JCSDA Research Progress Assessment Land Surface

1. JCSDA Research Progress AssessmentLand Surface JCSDA Science Steering Committee April 26-27, 2005 Dan Tarpley

2. Improved Weather and Climate Forecast Skill Through Use of Satellite Land Data • New and improved satellite products for prescribed model boundary conditions • Improved land surface forward radiation models • Improved land surface model physics to better use satellite data and support forward modeling • Sensitivity studies on new satellite products • Land data assimilation science • Transition to operations

3. Research Progress from AO and Internally Funded Projects

4. Integration of Satellite Observations with the Noah Land Model for Snow Data Assimilation Xubin Zeng, Mike Barlage, Mike Brunke, Jesse Miller, University of Arizona, Tucson Summary of Accomplishments (a) Generate and deliver a global 5km maximum snow albedo data set; deliver utility code; and finish the preliminary impact study. (b) Intercompare bulk algorithms for the computation of sea ice surface turbulent fluxes as used in NCEP and other models; develop new formulations for sea ice roughness lengths. (c) Preliminary evaluation of wintertime vegetation data and its coupling with snow processes in the Noah land model. (d) Use and help improve the Noah model testbed. Additional impact study of snow albedo data; Study of wintertime vegetation data; and Improve snow processes in the Noah model

5. 0.05° Dataset Inclusion in NLDAS

6. Retrieval of Time-Varying Land Cover and Vegetation Properties from MODIS in Support of the NCEP-WRF Land Surface Model Contributors: Mark Friedl, Angela Martin, Bin Tan, Bruce Anderson; Boston University • Summary of Accomplishments: • A global land cover/land use database at 1km and 1/8 degree spatial resolution for NCEP Noah model has been compiled; Comparison with existing NLDAS database has been performed and results indicate significant differences. • Global datasets for albedo have been compiled from MODIS data at 1-km, 2-km, and 0.05 degrees; Results when compared with existing NCEP databases reveal a systematic bias in NCEP albedo. Current NLDAS Land Use Model Future: Finalize & deliver land cover and albedo data sets; develop corresponding global datat sets for leaf area index; test model sensitivity to input fields, both offline and online. MODIS NLDAS Land Use Model NLDAS MODIS

7. Sample Results: Albedo • 1/8 Degree NLDAS Albedo for June

8. Results: Albedo • 1/8 Degree Average Albedo for June from MODIS

9. Sample Results: Albedo • Comparison between NLDAS albedo and MODIS at 1/8 degree

10. Sample Results: Albedo • 2-Km Average Albedo for June from MODIS

11. Satellite-derived green vegetation fraction transition to VIIRS Cropland Urban Contributors: Kevin Gallo, ORA; Lei Ji, CSU/CIRA; Brad Reed, EROS; Jim Merchant and Patti Dappen, U. Nebraska-Lincoln. • Summary of Accomplishments • Continuity exists between similarly processed AVHRR and MODIS normalized difference vegetation index (NDVI) data, which suggests that transition to VIIRS may be acceptable. • Data sets being assembled to derive AVHRR and MODIS green vegetation fraction from Landsat data. Forest Future: Continued monitoring of AVHRR and MODIS until availability of VIIRS data. Development of green vegetation fraction from MODIS/AVHRR. Real time green vegetation fraction development. Grassland

12. MODIS daily, 250m MODIS 8-day, 500m MODIS 16-day, 1km AVHRR 14-day, 1km

13. Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM Kalman filter DA is superior than DI Contributors: GMU: Paul Houser (PI), Xiwu Zhan (Co-PI), Sujay Kumar, Kristi Arsenault: UMBC; Brian Cosgrove: SAIC No DA DI EKF TMI Obs o • Summary of Accomplishments • The Extended Kalman Filter data assimilation algorithm is implemented and tested for assimilating surface soil moisture observations into Noah land surface model; • Impact of assimilating TMI SM into Noah LSM is assessed; • Software design for assimilating AMSR-E SM into Noah LSM of LIS using EnKF is completed; • MODIS land cover data is adopted and tested for Noah LSM; Latent Heat Flux (W m-2): AVHRR run – MODIS3 run Future: AMSE-E SM and MODIS LST data will be assimilated into Noah LSM of LIS using EnKF.

