Sand and Dust Monitoring in RA II - · Sand and Dust Monitoring in RA II . ... (Kosa in Japanese)....
Transcript of Sand and Dust Monitoring in RA II - · Sand and Dust Monitoring in RA II . ... (Kosa in Japanese)....
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Xiang Fang
National Satellite Meteorological Center ,CMA
Sand and Dust Monitoring in RA II
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Outline
• Dust products in RA II
• Validation of multi-satellite products
• Action Proposed
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Operational Dust Monitoring and
Forecasting in RAII
AOD, et al.(MTSAT Himawari-8; COMS;
FY-2 FY-4; FY-3 ;EOS/MODIS)
(Obs. With Satellite)
PM10, AOD (Skyradiometer, Lidar),
Visibility, et al. from Weather Chart
(Obs. In situ.)
Operational Forecast
Model
Forecast Application
Issuance for Public
Assimilation
(Model)
Dust Information
Sharing
Validation
Dust products in RA II
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Dust observation in situ
Minamitorishima
Yonagunijima
Ryori
Gwangdeoksan
Ullengdo
Sokcho
Cheorwon
Baengnyeongdo
Ganghwa
Munsan Seoul
Gwanaksan
Chuncheon Daegwallyeong
Andong
Yeongwol Uljin
Daegu Ulsan
Suwon Gyeokgryeolbi-do
Anmyeondo
Cheonan Cheongwon
Chupungnyeong
Gunsan
Gwangju Heuksando
Jindo
Gosan
Jinju Gudeoksan
Jeonju
PM Mass Concentration Data-KMA PM Mass Concentration Data-JMA
AOD
PM10 ,et al.
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Visibility
Routine SDS Observational and visibility Data -Asia
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Lidar
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Current operational dust forecast systems
Country Model Area
China CUACE/Dust Asia
Japan MASINGAR-mk2 Global
Korea UM-ADAM2 Asia
Mongolia MGL-ADAM E. Asia
Now, Only CUACE/Dust model has assimilated satellite product (IDDI).
So, it is essential for improving satellite data assimilation.
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Dust products from satellite data
Orbit Satellite Instrument Product Waveband
Polar Orbit
FY-3B/C
TOU AI(Absorption Aerosol
Index ) UV
MERSI AOD, Angstrom exponent VIS,NIR
VIRR Dust Score VIS,NIR,IR
FY-3C VIRR
Dust Optical Thickness IR
Dust Particle Effective Radii IR
Dust Column Density IR
GEO Orbit
FY-2D/E/F/G S-VISSR IDDI IR
MTSAT-2 JAMI AOD VIS
Himawari-8 AHI AOD, Angstrom VIS,NIR
KOMPSAT GOCI AOD, AI VIS
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MTSAT-2 AOD Surface weather observation in Japan
Blue circles are weather stations where dust
was visually observed by human eyes.
n.b. AOD cannot be estimated over cloudy area.
Aerosol optical depth (AOD) product is used for monitoring dust events.
MTSAT AOD for Asian dust monitoring
Dust Event: 29 Apr – 2 May 2011
Satellite Products from JMA
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MTSAT AOD for Asian dust monitoring
Aerosol optical depth (AOD) over the ocean estimated from MTSAT data.
Method: Generate a lookup table which relates MTSAT visible band’s reflectivity to aerosol property using radiative transfer simulation. Estimate aerosol property from MTSAT data using the lookup table. MTSAT band: VIS (0.55 - 0.90 μm)
Ångström exponent (proxy for particle size) is fixed
Aerosol type is assumed to be Asian dust. Not optimized for other types of aerosol (e.g. haze)
User: JMA routinely uses MTSAT aerosol product for monitoring Asian dust events (‘Kosa’ in Japanese).
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Himawari-8 AOD for Asian dust monitoring
Aerosol optical depth (AOD) and Ångström exponent (proxy for particle size) to be estimated from Himawari-8/AHI data. Ångström exponent only over the ocean.
