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Biases that matter for applications where long-term/large-area averages are used: clear-sky bias Levy et al (MODIS C5 data) Top: May 2003 monthly mean AOD using equal- day weighting (for days with data) Bottom: bias in monthly mean from using per- pixel weighting This result hints at clear/cloud differences: can we analyze them directly?
Citation preview
Integrating satellite fire and aerosol data into air quality models: recent improvements and ongoing
challenges
Edward HyerNaval Research Laboratory
6 January 2016
In This Talk
• Why data assimilation? An example using trend analysis
• Improvements on many different fronts:– New data sources– Improved data processing methods– Improved data assimilation methods
Biases that matter for applications where long-term/large-area averages are used: clear-sky bias
• Levy et al. 2009 (MODIS C5 data)
• Top: May 2003 monthly mean AOD using equal-day weighting (for days with data)
• Bottom: bias in monthly mean from using per-pixel weighting
This result hints at clear/cloud differences: can we analyze them directly?
Biases that matter for applications where long-term/large-area averages are used: clear-sky bias
• Zhang and Reid (GRL 2009) used a model with data assimilation (ocean only) to examine clear-sky bias
• 24-hour model forecast mean vs. model sampled at locations with usable MODIS data
• +/-15% bias for June-August (2006-2008) shown
• This study needs to be repeated with ocean+land assimilation Combined implication of these two studies:
1. trends in cloud cover can show up as trends in observed AOD;2. Data assimilation can yield a more accurate trend
Data Assimilation for Aerosol Optical DepthMODIS AODMODIS RGB
NAAPS “Natural” NAAPS + NAVDAS
•Approach used by Navy operational aerosol model shown at left•Similar approaches are now used in multiple global+regional modeling systems
Data Assimilation for Aerosol Optical DepthMODIS AODMODIS RGB
NAAPS “Natural” NAAPS + NAVDAS
•Three motivations for Aerosol Data Assimilation1. Analysis covers
the domain and is consistent with the (model) meteorology
2. Analysis can be used to initialize short-term forecast
3. Re-analysis provides the most complete description of the aerosol field over time
AOD trend based on global model reanalysis 2003-2013
AOD trend 2003-2013(AOD/year x 100) [shaded = 95% statistical significance]
• Lynch et al. paper now in Geophysical Model Development Discussions
• Analyzed weather fields + satellite data used to constrain precipitation
• AOD constrained by assimilation of MODIS+MISR
http://www.geosci-model-dev-discuss.net/8/10455/2015/gmdd-8-10455-2015.html
AOD trend based on global model reanalysis 2003-2013
AOD trend 2003-2013(AOD/year x 100) [shaded = 95% statistical significance]
• Lynch et al. paper has:• Very thorough description of
global aerosol model• Detailed discussion of all data
inputs and model processes• Comparative analysis of
reanalysis trends vs observation-only trend studies
http://www.geosci-model-dev-discuss.net/8/10455/2015/gmdd-8-10455-2015.html
What can make model/satellite hybrids more accurate?
• More data– Geostationary data already shown to be important for
mesoscale aerosol modeling• Saide et al. GRL 2014; Lee et al. ACP 2014• More to come! (e.g. Yumimoto et al., GRL, in review)
– Lidar gives vertical constraint• Campbell et al. JAMC 2015
– What about nighttime?• More accurate data• More consistent data• Better data assimilation approaches
Potential for nighttime AOD retrieval using Day/Night Band on VIIRS
• Aerosol causes blurring of city lights• There are many challenges to quantify this signal
Cape Verde, clear versus dusty skies
Clear Dusty
Potential for nighttime AOD retrieval using Day/Night Band on VIIRS
• Initial results show method has some skill
• Right: VIIRS DNB-derived AOD compared with AOD from HSRL at Huntsville, AL
• Paper in Atmospheric Measurement Techniques: McHardy et al., 2015
http://www.atmos-meas-tech.net/8/4773/2015/
Potential for nighttime AOD retrieval using Day/Night Band on VIIRS
• Lots of work to turn this into a product
• Will work best in medium-size cities
• If a viable product can be made, satellite data can help us analyze nighttime particulate air quality!
