Alessandrini, Stefano

Anguelova, Magdalena

Arellano, Avelino F.

Darmenov, Anton S.

Kumar, Rajesh

Monache, Luca Delle

Raman, Aishwarya

English

NASA Technical Reports Server

Challenges in developing better observational 
constraints and models for aerosols : Emerging ideas for 
design and use of future observing systems
Aishwarya Raman1,2
Avelino F. Arellano1, Luca Delle Monache5, Stefano Alessandrini4, Rajesh Kumar4, Anton S. Darmenov1, 
Magdalena Anguelova6
NASA GSFC/GMAO1, SSAI 2, University of Arizona3, NCAR4,
Scripps Institution of Oceanography at UC San Diego 5
U.S. Naval Research Laboratory6
https://www.sciencemag.org/news/2016/08/nasa-aircraft-probe-
namibian-clouds-solve-global-warming-puzzle
1
https://ntrs.nasa.gov/search.jsp?R=20200002182 2020-05-24T04:35:42+00:00Z
NASA Decadal survey, ACCP SCIENCE AND Applications: Science objectives 
https://science.nasa.gov/science-pink/s3fs-
public/atoms/files/ACCP_SATM_Rel_E_TAGGED.pdf
Aerosol interaction with 
meteorology
2
2
Aerosol modeling  : Status and future
Observations Aerosol modeling
Data Assimilation Evaluation and 
developmentObs + Model : 
Calculate 
increments
• Multi-platform 
multi-spectral aerosol 
retrievals from satellites 
• In-situ aerosol mass and 
optical properties
• Best possible 
meteorology : Multi-model 
ensemble
• Aerosol mass, size distribution and 
optical properties 
• Point by point 
observation 
uncertainty
• Choice of  aerosol variables
• Clear sky radiance
• Use of  aerosol variables not used in 
assimilation – process-based 
improvements to models
3
4Observation constrained modeling
Good representation of  atmospheric aerosol 
sources and sinks
AOD
Dust AOD
Smoke AOD
Angstrom exponent
Depolarization ratio
PM total
Speciation
AERONET
MAN
Lidar profiles 
Dual-pole 
radar
Passive microwave 
retrievals
5Observation constrained modeling (Contd)
Observations
Obs
uncertainty 
point by point
Model errors 
Model
Observation 
operator
Direct assimilation 
(e.g. AOD)
Inversion for 
emission
Identify key 
mechanisms for 
aerosols
Improvements to 
parameters [Johnson et al. 
2019] 
6Aerosol regional modeling
7Case study of dust storms in Arizona : Mile 
high wall of dust hits Phoenix
8Aerosol optical properties, dust concentrations, 
and transport  
A. Raman et al., 2014 
PM10
PM2.5
Wind MODIS AOD (Terra + Aqua)
9Aerosol trends over the dusty 
SouthWest
Change in TERRA AOD Standardized anomaly over a decade shows 
statistically significant decrease in anomalies over dusty hotspots and 
North American Monsoon (NAM) alley for pre-monsoon and monsoon.  
Dust NAM
PRM, MON, POM
A. Raman et al., 2016
10
Modeling of Arizona dust storms
285 > 
310 
K
Conceptual Diagram of Cold Pool 
Formation and Movement 
WRF-Chem Simulated Cold Pool
(2011/07/05 8pm Local Time)
Strong SE winds 23-25ms-1 
and cold pool 
development !
WRF-Chem 1.2 km inner 
domain simulation of  
July 5, 2011 haboob 
- Decrease in 2m 
temperature
- Downbursts and 
surface divergence 
- Dust uplift
Lader, G., A. Raman et al., 2016 NOAA Tech memo NWS-WR 290 
11
Modeling of Arizona dust storms (Contd)
HYSPLIT ensemble trajectories of  dust transported 
from Phoenix to Northwestern AZ. Also seen from 
CALIPSO aerosol classification
12
Limitations and future direction
𝐹 = 𝐶𝑆𝑠𝑝 𝑢10 − 𝑢∗𝑡 𝑢10
2 𝑖𝑓 𝑢10 > 𝑢∗𝑡
S is the erodibility, sp is the fraction of  soil composition, u10 is the 10m wind 
speed, u*t is the threshold wind friction velocity, and C is a tunable constant
Missing dust sources  
using real time NDVI 
Lack of  
observations
Fine scale meteorology 
processes, dust impacts
Dual-pole radar reflectivities
and hydrometeor classification 
GEO+LEO, MISR 
plume height and 
spherical , non-spherical 
AOD
Convective scale  modeling, using wind 
probability instead of  wind speed
13
Black carbon  aerosols and uncertainties due to lack of  
observations
Improving co-emitted species such as 
Carbon-monoxide and Nitrogen Oxides to 
estimate Black carbon
14
• Reductions in BC, PM2.5 (PM
2.5 μm or less in diameter)
emissions from on-road
diesel engines have not been
