 10 … except during melt season. MODIS SCF successfully adds missing snow, MODIS SCF also improves timing of onset of snow season (not shown). De Lannoy et al. WRR (2011) submitted.

  16. Assimilation of AMSR-E snow water equivalent (SWE) SWE anomalies (40.38N, 106.66W) Assimilation of AMSR-E SWE retrievals did not help. De Lannoy et al. WRR (2011) submitted. cm AMSR-E MERRA SWE 1 Mar 2004 • AMSR-E SWE retrievals and MERRA differ a lot. • Working on intermediate approach betw. retrieval and full radiance assim.

  17. Assimilation of GRACE Terrestrial Water Storage (TWS) Assimilate GRACE TWS (2002-2009) for the Mackenzie river basin. RMSD [mm]: Snow water equivalent • Validate vs. • CMC snow product • GRDC runoff obs. Not shown: Trend issues w/ post-glacial rebound. Assimilation increments Assimilation reduces RMSD Subsurface water deficit SWE RMSD [mm/d]: Runoff Forman et al. (2011) in preparation.

  18. Summary and outlook • Projects underway for soil moisture, LST, snow, and TWS assimilation from various sensors. • Generally (but not always!) assimilation improves estimates of land surface fields. Encountered • expected problems for AMSR-E SWE and • unexpected problems for GRACE. • Include land assimilation in GEOS-5/DAS, focus on LST and soil moisture assimilation. • Continue preparations for SMAP Level 4 soil moisture product, incl. calibration of L-band radiative transfer model.

  19. Thanks for listening! Questions?

  20. EXTRA SLIDES

  21. Precipitation MERRA – GPCPv2.1 1981-2008 [mean=-0.03 mm/d] Aug 1994 [mean=-0.04 mm/d] Long-term bias Synoptic-scale errors  Correct MERRA precipitation with global gauge- and satellite-based precipitation observations to the extent possible. Reichle et al. J Clim (2011) submitted

  22. Precipitation corrections • MERRA • Reanalysis • Hourly • 0.5 deg • GPCP v2.1 • Satellite + gauges • Pentad • 2.5 deg Rescale MERRA separately for each pentad and 2.5 grid cell For each pentad and each 2.5 deg grid cell, the corrected MERRA precipitation (almost) matches GPCP observations. • MERRA + GPCPv2.1 • (hourly, 0.5 deg)

  23. Catchment model parameter changes

  24. Runoff Validation against naturalized streamflow observations from 9 “large” and 9 “small” basins (~1989-2009). Streamflow skill (3-month-average anomaly R) GPCP corrections yield significantly in 3 large basins. MERRA and MERRA-Land (0.5 deg) better than ERA-Interim (1.5 deg). Not shown: In all cases the revised interception parameters yield improved runoff anomalies (albeit not significant).

  25. Snow water depth MERRA-Land v. CMC snow analysis Pentad anomaly R (2002-2009) Note: No snow analysis in MERRA or MERRA-Land. CMC snow analysis Density [stations/10,000 km2] MERRA and MERRA-Land have similar skill. Low R values in areas without in situ observations. Not shown: Similar result for comparison against in situ data (583 stations) and for snow water equivalent (SWE).

  26. Categorical analysis of snow cover fraction vs. MODIS MERRA MERRA-Land MOD10C2, aggregated to monthly avg. SCF Feb 2004 MERRA SCF agrees well with MODIS SCF observations. False alarm rate increases in MERRA-Land. Apr 2004

  27. Snow cover extent (SCE) v. MODIS km2 Change of WEMIN parameter in “Fortuna” unhelpful for estimation of snow cover fraction. [Not sensitive to GPCP precipitation corrections.] See poster by Toure and Reichle for details.

  28. Observation error estimation via Triple Co-location Estimated anomaly error std-dev of surface soil moisture (2007-10) Obtain error std-dev from 3 independent estimates: ASCAT (active MW) GEOS-5 (model) • active MW • passive MW • modeling Active retrievals better in more vegetated areas. Passive retrievals better in more arid conditions. AMSR-E (passive MW) AMSR-E (passive MW) LPRM retrievals better than NSIDC. Work in progress! LPRM-X NSIDC GEOS-5 anom std-dev Units: [m3/m3] Consistent with model anomaly variability. small errors large errors

  29. Surface soil moisture: SMOS retrievals vs. GEOS-5 Mean (4/2010 – 4/2011) m3/m3 GEOS-5 soil moisture wetter than SMOS. SMOS Time series correlation coeff. GEOS-5 Reasonable agreement where expected.

  30. SMAP Level 4 soil moisture and carbon products • L4_SM Product: • Lead: Rolf Reichle (NASA/GMAO) • Assimilating SMAP data into a land model driven with observation-based forcings yields: • a root zone moisture product (reflecting SMAP data), and • a complete and consistent estimate of soil moisture & related fields. Surface meteorology SMAP observations Gross Primary Productivity Data Assimilation L4_C Product: Lead: John Kimball (U Montana) Combining L4_SM (SM & T), high-res L3_F/T_A & ancillary Gross Primary Productivity (GPP) inputs within a C-model framework yields: - a Net Ecosystem Exchange (NEE) product, & - estimates of surface soil organic carbon (SOC), component C fluxes (R) & underlying SM & T controls. L4_SM Product: Surface and root-zone soil moisture and temperature Land model Carbon model L4_C Product: Net Ecosystem Exchange

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