assimilation of modis and amsr-e land products into the noah...

Assimilation of MODIS and AMSRAssimilation of MODIS and AMSR--E Land E Land Products into the NOAH LSMProducts into the NOAH LSMXiwu ZhanXiwu Zhan11,, Paul HouserPaul Houser2 2 , , Sujay KumarSujay Kumar11

Kristi ArsenaultKristi Arsenault11, Brian Cosgrove, Brian Cosgrove33

11 UMBCUMBC--GEST/NASAGEST/NASA--GSFC; GSFC; 22 GMU/CREW; GMU/CREW; 33 SAIC/NASASAIC/NASA--GSFCGSFC

JCSDA 3JCSDA 3rdrd Workshop on Satellite Data AssimilationWorkshop on Satellite Data Assimilation

1.1. Project RationaleProject Rationale2.2. Objectives Objectives 3.3. ProgressProgress4.4. PlanPlan

OUTLINEOUTLINE

JCSDA 3rd Workshop, April 20-21, 2005 Slide Slide 22

RATIONALERATIONALELand surface information improves weather and climate Land surface information improves weather and climate prediction;prediction;NearNear--realreal--time land observations (MODIS, AMSRtime land observations (MODIS, AMSR--E) are E) are available;available;Few satellite land products are used in operational weather Few satellite land products are used in operational weather and climate prediction;and climate prediction;Lack of proven operational land assimilation methods have Lack of proven operational land assimilation methods have been a limit;been a limit;

GOAL:GOAL: Implement Kalman Filter to assimilate land satellite Implement Kalman Filter to assimilate land satellite data products into the Noah land surface model installed in data products into the Noah land surface model installed in the Land Data Assimilation Systems (NLDAS/GLDAS)the Land Data Assimilation Systems (NLDAS/GLDAS)


OBJECTIVESOBJECTIVESIdentify relevant MODIS & AMSR data products;Identify relevant MODIS & AMSR data products;Implement the Kalman Filter in LDAS/LIS;Implement the Kalman Filter in LDAS/LIS;Examine the efficiency and benefits of assimilating Examine the efficiency and benefits of assimilating the satellite data products into the NOAH LSM.the satellite data products into the NOAH LSM.

PROGRESSPROGRESSSatellite data products selected;

Data assimilation technique implemented;

Results of soil moisture data assimilation;

Results of using MODIS land cover data;

Results of using other data products.


Satellite Data Products to be Assimilated

1. AMSR-E SM/TB: top layer SM/TB observed, 4 layer Noah SM updated with Kalman filter DA;

2. MODIS land cover: replace AVHRR with MODIS LC;

3. MODIS snow cover: nudging model snow cover/depth/ SWE with MODIS and in situ (SnoTEL) snow data;

4. MODIS LST: update 4 layer soil temperature with MODIS LST using Kalman filter DA; could also use GOES LST?


Data Assimilation Techniques in LIS1. Direct Insertion (DI): replace LSM states with

corresponding observation data;

2. Kalman Filters (EKF/EnKF): correct LSM states by weighing model forecasts and observations with their error covariance:

Xa = Xb + K [Z – h(Xb)],K = PHT/[HPHT + R].

3. Land Information System: LIS (enhanced NLDAS/ GLDAS software system) includes plug-ins for both DI and EKF; This plug-in system design allows assimilating any state variable data using any LSM; The EnKF is being implemented using the plug-in system design and an ensemble generation algorithm recently developed.


Soil Moisture Data Assimilation in LISLIS-EKF: The Extended Kalman Filter is implemented in LIS to assimilated TMI 0-2cm soil moisture retrievals of the SGP’99 area into the Mosaic and Noah land surface models;

SGP’99 TMI SM: Jackson & Hsu (2001) retrieved and validated 0-2cm SM for an ~140km by 280km area in central OK for 14 days from July 8 to 21, 1999;

SMEX’02 SM: SM for SMEX’02 area simulated with Noah LSM in LIS compared with in situ observations;

AMSR-E SM: B01 version retrieval algorithm was used before Feb 15, 2005. B02 algorithm is used on an after that. B01 uses 10.7GHz TBs only while B02 uses both 6.9 and 10.7 GHz TBs.


TMI

DI

EKF

WetWetStartStart

SGP’99 TMI SM Data Assimilation with Mosaic LSM


NoDA

DI

EKF

WetWetStartStart

SGP’99 Latent Heat Flux from Mosaic LSM


No DADIEKFTMI Obso

Wet start, 0-2cm Layer


• For wet start case, KF DA advantage is more significant.


Wet start, 2-148cm Layer No DADIEKF


• KF DA uses the correlations between the different soil layers in the Mosaic LSM.