14. Evaluation and Application of the NASA-GSFC Land Information System Objective:A high performance, high resolution (1 km) global land modeling and assimilation system. Applications:Weather and climate model initialization and retrospective coupled modeling, Flood and water resources forecasting, Precision agriculture, Mobility assessment, etc. Christa Peters-Lidard (NASA/GSFC) and the LIS Team 1km MODIS Leaf Area Index (LAI) data

15. Example Output Sensible Heat Flux

16. Title:Development of Improved Forward Models for Retrievals of Snow Properties PIs: E. Wood (Princeton U) and D. Lettenmaier (U. Washington) • Purpose: • Develop a forward model of surface microwave emission for snow covered areas, and test its usefulness for assimilating operational Tb into NCEP/NWP models for improved snow estimation. • Progress so far: • An initial version of the model has been developed and is undergoing testing using field data from the NASA Cold Land Process eXperiment (CLPX). Sensitivity studies have started to evaluate the effect of grain size, partial coverage and incidence angle on the modeled brightness temperatures. • Future plans 05-06: • Further model development to try and incorporate the model into the NOAA Community Radiative Transfer Model system; • Continue model testing using additional NASA’s CLPX ground data, airborne PSR data and comparison to AMSR-E; • Continue sensitivity studies of Tb to snow and surface parameters

17. Task 2: Algorithm testing and validation Validation with CLPX Observations • Ground-Based Microwave Radiometer • Dense Snow Pit measurements • 12-13 Dec 2002 & 19-24 Mar 2003 • Snow on bare ground (no vegetation) • Assume snow measurements representative of entire LSOS (100 x 100 m) http://nsidc.org/data/docs/daac/nsidc0165_clpx_gbmr/index.html CLPX = Cold Land Process Experiment

18. Project Research Questions • Can a forward model of surface microwave emission be developed that is capable of providing realistic brightness temperatures for snow covered areas, and can the inputs for the forward model be provided by operational observations and NWP model output? • Is the modeled/predicted Tb sufficiently accurate and useful for assimilation into operational NWP?

19. Assimilation of Passive Microwave Radiances over Land: Use of the JCSDA Common Microwave Emissivity Model (MEM) in Complex Terrain Regions • Dr. Nancy Baker (PI) & Benjamin Ruston (co-Investigator) • Naval Research Laboratory, Monterey, CA • collaborators: Drs. Fuzhong Weng, Banghua Yan, & Andrew Jones • Purpose: • - Improve atmospheric profiling & radiance assimilation over land • - Improve the Land Surface Temperature (LST) • - Reduce rejects over desert & elevated terrain • Milestones: • - Developed IR emissivity & single-channel LST retrieval • - Implemented MEM into NAVDAS framework • - Performed initial 1dvar emissivity retrieval • Combined infrared & microwave sensors into a single retrieval • Submitted working code to NRL beta-testing platform • Future Plans: • - Complete CRTM implementation • - Transfer methodology to NCEP • - Mesoscale impact studies (UAE2) • Extend to SSMI/S assimilation • Complete strategy for bias correction

20. Emissivity Means 1-dvar retrieval incorporates HIRS/3, AMSU/A and AMSU/B to simultaneously retrieve profiles T & q, emissivity, and LST

21. Deriving Biomass Burning Emissions from GOES WildFire Products P.I: Shobha Kondragunta NOAA/NESDIS/ORA Co-I: Chris Schmidt UW-Madison

