1. Advances in data and methods for aerosol data assimilation in the Navy Aerosol Analysis and Prediction System Edward Hyer James Campbell Jeff Reid Doug Westphal Naval Research Laboratory 13 May 2015

2. Satellite Aerosol Data Assimilation Navy Aerosol Analysis and Prediction System (NAAPS) Navy Variational Data Assimilation System for Aerosol (NAVDAS- AOD) Use of LIDAR to support aerosol modeling / LIDAR data assimilation Quantitative Aerosol Measurements from Satellite Obs. Dust Source Database FLAMBE Smoke Emissions Aerosol Effects on Visibility / Atmospheric Correction Ensemble Methods for Prediction of Aerosol Model Verification and Observing System Simulation Experiments NAVGEM Meteorology Fields Air Quality Applications

3. Satellite Aerosol Data Assimilation Aerosol Constellation Time Series The Present: MODIS+VIIRS The Very Near Future: Himawari, GOES-R, Sentinel

4. Why Does Assimilation-Grade AOD Matter? Aerosol analysis and forecasting requires AOD for assimilation Assimilation has specific requirements – Minimize outliers – Correct persistent bias – Quantify residual uncertainty Level 2 AOD products are not good enough – Correlated bias – Limited error characterization 15 June 2012Hyer AQAST 34

5. NRL’s process for QA/QC of new satellite AOD products: 5 stages 1.L2/L2 comparison to AERONET at full resolution 2.L2/L2 comparison to MODIS Generation of candidate L3 AOD 3.L3/L3 comparison to currently assimilated datasets Test runs of NAAPS+NAVDAS-AOD using new data 4.Model/Model comparison of analyzed aerosol fields using different AOD inputs 5.Model/AERONET comparison and model verification

6. Stage 1: L2 comparison to AERONET Paired dataset includes each AERONET AOD and each L2 satellite AOD within distance/time constraint For a full year of data, N=1.5M+ – Degrees of freedom are much less than that – Analysis at full L2 resolution is still mandatory PROS: – Build model of retrieval error at L2 resolution – Permits comparison to anything retrievable by AERONET, or to higher-resolution data (e.g. surface albedo)

7. Stage 1: L2 comparison to AERONET Utility: – Diagnosis of retrieval behavior – Understanding sources of retrieval uncertainty CONS – AERONET geographic coverage is limited; especially sparse over open ocean Note that Marine Aerosol Network (MAN) measurements are available, providing additional remote ocean data Examples – VIIRS-AERONET pairs 2/2013-12/2014 VIIRS ocean retrieval only QA=high only – Top: bias (solid line) and outliers (shaded regions) in VIIRS AOD vs NOGAPS/NAVGEM wind speed Bottom line: wind speed incorporated in VIIRS retrieval results in minimal bias Outliers can still be seen at high wind speed – Bottom: bias and outliers in VIIRS AOD as a function of AERONET fine mode fraction AERONET AOD > 0.4 Bottom line: trend is not useful, but outlier distribution is: high and low outliers in coarse-mode cases, mostly low outiers in fine-mode cases Low outliers consistent with scale mismatch “Compliance plots” are based on EDR- AERONET pairs. The above plots are based on VIIRS aerosol products from IDPS (only QA=‘High’) and AERONET Level 1.5 for February 2013-November 2014. Solid lines show the mean AOD bias in each bin; gray bars indicate the fraction of retrievals falling outside of an expected error of 0.05+0. 2  AERONET. (Top) VIIRS EDR shows a small trend with wind speed, with increasing positive errors at high winds, but no overall trend (MODIS c5 used a static wind speed in the retrieval, and so showed a positive trend as a function of wind speed due to whitecapping at the ocean surface). (Bottom) Comparison of VIIRS bias as a function of AERONET fire mode fraction (only pairs with tA>0.4 were used) indicates that the VIIRS EDR has better performance retrieving fine-mode aerosols. Extreme high and low values of fine mode fraction are generally in plumes near the source. Negative errors for these plumes largely reflect the disparity of scale between the satellite and AERONET.

8. Satellite Aerosol Data Assimilation MODIS Collection 6 over-ocean MODIS Collection 6 – upgraded MODIS L1-L2-L3 products Collection 6 changes include algorithm changes and new sensor calibration As of 4/21/2015, C6 processed by NASA and LAADS daily production LANCE C6 production pending release of MODIS land products QA/QC processing algorithms in development at NRL C4 C6 Collection 4 vs NOGAPS, from Zhang and Reid 2006 Collection 6 vs NOGAPS Collection 6 changed treatment of wind speed bias, but appears undercorrected– NRL will continue to empirically correct for windspeed effects