SGP’99 TMI SM Data Assimilation with Noah LSM

Dry start, 0-10cm Layer No DADIEKFTMI Obso

• SM DA with Noah LSM needs special treatment for using 0-2cm SM obs to update 0-10cm top soil layer SM of the LSM

• Directly using 0-2cm SM for the 0-10cm SM of Noah LSM may be misleading.


Dry start, 10-40cm Layer

SGP’99 TMI SM Data Assimilation with Noah LSM

No DADIEKF • Second layer SM of

Noah LSM did not get updated;

• There is no SM correlation between the Noah LSM soil layers? Or

• The current code of either EKF or Noah LSM has bugs?.


SMEX’02 SM Simulations with Noah LSM

0-1cm

1-6cm


SMEX’02 SM Simulations with Noah LSM


AMSR-E Surface Soil Moisture RetrievalsVersion B00

Version B01

Version B02

• AMSR-E SM algorithm changes;• Newest algorithm starts on 2/15/05;• Some areas do not have retrievals;• Will be assimilated at 0.25° grids

globally;• May directly assimilate TB data if

retrievals are suspect.


AVHRRAVHRRUMD land coverUMD land cover

MODIS V4MODIS V4UMD land coverUMD land cover

MODIS V3MODIS V3UMD land coverUMD land cover

Rio Grande River Basin in New Mexico Below Elephant Butte DamArsenault et al. 2005

Impact of MODIS LC Data on Noah LSM SimulationsImpact of MODIS LC Data on Noah LSM Simulations

JCSDA 3rd Workshop, April 20-21, 2005 Slide Slide 1717Arsenault et al. 2005

Latent Heat Flux (W mLatent Heat Flux (W m--22)) Top 10 cm Soil Temperature (Celsius)Top 10 cm Soil Temperature (Celsius)Sensible Heat Flux (W mSensible Heat Flux (W m--22))

DifferencesDifferences between (1) AVHRR run and (2) MODISbetween (1) AVHRR run and (2) MODIS--V3V3

May 30, 2002 (18 Z)May 30, 2002 (18 Z)

These figures show the differences in latent heat flux, sensible heat flux and the top layer soil temperature for the Noah LSM.


Albuquerque, NM area Albuquerque, NM area –– May 30, 2002 (18Z)May 30, 2002 (18Z)

Latent Heat Flux (W mLatent Heat Flux (W m--2): AVHRR run 2): AVHRR run –– MODIS3 runMODIS3 run


LDAS LSM and MODIS Snow Cover ComparisonLDAS LSM and MODIS Snow Cover Comparison

Central Washington State Central Washington State –– Yakima Basin (February 24, 2003)Yakima Basin (February 24, 2003)

Comparison between the LDAS LSMs snow cover fields and Terra MODIS daily snow cover extent. (Purple indicates snow, yellow is clouds, and beige is snow-free land.)

Noah LSM underestimates and CLM2 overestimates snow cover when compared to the MODIS 1-day and also 8-day fields for most of the winter (for WY2002-2003).

In later spring months when snow melt occurs, the model snow cover has been found to identify snow in locations that MODIS algorithms fail to locate the snow beneath the tree canopies, when compared to in-situ measurements.

Noah 2.7.1 LSMNoah 2.7.1 LSM CLM2 LSMCLM2 LSM MODIS 1MODIS 1--DayDay


Impact of Assimilating MODIS Snow Cover Data21Z 17 January 2003

Control Run Mosaic SWE (mm)

Enhanced MODIS Snow Cover (%)

Assimilated Mosaic SWE (mm)

IMS Snow CoverSNOTEL and Co-op Network

SWE (mm)

Mosaic SWE Difference (mm)

01020304050607080

Sno

w W

ater

[mm

]

48.58 N, 109.23 WObservationsModel outputAssimilated output

Rodell et al., 2003


FOLLOWING YEAR PLANFOLLOWING YEAR PLAN

1. Assimilate global 0.25 AMSR-E SM retrievals and TB observations into LIS-Noah LSM using the EKF-implemented LIS;

2. Implement the Ensemble Kalman Filter in LIS;3. Assimilate MODIS LST into LIS-Noah LSM using the EnKF-

implemented LIS;4. Assess and publish the efficiency/benefits of assimilating MODIS

LC, LST, snow cover, and AMSR-E SM/TB.

DA Evaluation, impact studies, and code transfer33

EKF/EnKF DA and software development22

Data Identification, QC, and Importation11

Task 3Task 3Task 2Task 2Task 1Task 1YearYear

assimilation of modis and amsr-e land products into the noah...

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