JCSDA Workshop on Satellite Data Assimilation

1. JCSDA Workshop on Satellite Data Assimilation • Project Title: Detection and Correction of Aerosol Contamination in Infrared Satellite Sea Surface Temperature Retrievals • Principal Investigators: James Cummings, Doug Westphal Naval Research Laboratory, Monterey, CA • Co-Investigators: Jeff Hawkins, Doug May, Andy Harris • Budget: $110 FY03 $115 FY04 $150 FY05 • Talk Outline: Project Objectives and Tasks • Progress to Date • Future Plans

2. JCSDA Workshop on Satellite Data Assimilation Project Objectives • Detection of aerosol contamination in infrared satellite sea surface temperature (SST) retrievals using Navy Aerosol Analysis Prediction System (NAAPS) aerosol distributions. • Correction of satellite SSTs for aerosol contamination using NAAPS aerosol products.

3. JCSDA Workshop on Satellite Data Assimilation Project Tasks • Collocate NAAPS optical depth forecast fields valid for the time SST retrievals are generated (Doug May, NAVOCEANO). • Estimate SST retrieval reliability relationship to AOD content (Doug May, NAVOCEANO - Jim Cummings, NRL) • Develop SST quality control schemes to recognize aerosol contamination (Jim Cummings, NRL). • Correct satellite SSTs for aerosol contamination (Andy Harris, NESDIS). • Validate NAAPS aerosol products using using independent data - improve NAAPS model (Jeff Hawkins, Doug Westphal, NRL).

4. SST Retrievals and NAAPS Collocations at NAVOCEANO On going since February 2004 NAAPS AOD forecast fields obtained via ftp from NRL 4 times daily Append AOD value closest in time and location to each MCSST retrieval total AOD used (sum of dust, smoke, sulfate components) globally for N-16 and N-17 (26 Jan 2004) Global SST observation data file with NAAPS AOD values provided daily at 1000 UT to US GODAE server in Monterey New capabilities added May 2004 NAAPS AOD components plus total AOD collocated with MCSST Cloud cleared radiances for AVHRR channels 3,4,5 saved with AOD values JCSDA Workshop on Satellite Data Assimilation

5. QC of SST Retrievals with NAAPS Collocations at NRL Develop discriminant analysis functions to distinguish aerosol contaminated vs. uncontaminated SST retrievals SST retrievals from verified Saharan dust events are used as training data sets Discriminant functions computed using NAAPS AOD components (dust, sulfate, smoke), AVHRR channels 3,4,5 brightness temperatures, and SST innovation from 6 hourly global 9 km SST analysis Provides probabilistic framework for QC outcome is probability SST retrieval is contaminated allows simple query capability when gathering data for assimilation JCSDA 2nd Workshop on Satellite Data Assimilation

6. QC Discriminant Analysis Training Data Sets • Jun 2-6, 2004 • Jul 15-17 and 20-25, 2004 • Sep 12-15, 2004 • Oct 10-13, 2004 • Nov 2-4 and 6-8, 2004 • Dec 13-15 and 28-29, 2004 • Jan 5-8, 2005 • Feb 10-13, 2005 Case 20050212 Case 20040725

7. Case 20040725: Visible & AOD s u n g l i n t s u n g l I n t 14:18 GMT 12:48 GMT 15:55 GMT

8. Resultant Composite AOD Image 14:18 GMT 12:48 GMT 15:55 GMT Composite

9. NPS AOD Image Reduction: Matching NAAPS Domain NAAPS Dust AOD valid: 2004072512 0.1 0.4 1.6 6.4 Original: 2250x1200 pixels Intermediate: 60 x 60 pixels Final: 20 X 20 pixels

10. MODIS (GSFC) AOD Image Reduction: Matching NAAPS Domain NAAPS Dust AOD valid: 2004072512 0.1 0.4 1.6 6.4 Original: 2250x1200 pixels Intermediate: 60 x 60 pixels Final: 20 X 20 pixels

11. NAAPS vs NPS AOD (left) & NAAPS vs MODIS (GSFC) AOD (right) 202 observations 231 observations

12. NAAPS vs NPS AOD Scatter Plot: NPS vs NAAPS AOD for Case 20040725 202 observations AOD  .50 R2: .37 AOD .25 .30 .40 .50 R2: .68 .53 38 .29

13. Suggested Improvements • Cloud Filtering • Conversion of Image data to NAAPS grid • Include AERONET measurements

14. RT Modeling of aerosol effects Consider Merchant et al. notation… SST = aTkCk is aerosol ‘mode vector’ a is vector of retrieval coefficients So, need to ascertain weights of mode vector for 3.7, 11 and 12 µm channels, i.e.

15. Effect on brightness temperatures

16. Dependency on total transmittance Primary cause of scatter is attenuation of near-surface aerosol effect by intervening atmosphere Can be mitigated by linear fit to total clear-sky transmittance Different aerosol types have significantly different coefficients

17. Role of air-sea temperature difference Residual error in fit depends on air-sea temperature difference Magnitude and range of ASTD-dependence is a function of total clear-sky transmittance Could parameterize∂T/∂Χ as a function of both t and ASTD…

18. Suggested form of k-estimation k-coefficients will be different for different aerosol types

19. Conclusions from RTM studies Merchant et al. approach requires modification in the tropospheric case because aerosols are not at the top of the atmosphere Similar reason for greater success of Nalli & Stowe methodology in stratospheric aerosol case Addition of total atmospheric transmittance (from NWP or e.g. SSM/I water vapor) should assist in correcting for much of the scatter Air-sea temperature difference (NWP) useful addition Still need discrimination of aerosol type (e.g. via NAAPS)

20. Conclusions from RTM – part 2 NAAPS data can permit full RT treatment of problem, but this is costly → reduced predictor approach proposed here More work required in order to develop and validate this approach May be desirable to adopt an interim empirical approach using satellite-derived AODs (analyses) and ancillary clear-sky transmittance, air-sea temperature differences (NCEP fields?) Beware of cross-talk between AOD & WV, ASTD Stratospheric aerosols have much greater impact for given AOD – suggest using alternative sources (e.g. HIRS retrieval, or another analysis/product)