9. C6 Satellite Aerosol Data Assimilation MODIS Collection 6 over-ocean MODIS Collection 6 – upgraded MODIS L1-L2-L3 products Collection 6 changes include algorithm changes and new sensor calibration As of 4/21/2015, C6 processed by NASA and LAADS daily production LANCE C6 production pending release of MODIS land products QA/QC processing algorithms in development at NRL C5 Collection 6 has modified surface reflectance estimation, but biases remain– empirical correction must be recomputed Albedo thresholds in QA/QC will be revisited: Collection 6 includes “Deep Blue” alternate algorithm for use over bright surfaces (urban/desert)

10. Stage 2: L2/L2 comparison to MODIS Every MODIS-Aqua scene is checked to see if there is an overlapping VAOOO scene within 5 minutes For each MYD04 10km Level 2 footprint in the scene, VAOOO footprints whose centers fall within the MYD04 footprint are selected – Even if MODIS does not retrieve AOD for that footprint, it is included in the matched product Matched product includes all MYD04_L2 SDS For VAOOO, product includes: – AOT/EPSP for: Mean, all QA Mean, QA = Moderate+|High Nearest EDR retrieval – all QA|Moderate|High QA

11. Stage 2: L2/L2 comparison to MODIS Data Volume is large, e.g. Ocean case study: – 2/1/2013-2/1/2014: full year of matchup data used – 0-40S, 140-180W – N=3,366,835 MODIS footprints – 881,975 valid MODIS (187,902 QA=very good) – 1,162,688 valid VIIRS (QA=high [other QA levels not considered]) – MODIS QA values: 6% very good, 0% good, 20% marginal, 74% not retrieved – 131,232 footprints with both MYD04 and VAOOO highest QA retrievals

12. Stage 2: L2/L2 comparison to MODIS PROS: – Large volume of paired data permits deep analysis of retrieval behavior – factors affecting successful or failed retrievals can be examined directly – geographic distribution of pairs is wide (though not comprehensive), – understanding of MODIS retrieval behavior can be leveraged. Example: Wind Speed Again VIIRS (QA=High, ocean-only) and MODIS (C5, QA=VG, ocean-only) AOD as a function of NOGAPS/NAVGEM wind speed. MODIS C5 LUTs are generated using a 6ms-1 fixed wind speed VIIRS LUTs include wind speed effects, retrieval uses GFS winds. CONS: No ‘ground truth’

13. After Stage 2, a candidate DA-ready dataset can be generated Application of in-granule filters Application of additional filters – May involve external data sources Application of AOD corrections – May involve external data sources Calculation of AOD uncertainty – “instrument error” model based on L2/AERONET comparison – “representativeness error” based on aggregation (EDR retrievals within grid cell for gridded products) Generation of DA-ready outputs

14. Stage 3: L3 comparisons of gridded AOD products Compare VIIRS after QA/QC with MODIS after QA/QC Compare VIIRS+MODIS AOD to MODIS-only PROS: – Products can be evaluated separately and jointly – Effects on assimilation system can be inferred by directly testing coverage and consistency – Effects of data filtering can be quickly examined CONS: No ground truth. – This analysis is less useful for diagnosing the retrievals’ behavior.

15. Stage 3: L3 comparisons of gridded AOD products Examples TOP: AOD coverage impact of adding VIIRS to MODIS. Red colors indicate observation density increasing by more than 5x. NOTE: MODIS processing includes much stricter filters than (this version of) VIIRS. Also, MODIS observations below 30S are excluded from NAVDAS-AOD. BOTTOM: AOD difference, annual mean (VIIRS+MODIS)/(MODIS-only) Substantial addition of aerosol mass in tropics Some real, some probably not real Reduction of aerosol mass in North Atlantic/Pacific Microphysical differences? Needs further examination

16. After Stage 3, an aerosol analysis is generated with NAVDAS-AOD Cycling runs combine 6-hour NAAPS forecasts with NAVDAS-AOD analysis Can be easily run for multiple months/years All run properties are identical except AOD data input to NAVDAS-AOD – VIIRS-only – VIIRS+MODIS – MODIS-only (reflects current NAAPS operational configuration)

17. Stage 4: comparison of analyzed aerosol fields from NAAPS/NAVDAS-AOD An aerosol re-analysis is generated using NAAPS including cycling assimilation of one or both AOD datasets. This results in a continuous global field of aerosol properties reflecting the information content of the AOD datasets. PROS: – Allows examination of spreading of information in space and time – Allows examination of model consequences of AOD data choices CONS: – Analysis is weakly linked to AOD retrieval. – Analysis contaminated by biases in underlying model sources/sinks. – Effects of AOD values and AOD observation density convolved.

