This study developed a new three-dimensional variational data assimilation (3DVAR) system to assimilate MODIS aerosol optical depth (AOD) observations. The system analyzes the 3D mass concentrations of 14 aerosol species in the WRF-Chem model as part of a one-step minimization procedure. Assimilating AOD observations from MODIS improved forecasts of AOD and surface PM10 concentrations compared to the control run without data assimilation, as shown through comparisons with AERONET and CALIPSO observations of a major dust storm over East Asia in March 2010.

1. Three-dimensional variational assimilation of
MODIS aerosol optical depth: Implementation and
application to a dust storm over East Asia
1
Liu, Zhiquan, et al. "Three‐dimensional variational assimilation of MODIS aerosol optical depth: Implementation and
application to a dust storm over East Asia." Journal of Geophysical Research: Atmospheres 116.D23 (2011).
Presenter: Trieu Xuan Hoa
First Year Student, International Ph.D Program in Environmental Science and Technology
Advisor: Prof. Tang-Huang Lin
2018/03/22 R2-116

2. Outline
2
1. Introduction
2. Methodology
3. Results & Conclusions
4. My thought & Future work
5. References

3. Introduction
3
➢ Monitoring the distribution of atmospheric aerosols is crucial to
understanding how aerosols impact regional air quality and human
health.
➢ There are a lot of uncertainties in numerical modeling and prediction
of aerosol. Data assimilation (DA) can offer a mean to reduce
uncertainties of the model in the aerosol field.
➢ The main objective of this study is to developed a new algorithm for
AOD data assimilation. Therefore 3-D mass concentration of aerosol
species can be analyzed in a one-step minimization procedure.
➢ This is the first attempt to use individual aerosol species as analysis
variables in a truly 3DVAR (Three-dimensional variational) DA
system.

4. Introduction
4
STUDY AREA
Figure 1: The study area
located in East Asia with 27
km horizontal grid spacing.

5. Methodology
5
DATA USED
➢ AOD data from AERONET (AErosol RObotic NETwork) at
seven sites over East Asia. It provides real-time aerosol optical
depth;
➢ AOD data from MODIS sensors on board Terra and Aqua;
➢ Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)
instrument on board the Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO);
➢ Surface PM10 (particulate matter with diameters less than 10
mm) at 83 surface station across China.

6. Methodology
6
➢ The WRF-Chem (Weather Research and Forecasting – Chemistry)
model was used to predict the transport of aerosol and gaseous
chemical species in a limited area.
➢ The GOCART (Goddard Chemistry Aerosol Radiation and
Transport) aerosol module is available within the WRF-Chem model
and produces forecast for 14 aerosol species.
MODEL USED
• Hydrophobic and hydrophilic organic carbon (OC1, OC2)
• Hydrophobic and hydrophilic black carbon (BC1, BC2)
• Sulfate
• Dust in 5 particle-size bins [dust{1,2,3,4,5}]
• Sea salt in 4 particle-size bins [seas{1,2,3,4}]

7. Methodology
7
Formulation of 3DVAR aerosol data assimilation
Aerosol analysis variables and
the background error covariance statistics
Observation Operators
Application to a Dust Storm and Experimental Design

8. Methodology
8
1. Formulation of 3DVAR aerosol data assimilation
𝐽 𝑥 =
1
2
𝑥 − 𝑥𝑏
𝑇
𝐵−1
𝑥 − 𝑥𝑏 +
1
2
𝐻 𝑥 − 𝑦 𝑇
𝑅−1
[𝐻 𝑥 − 𝑦]
which measures the weighted distance of the model state x to the model “background” 𝑥𝑏 and
the observations y.
In our case for aerosol data assimilation:
x are 14 aerosol species mass concentration in 3D space.
𝑥𝑏 the “background” of x, short-term forecast from WRF/Chem.
y can be any aerosol-related observations (in our case, MODIS AOD and surface PM10).
H is “observation operator”, which transforms the model state to observation space.
The background error covariance B and observation error covariance R.

