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Contract No. DE-AC36-08GO28308

Improved Gridded Aerosol Data 

for India

C. Gueymard

Solar Consulting Services

M. Sengupta

National Renewable Energy Laboratory

Technical Report

NREL/TP-5D00-58762

November 2013
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

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Improved Gridded Aerosol 

Data for India

C. Gueymard 

Solar Consulting Services

M. Sengupta 

National Renewable Energy Laboratory

Prepared under Task No. WFK6.1001

Technical Report

NREL/TP-5D00-58762

November 2013
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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

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List of Acronyms

AE Ångström exponent

AERONET Aerosol Robotic Network

AOD aerosol optical depth

AOD550 AOD at 550

CM-SAF The Satellite Application Facility on Climate 

Monitoring

GHI global horizontal irradiance

DNI direct normal irradiance

MATCH Model of Atmospheric Transport and Chemistry

MATMOD MATCH Optical Depth

MISR Multi-Angle Imaging Spectroadiometer

MODIS Moderate Resolution Imaging Spectroradiometer
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Executive Summary

Using point data from ground sites in and around India equipped with multi-wavelength 

sunphotometers, as well as gridded data from space measurements or from existing aerosol 

climatologies, an improved gridded database providing the monthly aerosol optical depth at 550 

nm (AOD550) and Ångström exponent (AE) over India is produced. Data from 83 

sunphotometer sites are used here as ground truth to calibrate, optimally combine, and validate 

monthly gridded data from 2000 to 2012. 

To compare the satellite and climatology data (at original resolutions of 1° x 1° or 0.5° x 0.5°) to 

the pointlike ground-truth data, scale height corrections are applied to the AOD550 data to 

compensate for the effect of local elevation.

Different optimal combinations of the satellite data are obtained for the three main seasons of the 

Indian subcontinent. Most generally, the Moderate Resolution Imaging Spectroradiometer data 

are found to be closest from ground truth, albeit with some bias, which is globally corrected for 

the whole subcontinent, separately for each season. One main difficulty resides in the numerous 

data breaks that are found in the satellite-based data bases, particularly over Rajasthan and 

during the monsoon season. An optimal combination of alternate satellite data and climatologies 

is applied in such cases.

Additionally, a statistical analysis is undertaken to evaluate how to optimally correct the 

monthly-mean AOD550 so that more accurate direct normal irradiance results can be obtained 

on a mean monthly basis. 

The resulting AOD550 and AE databases are used to derive the necessary input to the State 

University of New York radiation model, namely a data set of AOD at 700 nm, with a spatial 

resolution of 0.1° x 0.1°.
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Table of Contents

List of Figures ............................................................................................................................................ vi

List of Tables ............................................................................................................................................. vii

1 Introduction........................................................................................................................................... 1

2 Aerosol Optical Properties .................................................................................................................. 5

3 Sources of Data .................................................................................................................................... 7

4 AOD Data Interpolation ...................................................................................................................... 17

5 Gridded Data Derivation .................................................................................................................... 18

5.1 Seasonal and Geographical Considerations ................................................................................ 18

5.2 Calibration of Satellite Data and Missing Data Fill-In................................................................ 21

5.3 Production of Monthly-Mean Data Sets...................................................................................... 22

6 Aerosol Data Set Validation............................................................................................................... 25

6.1 Validation of the Monthly-Mean AOD550 Data Set .................................................................. 25

6.2 Validation of the Monthly-Mean AE Data Set............................................................................ 31

7 Statistical Correction of AOD Data ................................................................................................... 33

8 Conclusion and Recommendations ................................................................................................. 37

9 References .......................................................................................................................................... 38
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List of Figures

Figure 1. Intense haze over the eastern Indo-Gangetic Basin and the Bay of Bengal, as observed from 

satellite ..................................................................................................................................... 2

Figure 2. Aerosol brown cloud near or over the Thar Desert in Rajasthan, as observed from satellite........ 3

Figure 3. Trends in GHI (in percentage per decade) for some measurement stations. Source: Attri 2008 .. 4

Figure 4. Spectral variation of mean monthly AOD for three different months in Kanpur.......................... 6

Figure 5. Location of ground-truth sites used in this study. Kanpur and Anantapur are indicated by the 

orange and white circles, respectively. .................................................................................... 8

Figure 6. Observed monthly AOD550 at Kanpur for different years and the (thick, broken line) long-term 

average ..................................................................................................................................... 9

Figure 7. Observed monthly AOD550 at Anantapur for different years and the (thick, broken line).......... 9

long-term average ......................................................................................................................................... 9

Figure 8. Mean AOD550 over India for June 2008, according to (top left) MISR, (top right) MODISTerra, (bottom left) MODIS-Aqua, and (bottom right) MODIS-Aqua Deep Blue................ 11

Figure 9. AOD550 climatology over India for May, according to all climatology data sets listed in Table 

2, after regridding to 0.5° x 0.5° resolution ........................................................................... 14

Figure 10. Observed monthly AE at Kanpur for different years and the (thick, broken line) long-term 

average ................................................................................................................................... 15

Figure 11. Observed monthly AE at Karachi for different years and the (thick, broken line) long-term 

average ................................................................................................................................... 16

Figure 12. Seasonal validation of monthly AOD from MISR and MODIS-Terra for all ground-truth 

stations. Top plot: dry season; middle plot: monsoon season; bottom plot: post-monsoon 

season..................................................................................................................................... 19

Figure 13. Validation of monthly AE from MISR and MODIS-Terra for all ground-truth stations during 

the monsoon season. Top plot: coastal sites; bottom plot: inland sites.................................. 20

Figure 14. Long-term average AOD550 over India for January, March, May, June, September, and 

November............................................................................................................................... 23

Figure 15. Long-term average AE over India for January, March, May, June, September, and November24

Figure 16. Predicted versus measured monthly-mean AOD550 over India for the three seasons.............. 27

Figure 17. Measured versus predicted time series of mean monthly AOD at Gandhi College from 2006 to 

2011........................................................................................................................................ 28

Figure 18. Measured versus predicted time series of mean monthly AOD at Kanpur from 2002 to 2012. 29

Figure 19. Measured versus predicted time series of mean monthly AOD at Karachi from 2006 to 2011 29

Figure 20. Measured versus predicted time series of mean monthly AOD at Pune, India, from 2008 to 

