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Aerosol Observability and Data Assimilation Investigations in Support of
Atmospheric Composition Forecasts
Jeffrey S. Reid, Walter Sessions*, Peng Lynch*, Edward J. Hyer
Naval Research Laboratory
7 Grace Hopper Ave., Stop 2
Monterey, CA 93943-5502
Phone: (831) 656-4725 Fax: (831) 656-4769 email: email@example.com
*CSC Inc., Monterey, CA
Document Number: N0001413WX20162
In this final year of a 3 year effort by the Naval Research Laboratory Code 7544 to enable Navy
aerosol forecasting to take full advantage of available data feeds through the investigation of
fundamental atmospheric composition observability, we find that the next generation of data feeds and
associated technology present a number of challenges and opportunities which require attention. These
include: 1) The transition from NASA EOS sensors to the next generation of diversified operational
and near real time data sources; 2) The move to a constellation approach for global atmospheric
composition observing, 3) The expected near real time availability of US and European lidar data; 4)
The enhanced availability of surface and aircraft observations; and 5) Increased aerosol model
demands for such applications as joint surface-atmosphere retrievals, directed energy (DE), and
intelligence, surveillance, and reconnaissance (ISR). This increase in the number of potential data
sources, coupled with further efficacy demands, creates an imperative need to understand the nature of
global aerosol observability and the development of realistic uncertainties for composition
observations, retrievals and model products. Outstanding problems facing the community relate to
such issues as observation bias, representativeness, and information spreading for the myriad of
sporadic data sources available. To meet this need, in this grant we investigated the use of these
diverse flows of data using ensemble and ensemble data assimilation technologies to be incorporated
into the Navy Aerosol Analysis and Prediction System (NAAPS)/Navy Variational Analysis Data
Assimilation-Aerosol Optical Depth (NAVDAS-AOD) framework. This work also led to the
development of the world’s first quasi-operational global multi-model ensemble, drawing from the
world’s operational data centers. This is the nucleus of a potential operational multi-model ensemble.
As with other consensus-like products, such an ensemble is likely to remain top ranked.
Our overarching goal is to investigate applied science aerosol observability issues related to the proper
determination of observed product efficacy and information spreading. To this end the core
methodology involves the development and application of an ensemble version of NAAPS and its
subsequent ensemble based data assimilation system. At the same time, we have initiated the
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Aerosol Observability and Data Assimilation Investigations in Support of
Atmospheric Composition Forecasts
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Naval Research Laboratory,? Grace Hopper
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development of a multi-model ensemble across major centers including European ECMWF, Japanese
JMA, NASA GMAO, NOAA NCEP and the Spanish BSC. Subcomponents of our effort include:
1) The development of an ensemble-based NAAPS system which makes use of the FNMOC
NOGAPS ensemble data set: Here we will use the base NAAPS model, including the data
assimilation system and run in parallel with the 20 NOGAPS fields, further perturbed with 10
to 30 additional perturbations to source and sink functions. This version of NAAPS will be
prepared for eventual transition to 6.4 and FNMOC if the Navy so desires.
2) The development of key verification metrics of models and observations alike: Based on initial
simulation of the ensemble, we will investigate appropriate error metrics for Navy satellite and
model products that account for the non-Gaussian, spatially correlated nature of biases present
in the environment. This objective includes the development of probabilistic tools for forecasts.
These can then be applied to satellite observations for model verification.
3) Investigation into the nature of available satellite and in situ observations including their flow
dependant correlation lengths for use in NAVDAS-AOD: The optimal mix of meteorological
and source/sink perturbations for the NAAPS ensemble for given computational constraints
will be identified. A particular emphasis will be on the application of these methods to aerosol
4) The incorporation of NAAPS in the NCAR Data Assimilation Test-bed (DART): Once the
NAAPS ensemble model is running quasi-operationally and is well understood, we will port
NAAPS to the DART package at NCAR. This is the first step in developing our ensemble-
based data assimilation system.
5) The creation of a research grade ensemble-based data assimilation system: Based on our
experience with the DART system, we anticipate being able to have the ability to harvest and
develop a robust ensemble based data assimilation system. This will be used to supplement the
existing 3DVAR aerosol data assimilation system currently in use. Emphasis in this system
will be on sparse or isolated observations such as provided by lidars, sun photometers and
surface reports. In addition, ensemble co-variability studies will be carried out to address
phenomenological questions such as modes of atmosphere/aerosol coupling.
