1. Bureau of Meteorology MODIS Activity Ian Grant Bureau of Meteorology

2. Outline Aerosol validation Total water vapour validation Forecast atmospheric fields Bureau near real-time processing trial MODIS DB BRDF: plan & status

3. Validation of MOD04 aerosol Database established by MODIS aerosol team (MAPSS) of statistics over sunphotometer sites: - MOD04 50 km x 50 km - Sunphotometer 30 minutes - 470 nm, 660 nm 4 CSIRO sites (Mitchell) 15 Bureau sites (Forgan)

6. Aerosol validation MAPSS/MODIS comparison: MODIS grossly overestimates AOD Next: Investigate a case in detail via MOD02, MOD09 data. Raw Bureau AOD data is available Need to extract and validate “internal AOD” of MOD09? Coordinate with CRC-SI aerosol validation and improvement

7. Validation of Total Water Vapour Radiosondes (Bureau of Meteorology) GPS (Geoscience Australia, 16 sites) Radiosonde sites

8. Numerical Weather Model Fields Model run every 12 hours Forecast at 3, 6, …, 72 hours Resolutions 0.75º, 0.375º, 0.125º Forecasts for input to MOD09: - Surface Pressure, TWV? Analysis for validation: TWV

9. Numerical Weather Model Fields Surface pressure, Total Water Vapour?, Total ozone? LAPS forecast of TWV at 3, 6, …, 72 hours. Resolution 0.75º.

10. Bureau near real-time MODIS processing Acquaint Bureau users with products: NWP model input, forecasters Trial with Bureau Linux PC at ES&S antenna in Melbourne IMAPP: PDS → L1B → L2 Products: – MOD07 atmospheric temperature and humidity profiles, stability – Truecolour images (CSIRO Marine CAPS software)

11. Bureau near real-time MOD07 Cloud maskTotal TotalsLifted Index

12. Bureau near real-time images Truecolour images

13. A Future MODIS Application Bushfire CRC - Grassland Curing Project Develop techniques of satellite based curing assessment that are robust, reliable, validated and applicable across Australia and New Zealand Approved for July 2004 – June 2010 Conduct an extensive and systematic field measurement program Compare MODIS vegetation indices with curing, fuel moisture content A daily AVHRR-based map of Grassland Curing Index for south-eastern Australia has been distributed by the Bureau for four years

14. MODIS Direct Broadcast BRDF Project Coordinated by MODIS BRDF Team at Boston University Participants US:Boston U, U Maryland, USDA Forest Service Australia:CSIRO, Bureau of Meteorology, GA, DLI China:IRSA, BNU South Africa:CSIR Daily BRDF - Detect rapid land change: burns, snow - Track changing BRDF better: vegetation growth cycle - Reject cloud contamination Funded by NASA

15. MODIS DB BRDF Boston University group (Alan Strahler, Crystal Schaaf et al.) developed the MODIS BRDF module MOD43. (Crystal is on the NPP team) NASA funded the BU group to develop a DB version of MOD43 Groups offered to act as implementation testbeds, in: – Australia (CSIRO, BoM, GA, DLI) – China, South Africa, US DB code to aggregate MOD09 reflectances “L2Glite” is finished Initial implementation now happening at BNU, China – Tuning RMSE and WoD thresholds Australian DB sites are welcome to be next. Crystal here in March. BRDF inversion and MOD43 products will follow

16. IWMMM-4 Fourth International Workshop on Multiangular Measurements and Models 20-24 March 2006 UniLodge Hotel, Sydney www.eoc.csiro.au/iwmmm-4 An opportunity - to bring key sensor scientists to Australia - for Australian and international EO communities to interact - for Australian users to describe requirements to EO community

22. Validation of MOD04 aerosol Statistics of error in Aerosol Optical Depth (MOD04 - Sunphotometer)

23. DB BRDF: Discussion points

24. Institutional MOD09_L2 to MOD43_L3: Conceptual steps at a grid cell For each orbit: - Identify which swath pixels overlap the grid cell - Calculate pointers (  line,  sample, fractional overlap, etc.) For each UT day: - Group pointers from all orbits - Select swath pixels by geometry (obscov > 24%) (Only using geometry so far - Now introduce MOD09) - Select swath pixels by MOD09 QA (up to four) - Aggregate swath pixels to one value per orbit (average, weighted by obscov. This does the regridding) For each 16 days: - Invert the BRDF model

25. BRDF - DB Issues (1) Implement institutional details in DB: selection, aggregation, etc.? - Bow-tie and IFOV increase give multiple swath pixels at a grid cell Window length? Re-use any institutional code? BRDF as an IMAPP module? (Multipass is new to IMAPP?) Process in tiles (10º  10 º) for efficient memory use? 250-m resolution? Feed BRDF upstream, for aerosol and cloud mask?

26. BRDF - DB Issues (2) Invert BRDF each day or each orbit? - Terra + Aqua span 5 hours - Sub-day changes: Burns, flood, snow, cloud Iterate Atmospheric correction and BRDF inversion? - Use yesterday’s BRDF as first guess? - In thick aerosol (smoke)? - Iterative retrieval of aerosol, cloud mask? - In high-value region-season cases (fuel reduction, crop yields)? Build a flexible framework to accommodate these options? - Recognise the conceptual steps and keep them separate - CSIRO AVHRR BRDF as a testbed?

27. BRDF window length - VEGETATION approach BRDF shape from inversion in long window (last 10 looks) Average normalised clear looks in short window (last 10 days) Reject inversion outliers (up to three, red band) In practice, worst cases for inversion window length are: >25 days in 11% of cases (Europe) >50 days in 0.1% of cases (tropics) Weight more recent looks? Additive rather than multiplicative normalisation? (needs investigation) Duchemin et al., Remote Sens. Env., 81 (2002) 101-113

28. BRDF - DB Issues Implement institutional details in DB: selection, aggregation, etc.? Window length? Re-use any institutional code? BRDF as an IMAPP module? Process in tiles for efficient memory use? 250-m resolution? Feed BRDF upstream, for aerosol and cloud mask? Invert BRDF each day or each orbit? Iterate Atmospheric correction and BRDF inversion? Build a flexible framework to accommodate processing options?