1. 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 JCSDA 3 rd Workshop on Satellite Data Assimilation 1.Project Rationale 2.Objectives 3.Progress 4.Plan OUTLINE

2. Slide 2 JCSDA 3 rd Workshop, April 20-21, 2005 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. Slide 3 JCSDA 3 rd Workshop, April 20-21, 2005 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. Slide 4 JCSDA 3 rd Workshop, April 20-21, 2005 Satellite Data Products to be Assimilated 1.AMSR-E SM/T B : top layer SM/T B 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?

5. Slide 5 JCSDA 3 rd Workshop, April 20-21, 2005 Data Assimilation Techniques in LIS 1.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.

6. Slide 6 JCSDA 3 rd Workshop, April 20-21, 2005 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 T B s only while B02 uses both 6.9 and 10.7 GHz T B s.

7. Slide 7 JCSDA 3 rd Workshop, April 20-21, 2005 TMI DI EKF WetStart SGP’99 TMI SM Data Assimilation with Mosaic LSM

8. Slide 8 JCSDA 3 rd Workshop, April 20-21, 2005 No DA DI EKF WetStart SGP’99 Latent Heat Flux from Mosaic LSM

9. Slide 9 JCSDA 3 rd Workshop, April 20-21, 2005 No DA DI EKF TMI Obs o Wet start, 0-2cm Layer SGP’99 TMI SM Data Assimilation with Mosaic LSM For wet start case, KF DA advantage is more significant.

10. Slide 10 JCSDA 3 rd Workshop, April 20-21, 2005 Wet start, 2-148cm Layer No DA DI EKF SGP’99 TMI SM Data Assimilation with Mosaic LSM KF DA uses the correlations between the different soil layers in the Mosaic LSM.

11. Slide 11 JCSDA 3 rd Workshop, April 20-21, 2005 SGP’99 TMI SM Data Assimilation with Noah LSM Dry start, 0-10cm Layer No DA DI EKF TMI Obs o 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.

12. Slide 12 JCSDA 3 rd Workshop, April 20-21, 2005 Dry start, 10-40cm Layer SGP’99 TMI SM Data Assimilation with Noah LSM 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. Slide 13 JCSDA 3 rd Workshop, April 20-21, 2005 SMEX’02 SM Simulations with Noah LSM 0-1cm 1-6cm

14. Slide 14 JCSDA 3 rd Workshop, April 20-21, 2005 SMEX’02 SM Simulations with Noah LSM

15. Slide 15 JCSDA 3 rd Workshop, April 20-21, 2005 AMSR-E Surface Soil Moisture Retrievals Version 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 T B data if retrievals are suspect.

16. Slide 16 JCSDA 3 rd Workshop, April 20-21, 2005 AVHRR UMD land cover MODIS V4 UMD land cover MODIS V3 UMD land cover Rio Grande River Basin in New Mexico Below Elephant Butte Dam Arsenault et al. 2005 Impact of MODIS LC Data on Noah LSM Simulations

17. Slide 17 JCSDA 3 rd Workshop, April 20-21, 2005 Arsenault et al. 2005 Latent Heat Flux (W m -2 ) Top 10 cm Soil Temperature (Celsius) Sensible Heat Flux (W m -2 ) Differences between (1) AVHRR run and (2) MODIS-V3 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.

18. Slide 18 JCSDA 3 rd Workshop, April 20-21, 2005 Albuquerque, NM area – May 30, 2002 (18Z) Latent Heat Flux (W m-2): AVHRR run – MODIS3 run

19. Slide 19 JCSDA 3 rd Workshop, April 20-21, 2005 LDAS LSM and MODIS Snow Cover Comparison Central Washington State – 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 LSM CLM2 LSM MODIS 1-Day

20. Slide 20 JCSDA 3 rd Workshop, April 20-21, 2005 Impact of Assimilating MODIS Snow Cover Data 21Z 17 January 2003 Control Run Mosaic SWE (mm) Enhanced MODIS Snow Cover (%) Assimilated Mosaic SWE (mm) IMS Snow Cover SNOTEL and Co-op Network SWE (mm) Mosaic SWE Difference (mm) 48.58 N, 109.23 W Observations Model output Assimilated output Rodell et al., 2003

21. Slide 21 JCSDA 3 rd Workshop, April 20-21, 2005 FOLLOWING YEAR PLAN 1.Assimilate global 0.25 AMSR-E SM retrievals and T B 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. Year Task 1 Task 2 Task 3 1 Data Identification, QC, and Importation 2 EKF/EnKF DA and software development 3 DA Evaluation, impact studies, and code transfer