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

AMSR-E Science Team Meeting Asheville, NC 28-29 Jun 2011 Update on AMSR-E soil moisture and snow data assimilation. 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

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

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  1. AMSR-E Science Team Meeting Asheville, NC 28-29 Jun 2011 Update on AMSR-E soil moisture and snow data assimilation 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 • MERRA (atmospheric) reanalysis • Baseline skill of global estimates without land data assimilation • Soil moisture assimilation • AMSR-E and precipitation corrections • AMSR-E and ASCAT • Snow assimilation • AMSR-E and MODIS

  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-based precip. corrections), 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. Snow depth validation MERRA-Land v. CMC snow analysis Pentad anomaly R (2002-2009) Low R values in areas without in situ obs. CMC snow analysis Density [stations/10,000 km2] MERRA -Land shows good skill without station-based snow analysis. Not shown: MERRA and MERRA-Land have similar skill. Similar result for comparison against in situ data (583 stations) and for snow water equivalent (SWE).

  6. Outline • MERRA (atmospheric) reanalysis • Baseline skill of global estimates without land data assimilation • Soil moisture assimilation • AMSR-E and precipitation corrections • AMSR-E and ASCAT • Snow assimilation • AMSR-E and MODIS

  7. Key limitations of satellite soil moisture retrievals Satellite soil moisture retrievals: are sensitive to moisture and temperature only in a 5 cm surface layer (under 0-5 kg/m2 vegetation), have limited coverage in time and space, and are subject to measurement errors. Surface layer (0-5 cm) “Root zone” layer (0-100 cm) Models suffer from precipitation uncertainties and other errors  Use data assimilation to merge retrievals into land surface model.

  8. Objective What are the relative contributions of precipitation observations and (AMSR-E) soil moisture retrievals to the skill of soil moisture estimates in a land data assimilation system?

  9. Experiments NASA/GMAO land data assimilation system, including the GEOS-5 Catchment model • Four different precipitation forcing datasets: • MERRA • Reanalysis, 0.5 deg • MERRA+CMAP • MERRA+GPCPv2.1 • Pentad, 2.5 deg • Global • Satellite + gauges • MERRA+CPC • Daily, 0.25 deg • CONUS • Gauges • Separately assimilate two different AMSR-E soil moisture retrieval datasets: • NSIDC • LPRM (X-band) At the pentad (daily) and 2.5 deg (0.25 deg) scale, the corrected re-analysis precipitation matches the correcting observations. Combine into 12 experiments:

  10. Validating in situ observations • USDA SCAN stations (only 37 of ~120 suitable, single profile sensor, surface and root zone soil moisture), and • four AMSR-E CalVal watershed sites (RC, WG, LW, LR; distributed sensors, only surface soil moisture) x SCAN + CalVal

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

  12. Precipitation corrections v. retrieval assimilation Extract average skill contributions of precipitation corrections and retrieval assimilation: (reference)

  13. Precipitation corrections v. retrieval assimilation R = skill improvement over reference model integration • Precip. corrections and retrieval assimilation contribute: • roughly evenly and • largely independently • to skill improvement. • Results from single sensor per watershed (SCAN data) are consistent with those from distributed CalVal in situ sensors. Liu et al. JHM (2011) doi:10.1175/JHM-D-10-05000.

  14. Outline • MERRA (atmospheric) reanalysis • Baseline skill of global estimates without land data assimilation • Soil moisture assimilation • AMSR-E and precipitation corrections • AMSR-E and ASCAT • Snow assimilation • AMSR-E and MODIS

  15. Active vs. passive microwave soil moisture retrievals Best result for assimilation of data from both sensors. AMSR-E better in dry conditions. ASCAT better in more humid conditions. Model and Assimilation Model and Retrievals Model: Catchment model ASCAT: Radar; C-band, TU Vienna AMSR-E: Radiometer; X-band, LPRM Period: 2007-2010

  16. Soil moisture error std-dev (Triple Co-location) Model soil moisture variability Three estimates: AMSR-E, ASCAT, model. Assume independent errors. Convert data to standard-normal deviates. Obtain dim.-less error std-dev estimate. Scale error std-dev into units of model variability. m3/m3 AMSR-E (LPRM-C) ASCAT model m3/m3 Dominant pattern in err-std-dev is soil moisture variability. AMSR-E minus ASCAT Red: AMSR-E better in south-west deserts but also in eastern US. Blue: ASCAT better in central US. m3/m3

  17. Outline • MERRA (atmospheric) reanalysis • Baseline skill of global estimates without land data assimilation • Soil moisture assimilation • AMSR-E and precipitation corrections • AMSR-E and ASCAT • Snow assimilation • AMSR-E and MODIS

  18. 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.

