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Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM

JCSDA 3 rd Workshop on Satellite Data Assimilation. Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM. Xiwu Zhan 1 , Paul Houser 2 , Sujay Kumar 1 Kristi Arsenault 1 , Brian Cosgrove 3 1 UMBC-GEST/NASA-GSFC; 2 GMU/CREW; 3 SAIC/NASA-GSFC. OUTLINE. Project Rationale

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Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM

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  1. JCSDA 3rd Workshop on Satellite Data Assimilation Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM Xiwu Zhan1,Paul Houser2 , Sujay Kumar1 Kristi Arsenault1, Brian Cosgrove3 1 UMBC-GEST/NASA-GSFC; 2 GMU/CREW; 3 SAIC/NASA-GSFC OUTLINE • Project Rationale • Objectives • Progress • Plan

  2. RATIONALE • Land surface information improves weather and climate prediction; • Near-real-time land observations (MODIS, AMSR-E) are available; • Few satellite land products are used in operational weather and climate prediction; • Lack of proven operational land assimilation methods have been a limit; • GOAL: Implement Kalman Filter to assimilate land satellite data products into the Noah land surface model installed in the Land Data Assimilation Systems (NLDAS/GLDAS)

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

  4. Satellite Data Products to be Assimilated AMSR-E SM/TB: top layer SM/TB observed, 4 layer Noah SM updated with Kalman filter DA; MODIS land cover: replace AVHRR with MODIS LC; MODIS snow cover:nudging model snow cover/depth/ SWE with MODIS and in situ (SnoTEL) snow data; MODIS LST: update 4 layer soil temperature with MODIS LST using Kalman filter DA; could also use GOES LST?

  5. Data Assimilation Techniques in LIS • Direct Insertion (DI): replace LSM states with corresponding observation data; • 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]. • 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.

  6. Soil Moisture Data Assimilation in LIS • LIS-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.

  7. SGP’99 TMI SM Data Assimilation with Mosaic LSM Wet Start TMI DI EKF

  8. SGP’99 Latent Heat Flux from Mosaic LSM Wet Start No DA DI EKF

  9. SGP’99 TMI SM Data Assimilation with Mosaic LSM Wet start, 0-2cm Layer No DA DI EKF TMI Obs o • For wet start case, KF DA advantage is more significant.

  10. SGP’99 TMI SM Data Assimilation with Mosaic LSM Wet start, 2-148cm Layer No DA DI EKF • KF DA uses the correlations between the different soil layers in the Mosaic LSM.

  11. SGP’99 TMI SM Data Assimilation with Noah LSM Dry start, 0-10cm Layer No DA DI EKF TMI Obs • 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. o

  12. SGP’99 TMI SM Data Assimilation with Noah LSM Dry start, 10-40cm Layer No DA DI EKF • 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?.

  13. SMEX’02 SM Simulations with Noah LSM 0-1cm 1-6cm

  14. SMEX’02 SM Simulations with Noah LSM

  15. AMSR-E Surface Soil Moisture Retrievals Version B02 Version B00 Version B01 • 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.

  16. Impact of MODIS LC Data on Noah LSM Simulations MODIS V3 UMD land cover MODIS V4 UMD land cover AVHRR UMD land cover Rio Grande River Basin in New Mexico Below Elephant Butte Dam Arsenault et al. 2005

  17. Differences between (1) AVHRR run and (2) MODIS-V3 May 30, 2002 (18 Z) Latent Heat Flux (W m-2) Sensible Heat Flux (W m-2) Top 10 cm Soil Temperature (Celsius) These figures show the differences in latent heat flux, sensible heat flux and the top layer soil temperature for the Noah LSM. Arsenault et al. 2005

  18. Latent Heat Flux (W m-2): AVHRR run – MODIS3 run Albuquerque, NM area – May 30, 2002 (18Z)

  19. LDAS LSM and MODIS Snow Cover Comparison Central Washington State – Yakima Basin (February 24, 2003) Noah 2.7.1 LSM CLM2 LSM MODIS 1-Day • 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.

  20. Observations Model output Assimilated output 48.58 N, 109.23 W Impact of Assimilating MODIS Snow Cover Data 21Z 17 January 2003 SNOTEL and Co-op Network SWE (mm) Rodell et al., 2003 Enhanced MODIS Snow Cover (%) IMS Snow Cover Control Run Mosaic SWE (mm) Assimilated Mosaic SWE (mm) Mosaic SWE Difference (mm)

  21. FOLLOWING YEAR PLAN Assimilate global 0.25 AMSR-E SM retrievals and TB observations into LIS-Noah LSM using the EKF-implemented LIS; Implement the Ensemble Kalman Filter in LIS; Assimilate MODIS LST into LIS-Noah LSM using the EnKF-implemented LIS; Assess and publish the efficiency/benefits of assimilating MODIS LC, LST, snow cover, and AMSR-E SM/TB.

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