This presentation focuses on MERRA Aerosol Reanalysis (MERRAero) which is the first aerosol reanalysis produced at GMAO. This presentation involve an overview of MERRAero. The evaluation of MERRAero absorption and the evaluation of MERRAero Surface PM 2.5 will also be discussed

Buchard, Virginie

Colarco, Peter

da Silva, Arlindo

Darmenov, Anton

Govindaraju, Ravi

Randles, Cynthia

NASA Technical Reports Server

Evaluation of MERRAero	

(MERRA Aerosol Reanalysis) 
	

Virginie	  Buchard,	  
(virginie.buchard@nasa.gov)	  
Arlindo	  da	  Silva,	  Cynthia	  Randles,	  	  Peter	  Colarco,	  Anton	  Darmenov,	  Ravi	  Govindaraju	  	  
	  	  
JCSDA	  workshop,	  14	  May	  2015	  
https://ntrs.nasa.gov/search.jsp?R=20160004700 2019-08-31T03:51:32+00:00Z
Introduction	  
	

•  MERRA Aerosol Reanalysis (MERRAero) 	

is the first aerosol reanalysis produced at GMAO :	

§  based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols, 	

§  assimilation of bias corrected Aerosol Optical Depth (AOD) from the MODIS sensor 
on both Terra and Aqua satellites.	

	

Introduction	  
	

•  MERRA Aerosol Reanalysis (MERRAero) 	

is the first aerosol reanalysis produced at GMAO :	

§  based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols, 	

§  assimilation of bias corrected Aerosol Optical Depth (AOD) from the MODIS sensor on 
both Terra and Aqua satellites.	

•  Two different ways of validating MERRAero: 	

	

1)  Absorption:	

	

•  Effect of aerosols on climate system depends :	

§  on the total aerosol concentration,	

§  on the radiative or optical properties of the particles.	

       	

        In MERRAero :  absorption is not constrained by observations.	

Ø   We used OMI/AURA AI measurements and AAOD retrieval as independent validation 
for MERRAero absorption.	

Introduction	  
2)  Surface concentration:	

	

•  PM2.5 fine particulate matter with diameter less than 2.5 um:	

•  Negative effects on regional air quality and on human health,	

•  Ways to monitor:	

•  Global monitoring networks -> but offer sparse geographic coverage,	

•  Satellite measurements, especially AOD -> large spatial coverage but limited to cloud free 
conditions, uncertainties on AOD retrievals and how do you relate  PM2.5 to AOD,	

•  Combination of satellites retrievals and model,	

•  Data assimilation of AOD in modelà MERRAero.	  	  
	

	

Ø  We used EPA-AQS and IMPROVE surface PM2.5 measurements to assess the quality of 
simulated surface PM2.5 in MERRAero.	

Outline	  
•  MERRAero overview	

•  Evaluation of MERRAero absorption	

•  Evaluation of MERRAero surface PM2.5	

•  Conclusions	

Summary	  of	  GEOS-­‐5	  	  
Reanalysis	  Activities	  
6 
Name	   Nominal	  
Resolution	  
Period	   Aerosol	  
Data	  
Available	  
MERRA-­‐1	   50	  km	   1979-­‐present	   NONE	   now	  
MERRAero	   50	  km	   2002-­‐present	   MODIS	  C5	   now	  
FP	  for	  Inst.	  
Teams	  
50	  km	   1997-­‐	   MODIS	  C5	   In	  progress	  
NCA	   25	  km	   2010-­‐11	   MODIS	  C5,	  MISR	   Now	  
MERRA-­‐2	   50	  km	   1979-­‐present	   AVHRR,	  MODIS	  
C5,	  
MISR,AERONET	  
Summer	  2015	  
MERRA-­‐2	  
Dynamical	  
Downscaling	  
12.5	  km	   2000-­‐2015	   AVHRR,	  MODIS	  
C5/C6,	  MISR,	  
AERONET	  
Q4	  2015	  
Not	  discussed	  here	  
MERRAero	  Overview	  (1)	  
Feature	   Description	  
Model	   GEOS-5 Earth Modeling System (w/ GOCART)	

Constrained by MERRA Meteorology (Replay)	

Land sees observed precipitation (like MERRALand)	

Driven by QFED daily Biomass Emissions	

Aerosol	  Data	  
Assimilation	  
Local Displacement Ensembles (LDE)	

NNR AOD: Neural Net Retrievals	

•  Based on MODIS “Level 2” reflectances 	

•  Trained on AERONET Retrievals	

Stringent cloud screening	

Period	   mid 2002-present (Aqua + Terra)	