6) Collaborate with other centers to derive an aerosol consensus for severe events: It has been
shown that for many extreme events, consensus forecasting is a top perfonner. In this objective
we wish to investigate the applicability of performing multi-model consensus of GMAO,
ECMWF and other centers to investigate the predictability and extent of significant aerosol
As outlined in previous reports, in FY12 and 13, we developed the basic architecture of both the
NAAPS single and ICAP-multi-model ensembles. Work perfonned in FY13 focuses on the application
of these systems for better understanding aerosol observability. The spatial covariance fields are
generated in an ensemble version of NAAPS (henceforth eNAAPS). Nominally eNAAPS is run quasi-
operationally using the 20 NOGAPS ensemble members generated by FNMOC every 12 hours.
Additionally, other members, based on perturbations to the NAAPS source function, are created.
Results from the initial stages of analysis led to a series of upgrades for eNAAPS sources and
microphysics (such as a new pollution specie to replace sulfate) which are available for the
detenninistic operational version once the NAVGEM porting is complete.
Collinear with the development of eNAAPS has been the continued development of the multi-model
ensemble. Based on members of the International Cooperative for Aerosol Prediction (ICAP), the
ICAP Multi-model ensemble (or ICAP-MME) is the world’s first quasi-operational aerosol ensemble,
and is already seeing significant use across centers. There are four multi species models is ICAP-
MME: ECMWF-MACC, JMA-MASINGAR, NASA GMAO-GEOS-5, and a locally run NRL NAAPS
next generation. In addition to these, for the dust species only, we also incorporate NOAA NCEP’s
NGAC and the Barcelona Supercomputing dust models. Data for all models now goes back 18
months, with the time series for the core models going back 28 months.
Tools and display systems were also developed for analysis of ensemble products. Basic tools for
analyzing ensemble variably, correlation length scales, and the most commonly employed “spaghetti
plots” and ra nk histograms were largely adapted from existing Matlab code into Perl, Python and IDL
framework currently used in 7544 and FNMOC. Online verification products based on AERONET,
satellite data and own analyses are being developed. In a similar manner to eNAAPS, we cooperate
with other meteorological centers over the globe which are developing global aerosol prediction
capabilities. By pulling data from such centers as ECMWF, JMA, NASA GMAO, and NOAA NCEP,
we created a first of its kind global aerosol multi-model ensemble. For comparison purposes, products
generated are similar in nature to eNAAPS.
For ensemble based data assimilation we ported NAAPS into the NCAR Data Assimilation Research
Testbed (DART) in years one and two. DART hosts numerous ensemble-based data assimilation tools
including the core components of an EnKF system. Both NOGAPS and COAMPS have already been
ported to DART, and ensemble based COAMPS dust source functions studies are already underway
through a joint project with Dr. Hansen. Hence, in house knowledge already exists to expedite this
process. Included in this budget is additional travel money so that NRL developers can spend the
required time at NCAR to make this port happen.
Finally, additional verification tools need to be generated to evaluate improvements to the model. This
not only allows model parameterizations, but also the satellite data assimilated into the models to be
investigated and improved. In particular, technologies need to be created that can quickly evaluate
new or updated satellite products, such as those being generated by VIIRS.
In FY 13, the following tasks were completed:
1) eNAAPS was fully exercised. Most importantly, in conjunction with Dean Hegg (University of
Washington) we ported the current NAAPS sulfate model to a more generic “pollution” specie
to account for the more dominant secondary organic aerosol loadings in the atmosphere. Once
NAVGEM development and tuning is complete, we feel the NAAPS ensemble is ready for
transition to 6.4.
2) We performed an extensive analysis of the ICAP multi-model ensemble for a one year period
of perfonnance. We rigorously compared ICAP-MEE and its single model components against
a series of continuous (e.g., RMSE, MAE etc.) and threat score based metrics. Regions of
common success and difficulty over the globe were mapped.
3) Publications on eNAAPS and ICAP-MME were initiated, and at the end of the fiscal year
rough drafts of findings will be complete
Research results for this project can be divided into areas of ensemble development, verification, and
data assimilation. In FY11 & 12, focus was on development and data assimilation. For FY13, focus has
been on verification and analysis, with additional effort on the creation of a pollution specie in
NAAPS, and the tuning of the dust and smoke parameters.