9. Methodology
9
2. Aerosol analysis variables and the background error covariance
statistics
➢ “NMC” (National Meteorological Center) method was used to compute aerosol
background error covariance (B) statistics using WRF-Chem model forecasts.
➢ For implementation of AOD DA, the 3-D mass concentrations of the 14 WRF/Chem
GOCART aerosol species within the entire domain and at all model levels comprised the
analysis variables in the GSI 3DVAR minimization procedure

10. Methodology
10
➢ MODIS AOD:
Use Community Radiative Transfer Model (CRTM) of Joint Center
for Satellite Data Assimilation (JCSDA) as the observation operator
3. Observation Operators
➢ The CRTM-AOD module was incorporated into the Gridpoint
Statistical Interpolation (GSI) system

11. Methodology
11
 Experimental Design:
 Application to a Dust Storm: A dust storm that started in Mongolia
blasted Beijing on 20 March 2010.
4. Application to a Dust Storm and Experimental Design
➢ No data assimilation (continuous WRF-Chem forecast)
➢ AOD data assimilation

12. 12
Results: Comparison to AERONET AOD
Figure 2: AERONET sites in (a)
Nanjing, (b) Jhongli city of Taiwan, (c)
Dongsha Island, (d) Hong Kong,
(e) Ubon Ratchathani, and (f) Bangkok.
Model output is hourly.
- Red line denotes the AERONET
observations
- Blue line denotes the DA
experiments
- Green line denotes the NoDA
experiments.

13. 13
Results: Comparison to AERONET AOD
Figure 3: Model 550 nm AOD forecasts
from (a, d) the NoDA experiment and (b,
e) the DA experiment overlaid with
CALIPSO path,
and (c, f) 532 nm AOD values along the
CALIPSO path from CALIOP
observations (red) and 550 nm model
AOD output from DA (blue) and NoDA
(green) experiment.
Figures 11a–11c are valid around 17:00
UTC 19 March and Figures 11d–11f
around 20:00 UTC
20 March.
CALIPSO AOD

14. Results: Comparison to Surface PM10
14

15. Conclusions
15
➢ The GSI 3DVAR DA system was expanded to assimilate MODIS
AOD observations using 3-D mass concentrations.
➢ Promising results for both dust storm and general air-quality
applications.
➢ Simultaneous assimilation of surface PM10 and MODIS AOD
produced better analysis and forecast of PM10 and AOD.
➢ One-step 3DVAR method of assimilation MODIS AOD permits
concentrations of individual aerosol species.

16. My thought and Future work
16
➢ This method brought better effect than some previous studies, but
the further investigation is needed to more effectively use available
aerosol-related observations. Therefore, developing a more advanced
method considering the forecast bias is desired.
➢Doing research about Assimilate Multi AOD products and aerosol
related observations to build aerosol simulation model.

17. References
17
Liu, Zhiquan, et al. "Three‐dimensional variational assimilation of
MODIS aerosol optical depth: Implementation and application to a
dust storm over East Asia." Journal of Geophysical Research:
Atmospheres 116.D23 (2011).

18. 18
Thank you!

19. Results
19
Fig 5: Scatter plots of observed
concentrations of OC (a), NO3
(b), SO4 (c) and NH4 (d) versus
simulated concentrations during
01:30 to 02:30 PDT
initialisations.

20. Results
20
- The first step: Analyzed from aerosol observations
- The second step: Partitioned using a postprocessing procedure into
3-D mass concentrations of different aerosol species, making
assumptions regarding vertical distribution and relative ratio of
individual species’ mass to total aerosol mass.
The newly developed 3DVAR aerosol DA system uses individual
aerosol species of the WRF/Chem built-in GOCART module as
“control variables.” Therefore, 3-D mass concentrations of the aerosol
species are analyzed in a one-step minimization procedure, obviating
the need for a second-step postprocessing required by previous
studies.