Method: Generate a lookup table which relates Himawari-8/AHI visible and near-infrared bands’ reflectivities to aerosol property using radiative transfer simulation. Estimate aerosol property from Himawari-8/AHI data using the lookup table. AHI bands: 0.64, 0.86 μm (ocean), 0.64, 2.25 μm (land)
Aerosol type is assumed to be Asian dust. Not optimized for other types of aerosol (e.g. haze)
Validation plan: Comparison with surface observation of AOD
User: JMA will routinely use Himawari-8 aerosol product for monitoring Asian dust events (‘Kosa’ in Japanese).
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COMS
Satellite Products from KMA
Asian dust Index May 2011 12
Instrument Product Area
MI Aerosol Index (AI)
Aerosol Optical Depth (AOD)
GOCI AOD
Fine-Mode Fraction(FMF)
Single scattering Albedo (SSA)
Aerosol Type
KMA has developed the new sand
and dust detection algorithm using 4
channels of COMS/MI: 3 IR
channels with VIS channel, to
improve the previous algorithm
which uses 2 IR channels. KMA is
testing and validating the
performance of this algorithm with
surface observation data.
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Retrieval results from GOCI :
Dust case (2012.04.27)
13
GOCI RGB
MODIS AOD (2 times per day)
GOCI AOD (1hr interval)
GOCI FMF
MODIS FMF GOCI Aerosol Type
(AOD > 0.3)
GOCI SSA
0.0 2.0 0.85 1.0 0.0 1.0
HA, fine MA, fine NA, fine Mixture Dust NA/coarse
FMF 0.6 ~ 1.0 0.6 ~ 1.0 0.6 ~ 1.0 0.4~0.6 0.1~0.4 0.1~0.4
SSA 0.85~0.90 0.90~0.95 0.95~0.99 0.85~0.99 0.85~0.95 0.95~0.99
Yellow Dust
MI AOD (15min interval)
0.0 2.0
Low FMF (~0.3): coarse particle
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Validation results of AODs from GOCI and MI • AERONET level 2.0 AOD data (2011.03.01-2013.02.28)
- Spatial co-location : within 25km at each AERONET site
- Time co-location : ±30min AERONET at each satellite center measurement time
14
AERONET lev2.0 Data
[110 0E – 150 0E, 20 0N-45 0N]
2012. 03. 01 ~ 2012. 05. 31
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• NOAA and MODIS data are used for detection of SDS in the Environmental Information Center, National Agency for Meteorology and Hydrology (NAMEM), Mongolia
Dust storm by MODIS
Satellite products from Mongolia
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IDDI: Infrared Difference Dust Index with infrared window channel could be ratio to dust density.
China Satellite – Geo orbit products
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2013-01-20 17
3.14-3.15, 2009
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Forecasting application in CMA – DUST-IDDI
29 Apr. 2011, FY-2D IDDI product
Forecasting result of GRAPRS-CUACE/Dust without DUST-IDDI
Developed DUST-IDDI numerical assimilation system with FY-2 IDDI product,
DUST-IDDI is coupled with dust numerical forecasting system named as GRAPES-CUACE/Dust,
could provide 72 hours forecasting results in Asian region as real-time services.
Forecasting result of GRAPRS-CUACE/Dust with DUST-IDDI
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20100321
AOD product over land from FY-4 (proxy data AQUA/MODIS)
21 Mar. 2010 RGB figure AOD@550nm
26 Mar. 2010 RGB figure
21 Mar. 2010
26 Mar. 2010
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• RGB figure monitoring
China Satellite – Polar orbit products
The dust weather event occurred in southeast
Mongolia , the middle of Inner Mongolia , the
Bohai Sea and the Korean Peninsula
caused by Mongolia cyclone during February 21-
23, 2015.
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AOD product from VIS and NIR spectral band based on look-up table method, sensitive to weak dust storm
under clear pixels.