22. Problem • PM25 emissions from biomass burning (forest fires) are currently not included in CMAQ simulations • This leads to uncertainties in PM25 forecasts during long range transport of forest fire smoke Long range transport of smoke (PM25) to US from forest fires in Canada during July 16-18, 2004

23. Summary and Work Plan • Summary • CMAQ simulations during a known biomass burning event underestimate PM25 and AOD fields. This is due to the absence of smoke emissions in the model • Satellite-derived PM25 emissions in near real time will be developed to be incorporated into CMAQ • Work Plan • Collect existing static (fuel load, emission factors) data bases from USFS and other sources • Analyze and assess the databases • Derive emissions for a known fire episode (use GOES fire products and other satellite information for fuel moisture and so forth) • Conduct impact studies in collaboration with USFS and EPA • Transition technology to NWS Community Multiscale Air Quality (CMAQ) Model

24. What progress on the proposed work has been made over the performance period? • Products – making use of MODIS data for fixed fields • MODIS land classification/albedo (summer2005) • MODIS maximum snow albedo (Jan 2005) • AVHRR/MODIS intercomparisons of NDVI • Land data assimilation infrastructure • LIS has been ported from GSFC to NCEP, where it is used to execute the uncoupled GLDAS • Exercised uncoupled NLDAS test-bed for external PI (snow & vegetation data) • Radiative transfer/forward modeling • Forward physical model of snow emission • Retrieval of infrared and microwave emissivity over desert and complex terrain • Data assimilation science • Extended Kalman filtering of TMI and AMSR • Adjoint/Tangent linear assimilation of GOES LST • Assimilation of infrared and microwave window channels over land • Transition to operations

25. Transition to operations Objective demonstration of prediction model improvement on NCEP platform using NCEP performance measures • Uncoupled test bed infrastructure1 • NLDAS and GLDAS/Land Information System • Can be executed at PI’s home institution or NCEP • Use NCEP performance measures (e.g. state variable itself, impact of state variable on forward modeled radiances) • Assess computational resource demand • Coupled test bed infrastructure • EDAS with companion regional prediction model • GDAS with companion global prediction model • Executed at NCEP with NCEP performance measures • Assess computational resource demand • Implement in operations if positive impact warrants

26. Example Pathway to Operations • Develop validated satellite-based land product or land assimilation approach • Test in uncoupled LDAS • Retrospective • Test in coupled LDAS/GLDAS and companion prediction model • Retrospective • Real-time parallel • Implement in operations if positive impact warrants

27. Stop

29. Summary Report Guidelines • What progress has been made over the performance period? • Scientific direction and progress (wrt JC) • Will code will be resource affordable when its ready for integration into JCSDA systems? • Will delivered codes execute sufficiently fast in operational setting and timelines • Outstanding scientific issues: • Prioritize impact sensitivities • Horizontal correlations negligible or not in LDAS context • Institutional or infrastructure issues • I/O volumes of hi-res satellite land data • Preparation for NPOES • What work is planned in this area for next year, are there any coordination issues?

30. Example Deliverables for Next Year • Put in place and demonstrate uncoupled and coupled LDAS and prediction model testbeds. • Get PI feedback from pilot executions of above testbeds • Deliver new hi-res MODIS-based max snow albedo database and impact study in the uncoupled and coupled testbed. • Deliver hi-res MODIS-based land-use/land-cover and mapping tools for desired grids • Develop consistent wintertime land products (snow albedo, snow fraction, vegetation fraction and LAI)

31. Example Deliverables for Next Year • Put in place and demonstrate uncoupled and coupled LDAS and prediction model testbeds. • Get PI feedback from pilot executions of above testbeds • New MODIS-based max snow albedo database and impact study in the uncoupled and coupled testbed. • New MODIS-based land-use/land-cover and mapping tools for desired grids • Develop consistent wintertime land products (snow albedo, snow fraction, vegetation fraction and LAI)

32. Green Vegetation Fraction – June 16, 2003