significant (e.g. Dallmann, T. R. and
Harley, R. A, 2010).
• Large uncertainties in BC
mixing state in aerosol
models.
Black carbon aerosols : Requirements for 
observations
Using co-emitted species
Co-emitted gases : Utility in source attribution 
15A. Raman and A.F. Arellano., 2017
16
Co-emitted gases : Utility in Elemental carbon 
enhancement trends
2000-2007 2008-2015 2000-2007 2008-2015
A. Raman and A.F. Arellano., 2017
17
Co-emitted gases : Utility in BC emission 
trends (Contd)
2000-2007 2008-2015 2000-2007 2008-2015
A. Raman and A.F. Arellano., 2017
18
Take home messages
🔴 CO and NOx provide a novel pathway to improve observational 
coverage for sparsely measured , co-emitted aerosol species like 
Black Carbon.
🔴 Such approach can significantly improve characterization of  
black carbon aerosol sources from satellite retrievals due to 
ample measurements of  CO and NOx from space. 
🔴 Developing tracers for regional and sectoral CO in addition to 
total CO can improve source attribution and emission fluxes for 
black carbon aerosol in models. 
19
Combining models and observations
Bouttier and Courtier, 1999
Djalalova.I et al. 2015 
Analog Ensemble Forecasting
20
Analog Ensemble Forecasting 
Delle Monache et al. 2011 
Delle Monache et al. 2006
Analog Ensemble 
Forecasting 
Analog Kalman 
Filter operates on 
the analog set of  
forecasts and the 
predicted errors  in 
the historical 
forecasts
21
od
Predictor weights correspond to AOD-predictor 
correlation in the model
(a) (b) (c) (d)
(e) (f) (g) (h)
TPW PM10 PM2.5 WINDSPEED
Fig 5. Correlat ion values of AOD and predictors . (a) AOD Vs Total Precipitable Water, (b) AOD Vs PM10 ,  (c) AOD Vs PM2.5 , and (d) AOD Vs Horizont al windspeed at  
the surface collocated for Terra overpass t ime. (e) - (h) Sim ilar  to (a) - (d) but  for Aqua over pass t ime
A. Raman et al., in prep
22
Analog Ensemble Forecasting : Results  
(a) (b) (c)
(d) (e) (f)
Fig 10. Mean bias (Model - Observat ion)  for June - august , 2012 from  WRF-Chem  (a,d), ANEN (b,e), and KFAN (c,d) for points 
collocated with  Terra (top) and Aqua (bot tom) respect ively. 
23
Analog Ensemble Forecasting : Meteorology 
driven aerosols
JUN JUL AUG JUN JUL AUG
WE WE
CE CE
EA EA
SO SO
WRF-Chem
ANEN
KFAN
Aqua MODIS
WRF-Chem
ANEN
KFAN
TERRA MODIS
Fig 16. Boxplot  of AOD from  MODIS, WRF-Chem  , ANEN, and KFAN for June-August , 2012
Midline in the boxplot  represents the m edian of AOD , top and bot tom  lines of the box 
represents 25th and 75th percent ile, and t op and bot tom  lines outside t he box represents 
m axim um  and m inim um  values of AOD that  are not  out liers. The boxplots are shown for 
different  EPA regions and out liers are not  shown here. 
JUN JUL AUG JUN JUL AUG
WE WE
CE CE
EA EA
SO SO
WRF-Chem
ANEN
KFAN
Aqua MODIS
WRF-Chem
ANEN
KFAN
TERRA MODIS
Fig 16. Boxplot  of AOD from  MODIS, WRF-Chem  , ANEN, and KFAN for June-August , 2012
Midline in the boxplot  represents the m edian of AOD , top and bot tom  lines of the box 
represents 25th and 75th percent ile, and t op and bot tom  lines outside t he box represents 
m axim um  and m inim um  values of AOD that  are not  out liers. The boxplots are shown for 
different  EPA regions and out liers are not  shown here. 
24
Analog Ensemble Forecasting : Meteorology 
driven aerosols
Fig 15. RMSE of AOD for WRF-Chem , ANEN, and KFAN calculated with reference 
to Terra (left ) and Aqua (right ) ret r ievals for June, July, and August  of 2012. Points are grouped
into East  (EA), West  (WE), Southern (SO), and Cent ral (CE) regions based on EPA zones.     
(a) (b)
(c) (d)
(e) (f)
(g) (h)
RMSE for model and 
analog forecasts for 
summer for Western 
U.S
Terra Aqua
Change in RMSE is ~ 30% for June and July. Smaller reduction for 
August is due to the higher relative humidity (RH) during this period 
across U.S and the model errors in accurately predicting the total 
precipitable water and effects of  higher on RH on model AOD. 
25
Take home messages
🔴 Analog Ensemble forecasting with a combination of  Kalman Filter 