Resolution	   Horizontal: nominally 50 km	

Vertical: 72 layers, top ~85 km	

Aerosol	  Species	   Dust, sea-salt, sulfates, organic & black carbon	

Analysis	  Splitting	  
MERRAero	  Overview	  (2)	  
MERRAero	  Overview	  (3)	  
Analysis	  Splitting	  with	  Ensembles	  
o  Construct	  perturbation	  
ensembles	  by	  means	  of	  
isotropic	  displacements	  
around	  gridbox	  
o  Weigh	  each	  ensemble	  
member	  by	  its	  ﬁt	  to	  2D	  
AOD	  analysis	  
o  For	  eﬃciency,	  perform	  
the	  AOD-­‐to-­‐mixing	  ratio	  
calculation	  in	  1D	  
 
First Guess 
“Truth” “Truth” 
MERRAero	  Overview	  (4)	  
Local	  Displacement	  Ensembles	  (LDE)	  
MERRAero	  Overview	  (5)	  
Independent	  AERONET	  AOD	  evaluation	  	  
Evaluation	  of	  MERRAero	  
Absorption	  
AI	  observed/simulated	  comparison	  (1)	  
AI	  :	  Qualitative	  indicator	  of	  the	  presence	  of	  absorbing	  aerosol	  (	  AI	  >	  0	  )	  
AI	  is	  derived	  from	  the	  change	  in	  the	  spectral	  dependence	  of	  the	  back-­‐scattered	  UV	  
radiances	   induced	  by	  aerosols	   relative	  to	  the	  Rayleigh	  scattering	  between	  354	  and	  
388	  nm	  [Torres	  et	  al.,	  1998,	  2007].	  
AI =  100

log10
✓
IObs354
Icalc(Ray)354 (R
Obs
388⇤)
◆ 
AI	  depends	  on	  :	  	  
•  aerosol	  concentration	  
•  aerosol	  layer	  height	  
•  aerosol	  optical	  properties	  
MERRAero simulated radiances and AI: Radiative transfer code VLIDORT	

                                                                                                          [Spurr et al., 2002, 2006].	

§  Globally	  MERRAero	  AI	  captures	  major	  features	  but	  :	  
•  Tend	  to	  underestimate	  over	  North	  America,	  South	  America	  and	  more	  
importantly	  in	  the	  Southern	  Africa	  Biomass	  burning	  region.	  
§  MERRAero	  AAOD	  greater	  than	  OMI	  AAOD	  at	  388	  nm:	  
•  Saharan	  dust	  region	  à	  conﬂicted	  diagnosis	  
OMAERUV product  [Torres et al., 2007]	

AI	   AAOD	  
AI	  observed/simulated	  comparison	  (2)	  
RESULTS	  (baseline	  simulation)	  
Baseline  
simulation : 	  
Fraction of MERRAero AAOD from DUST aerosol > 0.7	

AI	   AAOD	  
Saharan	  dust	  	  
OMAERUV product 	

 [Torres et al., 2007]	

AI	  observed/simulated	  comparison	  (3)	  
Baseline  
simulation : 	  
Fraction of MERRAero AAOD from DUST aerosol > 0.7	

AI	   AAOD	  
Saharan	  dust	  	  
Updated  
simulation : 	  
AI	  observed/simulated	  comparison	  (3)	  
AI	   AAOD	  
More	  details	  can	  be	  found	  in	  Buchard	  et	  al.,	  ACP,	  2015	  	  
AI	  observed/simulated	  comparison	  (4)	  
RESULTS	  (updated	  simulation)	  
Evaluation	  of	  MERRAero	  Surface	  
PM2.5	  
à	  Carbonaceous	  from	  anthropogenic	  and	  biomass	  
burning	  sources	  	  
à	  Dust	  from	  local	  sources	   à	  High	  values	  of	  sulfate	  in	  the	  Ohio	  River	  Valley	  due	  to	  
abundance	  of	  SO2	  emitted	  by	  several	  power	  plants	  	  
à	  Too	  much	  penetration	  in	  land	  
Evaluation	  of	  Surface	  PM2.5	  (1)	  
MERRAero	  Annual	  mean	  PM2.5	  (ug/m3)	  	  	  
Nitrate	  is	  also	  a	  component	  of	  PM2.5:	  nitrate	  particle	  formation	  is	  not	  considered	  in	  MERRAero.	  	  	  
Evaluation	  of	  Surface	  PM2.5	  (2)	  
AERONET	  
EPA	  AQS	  
SUBURBAN	  
RURAL	  
IMPROVE	  stations	  à	  mainly	  RURAL	  &	  more	  spread	  across	  the	  country.	  
EPA	  AQS	  stations	  à	  Eastern	  region	  
AOD	  (AERONET)	  &	  PM2.5	  measurements	  (IMPROVE	  and	  EPA	  networks)	  
-­‐>	  Comparisons	  by	  regions	  
NW	  
SW	  
NC	  
SC	  
NE	  
SE	  
AERONET	  comparisons:	  	  
	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  	  AOD	  550	  nm	  Seasonal	  variability	  
	  