http://www.atmos-meas-tech.net/8/4773/2015/
Making more consistent AOD products from MODIS data
• MODIS AOD products use the instrument scan to aggregate pixels
• This leads to distortion and overlap at the swath edge
• Right: figure from Sayer et al. AMT 2015
http://www.atmos-meas-tech.net/8/5277/2015/amt-8-5277-2015.html
ODD SCANS
EVEN SCANS
ALL SCANS
Making more consistent AOD products from MODIS data
• Sayer et al. AMT 2015 tried to do better:
1. Aggregation by pixel proximity rather than by scan
2. Variable aggregation to get consistent footprint
http://www.atmos-meas-tech.net/8/5277/2015/amt-8-5277-2015.html
Variable Aggregation
Standard Aggregation
Making more consistent AOD products from MODIS data
• This method obviously makes better imagery
• But it will also help data assimilation– Better understood error of
representation– Simpler uncertainty model
• This method can (and should) be usefully applied to any polar orbiter data
http://www.atmos-meas-tech.net/8/5277/2015/amt-8-5277-2015.html
Variable Aggregation
Standard Aggregation
Capturing Meteorological Context in Aerosol Data Assimilation
• 1 August 2013• Dust front has clear
boundary• Air masses ahead and
behind dust from likely have important differences!
• AOD data for assimilation (colored) is sparse!
• How can we make the AOD analysis reflect these conditions?
Capturing Meteorological Context in Aerosol Data Assimilation
• Rubin et al. ACPD 2015• Top: analysis using 2-D variation
assimilation– ‘bullseyes’ in analysis
increment– Aerosol mass added ahead of
frontal boundary• Bottom: analysis using
ensemble Kalman filter– Uses 20-member ensemble of
meteorology and source magnitude perturbations
– Smoother analysis increment field
– Aerosol mass contained behind dust front
http://www.atmos-chem-phys-discuss.net/15/28069/2015/acpd-15-28069-2015.html
Capturing Meteorological Context in Aerosol Data Assimilation
• Rubin et al. ACPD 2015• Top: analysis using 2-D variation
assimilation– ‘bullseyes’ in analysis
increment– Aerosol mass added ahead of
frontal boundary• Bottom: analysis using
ensemble Kalman filter– Uses 20-member ensemble of
meteorology and source magnitude perturbations
– Smoother analysis increment field
– Aerosol mass contained behind dust front
http://www.atmos-chem-phys-discuss.net/15/28069/2015/acpd-15-28069-2015.html
This is a complex and computationally expensive method, but simulation of air quality in observation-poor areas (which include urban areas and coastal zones) requires some means of accounting for synoptic and mesoscale conditions when spreading information.
What can make model/satellite hybrids more accurate?
• More data– Geostationary – Lidar – Nighttime?
• More accurate data• More consistent data
– Spatial representation matters as we get to finer model resolution
– Consistent data simplifies error estimation for assimilation
• New datasets coming online bring dramatic improvements in all aspects of product quality
• Better data assimilation approaches– Incorporate dynamic
meteorology– A joint analysis of
aerosol and meteorology is the ideal; this turns out to be a very hard problem
• Thanks for your time!
Biases that matter for applications where long-term/large-area averages are used: persistent systematic bias
Example: MODIS Collection 6 AOD slope (using only AERONET AOD > 0.2) as a function of the fraction of AERONET AOD from the fine mode• This is a big improvement over Collection 5! (C5 Figure in Hyer et al. 2011)• But the gray bars (fraction of high/low outliers) tell a similar story:• In regions with predominantly coarse aerosols, expect MODIS C6 to underestimate
slightly; for fine-mode aerosols, expect a slight overestimate