2012........................................................................................................................................ 30

Figure 21. Measured versus predicted time series of mean monthly AOD at Anantapur from 2002 to 

2009. The vertical dashed line separates the periods of high and low measured AOD. ........ 30

Figure 22. Measured versus predicted time series of mean monthly AOD at Dibrugarh, India, from 2002 

to 2009 ................................................................................................................................... 31

Figure 23. Predicted versus measured monthly-mean AE over India for (top) coastal areas and (bottom) 

continental areas..................................................................................................................... 32

Figure 24. Daily variability of the anomaly in the AERONET AOD550 (relative to the monthly mean) at 

(top) Kanpur and (bottom) Banizoumbou during all months of May of the record............... 34

Figure 25. Frequency distributions of the (left) daily measured AOD550 at Kanpur and (right) 

Banizoumbou during all months of May. Monthly statistics are also indicated. ................... 35

Figure 26. Calculated effective monthly values of ß over Asia, compared to the observed monthly median 

ß at AERONET sites.............................................................................................................. 36
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List of Tables

Table 1. Sources of Gridded Satellite Data for AOD and AE* .................................................................. 10

Table 2. Sources of Gridded Climatological Data for AOD and AE.......................................................... 12

Table 3. Number of Monthly Data Points Used for the Validation of Each Satellite Data Set From 2000 to 

2012 and Coincidence With Ground Truth............................................................................ 18

Table 4. Performance Statistics of the 2000 to 2012 AOD550 Database for Two Specific Sites and for All 

Ground-Truth Sites ................................................................................................................ 27

Table 5. Stations Used for the Validation of AOD550 Time Series........................................................... 28

Table 6. Performance Statistics of the 2000 to 2012 Monthly AE Database for All Ground-Truth Sites.. 32
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1 Introduction

The goal of this study is to develop a complete data set of aerosol optical depth (AOD) for India, 

to be used as input to the State University of New York satellite model in the process of deriving 

high-resolution solar irradiance estimates for India. This constitutes a revision of a previous 

study, which led to the National Renewable Energy Laboratory’s release of a series of solar 

resource maps in 2010. The period of interest was then 2002 to 2008; it has been extended to 

2002 to 2012 for this study, which will lead to a new release of solar resource maps and 

irradiance time series by the National Renewable Energy Laboratory. 

The State University of New York model works at a spatial resolution of 0.1° x 0.1° (about 10

km x 10 km), and provides solar irradiance estimates at hourly intervals, using satellite imagery 

of cloud cover. Ideally, the aerosol input to the model should be at the same resolution, both 

spatially and temporarily. Unfortunately, such stringent requirements cannot be matched directly 

with any currently available source of aerosol data. Other issues are the limited accuracy of the 

existing sources of AOD data and their frequent missing data gaps. A large part of the present 

study is devoted to finding acceptable solutions to these issues. Considering the various

constraints of this study, the target spatial resolution of the primary data is 0.5° x 0.5° (about 50

km x 50 km), from which the end resolution necessary for the model (0.1° x 0.1°) is derived by 

interpolation, as discussed in Section 4. The adopted temporal resolution is one month because 

satellite observations have too many missing points on a daily basis. 

The issue of accuracy is critical in the context of solar resource assessment, particularly for 

concentrating technologies (concentrating solar power or concentrating photovoltaics), because 

AOD has a strong impact on direct normal irradiance (DNI). It has a smaller impact on global

horizontal irradiance (GHI), as discussed in the literature; e.g., [1], which makes flat-plate 

technologies (such as photovoltaics) much less sensitive to AOD. The specific aerosol conditions 

of India must also be discussed, because they are dramatically different from those in most other 

areas of the world.

Asia is known for its large aerosol burden, due to a combination of various causes: (1) large 

sources of local dust, which tends to rise easily under hot, dry conditions; (2) long-range 

transport from dry areas or deserts in Asia or Africa; (3) smoke from biomass burning, as a result 

of agricultural practices in particular; and (4) anthropogenic pollution. This mixture of large 

quantities of aerosols frequently results in strong haze. When laden with significant industrial 

and transportation pollution from large population centers, it creates what is referred to as the 

Asian brown cloud. The finest aerosol particles may be washed out by rain during the monsoon 

period. This is known as the scavenging effect. Winds can import or export aerosols, depending 

on season and meteorological conditions. Finally, an important case is that of the Indo-Gangetic 

basin, in the north of India. Considerable aerosol sources exist there because of the presence of 

agriculture, industries, and an extremely high population density. Influx of aerosols from desert 

areas west of India is frequent. These aerosols cannot escape to the north because the Himalayas 

create an effective barrier, resulting in southward outflow toward the Bay of Bengal (Figure 1). 

An almost permanent haze is noticeable over the region. This can impair the solar resource 

significantly. In contrast, most parts of Rajasthan have a significantly lower aerosol load and thus 

a much better solar resource. A strong spatial gradient in aerosol load exists between the “clean” 

parts of Rajasthan and the northwest Indo-Gangetic Basin, which complicates the precise solar 
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resource mapping in that area of India. This spatial gradient is generally evident from satellite 

imagery, actually showing a clear-cut separation between low- and high-turbidity areas over 

Rajasthan, in particular (Figure 2). 

Figure 1. Intense haze over the eastern Indo-Gangetic Basin and the Bay of Bengal, as observed 

from satellite. Photo from NASA
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Figure 2. Aerosol brown cloud near or over the Thar Desert in Rajasthan, as observed from 

satellite. Photo from NASA

Some studies have examined the long-term trends in aerosol emissions and/or AOD, showing 

significant increases over large parts of India, particularly the Indo-Gangetic Basin [2-4]. The 

Delhi area appears most affected, with upward trends in AOD exceeding 20% per decade. This 

can certainly explain, at least in part, the concomitant decrease in measured GHI that has been 

noted during the last decades [3, 4]. The dimming in GHI over India started around 1960. During 

the four decades that followed, calculations indicate that the increased AOD caused an average 

GHI dimming of 3 W m-2 to 4 W m-2 per decade, which corroborates the negative trend 

observed in the long-term GHI measurements from the Global Energy Balance Archive network 

