SPIE Remote Sensing 2013 - Dresdsen 1 APPLICATION OF MAIAC HIGH SPATIAL RESOLUTION AEROSOL...

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SPIE Remote Sensing 2013 - Dresdsen

APPLICATION OF MAIAC HIGH SPATIAL RESOLUTION AEROSOL RETRIEVALS

OVER PO VALLEY (ITALY)Barbara Arvani(1),(*), R. Bradley Pierce(2), Alexei I. Lyapustin(3), Yujie Wang(4), Sergio Teggi(1), Grazia Ghermandi(1)

(1) University of Modena and Reggio Emilia, Italy(2) NOAA/NESDIS Advanced Satellite Products Branch, Madison (WI), USA(3) NASA Goddard Space Flight Center, code 613, Greenbelt, Maryland 20771 USA(4) University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA(*) Cooperative Institute for Meteorological Satellite Studies, Madison (WI), USA

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• Outline:• Objectives • Area of interest: Po Valley Domain• Dataset

• PM10 ground stations• MODIS: MYD04 and MAIAC AODs• AERONET AOD• Planetary Boundary Layer height (ZPBL)

• MYD04 and MAIAC test using AERONET data• Vertical distribution of aerosols: ZPBL correction • PM10 – AOD: Monthly trend • PM10 – AOD: Correlation• PM10 – AOD: Effect of ZPBL • Conclusion


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• What

Ground measurements: • Particulate Matter with aerodynamic diameter less than 10 m (PM10) obtained by regional monitoring agencies in the Po Valley, North of Italy

Aerosol Optical Depth (AOD): • Obtained from the standard MODIS Aqua Collection 5.1 (MYD04 product), with a spatial resolution of 10 x 10 km2

• By MAIAC (Multi-Angle Implementation of Atmospheric Correction) algorithm, with a spatial resolution of 1 x 1 km2

Study of correlation between Particulate Matter measured by ground monitoring stations and aerosol information retrieved by MODIS sensor


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• Why• PM is one of the major pollutant affecting air

quality in urban areas• Ground stations:

• Continuous over time • Accurate• Sparse: often do not provide an accurate

estimate of the spatial distribution of PM10

• MODIS AOD: • Defined on regular grid • If a correlation AOD PM10 would be

defined the AOD grid could be used to improve the spatial distribution obtained from ground measurements

• E.g.: additional PM10 values could be obtained directly from AODs

• E.g.: AOD could be used for geostatistical interpolation as correlated variable (co-kriging)

Isolines of concentrations

Ground station

MODIS data


• Where Po Valley• Northern Italy• Extends about 400 km in the West-East

direction and about 100 km in the North-South direction.

• The largest industrial, trading and agricultural area of Italy

• High population density (20 mil. inhab.)• Area with the most severe air pollution

problems in the Country• Alpine and Apennines chains act as a

barrier to winds blowing from Northern Europe and the Mediterranean favoring the stagnation of pollutants


• Ground station PM10 data• 126 PM10 monitoring stations

installed by the regional environmental agencies (ARPA) divided into 4 administrative regional districts:

• Piemonte: 27 stations• Lombardia: 59 stations • Emilia Romagna: 37 stations• Veneto: 3 stations

• PM10 concentrations (µg/m3) are provided as daily average value

• Period studied: 2012


• MODIS AOD: MYD04 product• MYD04: MODIS Aqua Collection 5.1

(Level 2) [Remer, L. A., Tanre, D., Kaufman, Y. J., Levy, R., & Mattoo, S. (2006). Algorithm for remote sensing of tropospheric aerosol from MODIS: Collection 005. National Aeronautics and Space Administration.]