18. Stage 4: comparison of analyzed aerosol fields from NAAPS/NAVDAS-AOD Examples: Global maps of NAAPS analyzed aerosol optical depth show how assimilation of different data affects the model analysis. (A-Left) AOD analysis (VIIRS+MODIS)/(MODIS-only) Increase (fractional) around ITCZ Decrease (from a very low amount) over Antarctica Likely the result of removal of slow-sinking elevated aerosol in NAAPS Note: this map shows fractional change of AOD: absolute concentrations are low over Antarctica in all analyses. (B-Upper Right) Change in fractional contribution of dust to total AOD. Saharan outflow. NAAPS positive bias near source + more observations = reduced dust fraction (C-Lower Right) Change in smoke AOD fraction Southern Africa: positive bias in NAAPS + more observations = lower smoke fraction Seen over land because of recirculation A B C

19. Stage 5: Comparison of NAAPS analyzed AOD to AERONET NAAPS analyzed AOD is compared to AERONET for model verification This is our final determination if assimilation of AOD data is making NAAPS “better” or “worse” PROS: – Ground truth—a better match to AERONET is a better analysis/forecast (assuming you are not assimilating AERONET) CONS: – This analysis does not provide much insight into the details of the model processing and the AOD data.

20. Stage 5: Comparison of NAAPS analyzed AOD to AERONET EXAMPLE: Verification of VIIRS impact on NAAPS assimilation done using AERONET L1.5 R2 NAAPS w/MODIS (by AERONET site) R2 NAAPS w/MODIS+VIIRS (by AERONET site) MODIS+VIIRS better MODIS-only better NAAPS was run with the AOD assimilation package NAVDAS-AOD for the period 20130201-20140201, in two configurations: The first configuration was close to the current Navy operational configuration including assimilation of MODIS AOD using the NRL/UND Level 3 AOD product. The second configuration combined the NRL/UND MODIS AOD with a VIIRS AOD product based on the VAOOO EDR product from JPSS. This product used all in-granule quality flags as well as additional textural filtering. Analyzed AOD output from NAAPS was compared to AERONET L1.5 observations, and statistical comparison of NAAPS vs AERONET was compiled for each AERONET station. The balance of evidence for improvement of NAAPS analysis is based on improvement of statistical performance vs AERONET at a majority of stations. For the runs compared in this analysis, RMSE was reduced at 234 of 399 AERONET stations (not shown). The correlation (r 2 ) between the runs increased at 272 of 399 stations. The figures above show this as (bottom) a scatter plot of the single-station r 2 for each run, and (top) a map indicating the locations of stations where r 2 was significantly different between the two runs. Stations where r 2 was within 0.05 for the two runs are shown in gray; for other stations, the colors indicate the runs, and the size of symbols is proportional to the r 2. Thus, stations where the blue circle is larger than the red show better results for the VIIRS+MODIS run relative to MODIS-only. LEFT: Correlation coefficient r 2 for each AERONET station for VIIRS+MODIS and MODIS- only. Larger = better. (gray = small/no difference). RIGHT: scatter plot of per-station r2 of analyzed AOD. VIIRS+MODIS has better r 2 at 272 of 399 stations RMSE also tested: VIIRS+MODIS has lower RMSE at 234 of 399 stations

21. Upcoming Improvements Complete upgrade of operational inputs later this year – VIIRS over-ocean AOD – MODIS Collection 6 Dark Target – MODIS Collection 6 Deep Blue NAAPS now has a sweep run capability– potential for exploitation of late-arriving observations – MISR AOD? – CALIOP? – ISS CATS? Future directions: – geostationary AOD assimilation with Himawari and GOES-R – Nighttime observations of aerosol?

22. At NOAA Comprehensive Large Array-data Stewardship System (CLASS): Intermediate Product (IP) –0.75-km pixel AOT (550 nm); valid range: 0-2 Aerosol Particle Size Parameter AMI (Aerosol Model Information) quality flags Environmental Data Record (EDR) –6-km cell aggregated from 8x8 IPs filtered by quality flags AOT (10 M bands + 550 nm) APSP (over-land product is not recommended!) quality flags –0.75 km SM (not recommended) quality flags At NOAA/NESDIS/STAR Gridded 550-nm AOT EDR –regular equal angle grid: 0.25°x0.25° only high quality AOT EDR is used OceanAccuracyPrecision AOTRequirementSNPP/VIIRSRequirementSNPP/VIIRS  0.3 0.0800.0070.1500.041 ≥ 0.30.1500.0200.3500.144 LandAccuracyPrecision AOTRequirementSNPP/VIIRSRequirementSNPP/VIIRS  0.1 0.0600.0120.1500.058 0.1 - 0.80.0500.0160.2500.117 >0.80.2000.1860.4500.414 AOT EDR Product Maturity: Validated Status of SNPP VIIRS Aerosol Products

23. 23 Document links to ATBD, user’s guide, etc. Products page has a link to FTP site for data download Latency for daily global gridded product availability is 1-2 days NOAA Cal/Val web: VIIRS aerosol information and gridded AOT http://www.star.nesdis.noaa.gov/smcd/emb/viirs_aerosol/index.php Software to display VIIRS aerosol products and convert data to MODIS- like EOS HDF format are available for download