[3]. As shown in Figure 3, similarly pronounced negative trends, of up to 7% per decade, have 

been reported, based on measured GHI data from the India Meteorological Department [5].
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Figure 3. Trends in GHI (in percentage per decade) for some measurement stations. Image from

Attri, 2008
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2 Aerosol Optical Properties 

Two common aerosol optical properties are used here: the AOD at 550 nm (AOD550 or a550) 

and the Ångström exponent (AE or α). The AOD at any other wavelength , a, can be derived 

through 

a = a550 (0.55/)

α

 (1) 

where  is expressed in μm. Equation (1) can be used, for instance, to obtain the AOD500 or the 

Ångström’s turbidity coefficient, ß, which is the AOD at 1 μm. Both of them have a strong 

presence in the literature, and have been around long before 550 nm became a de facto standard 

wavelength for satellite instruments. Ground-based sunphotometers usually report AOD at 

various wavelengths between 330 nm and 1,050 nm. (For instance, most Cimel instruments used 

by the Aerosol Robotic Network (AERONET) report AOD at 340 nm, 380 nm, 440 nm, 500 nm, 

670 nm, 870 nm, and 1,020 nm, simultaneously.) AOD550 is most generally not measured with 

these instruments, and therefore must be derived. This can be accomplished by using the log-log 

transform of Equation (1): 

 lna = lna550 + α ln(0.55/). (2) 

A linear fit of the simultaneous a measurements to Equation (1) with the usual least-squares 

method provides both the intercept, lna550, and the slope, α. This process is illustrated in Figure 

4 for three different months with widely different a and α in Kanpur, India. 

The current version of the State University of New York model relies on the AOD at 700 nm, 

which can be derived from Equation (1) as: 

a700 = a550 (0.55/0.70)α. (3) 

In what follows, a methodology is developed to obtain gridded data of a550 and α without any 

missing data break, and with the appropriate statistical representativeness so as to derive hourly 

DNI and GHI with as little bias as possible. The desired a700 data set is then simply derived by 

applying Equation (3). 
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Figure 4. Spectral variation of mean monthly AOD for three different months in Kanpur 

 
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3 Sources of Data

Three widely different kinds of data are used here: (1) point data from ground sites equipped 

with sunphotometers (highest accuracy, but periods of missing data are frequent); (2) gridded 

data from space observations (low accuracy, with frequent missing data); and (3) gridded data 

from monthly climatologies (low to intermediate accuracy, no missing data). Data of the first 

kind are conventionally used as ground truth to validate and optimally combine data from the 

other sources. 

Ground-based multi-wavelength sunphotometric measurements are the most accurate source of 

AOD data. Individual data points have an estimated uncertainty of approximately ±0.01 AOD 

unit [6]. Most validation studies of gridded aerosol products have used the National Aeronautics 

and Space Administration’s AERONET data as ground truth. In the present case, this would not 

have provided enough sites or monthly data points to guarantee sufficient spatial coverage and 

statistical significance. To increase the number of ground-truth sites, the area of study has been 

increased to encompass not only India, but a much larger area, defined by latitudes 4° N to 39° N 

and longitudes 52° E to 103° E. Furthermore, the existence of sunphotometer data from sources 

other than AERONET has been queried. The literature shows that a sizeable number of 

sunphotometric stations belonging to various institutions (particularly the Indian Space Research 

Organization) exist in India. After considerable effort, it has been possible to obtain monthlyaverage AOD data for 18 stations, thanks to the intervention of the Ministry of New and 

Renewable Energy. The quality of this data set appears reasonable, but not as consistent or 

accurate as the AERONET data. Only a few data points had to be rejected as obvious outliers.

To mitigate this problem of insufficient ground-truth data, the literature has been searched 

thoroughly for any published data of AOD and AE. Of approximately 200 articles that have been 

scrutinized, about 10% contained valuable data (usually displayed in graphics, which had to be 

digitized), which ultimately resulted in 15 additional data series. Note that there were some 

instances in which different authors reported conflicting data for the same site and same period. 

Attempts to resolve such inconsistencies through personal communications with the concerned 

authors were unsuccessful (i.e., no response). Thus, dubious or conflicting data points had to be 

eliminated.

Through a combination of the AERONET and literature data, the total number of potential 

ground-truth sites then reached 95. From this total, 12 sites were not usable because either they 

were at too high altitude in the Himalayas (where the spatial variability in AOD is high and may 

seriously affect the validation); they were decommissioned before the first satellite data became 

available, in 2000; or their data were found to be of too low quality. As a result, only 83 sites 

were kept to establish the final ground-truth data set. The location of these sites is shown in 

Figure 5. It is obvious that the geographical distribution of the existing ground-truth stations is 

uneven. In particular, the lack of such stations where AOD is low and the solar resource is high 

(such as in large parts of Rajasthan) creates an important sampling problem. Two of the sites, 

Kanpur, India, and Anantapur, India, are indicated in Figure 5 by the orange and white circles, 

respectively. Kanpur is representative of the very hazy conditions of the Indo-Gangetic Basin, 

and has been the longest-operating AERONET station in India (since 2001). Its monthly-average 

AOD observations, reduced to a wavelength of 550 nm for comparative purposes, are shown in 

Figure 6. The seasonal variation of the 12-year long-term average is also shown. In contrast, the 
8

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monthly AOD is lower and has a different seasonal pattern at Anantapur, in southern India 

(Figure 7). The data (available during the period from 2001 to 2009 only) for the latter 

sunphotometric station was extracted from the Ministry of New and Renewable Energy data set.

Figure 5. Location of ground-truth sites used in this study. Kanpur and Anantapur are indicated by 

the orange and white circles, respectively. Image © Google Earth
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Figure 6. Observed monthly AOD550 at Kanpur for different years and the (thick, broken line) 

long-term average 

Figure 7. Observed monthly AOD550 at Anantapur for different years and the (thick, broken line) 

long-term average 

In parallel, the sources of gridded data from satellite observations are shown in Table 1. A 

preliminary comparison of the four satellite AOD data sets described in Table 1 reveals two 

important things: (1) the magnitude of their distributions of AOD over India differ largely; and 

(2) there are a lot of missing data pixels, as a result of high ground albedo, high cloudiness, or 

other factors. These issues are illustrated in Figure 8 for a single common month (June 2008) 

when AOD is usually very high. Other patterns of absolute differences and missing data are 

observed for essentially all months, so that a comparison of gridded versus ground-truth AOD is 

needed to obtain performance statistics and select the best data source. This process is described 

in Section 5. 
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Monthly AOD climatologies have been derived from the data sets found in Table 1 by simple 

averaging of each grid-cell data point over the whole record period (Table 2). These 

climatologies still have missing data, however. To resolve this issue, continuous climatologies 

(with no missing data) from other sources have also been considered, as described in Table 2. A 

comparison of the seven AOD climatologies is shown in Figure 9 for the month of May. Again, 

it is obvious that significant differences exist between these data sets.

