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Sensing 2013 Dresdsen 1 APPLICATION OF MAIAC HIGH SPATIAL RESOLUTION AEROSOL RETRIEVALS OVER PO VALLEY ITALY Barbara Arvani 1 R Bradley Pierce 2 Alexei I Lyapustin 3 ID: 567031

remote aod maiac sensing aod remote sensing maiac spie modis 2013 dresdsen pm10 zpbl myd04 valley data correlation aerosol

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Slide1

SPIE Remote Sensing 2013 - Dresdsen

1

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), USASlide2

SPIE Remote Sensing 2013 - Dresdsen2

Outline

:

Objectives Area of interest: Po Valley DomainDatasetPM10 ground stationsMODIS: MYD04 and MAIAC AODsAERONET AODPlanetary Boundary Layer height (ZPBL)MYD04 and MAIAC test using AERONET dataVertical distribution of aerosols: ZPBL correction PM10 – AOD: Monthly trend PM10 – AOD: CorrelationPM10 – AOD: Effect of ZPBL Conclusion Slide3

SPIE Remote Sensing 2013 - Dresdsen3

What

Ground measurements:

Particulate Matter with aerodynamic diameter less than 10 m (PM10) obtained by regional monitoring agencies in the Po Valley, North of ItalyAerosol Optical Depth (AOD): Obtained from the standard MODIS Aqua Collection 5.1 (MYD04 product), with a spatial resolution of 10 x 10 km2By MAIAC (Multi-Angle Implementation of Atmospheric Correction) algorithm, with a spatial resolution of 1 x 1 km2Study of correlation between Particulate Matter measured by ground monitoring stations and aerosol information retrieved by MODIS sensorSlide4

SPIE Remote Sensing 2013 - Dresdsen4

Why

PM is one of the major pollutant

affecting air quality in urban areasGround stations: Continuous over time AccurateSparse: often do not provide an accurate estimate of the spatial distribution of PM10MODIS 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 concentrationsGround station

MODIS dataSlide5

SPIE Remote Sensing 2013 - DresdsenWhere

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 ItalyHigh population density (20 mil. inhab.)Area with the most severe air pollution problems in the CountryAlpine and Apennines chains act as a barrier to winds blowing from Northern Europe and the Mediterranean favoring the stagnation of pollutantsSlide6

SPIE Remote Sensing 2013 - DresdsenGround station PM

10

data126

PM10 monitoring stations installed by the regional environmental agencies (ARPA) divided into 4 administrative regional districts:Piemonte: 27 stationsLombardia: 59 stations Emilia Romagna: 37 stationsVeneto: 3 stations PM10 concentrations (µg/m3) are provided as daily average valuePeriod studied: 2012Slide7

SPIE Remote Sensing 2013 - DresdsenMODIS 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 ServiceAOD: 0.55 µmGrid cells: 10 x 10 km2 Time step: daily Data: depending on cloud coveragePeriod studied: 2012Slide8

SPIE Remote Sensing 2013 - Dresdsen

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 parametersAOD is computed at 0.47 mDetails 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.Slide9

SPIE Remote Sensing 2013 - DresdsenCo-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

RSlide10

SPIE Remote Sensing 2013 - Dresdsen

MODIS AOD: AERONET Test

Ispra

Modenaλ = 0.55 µmλ = 0.47 µmMODIS 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.Slide11

AOD represents the columnar content of aerosolPM

10 is the ground concentration

Direct comparison of AOD and

PM10 maybe problematicAssumption: most of the aerosols are distributed in the Planetary Boundary Layer The ratio AOD/ZPBL should be more appropriatefor this analysisSPIE Remote Sensing 2013 - DresdsenVertical distribution

AODPBLPM

10

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).Slide12

SPIE Remote Sensing 2013 - DresdsenZPBL

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)WinterSummerSlide13

SPIE Remote Sensing 2013 - DresdsenZPBL: CALIPSO

Coarse

spatial resolution of NCEP ZPBL: alternative sources ZPBL retrieved from the

CALIPSO satellite measurementsWinker, 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 ObservationFirst comparison: CALIPSO ZPBL data are systematically higher than NCEP ZPBLThis study has just begun: too early to draw conclusionsSlide14

SPIE Remote Sensing 2013 - DresdsenPM

10

-MODIS comparison: Po Valley Monthly

trendLarge amount of data: Po Valley monthly trendsPo Valley average values of PM10 and AOD per each dayMonthly means of these values (MYD04: 2012, MAIAC 03/01-10/16/2012).PM and AOD show opposite trends in the fall-winter periodThis disagreement disappears if the ratio AOD/ ZPBL is considered in place of the simple AOD14Slide15

SPIE Remote Sensing 2013 - DresdsenPM

10

-MODIS comparison: PM10 vs. AOD

PM10 and AOD show a large spread of valuesPM10 – AOD correlation: method used by Gupta et al. (2006):PM10 values are grouped into 10 bins of 5 µg/m3 interval.Statistic of the AOD values falling into each bin: MeanMedian 25th percentile 75th percentileLinear 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

PM

10

central

values

Linear

R

egression equation

AOD

PM

10Slide16

SPIE Remote Sensing 2013 - DresdsenPM

10

-MODIS comparison: PM10 vs. AOD

Po Valley, March 1st – October 16th 2012 correlation between bin-averaged AOD and PM concentration is high in both casesSlide17

SPIE Remote Sensing 2013 - DresdsenPM

10

-MODIS comparison: PM10 vs. AOD

Po Valley, March 1st – October 16th 2012 , AOD  AOD/ZPBLCorrelation between bin-averaged AOD and PM concentration is high in both cases and improves slightlySlide18

SPIE Remote Sensing 2013 - Dresdsen

PM

10-MODIS comparison: PM

10 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 casesThe ratio AOD/ZPBL in place of the simple AOD produced improvements in some cases and worsening in other cases MAIAC: 1 x 1 km2Ground measurements: local meaningGDAS ZPBL: 0.5° x 0.5°Spatial Inconsistency

 

 

 

PM

10

vs.

AOD

PM

10

vs.

AOD/ZPBL

Region

N. Stations

N. Data (~)

MYD04

MAIAC

MYD04

MAIAC

MYD04

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

0.83Slide19

SPIE Remote Sensing 2013 - DresdsenConclusions 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 regionThis last result indicates a probable spatial inconsistency between the data setsSimilar test using the ZPBL extracted from the CALIPSO satellite products are under progressThe correlation between ground PM10 and MAIAC AOD should permits a better reconstruction of the PM10 spatial distribution over most of the Po valley.Slide20

SPIE Remote Sensing 2013 - DresdsenThank you!

Any question?