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