Although the target period for the end-product data set is from 2002 to 2012, all data points 

available for the period from 2000 to 2012 have been used for development.

Table 1. Sources of Gridded Satellite Data for AOD and AE*

Variable Data Source Period Original Resolution

AOD550 MISR v4 2000–2012 0.5° x 0.5°

MODIS-Terra v5.1 2000–2012 1° x 1°

MODIS-Aqua v5.1 2002–2012 1° x 1°

MODIS-Aqua “Deep Blue” v5.1 2002–2012 1° x 1°

AE MISR v4 2000–2012 0.5° x 0.5°

MODIS-Terra Land v5 2000–2010 1° x 1°

MODIS-Terra Ocean v5 2000–2007 1° x 1°

MODIS-Aqua Land v5.1 2003–2009 1° x 1°

MODIS-Aqua Ocean v5.1 2002–2010 1° x 1°

*From the National Aeronautics and Space Administration
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Figure 8. Mean AOD550 over India for June 2008, according to (top left) MISR, (top right) MODISTerra, (bottom left) MODIS-Aqua, and (bottom right) MODIS-Aqua Deep Blue
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Table 2. Sources of Gridded Climatological Data for AOD and AE

Variable Data Source Resolution Source Continuous

AOD550 MISR v4 0.5° x 0.5° Table 1

MODIS-Terra v5.1 1° x 1° Table 1

MODIS-Aqua v5.1 1° x 1° Table 1

MODIS-Aqua Deep Blue v5.1 1° x 1° Table 1

CM-SAF 1° x 1° www.cmsaf.eu/ • MATMOD 1° x 1° [7] • Meteonorm 1° x 1° [8] •

AE MISR v4 0.5° x 0.5° Table 1

MODIS-Terra Land v5 1° x 1° Table 1

MODIS-Terra Ocean v5 1° x 1° Table 1

MODIS-Aqua Land v5.1 1° x 1° Table 1

MODIS-Aqua Ocean v5.1 1° x 1° Table 1

CM-SAF 1° x 1° www.cmsaf.eu/ • MATMOD 1° x 1° [7] •
13

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14

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Figure 9. AOD550 climatology over India for May, according to all climatology data sets listed in 

Table 2, after regridding to 0.5° x 0.5° resolution

Compared to AOD550, AE appears to have a much more limited interannual variation, and a 

more distinct seasonal variation. This is illustrated for Kanpur in Figure 10. AE’s steady decrease 

during the dry season corresponds to the buildup of dust particles of large size, for which α is 

typically low (because α is inversely related to the mean aerosol particle size). These large 

particles are then scavenged (by rain) during the monsoon season, so that α increases steadily 

until the next dry season.
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Figure 10. Observed monthly AE at Kanpur for different years and the (thick, broken line) longterm average 

The geographical feature that is important to consider in the case of AE is the proximity to the 

coast. Maritime aerosols have a large size and are thus characterized by a low α, normally less 

than 0.5. Coastal areas (and even more so, islands) are therefore characterized by a lower α than 

inland areas in any season. This becomes obvious when comparing the situation at the inland site 

of Kanpur, India (Figure 10) to that of the coastal site of Karachi, Pakistan (Figure 11). The 

general U-shape trend of the seasonal variation of α still exists, because maritime areas are still 

strongly affected by dust and other types of aerosols transported from continental sources. In 

parallel, Table 1 and Table 2 indicate that the MODIS retrieval algorithms for AE vary 

depending on the expected presence of maritime aerosols, thus their outputs were separated into 

“Land” and “Ocean” data sets. Consequently, in contrast to the comparative analysis between 

satellite data and ground truth that can be carried out only on a seasonal basis for AOD550, that 

of AE additionally requires a separate analysis for the coastal and inland areas. 
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Figure 11. Observed monthly AE at Karachi for different years and the (thick, broken line) longterm average 

 
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4 AOD Data Interpolation

Table 1 and Table 2 reveal that the MISR data has better spatial resolution than the other data 

sources. All the 1° x 1° AOD data sets were thus regridded to 0.5° x 0.5° so that they could be 

compared to the MISR data. This also improves comparisons with ground observations because

the point-source ground-truth data is then compared to gridded data that is spatially averaged 

over a much smaller cell area, which is therefore more representative of the ground-truth site 

conditions. 

Because AOD is a strong function of elevation, this regridding must take the local elevation into 

consideration. This can be done via the convenient scale-height method, which assumes that the 

AOD at elevation h is lower than that at sea level (h=0), in such a way that

AOD(h) = AOD(0) exp(-h/Ha) (4)

where Ha is the aerosol scale height. The method is convenient, but approximate because Ha

varies, depending on region, season, etc. Based on a previously published study [7], a typical 

value of Ha is approximately 2,500 m.

The overall interpolation is executed in three successive steps: (1) the 1° x 1° data points for each 

grid cell are first reduced to sea level through the reverse form of Equation (4); (2) a double 

linear interpolation is used to obtain 0.5° x 0.5° data at sea level; and (3) finally, Equation (4) is 

used to obtain the gridded values at their final resolution. The first and last step involve a digital 

elevation model of the study area at 1° x 1° and 0.5° x 0.5°, respectively.