20090315 20090316 20090317
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23 Apr.2010-3,May
24 Apr.2010 FY2D IDDI
Absorption Aerosol Index (AAI) using 331nm and 360nm, FY3 TOU present the semi-quantity information of absorption aerosols.
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Dust identification product - dust score based on multi-threshold method using 12 dust identification indexes for monitoring.
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Global dust monitoring with FY-3A/VIRR. Result shows in binary image.
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Quantitative retrieval from TIR
using infrared split-window channel, sensitive to strong dust storm and less
affected by surface type, could be used in dust source region.
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20130309,FY-3A/VIRR RGB
AOD overlay RGB
Test result:
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Related to the SCOPE-Nowcasting Pilot Project 4, JMA had shared the data in situ and MTSAT AOD products with CMA. CMA had finished the validation of the products of MTSAT, FY-3, FY-4 using same validate method and shown the result in AMSUC-5.
Satellite L2 Data
MTSAT : AOD from MTSAT-1R or MTSAT-2 supplied by MSC/JMAM
Spatial resolution: 0.25°*0.2°(lon*lat)
Case area range: 17-52°N, 114-160°E.
Case time period: 16-19 Mar. 2009, 20-24 Mar. 2010, 23-28
Apr. 2010, 29 Apr - 02 May 2011.
FY-3/MERSI Spatial resolution: 1km.
Case area range: 17-52°N, 110-160°E. (Product: global)
Case time period: 16-19 Mar. 2009, 20-24 Mar. 2010 , 23-28
Apr. 2010 , 01-02 May 2011 (FY-3A)
FY-4 ( using AQUA/MODIS as proxy data) Spatial resolution: 1km.
Case area range: 17-52°N, 100-125°E.
Case time period: 19-26 Mar. 2010 , 23-28 Apr. 2010
Ground-based Data AERONET
The level 2 data which are cloud screened and quality
assured are used in the validation.
AOD matchup:
Spatial window :
0.25°*0.2°(lon*lat) for MTSAT ;
0.25°*0.25°for FY-3/4
Temporal window : ±20min
AOD match up:
Spatial window :
0.25°*0.2°(lon*lat) for MTSAT ;
0.25°*0.25°for FY-3/4
Temporal window : ±20min
Validation of multi-satellite products
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Land
Scatter plot of AOD from FY-3/MERSI and FY-4 (proxy) against AERONET
AOD from FY-4 using MODIS as proxy data is overestimated over Land. There is no
systematic bias for AOD from FY-3 when AOD smaller than 1.5 .
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Ocean
Scatter plot of AOD from FY-3/MERSI and MTSAT against AERONET
When compared with AERONET, AOD from MTSAT is underestimated over ocean.
AOD from FY-3 tends to be overestimated at low value while underestimated at high
value.
RMSE of AOD from these 3 satellite products during the process of dust storm is
nearly 0.2 .
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Conclusions about Validation
• The AOD product over ocean from MTSAT has better distribution pattern and good quality
for monitoring floating dust in the dust storm process. The AOD algorithm for FY-4 (using
MODIS as proxy data) is credible, which is proved by the validation result. FY-4 is expected to
provide valid AOD product for dust storm monitoring.
• Limited by the observation frequency, the AOD product from FY-3 can only provide partial
information of the dust storm transmission. The large area affected by sun glint over ocean is
against the continuity of AOD distribution. From the RMSE, it shows that the AOD data
quality of FY-3 is a little poorer than MTSAT over ocean and FY-4 using MODIS as proxy data
over land. FY-3 AOD product is suitable for the aerosol climate background estimation.
• This validation is preliminary work for the limited samples.
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The first session is invited to comment on the comparison between dust products of different satellites. It is further invited to consider the following suggestions:
• Enhancing the comparison between dust products of different satellites. The validation and comparison of Himawari-8 and FY-4 AOD should be done in the future.
• Establishing a website for Asian dust monitoring which should include dust products from all countries, and provide access to users.
• Assimilating the satellite products into operational dust forecast models is improved .
Action Proposed
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Thanks for your attention!