• LAADS Web Service

• AOD: 0.55 µm• Grid cells: 10 x 10 km2 • Time step: daily • Data: depending on cloud coverage• Period studied: 2012


• MODIS AOD: MAIAC product• MAIAC: new algorithm recently implemented by

Alexei I. Lyapustin and his team group of research

• MAIAC retrieval: aerosol parameters over land at a spatial resolution of 1 x1 km2 simultaneously with BRDF parameters

• AOD is computed at 0.47 m• Details and discussion of the algorithm can be

found in literature:Lyapustin, A., Wang, Y., Laszlo, I., Kahn, R., Korkin, S., Remer, L., Levy, R., and Reid, J. S.,

“Multiangle implementation of atmospheric correction (MAIAC): 2. aerosol algorithm,” Journal of Geophysical Research: Atmospheres 116(D3) (2011).

Lyapustin, A., Wang, Y., Hsu, C., Torres, O., Leptoukh, G., Kalashnikova, O., and Korkin, S., “Analysis of MAIAC dust aerosol retrievals from MODIS over North Africa,” AAPP — Physical, Mathematical, and Natural Sciences; Vol 89, SUPPLEMENT NO 1 (2011): ELS XIII Conference –, – (2011).

• Cloudy (from Cloud Mask field) and Snow-Water pixels (from Land-Water-Snow field) are excluded • Period studied: March 1st – October 16th 2012.


• Co-location• Comparison: MYD04 and MAIAC

data must be co-located with ground stations

• The Nearest Neighbor method with fixed search radius was used:

• MYD04: R = 0.2o (20 – 25 km)

• MAIAC: R = 0.02o (2.0 – 2.5 km)

• MAIAC: for each location there could be multiple values per day due to the MODIS swaths overlapping:

• the daily mean of the nearest neighbour values was considered. 9

R


• MODIS AOD: AERONET Test

Ispra

Modena

λ = 0.55 µm λ = 0.47 µm

• MODIS AOD by MYD04 and MAIAC have been tested by comparing them with the AOD measured by the AERONET station of Ispra.

• Comparison: method suggested by Chu et al.(2002):

Chu, D. A., Kaufman, Y. J., Ichoku, C., Remer, L. A., Tanra, D., and Holben, B. N., “Validation of modis aerosol optical depth retrieval over land,” Geophysical Research Letters 29(12), MOD2–1–MOD2–4 (2002).

• Period: March 1st – October 16th

• Good level of correlation in both cases

• Similar test done using the AERONET station of Modena are under progress.


• AOD represents the columnar content of aerosol• PM10 is the ground concentration

Direct comparison of AOD and PM10 maybe problematic

• Assumption: most of the aerosols are distributed in the Planetary Boundary Layer

The ratio AOD/ZPBL should be more appropriatefor this analysis

• Vertical distribution

AOD

PBL

PM10

ZPBL (km)

• Tsai, T.-C., Jeng, Y.-J., Chu, D. A., Chen, J.-P., and Chang, S.-C., “Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008,” Atmospheric Environment 45(27), 4777 – 4788 (2011).

• Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., and Kumar, N., “Satellite remote sensing of particulate matter and air quality assessment over global cities,” Atmospheric Environment 40(30), 5880 – 5892 (2006).


• ZPBL• ZPBL: values used in this study derive from the 6-hourly, 0.5° x 0.5°analysis products from the NOAA National Centre for Environmental Prediction (NCEP), Global Data Assimilation System (GDAS)

Winter Summer


• ZPBL: CALIPSO• Coarse spatial resolution of NCEP ZPBL: alternative

sources • ZPBL retrieved from the CALIPSO satellite

measurements• Winker, D. M., Pelon, J., McCormick, M. P., “The calipso mission: Spaceborne

lidar for observation of aerosols and clouds,” Proc. Spie, 4893 (1), 11 (2003).• Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt,

W. H., Young, S. A., “Overview of the calipso mission and calipso data processing algorithms,” J. Atmos. Oceanic Technol. 26, 2310–2323 ( 2009).