Equation (4) has also been applied to the ground-truth data, so that their corrected values are 

scaled to the average elevation over the 0.5° x 0.5° cell where the site is located. If the ground 

site’s elevation is hg and the 0.5° x 0.5° cell’s mean elevation is hi, the corrected ground-truth 

AOD is obtained from

AOD(hi) = AOD(hg) exp(-hi/Ha) / exp(-hg/Ha). (5)

The effect of elevation on AE is most likely much smaller than that on AOD, but is not known 

precisely. Therefore, it has been considered that the AE values for 1° x 1° cells are conserved 

when regridding to 0.5° x 0.5°.
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5 Gridded Data Derivation

5.1 Seasonal and Geographical Considerations

The scarcity of ground-truth data, particularly during the cloudier monsoon months, has a 

negative impact on the number of valuable data points and on the overall statistical significance 

that would be achieved by the desirable monthly analysis. A seasonal analysis is therefore 

preferable. The literature on aerosols in India clearly separates the aerosol data into three distinct 

seasons: pre-monsoon (dry season), monsoon (wet season) and post-monsoon. The exact period 

and strength of the monsoon season actually varies with latitude and also has a marked 

interannual variability. For simplicity, the monsoon season is defined here as the four-month 

period from June to September, after which the three-month post-monsoon season starts, October 

to December. The dry season is therefore defined as the five-month period from January to May. 

Considering the notable differences in AOD between all available satellite-gridded data sets 

(Figure 8) and between all available climatological data sets (Figure 9), it is important to 

compare each of them against ground truth. The number of data points for which coincident 

ground truth and gridded data points are available during the period from 2000 to 2012 is shown

in Table 3. It is clear that, for any season, the MODIS-Aqua Dark Blue data set is at a 

disadvantage as a result of its large fraction of missing data. If a low fraction of data breaks were 

the only selection criterion, MISR and MODIS-Terra would be the sources of choice. 

Table 3. Number of Monthly Data Points Used for the Validation of Each Satellite Data Set From

2000 to 2012 and Coincidence With Ground Truth

Season Ground Truth MISR MODIS-Terra MODIS-Aqua MODIS-Aqua 

Dark Blue

Dry 828 757 729 643 457

Monsoon 479 374 382 355 271

Post-Monsoon 422 371 363 333 248

ALL 1729 1502 1474 1331 976

Coincident Availability 100.0 86.9 85.3 77.0 56.4

For each of the three identified seasons, Figure 12 provides a visual comparison between the 

monthly satellite-derived AOD data points (from MISR and MODIS-Terra) and ground truth. 

The scatter around the ideal 1:1 line is large in all cases. However, the correlation between the 

satellite-derived and measured AOD data points is much better in the case of MOSIS-Terra than 

with MISR, even though the latter has a finer nominal resolution. The scatter is more pronounced 

during the monsoon season, which can be explained by the predominantly cloudy weather, and 

therefore a higher probability of spatial heterogeneity in cloud cover, yielding a lower probability 

of spatial and temporal coincidence between the satellite and ground observations. Overall, the 

two satellite-derived data sets tend to underestimate AOD compared to ground truth. The only 

potential exception is for MODIS-Terra during the monsoon season, but the light overall 

overestimation might then be caused by a few outliers, themselves likely resulting from cloud 

contamination in the satellite data.

A similar analysis shows that, among the seven AOD climatologies mentioned in Table 2, the 

better ones are MODIS-Terra, MODIS-Aqua, MATMOD, and Meteonorm. It is clear from 

Figure 9 that the MISR and CM-SAF climatologies tend to severely underestimate the high AOD 

conditions over the Indo-Gangetic Basin.
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Figure 12. Seasonal validation of monthly AOD from MISR and MODIS-Terra for all ground-truth 

stations. Top plot: dry season; middle plot: monsoon season; bottom plot: post-monsoon season 
20 

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A comparison of the satellite-derived α for both coastal and inland sites during the monsoon 

season is shown in Figure 13, again using data from the MODIS-Terra and MISR instruments. 

The observed large scatter in these plots confirms previous findings [9] that the quality of AE 

products from MODIS was particularly low. Unfortunately, none of the satellite-derived data sets 

for any of the seasons or geographical areas is of good quality. Although some of the bias can be 

reduced globally, as discussed in the next subsection, the low quality of the satellite data is 

obviously an area of concern, and a non-negligible source of error in the final product (AOD at 

700 nm). 

Figure 13. Validation of monthly AE from MISR and MODIS-Terra for all ground-truth stations 

during the monsoon season. Top plot: coastal sites; bottom plot: inland sites 
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5.2 Calibration of Satellite Data and Missing Data Fill-In

Figure 12 and Figure 13 indicate that, for both AOD550 and AE, the satellite data manifest both 

scatter and bias, which may depend on season and geographical location. The scatter is 

essentially a result of six sources of difference between the data sets being compared, sorted in 

decreasing order of assumed importance:

1. Errors in the satellite data (miscalibration of the retrieval algorithm, cloud contamination, 

ground reflectance artifacts, etc.)

2. Spatial mismatch between the satellite-based data (grid-cell average) and the groundbased data (point source)

3. Temporal mismatch between the satellite and ground data (The days used to derive the 

monthly average may not be the same.)

4. Errors introduced by the elevation correction

5. Errors introduced by the interpolation technique

6. Errors in the ground-truth data

The relative contribution of each source of noise is unknown, but it is likely that a significant 

part of the total noise observed in Figure 12 and Figure 13 is caused by the sources identified as 

numbers 2 through 6, and not only by errors in the retrieved aerosol data themselves (number 1). 

Whereas the noise-causing random errors are essentially unavoidable and impossible to correct a 

posteriori, it is desirable to correct for the overall bias. This is actually possible under the 

condition that it is relatively constant over large geographical regions and during a specific 

season. The correction method relies on scatter plots such as those shown in Figure 12 and 

Figure 13.

For AOD, the MODIS-Terra and MODIS-Aqua data sets appear to have less bias than the MISR 

data (Figure 12). To remove such bias as much as possible, a linear correction method is applied, 

based on the linear fits obtained from scatter plots such as shown in Figure 12 and Figure 13. 

The major problem with the MODIS data, however, is that some regions (such as around 

Rajasthan) or periods (monsoon months) have systematic data breaks (Figure 8 and Figure 9). 

The first attempted solution is to use MODIS data wherever/whenever available and MISR or 

climatology data elsewhere. However, this results in both temporal and spatial discontinuities or 

inconsistent results, particularly over Rajasthan. Following unsuccessful attempts of patching 

MODIS data breaks with various possible arrangements, the best solution is obtained with the 

Meteonorm data set, after scaling its values with the normalized MISR monthly AOD values, 

i.e., the values for individual months divided by the corresponding long-term averages. Finally, a 

weighted average of MODIS-Terra, MODIS-Aqua, and this scaled climatology has been 

developed for the whole subcontinent, yielding virtually indiscernible discontinuities over 

Rajasthan or during monsoon months.