à	  MERRAero	  AOD	  seasonal	  cycles	  follows	  AERONET	  seasonal	  cycles	  	  	  	  	  
NE	  
SW	  
NC	  
SC	   SE	  
NW	  
Evaluation	  of	  Surface	  PM2.5	  (3)	  
PM2.5	  -­‐	  	  Impact	  of	  AOD	  assimilation	  
Seasonal	  and	  regional	  RMS	  of	  the	  diﬀerences	  (model	  -­‐	  observation)	  for	  MERRAero	  and	  the	  GEOS-­‐5	  model	  without	  
AOD	  assimilation.	  MERRAero	  and	  GEOS-­‐5	  sampled	  at	  IMPROVE	  and	  EPA-­‐AQS	  stations.	  
à	  Positive	  impact	  on	  surface	  PM2.5	  with	  RMS	  generally	  lower	  for	  MERRAero.	  
SW	   SC	   SE	  
NE	  NC	  NW	  
Evaluation	  of	  Surface	  PM2.5	  (4)	  
PM2.5	  Seasonal	  variability	  
•  IMPROVE	  and	  EPA	  PM2.5	  highly	  correlated,	  except	  in	  the	  Western	  regions.	  	  
•  IMPROVE	  à	  lower	  values	  of	  PM2.5à	  mostly	  rural	  stations.	  
•  MERRAero	  PM2.5	  better	  correlated	  with	  IMPROVE	  stations.	  
•  Compared	  to	  EPA:	  better	  agreement	  during	  the	  summer;	  bias	  during	  the	  winter.	  
Total	  PM2.5	  
NE	  
SW	  
NC	  
SC	   SE	  
NW	  
Evaluation	  of	  Surface	  PM2.5	  (5)	  
For	  quality	  control:	  buddy	  check	  of	  Dee	  et	  al.	  (2001)	  
PM2.5	  –	  per	  species	  
NW	   NE	  
Ammonium	  Sulfate	   Ammonium	  Nitrate	  Ammonium	  Nitrate	  Ammonium	  Sulfate	  
OC	   OC	   BC	  BC	  
dust	   dust	  Sea	  salt	   Sea	  salt	  
Evaluation	  of	  Surface	  PM2.5	  (6)	  
à	  Lack	  of	  nitrate	  and	  underestimation	  of	  source	  emissions	  of	  OC	  over	  suburban	  areas.	  
•  MERRAero	  :	  Assimilation	  of	  bias	  corrected	  MODIS	  AOD.	  It	  does	  not	  assimilate	  any	  data	  capable	  
of	  directly	  constraining	  its	  vertical	  placement	  or	  composition.	  
•  Evaluation	  of	  Absorption:	  
–  Absorption	  AOD	  is	  not	  assimilated,	  
•  Good	  agreement	  for	  dust	  after	  tuning	  of	  optical	  properties.	  
•  Evaluation	  of	  PM2.5:	  
–  Comparisons	  with	  independent	  AERONET	  AOD:	  
•  Seasonal	  AOD	  variability	  captured	  
–  Comparisons	  with	  PM2.5	  observations:	  
•  Better	  agreement	  with	  IMPROVE	  (mostly	  rural	  stations),	  magnitude	  and	  seasonal	  cycle	  are	  
generally	  in	  good	  agreement,	  particularly	  for	  the	  summer.	  
•  Some	  discrepancies	  during	  the	  winter	  are	  species	  dependent,	  in	  particular	  lack	  of	  nitrate	  and	  
underestimation	  of	  OC.	  MERRAero	  vertical	  structure	  might	  also	  be	  part	  of	  these	  discrepancies.	  
•  Future	  work:	  
–  Development	  of	  an	  Ensemble	  Kalman	  Filter	  based	  scheme	  for	  the	  aerosol	  data	  assimilation.	  
	  
Conclusions	  
MERRAero	  Vertical	  structure	  	  
HSRL	  and	  modeled	  extinction	  proﬁles	  during	  DISCOVER-­‐AQ	  and	  SEAC4RS	  ﬁeld	  campaigns.	  	  	  
Houston	  (August-­‐September	  2013),	  
California	  (January-­‐Februray	  2013),	  
Baltimore-­‐Washington,	  D.C.	  (July	  2011)	  
SEAC4RS	  (August-­‐September	  2013)	  
Evaluation	  of	  Surface	  PM2.5	  (7)	  


Evaluation of MERRAero (MERRA Aerosol Reanalysis)

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