Pollution development and tuning: Figure 1 demonstrates before and optimized versions of eNAAPS,
based on NOGAPS deterministic meteorology. The largest single improvement was due to the
incorporation of secondary organic aerosol mass into the sulfate specie, creating a “fine mode pollution
specie.” Areas of consistent difficulty for NAAPS including East Asia, the Indian sub-continent, and
the eastern United States, have been dramatically improved. The mechanism uses maps of isoprene,
terpene and aromatic emissions, largely from the MACC and MEGAN source functions, to develop a
new source for fine mode particles. This source is not static, but is based on environmental conditions
and the pre-existing aerosol load. Thus, this formulation does not technically add any additional
aerosol or gas species to be carried in the model. In addition to this, based on the previous ensemble
Kalman filter work, the dust source map and biomass burning emission have been improved. In Asia,
the Indian sub-continent and sub-Saharan Africa, RMSE values were improved by 40%. Mean
innovations to account for the lacking source function for fine mode organic aerosol particles were also
Ensemble development and analysis: In year three we examined a number of case studies of severe
dust and biomass burning events. An example of this is depicted in Figure 2, where both the eNAAPS
“spaghetti plot” and several other ICAP-MME members are presented. In all of these cases, while a
dust front was predicted by all model members, magnitudes differed greatly. Diversity in the models
could be tracked back to a dust source in the Taklimakan desert. Aerosol data assimilation in and of
itself does not help in this situation, as the dust is generated in a desert area where aerosol optical
thickness retrievals are rare and inaccurate. Further, during transport, dust was masked by a frontal
cirrus shield. However, using the NRL dust enhancement product we could determine semi-
quantitatively which ensemble members were performing correctly. In an operational setting,
forecasters could use the ensemble in this way with Navy imagery to accurately forecast a wave of dust
passing over the Korean Peninsula and the Sea of Japan.
Verification: Both eNAAPS and ICAP MME underwent extensive verification and analysis in FY13.
From this, a common climatology was generated and areas of common difficulty among models were
identified. Examples of both these aspects are presented in Figure 3. In the upper set of panels, total,
fine, and coarse mode AOT medians and geometric standard deviations are presented for the ICAP-
MME ensemble average. Inset as dots on this figure are AERONET verification values. Overall, we
find that all models from all centers tend to underestimate AOT globally against AERONET. Further,
while all models reproduce major aerosol features (e.g., dust plumes in Africa and Asia, major biomass
burning regions, etc.), there are at times very large differences in mean amplitude and minimum AOT.
Particularly large differences exist in in the mid-latitude oceans, presumably due to differences in sea
salt source functions. In both cases, these are important findings that will lead to future work to further
improve aerosols source/sink functions and data assimilation.
We expect much of this work to be of immediate use to the warfighter. Just as the current NRL
aerosol page is frequently used in the METOC community, we expect these ensemble products to be of
immediate applicability. To begin with, in key portions of the globe we can generate dust, smoke, or
pollution “Spaghetti plots” for each of the meteorological members (Figure 4) or independent models.
These can also be shown spatially as areas of high variability (Figure 2). In these areas we will
collaborate with Dr. Hansen and Dr. Whitcomb who are working on METOC impacts, decision aids,
We have begun discussions with FNMOC to transition eNAAPS to operations. We are currently
waiting for the next set of revisions of NAVGEM to take place. Once the NOGAPS ensemble
transitions to the NAVGEM ensemble, we anticipate a final round of model optimization to take place
before we initiate 6.4 work.
This project is tightly coupled to a number of ONR 32 programs, particularly those of Professor
Jianglong Zhang at the University of North Dakota. Our primary transition partner is Douglas
Westphal, who is principal investigator on the Large-Scale Aerosol Model Development (PI: Doug
Westphal). New data-processing and visualization systems are being adapted for aerosol research
through the COAMPS-On Scene (COAMPS-OS Cr ) 1 IVPS charts program (PI: John Cook). We have
also begun working with Jim Hansen on his ONR-funded project for the use of ensemble data
assimilation in the prediction of atmospheric constituents.
Toth, T. D., J. Zhang, J. Campbell., J. S. Reid, et ah, 2013, Investigating elevated Aqua MODIS
aerosol optical depth retrievals over the mid-latitude Southern Oceans through intercomparison
with co-located CALIOP, MAN, and AERONET datasets, J. Geophys. Res, in press
Eck, T. F., B. N. Holben, J. S. Reid, et ah, (2013), A seasonal trend of single scattering albedo in
southern African biomass burning particles: Implications for satellite products and estimates of
emissions for the world's largest biomass burning source, J. Geophys. Res., 118,
Johnson, R. S., Zhang, J., Hyer, E. J., Miller, S. D., and Reid, J. S. (2013), Preliminary investigations
toward nighttime aerosol optical depth retrievals from the VIIRS Day/Night Band, Atmos. Meas.