SummerCALIPSO: Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation

First comparison: CALIPSO ZPBL data are systematically higher than NCEP ZPBL

This study has just begun: too early to draw conclusions


• PM10-MODIS comparison: Po Valley Monthly trend• Large amount of data: Po Valley monthly trends

1. Po Valley average values of PM10 and AOD per each day

2. Monthly means of these values (MYD04: 2012, MAIAC 03/01-10/16/2012).• PM and AOD show opposite trends in the fall-winter period• This disagreement disappears if the ratio AOD/ ZPBL is considered in place of the simple

AOD

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• PM10-MODIS comparison: PM10 vs. AOD• PM10 and AOD show a large spread of values

• PM10 – AOD correlation: method used by Gupta et al. (2006):

1. PM10 values are grouped into 10 bins of 5 µg/m3 interval.

2. Statistic of the AOD values falling into each bin: • Mean• Median • 25th percentile • 75th percentile• Linear regression between the central

value of the PM10 bins and the AOD.

Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y., and Kumar, N., “Satellite remote sensing of particulate matter and air quality assessment over global cities,” Atmospheric Environment 40(30), 5880 – 5892 (2006).

AOD statistc PM10 central values

Linear Regression equation

AOD

PM10


• PM10-MODIS comparison: PM10 vs. AODPo Valley, March 1st – October 16th 2012

correlation between bin-averaged AOD and PM concentration is high in both cases


• PM10-MODIS comparison: PM10 vs. AODPo Valley, March 1st – October 16th 2012 , AOD AOD/ZPBL

Correlation between bin-averaged AOD and PM concentration is high in both cases and improves slightly


• PM10-MODIS comparison: PM10 vs. AODLinear regressions per each region: Coefficient of Determination (R2)

•PM vs. AOD: good correlation levels in all cases but Venice-MYD04 case

•MYD04 performs slightly better than MAIAC in Piemonte and Lombardia, the opposite situation occur in Emilia Romagna and Veneto.

•PM vs. AOD/ZPBL: also in this case good correlation levels in most of the cases•The ratio AOD/ZPBL in place of the simple AOD produced improvements in some cases and worsening in other cases

•MAIAC: 1 x 1 km2

•Ground measurements: local meaning•GDAS ZPBL: 0.5° x 0.5°

Spatial Inconsistency

PM10 vs. AOD PM10 vs. AOD/ZPBL

Region N. StationsN. Data (~)

MYD04 MAIAC MYD04 MAIACMYD04 MAIAC

Piemonte 27 2400 4400 0.91 0.87 0.99 0.99

Lombardia 59 5800 9700 0.96 0.85 0.95 0.55

Emilia Romagna 37 3800 6400 0.86 0.89 0.84 0.90

Veneto 3 300 500 0.73 0.89 0.66 0.83


• Conclusions and Further Developments• MAIAC 1km retrieval provides high resolution information on Aerosol Optical Depth

within the highly industrialized Po valley of Northern Italy

• Correlation between ground stations measurements of PM10 and AOD obtained by MODIS standard products (MYD04, 10 x 10 km2) and by MAIAC algorithm (1 x 1 km2) over the Po valley seems satisfactory

• PM10 vs. AOD MYD04 and PM10 vs. AOD MAIAC linear regression should be improved if the AOD is normalized by the PBL depth:

• As first step, we used PBL Depth data derive from 6 hourly 0.5° 0.5∙ ° degree analysis files from the NOAA NCEP Global Data Assimilation System (GDAS)

• The results are positive if the entire Po Valley domain is considered, but they show some ambiguities if they are considered separately for each region

• This last result indicates a probable spatial inconsistency between the data sets

• Similar test using the ZPBL extracted from the CALIPSO satellite products are under progress

• The correlation between ground PM10 and MAIAC AOD should permits a better reconstruction of the PM10 spatial distribution over most of the Po valley.


Thank you!

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SPIE Remote Sensing 2013 - Dresdsen 1 APPLICATION OF MAIAC HIGH SPATIAL RESOLUTION AEROSOL...

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