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INTERCOMPARISON OF AEROSOL OPTICAL THICKNESS DERIVED FROM MODIS AND IN-SITU GROUND DATASETS OVER JAIPUR -A SEMI ARID ZONE IN INDIA Swagata Payra, Manish Soni, Anikender Kumar, Divya Prakash, and Sunita Verma Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.5b02225 • Publication Date (Web): 09 Jul 2015 Downloaded from http://pubs.acs.org on July 12, 2015

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INTERCOMPARISON OF AEROSOL OPTICAL

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THICKNESS DERIVED FROM MODIS AND IN-

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SITU GROUND DATASETS OVER JAIPUR - A

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SEMI ARID ZONE IN INDIA

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Swagata Payra1, Manish Soni1, Anikender Kumar2, Divya Prakash1 and Sunita Verma1,*

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Centre for Excellence in Climatology, Birla Institute of Technology Mesra, Jaipur Campus, Jaipur 302017, Rajasthan, India

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Department of Chemical and Environment Engineering, National University of Colombia, Bogota, Colombia.

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Abstract

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First detailed seasonal validation has been carried out for the MODerate Resolution Imaging Spectro-

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radiometer (MODIS) terra and aqua satellites level 2.0 collection version 5.1 AOT (τMODIS) with Aerosol

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Robotic Network (AERONET) level 2.0 AOT (τAERONET) for the year 2009-2012 over semi arid region

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Jaipur, north western India. The correlation between τMODIS vs τAERONET at 550 nm is determined with

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different spatial and temporal size windows. The τMODIS overestimates τAERONET within a range of

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+0.06±0.24 during the pre-monsoon (Apr-Jun) season while it underestimates the τAERONET with -

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0.04±0.12 and -0.05±0.18 during dry (Dec-Mar) and post-monsoon (Oct-Nov) seasons, respectively.

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Correlation without (with) Error Envelope has been found for pre-monsoon 0.71 (0.89), post-monsoon

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0.76 (0.94) and dry season 0.78 (0.95). The τMODIS is compared with τAERONET at three more ground

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AERONET stations in India i.e. Kanpur, Gual Pahari and Pune. Furthermore, the performance of MODIS

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Deep blue and Aqua AOT550 nm (τDB550nm and τAqua550nm) with τAERONET is also evaluated for all considered

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sites over India along with a US desert site at White Sand, Tularosa Basin, New Mexico. The statistical

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results reveal that τAqua550nm performs better over Kanpur, Pune whereas τDB550nm performs better over

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Jaipur, Gual Pahari and White Sand HELSTF (US site).

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Keywords: MODIS, AERONET, Aerosols, Dust, Climate, Deep Blue

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*Correspondence:

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Dr. Sunita Verma, Phone:+ 91-141-2385094 (extn - 306), Email:[email protected], Centre of

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Excellence in Climatology, Birla Institute of Technology Mesra, Jaipur Campus, 27 Malviya Industrial

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Area, Jaipur 302017, Rajasthan, India

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1. Introduction

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Aerosols play an important role in the climate of the Earth-atmosphere system by means of their

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direct and indirect impact on climate.1 Atmospheric aerosols are able to alter Earth’s Radiation budget by

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scattering and absorption of solar radiation. Due to aerosol’s highly variable spatial and temporal

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distribution, large uncertainty exists in the aerosol radiative forcing and for that reason the level of

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scientific understanding is very low.2

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The in-situ as well as satellite measurements can help in the scientific understanding of aerosols.

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Aerosols observations through remote sensing can provide detailed knowledge on a long time scale

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covering large spatial area.3 Ground based observation is a point measurement and cannot provide spatial

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variation unless a large network is working. In satellite observation, same instrument makes observation

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globally. The aerosol concentration can be compared at different locations which will not be affected by

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the calibration errors of the instrument.4 Satellite data provide us to quantitatively determine the aerosol

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optical properties and thus global aerosol budget.

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considerable errors in the retrieved aerosol products where the surface reflectance is high while for

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ground based data have no such constraints and therefore it provide the measurement of microphysical

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and optical properties of ambient columnar aerosols with high accuracy.7 The bias or error in Aerosol

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Optical Thickness (AOT) retrievals from satellite could arise from various sources e.g. incorrect

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assumptions about surface reflectance, aerosol type, status of sensor calibration and observation

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geometry, etc.8

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However, the satellite measurement may introduce

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The validation of satellite based measurement with ground based observation is important to

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establish its quality and suitability to be used in climatology and weather modeling. Ground based data is

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used for validation of satellite measured aerosol properties. 9 The MODIS-derived aerosol properties over

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land

4, 7, 9-13

and over the ocean

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have been validated by a large number of ground based stations


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worldwide. There are several studies on the validation of aerosol optical thickness data of ground based

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instrument with satellite based measurement and is in progress since sensors started to provide the data. 4,

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7, 11-12, 15-23

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The retrieval of AOT over land continues to be a challenge, due to large surface heterogeneity

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especially over India. This can introduce an error in estimated surface reflectance, which goes into

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radiative transfer calculations to retrieve AOT from satellite data. The averaging of pixels and cloud

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contamination is another source of error in remote sensing. To improve the accuracy of the MODIS data,

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it is essential to compare and validate the MODIS data with independent ground-based measurements.