  19. Assimilation of MODIS snow cover fraction (SCF) MODIS 12 Oct 09 18 Nov 09 7 Jan 10 2 Mar 10 10 Apr 10 31 May 10 Noah SCF assim. MODIS SCF successfully adds missing snow, … except during melt season. MODIS SCF also improves timing of onset of snow season (not shown). De Lannoy et al. WRR (2011) submitted.

  20. Assimilation of AMSR-E snow water equivalent (SWE) AMSR-E 12 Oct 09 18 Nov 09 7 Jan 10 2 Mar 10 10 Apr 10 31 May 10 Noah SWE assim. AMSR-E does not observe thin snow packs  no improvement at start and end of snow season. Some improvement at lower elevations with shallow snow pack (COOP sites). Problematic in mountains with deep snow packs (SNOTEL sites). De Lannoy et al. WRR (2011) submitted.

  21. AMSR-E snow water equivalent (SWE) SWE anomalies (40.38N, 106.66W) Example: Anomaly time series at SNOTEL site. De Lannoy et al. WRR (2011) submitted. Example: Snapshot over North America cm AMSR-E MERRA SWE 1 Mar 2004 AMSR-E SWE retrievals and MERRA differ a lot in absolute terms.

  22. AMSR-E snow water equivalent (SWE) Anomaly R (2002-09) AMSR-E v. MERRA MERRA v. CMC all months Jan Feb AMSR-E SWE retrievals and MERRA differ a lot in terms of anomaly correlation, while MERRA is consistent with CMC (ultimately based on in situ obs. of snow depth). Mar  AMSR-E SWE retrievals not ready for assimilation.

  23. AMSR-E and global snow models Tedesco et al., IEEE/TGARS, 2010: (DOI:10.1109/TGRS.2009.2036910) Novel retrieval approach using dynamic snow depth from land surface model, but … “… the novel retrieval approaches, […] do not outperform [snow depth] estimates from the land surface model alone ...” “… it is possible to simulate […] brightness temperatures by means of [electromagnetic] models driven with inputs derived from snow pit measurements.” “ … [but such approaches,] at the global scale, face tremendous difficulties with modeling key processes such as grain size evolution.”

  24. Snow temperature gradient index (TGI) Model: Josberger and Mognard, 2002 ΔTB [K] AMSR-E: ΔTB = TBv18–TBv36 R(TGI, ΔTB) one grid cell Works best in cold, dry snow conditions. one season no anomalies! Use TGI as surrogate for snow grain size?

  25. AMSR-E TB differences vs. snow model states R(SWE, TBv10–TBv18) R(SWE, TBv10–TBv36) R(SWE, TBv18–TBv36) Feb Similar results for R(SWE*TSURF, ΔTB) [not shown]. R(TGI, TBv10–TBv18) R(TGI, TBv10–TBv36) R(TGI, TBv18–TBv36) Feb Multi-regression approach not likely to work. Physically-based radiative transfer model may (or may not) work.

  26. Summary and outlook • Soil moisture assimilation • Improved surface and root-zone soil moisture from assimilation of AMSR-E and ASCAT surface soil moisture retrievals • Precipitation corrections and retrieval assimilation contribute independent and comparable amount of information • There are differences between AMSR-E products • Snow assimilation • AMSR-E SWE retrievals not ready for assimilation • Still trying to find a way to use AMSR-E TB for SWE assimilation into global models

  27. Thanks for listening! Questions?

  28. EXTRA SLIDES

  29. Soil moisture data assimilation Surface meteorology, incl. precipitation Satellite retrievals of surface soil moisture Data Assimilation Soil moisture data product: Surface and root-zone soil moisture Land model • Assimilating satellite soil moisture retrievals into a land model driven with observation-based forcings yields: • a root zone moisture product (reflecting satellite soil moisture data), and • a complete and consistent estimate of soil moisture & related fields.

  30. Key limitations of model soil moisture Land model estimates of soil moisture depend critically on the quality of the precipitation forcing data. (Re-)analysis precipitation forcing suffers from: short-term errors in diurnal cycle, and long-term bias. Reichle et al. J Clim (2011) submitted Correct (re-)analysis precipitation with global gauge- and satellite-based precipitation observations to the extent possible.

  31. 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

  32. 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)

  33. 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 significant improvements 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).

  34. 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

  35. 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

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