provides improvement in AOD in seasons when AOD is mostly driven 
by wind and aerosol emissions. 
🔴 Despite the strong dependence on observations, analogs are heavily 
driven by the choice and quality of  the predictors which is evident 
from the case of  U.S East coast where during the month of  August, 
errors in model predictions of  precipitable water and wet scavenging 
prohibits improvement to AOD. 
26
Aerosol global modeling  
27
Implementing a process driven sea salt aerosol 
emission  parameterization in GEOS
Sea salt emissions =
𝑓 𝑊ℎ𝑖𝑡𝑒𝑐𝑎𝑝 𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛
f  (Size distribution of  sea 
salt particles)
Whitecap fraction :  Area of  the ocean surface covered by active wave breaking  
(Stage A or active ) and mature foam (stage b). 
National Aeronautics and Space Administration
Whitecap models : Wind dependence
Brumer et al., 2017
W = 𝑎𝑈10
𝑛
(e.g. Monahan, 1971)
𝑊 = 𝑎𝑢∗
𝑛
(e.g. Wu, 1988)
𝑊 = 𝑎 𝑈10 − 𝑏
𝑛
(Goddijn-Murphy et al. (2011))
W = 𝑓 𝑊𝑖𝑛𝑑 ℎ𝑖𝑠𝑡𝑜𝑟𝑦
(e.g. Callaghan et al., 2008)
At a given wind speed, W variability is ~1-2 orders of  magnitude 
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
28
National Aeronautics and Space Administration
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
o Windsat W : 1o x 1o multi –frequency 
retrievals [Anguelova et al., 2019]
o 10 GHz includes more active W and 37 
GHz include fresh + mature (foam)  W
o GEOS-UMWM 
o 0.5o x 0.5o resolution runs for 
2014 replayed to MERRA-2
o Wind, sea-ice, air density input 
to UMWM from GEOS
Observation constrained modeling
29
National Aeronautics and Space Administration
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
W parameterization development
NDBC Wind/wave 
collocated stations 
Co-locate Windsat
Steepness, Re, 
dissipation rate, 
peak, air-sea 
temperature 
difference,
peak wave 
velocity
Model fitting
Sample GEOS-UMWM at NDBC
Test against 
independent 
Windsat W 
30
A. Raman and A. Darmenov , in prep
National Aeronautics and Space Administration
Global Modeling and Assimilation Office
gmao.gsfc.nasa.govGMAO
Variability with wind speed
▪ Whitecap decreases for 
higher windspeed. 
▪ In order to capture this 
behavior in models, 
additional terms based on 
wind stress were added to 
the Seastate W model for 
wind speed > 20 m/s. 
31
A. Raman and A. Darmenov , in prep
32
Aerosols as a part of the Earth System : What do 
we need from models and observations? 
➢Observations of  vertical profiles of  aerosol concentrations 
and number distribution. 
➢Point by point data uncertainties from satellite retrievals. 
➢Ensemble Forecasting of  aerosols
➢Use of  aerosol products from satellites to improve model 
parameterizations in addition to direct assimilation of  
AOD [e.g. R.Kahn , 2020] 
33
Acknowledgements
Thanks to NASA ACMAP , NASA ROSES, Modeling and Analysis 
Program, GMAO core, NCAR graduate visitor program for research 
funding and University of  Arizona for computing resources. 
Thanks to Magadalena Anguelova, NRL for Windsat retrievals.  
Thank you
34
Extra slides
35
Importance of meteorology for aerosol analogs
(a) (b)
(c) (d)
Fig.13 Skillscore for analog predict ions of AOD for June-August , 2012. Skill score is 
calculated here using Mean Square Errors in com parison with MODIS Terra and Aqua ret rievals. 
(a , b)  KFAN and ANEN AOD est im ated with reference to WRF-Chem AOD 
Mean Square Errors for Terra overpass. (c, d)  sim ilar to (a, b) for Aqua overpass. 
  
  
Negative skill 
scores in the East 
coast:
- Strong 
correlation with 
meteorology, in 
particular 
precipitable water
- Impact of  wet 
scavenging 
processes. 
36
Using PM2.5/CO    ratios in WRF-Chem to 
improve PM2.5 concentrations
37
A. Raman et al. IGAC 
2014
38
39


Challenges in Developing Better Observational Constraints and Models for Aerosols : Emerging Ideas for Design and Use of Future Observing Systems

Challenges in Developing Better Observational Constraints and Models for Aerosols : Emerging Ideas for Design and Use of Future Observing Systems

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