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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

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:[email protected]


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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).


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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?


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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:


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

Precipitation corrections v. retrieval assimilation

Extract average skill contributions of precipitation corrections and retrieval assimilation:

(reference)


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.”


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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?


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

Thanks for listening!

Questions?


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

EXTRA

SLIDES


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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.


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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)


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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).


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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


Global modeling and assimilation office nasa goddard space flight center phone 301 614 5693

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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