Tech., 6, 1245-1255, doi:10.5194/amt-6-1245-2013.
COAMPS-OS® is a registered trademark of the Naval Research Laboratory.
Khade, V. M., J. A. Hansen, J. S. Reid , and D. L. Westphal, (2013), Ensemble filter based estimation
of spatially distributed parameters in a mesoscale dust model: experiments with simulated and
real data, Atmos. Chem. Phys., 13, 3481-3500, doi:10.5194/acp-13-3481-2013.
Shi, Y., Zhang, J., Reid, J. S ., Hyer, E. J., and Hsu, N. C., (2013), Critical evaluation of the MODIS
Deep Blue aerosol optical depth product for data assimilation over North Africa, Atmos. Meas.
Tech. 6, 949-969, doi:10.5194/amt-6-949-2013, 2013.
Eck, T. F., B. N. Holben, J. Schafer; T. Berkoff, J. S. Reid , et ah, (2012) Observed enhancements in
aerosol optical depth in the vicinity of cumulus clouds during DISCOVER-AQ , American
Geophysical Union Fall Meeting, Dec. 3-7 San Francisco, CA, A13K-0328.
Geiszler, D. A., J. Cook, S. Chen, M. Frost, P. Harasti, C. Hutchins, Q. Zhao, J. S. Reid, D. Martinez,
R. A. Allard, T. J. Campbell, T. A. Smith, and L. McDermid, (2013), COAMPS-OS®: An
atmospheric/ocean/wave prediction system for the coastal environment. American meteorological
Society Annual Meeting, January 5-12, Austin TX.
Hyer, H. J„ J. S. Reid , J. Zhang, C. A. Curtis, (2013), Satellite aerosol observations for air quality:
matching the scales of observations and applications, American Geophysical Union Fall Meeting,
Dec. 3-7 San Francisco, CA, A11L-01.
Hyer, E. J„ J. Zhang, J. S. Reid , W. R. Sessions. C. A. Curtis, and D. L. Westphal, (2013), Operational
assimilation of aerosol optical depth from NPP VIIRS in a global aerosol model, American
meteorological Society Annual Meeting, January 5-12, Austin TX.
Johnson, R. S., J. Zhang, J. S. Reid, E. J. Hyer, S. D. Miller, (2012), Impact of VIIRS on Multi-Sensor
Aerosol Optical Depth Data Assimilation, American Geophysical Union Fall Meeting, Dec. 3-7
San Francisco, CA, A13J-0318.
Shi, X., J. Zhang, J. S. Reid , B. Liu, R., Deshmukh, (2013), Critical evaluation of cloud contamination
in MISR aerosol product using collocated MODIS aerosol and cloud products, American
Geophysical Union Fall Meeting, Dec. 3-7 San Francisco, CA, A13J-0310.
Zhang, J., J. S. Reid, J. R. Campbell, E. J. Hyer , R. S. Johnson, Y. Shi, and D. L. Westphal, (2013),
Sensitivity of aerosol climate forcing to the existing satellite observations, American
Meteorological Society Annual Meeting, January 5-12, Austin TX.
- 0.1 - 0.01 0 0.01 0.1
Mean AOT Innovation (550 nm)
2011 Natural RunAOTs
0.5 0.7 1.0 1.2
Aerosol Optical Thickness (550 nm)
Figure 1. Original and optimized Aerosol Optical Thickness (AOT) and mean AOT innovation with
the original NAAPS, and an optimized version which included the incorporation of secondary
organic aerosol in the model.
Figure 2. An example of differences within eNAAPS and the ICAP Multi-Model Ensemble (ICAP-
MME) for a single 72 hour dust event in November 2012.
Coarse Fine Total Coarse Fine Total
Median AOT (550 nm)
AOT Geometric Standard Deviation
0 0.04 0.08 0.16 0.32 0.64 1.28
Pointwise AOT Maximum and Minimum (550 nm)
Figure 3. Bi-seasonal sets of statistics for the ICAP Multi-model ensemble for a 1 year test period.
Upper set: the seasonal ICAP-MME median and geometric standard deviation of total, fine and
coarse mode AOT. Lower set: a point-wise comparison of the maximum and minimum mean aerosol
optical thickness (AOT) drawn from the ensemble members. That is, each point on the map draws
from the average maximum and minimum from the ensemble members, thus showing the maximum
average or minimum average AOT for any point. Shown are total, fine, and coarse mode AOT.
Inset are averages derived from AERO NET