For AE, various combinations of the monthly data from MISR and MODIS-Terra, with fill-in 

provided by combinations of the CM-SAF and MATMOD climatologies, appropriately scaled 

with normalized monthly values from MODIS or MISR whenever available, were used. The 

weighting of these combinations depends on location (coastal/inland) and season.
22

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5.3 Production of Monthly-Mean Data Sets

A Fortran code has been written to prepare the production of monthly values of AOD550 at a 

resolution of 0.5° x 0.5° over India. A similar code has been written to produce the monthly AE 

values. These codes read all the necessary AOD or AE data sets, detect missing values, select the 

appropriate source database(s) for each pixel and each month, apply bias corrections and 

substitutions whenever needed, and print the results in a single text file. Each line corresponds to 

a latitude/longitude combination, and each column corresponds to a specific month. Sample 

results for the long-term monthly averages of AOD and AE are shown in Figure 14 and Figure 

15, respectively.
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Figure 14. Long-term average AOD550 over India for January, March, May, June, September, and 

November
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Figure 15. Long-term average AE over India for January, March, May, June, September, and 

November
25

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6 Aerosol Data Set Validation

Because of the lack of ground-truth data, the performance of this new data set cannot be assessed 

against independent data. However, the validation undertaken here uses the production data set, 

which has a smaller size (latitudes 3° to 39° N, longitudes 65° to 100° E) and less control 

stations (60) than the development data set (latitudes 4° to 39° N, longitudes 52° to 103° E, 83 

stations), but is also more centered on India. 

6.1 Validation of the Monthly-Mean AOD550 Data Set

A comparison between the predicted and measured AOD550 is shown in Figure 16, which is

similar to Figure 12, except that the Y-axis now indicates the production values, after correction 

and/or filling of the available satellite data. Although the original bias and scatter has been 

reduced, particularly for the monsoon season, it is obviously still far from perfect. A summary of 

the quality statistics (Mean Bias Deviation (MBD) and Root Mean Square Deviation (RMSD)) is 

provided in Table 4 for each of the three seasons and for the whole data set. Specific results for 

Kanpur and Anantapur are also provided separately, showing that they are comparable to the 

overall results. This could be expected because the ground-truth data was not merged with the 

gridded satellite data (because this would have created undesirable “Swiss-cheese” local 

artifacts), but used only to calibrate it on a regional basis. 

Compared to the AERONET ground-truth data of AOD550, for which the uncertainty is 

approximately ±0.01 [6], it is estimated that the overall uncertainty in the production data set is 5

to 10 times larger. Availability of more ground-truth sites and of longer data series (so that a 

finer monthly analysis becomes possible) would help this situation. Still, improved satellite 

retrievals, particularly complemented by assimilation or optimal combinations with AOD 

predictions from chemical-transport models, would be the essential key to reduce the current 

uncertainty.

Another validation approach is to compare the proposed data to time series of ground-truth 

measurements at specific sites. This is advisable here at only a few ground-truth sites, because

most sites have too short periods of record or too many missing months. This is done here at six 

sites, for which the coordinates and mean measured AOD are shown in Table 5. The time-series 

comparisons for these six sites are shown in Figure 17 through Figure 22. In general, the 

predicted AOD correctly follows the actual (large) seasonal and interannual variations that are

measured locally. This is particularly the case at Karachi (Figure 19), possibly because it is a 

coastal location, and AOD satellite retrievals over water are more accurate than over land. 

Exceptions to this good agreement do occur, however, for some months at most sites. In addition 

to the relatively high uncertainty in the satellite retrievals already discussed, these exceptions can 

alternatively be explained by cloud artifacts in the satellite observations (or even in the ground 

sunphotometer data), by a too-low (or non-matching) number of data days in a month 

(particularly during the monsoon period), and in some cases by an incorrect sunphotometer 

calibration. The latter cause could explain the sudden downward step in measured AOD at 

Anantapur in mid-2006 (Figure 21). Before that time, the experimental data might well have 

been affected by a high bias of approximately ±0.20 in AOD550. (It is unlikely that natural 

causes can explain the strange behavior of the measured time series there.) Unfortunately, it is 

impossible to assess the historical quality of the data recorded at Anantapur or at the other sites 

in the Ministry of New and Renewable Energy data set because there are too many unknowns 
26 

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about the protocols they follow for the calibration of the instrument, the conditions of operation, 

the filtering of cloud-contaminated data, or the reduction and a posteriori quality control of the 

data. 
27 

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Figure 16. Predicted versus measured monthly-mean AOD550 over India for the three seasons. 

Table 4. Performance Statistics of the 2000 to 2012 AOD550 Database for Two Specific Sites and 

for All Ground-Truth Sites 

Statistic Dry Season Monsoon Season Post-Monsoon Season Year 

Meas. Predicted Meas. Predicted Meas. Predicted Meas. Predicted 

Kanpur

N 57 57 35 35 28 28 120 120

Min 0.258 0.348 0.351 0.321 0.437 0.562 0.258 0.321

Max 0.951 0.963 1.257 1.071 0.914 0.915 1.257 1.071

Mean 0.526 0.608 0.563 0.593 0.640 0.699 0.563 0.625

MBD 0.082 0.030 0.059 0.062

RMSD 0.127 0.137 0.092 0.123

Anantapur

N 45 45 17 17 25 25 87 87

Min 0.193 0.092 0.092 0.098 0.209 0.104 0.092 0.092

Max 0.714 0.445 0.508 0.403 0.517 0.424 0.714 0.445

Mean 0.388 0.300 0.282 0.289 0.335 0.225 0.352 0.277

MBD -0.087 0.007 -0.109 -0.075

RMSD 0.152 0.105 0.143 0.141

All Sites 

N 641 641 313 313 303 303 1257 1257

Min 0.016 0.044 0.016 0.089 0.017 0.042 0.016 0.042

Max 1.196 1.115 1.257 1.209 1.141 0.984 1.257 1.209

Mean 0.406 0.390 0.399 0.420 0.371 0.335 0.396 0.384

MBD -0.016 0.021 -0,036 -0.012

RMSD 0.099 0.142 0.144 0.138
28 

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Table 5. Stations Used for the Validation of AOD550 Time Series 

Station Latitude Longitude Elevation (m) Source Period Mean Annual 

AOD550 

Gandhi College 25.871 84.128 60 AERONET 2006–11 0.539 

Kanpur 26.513 80.232 123 AERONET 2001–12 0.563 

Karachi 24.87 67.03 49 AERONET 2006–11 0.472 

Pune 18.537 73.805 559 AERONET 2008–12 0.356 

Anantapur 14.2 77.7 25 India 2001-09 0.413 

Dibrugarh 27.3 94.6 111 India 2001-09 0.307 

 

Figure 17. Measured versus predicted time series of mean monthly AOD at Gandhi College from 

2006 to 2011 
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Figure 18. Measured versus predicted time series of mean monthly AOD at Kanpur from 2002 to 

2012 

Figure 19. Measured versus predicted time series of mean monthly AOD at Karachi from 2006 to 

2011 
30 

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Figure 20. Measured versus predicted time series of mean monthly AOD at Pune, India, from 2008 

to 2012 

Figure 21. Measured versus predicted time series of mean monthly AOD at Anantapur from 2002 

to 2009. The vertical dashed line separates the periods of high and low measured AOD. 
31 

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Figure 22. Measured versus predicted time series of mean monthly AOD at Dibrugarh, India, from 

2002 to 2009 

6.2 Validation of the Monthly-Mean AE Data Set 

The monthly AE data produced here are compared to the ground-truth data shown in Figure 23, 

separately for the coastal and inland locations. It is obvious that the problems of the original data 

(noted earlier in Section 5.1) are still present here. Although the corrections applied to the 

original data have reduced its bias, a substantial compression of the monthly dynamic range is 

still present, which translates into substantial noise and a lack of values below 0.5 or above 1.3. 

It should be noted, however, that in relative terms the experimental uncertainty in AE is larger 

than that in AOD. This is a consequence of the way that α is calculated in practice (see Equation 

(2) and Figure 4): a relatively small error in the AOD from one radiometer channel may yield a 

noticeable change in the slope of the best-fit line, hence in α. Still, the observed large scatter in 

the satellite-based data can be attributed to the various inadequacies of the current retrieval 

algorithms, and to the diminishing AOD signal observed by any sensor at long wavelengths 

(itself conducive of larger errors). The overall performance statistics in Table 6 show a low bias 

on an annual basis. 
32 

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Figure 23. Predicted versus measured monthly-mean AE over India for (top) coastal areas and 

(bottom) continental areas 

Table 6. Performance Statistics of the 2000 to 2012 Monthly AE Database for All Ground-Truth 

Sites 

Statistic Coastal Continental ALL 

Meas. Predicted Meas. Predicted Meas. Predicted 

N 356 356 867 867 1194 1194 

Min 0.149 0.380 0.145 0.461 0.145 0.380 

Max 2.276 1.362 2.252 1.443 2.276 1.443 

Mean 0.874 0.903 0.898 0.924 0.896 0.915 

MBD 0.029 0.026 0.019 

RMSD 0.302 0.292 0.290 

 
33

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7 Statistical Correction of AOD Data

The analysis described above to produce the AOD data set was based exclusively on mean

monthly values. This is because the raw satellite data used here are available only as monthly 

means. However, recent unpublished studies pointed out that using the mean monthly AOD often 

results in noticeable underestimations of the mean monthly direct irradiance. The National 

Renewable Energy Laboratory’s previous irradiance maps of India were based on the assumption 

that the monthly mode AOD value should be used [10], based on the fact that the monthly modal 

value of AOD550 is lower than the monthly mean, and its use would thus result in higher direct 

irradiance. This might have been an overcorrection, however, resulting in too-high direct 

irradiances over India, according to some observers. A later study [11], which was devoted to the 

core issue of selecting the most appropriate monthly statistic for AOD, suggested that the desired 

monthly effective AOD was close to the median of daily values, and that the median/mean ratio

was close to 0.8 on average over the world, with however significant scatter. Because that study 

was not specific to India and because a lot of unanswered questions still subsisted, a new 

analysis—now specific to the actual aerosol conditions of India—is undertaken here. 

In India, the same as over any other geographic area, the aerosol load, and hence AOD550, 

varies on a daily basis. That daily variability is more or less pronounced depending on 

geographic area [1]. An example of daily variability of AOD550 during a specific month of the 

year (May) is shown in Figure 24 for two sites: Kanpur, India, and Banizoumbou, Niger, based 

on AERONET data obtained during a period of 11 and 12 years, respectively. In these plots, all 

months of May with available data are artificially concatenated, and the daily AOD550 is shown 

as an anomaly, i.e., the difference between the actual daily value and the long-term monthly 

mean. Although the long-term mean AOD550 is comparable at the two locations for that month 

(0.708 at Kanpur versus 0.676 at Banizoumbou), the daily variations denote a critical difference: 

the individual daily values show much less excursions around the mean at Kanpur than at 

Banizoubou. This can be explained by the relatively permanent dust and pollution load over 

Kanpur, in contrast to the frequent alternation of very clear days and very hazy days (as a result 

of transported dust or smoke from nearby sources, for example) at Banizoumbou. 

The corresponding frequency distributions of AOD550 for these two sites and months are shown 

in Figure 25. Although both plots display the typical log-normal shape that could be expected 

[11], it is obvious that this shape is closer to that of a normal distribution at Kanpur than at 

Banizoumbou—a consequence of the difference in aerosol regime just mentioned. Another 

visible difference (and a direct consequence of the former observation) is that the spread between 

the monthly mean, median, and mode is much smaller at Kanpur than at Banizoumbou. For 

instance, the median/mean ratio for May is 0.931 at Kanpur versus 0.827 at Banizoumbou. 

Therefore, whereas at the latter site the value of that ratio is close to the world average, it is 

significantly higher at Kanpur. 

Another aspect of the observed differences in variability between Kanpur and Banizoumbou is 

that the standard deviation of daily AOD values is smaller at Kanpur (0.249 in May) than at 

Banizoumbou (0.418). Still, the magnitude of the daily standard deviation is high at Kanpur (it 

even reaches 0.411 in June there). This indirectly provides an estimate of the typical error that is 

introduced in the predicted DNI when using monthly AOD values rather than (more desirable) 

daily values. Based on the analysis in [1], this propagation of error from AOD to DNI could 
34 

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amount to approximately ±10% to ±25% in May or approximately ±20% to ±40% in June at 

Kanpur, which is very significant. 

Figure 24. Daily variability of the anomaly in the AERONET AOD550 (relative to the monthly mean) 

at (top) Kanpur and (bottom) Banizoumbou during all months of May of the record 

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

0 1 2 3 4 5 6 7 8 9 10 11 12

Kanpur, India

Daily anomalies for May

AOD550 Daily Anomaly

Year

-0.50

0.00

0.50

1.00

1.50

2.00

2.50

0 1 2 3 4 5 6 7 8 9 10 11 12

Banizoumbou, Niger

Daily anomalies for May

AOD550 Daily Anomaly

Year
35 

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Figure 25. Frequency distributions of the (left) daily measured AOD550 at Kanpur and (right) 

Banizoumbou during all months of May. Monthly statistics are also indicated. 

The study described in [11] has been repeated here, but only with stations in Asia and in or 

around India. The analysis uses daily AERONET data of AOD and precipitable water, w. The 

spectral AOD data points are reduced to pairs of (α, ß) data using Equation (2) and 

ß = a550 0.55α. (6) 

Individual daily values of ß, together with monthly-mean values of α and w, are used as inputs to 

the REST2 radiation model [12] to obtain the daily and monthly-mean DNI. A monthly effective 

ß, ßeff, is then obtained as the single (monthly) value of ß that yields the same monthly-mean 

DNI as previously, all other things being conserved. These calculations are repeated for each 

station and each calendar month with more than 40 daily data points. The present results, shown 

in Figure 26, confirm those in the previous study [12], to the effect that ßeff is very close to the 

monthly median ß. Some scatter exists, which can be attributed to the simplicity of the method, 

particularly regarding the modulating effects of α and w, which are not taken into account. 

Interestingly, the ratio between the effective and median ß is closer to one over India than at 

those sites in Asia that are outside India. 

A more detailed, seasonal analysis has provided the ratios ßeff/ßmean for each of the three seasons 

identified in 5.1. These ratios are found to be 0.950 for the dry season, 0.920 for the monsoon 

season, and 0.915 for the post-monsoon season. 

The monthly “effective” AOD550 values were obtained by multiplying the monthly-mean 

AOD550 values derived in Section 5.3 by these correction factors. The desired monthly 

AOD700 data were then obtained through Equation (3). Finally, a double linear interpolation, 
36 

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using the topographic correction scheme of Section 4, was applied to obtain a data set of 

AOD700 at 0.1° x 0.1° resolution. 

Figure 26. Calculated effective monthly values of ß over Asia, compared to the observed monthly 

median ß at AERONET sites 

The present statistical analysis introduces significant changes compared to the previous AOD 

data set that was used for the National Renewable Energy Laboratory’s 2010 India irradiance 

maps. The main difference is the much larger value of the monthly effective AOD (close to the 

median) compared to the monthly mode value that was used previously. That difference is 

illustrated in Figure 25 for one site and one month, but is a general characteristic. Considering 

the sensitivity of DNI on AOD [1], it can be expected that the newer modeled DNI data will be 

lower (typically by about 10 ±5%) than those in the 2010 release. This should actually help 

improve the accuracy of the newer irradiance maps by removing the high bias in the 2010 maps 

for DNI and GHI, which was recently found in some validation exercises, particularly in 

Rajasthan (e.g., [13]). 

 
37

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8 Conclusion and Recommendations

Various existing aerosol data bases have been tested and optimally combined to develop a data

set of monthly values of AOD and AE over India. Various existing databases have been tested 

and optimally combined to develop a data set of monthly values of AOD at 550 nm and of AE 

for the Indian subcontinent and the period from 2002 to 2012. These data sets are largely based 

on satellite information (particularly from MODIS Terra), after proper regional scaling to match 

ground truth as closely as possible, and data filling with climatological values to compensate for 

missing data. The end product of this investigation is a data set of AOD at 700 nm with a fine 

spatial resolution of 0.1° x 0.1°, to be used as input of the State University of New York satellite 

model to derive irradiance time series and solar resource maps.

Current limitations in satellite retrieval techniques and in availability of ground-truth data result

in significant scatter in the proposed data set. This issue constitutes a source of error in the 

eventual irradiance predictions (particularly those of DNI) that use this data set as input. 

Nevertheless, the present data set represents significant progress compared to previous attempts. 

In particular, the detailed analysis of the statistical attributes of the AOD frequency distributions 

that has been conducted here produced a way to correct the low bias that existed in the previous 

(2010) AOD data set. It is thus anticipated that the forthcoming solar irradiance maps, which will 

be based on the present data set, will have less bias than the 2010 irradiance maps released by the 

National Renewable Energy Laboratory. In particular, the DNI data series are expected to be 

approximately 10% ±5% lower than before, which should be enough to remove their noted high 

bias. 

Considering these current deficiencies in the source data, it is hoped that future versions of the 

MISR and MODIS algorithms will improve the accuracy of their retrievals. Additionally, a large 

increase in ground-truth data, using high-quality sunphotometers and experimental procedures,

would be necessary to develop better corrections to the satellite data, and a more convincing 

validation process. Similarly, high-quality DNI measurements would be necessary to assess the 

performance of the solar resource maps that will be developed from this study. Through an 

appropriate backward procedure, experimental DNI data could even be used to lower the current 

uncertainty in the historical time series of AOD data over India.
38

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9 References

1. Gueymard, C.A. "Temporal Variability in Direct and Global Irradiance at Various Time 

Scales as Affected by Aerosols." Solar Energy (86) 2012; pp. 3,544–2,553.

2. Datar, S.V., et al. "Trends in Background Air Pollution Parameters Over India."

Atmospheric Environment (30) 1996; pp. 3,677–3,682.

3. Porch, W., et al. "Trends in Aerosol Optical Depth for Cities in India." Atmospheric

Environment (41) 2007; pp. 7,524–7,532.

4. Ramanathan, V., et al. "Atmospheric Brown Clouds: Impacts on South Asian Climate 

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