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The spatial, temporal resolution and computation method of both is not same. The ground based

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AERONET sunphotometer measures AOT directly and no particular aerosol model is considered. The

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calibration and estimation of τAERONET can be done more reliably as the biases that can alter AOT values

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are also recorded at AERONET stations.24,25 Several authors have compared between τMODIS and τAERONET

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to show the seasonal dependence and variations in the correlations. The comparison between ground-

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based observations and MODIS satellite data retrievals has been carried out by various authors 4, 7, 21, 26-30

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over India. Mostly an overestimation by MODIS during summer or pre-monsoon, underestimation during

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winter and post monsoon is reported. 21, 31 Despite the increased proficiency and use of closer-to-realistic

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models in the retrieval algorithms, several studies have shown that discrepancies still exist between

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(products) retrieved AOT from satellite over arid, semi-arid regions and urban regions.30,32-33 However, to

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date a systematic seasonal study of MODIS and ground based observations as done in present study from

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Jaipur, a semi arid region India is not reported to our knowledge and is an important location for the study

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of aerosols as its proximity to the Thar desert.34

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Remote sensing technique is always good for Dark Target (DT) (τMODIS). Since the launch of

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MODIS, aerosol retrievals over lands uses a DT approach when 2.1 µm surface reflectance is greater than

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0.15, no aerosol retrievals are performed.30 However, the DT algorithm fails when reflectance is greater

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than 0.15 (Deserts, snow covered areas). Hsu. et al., 2004(32,33) developed a new algorithm named

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"Deep Blue (DB) Algorithm" to deal with situations like this. DB algorithm requires polarization


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correction for Terra Sensor of MODIS. Due to polarization problem of Terra satellite, the only deep blue

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data available after 2007 is of deep blue aqua AOT 550nm ( http://modis-atmos.gsfc.nasa.gov

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/products_calendar.html). This method is particularly useful for arid, semi arid and urban areas where

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AOT retrieval is problematic due to scattering of sun light in the presence of sand particles and greater

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surface reflectance. 32,33

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The objective of present study is to validate the MODIS satellite aerosol product with ground

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based AERONET observations over Jaipur in North Western India. An extensive evaluation and

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validation has been carried out between the measurements of level 2 Collection Version 5.1 MODIS

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onboard the Terra and Aqua satellites retrieved product (τMODIS) and the available Deep Blue AOT550nm

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(τDB550nm) against the seasonal ground based AERONET (2009-12) observations over Jaipur in North

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Western India and compared with three more sites over India. The study is divided into 5 sections. The

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section 1, 2 and 3 gives the brief introduction, site description and meteorology, respectively. Section 4

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describes the details of data used and its procedure of extraction along with introduction to Error

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Envelope (EE) criterion used for the study. In section 5.1.1 and 5.1.2 of results and analysis, the inter

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comparison between the τMODIS and τAERONET without and with EE Criterion of MODIS has been done.

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The τMODIS is also compared with τAERONET at three more ground AERONET stations i.e. Kanpur, Gual

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Pahari and Pune, India for both criterions. The analysis is further extended in section 5.1.3 to compare the

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performance of τDB550nm and τAqua550nm with τAERONET for all sites over India seasonally, and then annually

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with a US desert site at White Sand, Tularosa Basin, New Mexico. This is the first time that seasonal

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MODIS data has been reported from a site in North western India and inter-compared with more

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AERONET ground measurement sites over India.

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2. Site Description

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The measurement station is located at B M Birla Science and Technology Center ,Jaipur (26.9°

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N, 75.8° E), the capital of Rajasthan state. Jaipur, a hot semi arid region, at eastern boundary of the Thar

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Desert. This Desert is the major source of dust storm located in western India and Eastern Pakistan for


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Indian sub-continental during pre-monsoon season. The frequency of occurrence of dust storms is

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maximum during pre-monsoon season when dust is transported from the Thar desert.35. This site is

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strategically important due to its proximity to Thar desert.

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The Jaipur AERONET sites (Figure 1)36 chosen for validation purpose of τMODIS, τAqua550nm and τDB550nm against τAERONET observation along with Gual Pahari34 ,Kanpur21 and Pune31.

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Gual Pahari is an urban background site surrounded by farms and fields, and is located 25 km

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south of the nearest city, New Delhi. The Delhi pollution and rapid rate of urbanization contributes to

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aerosols load over Gual Pahari location. This location has sub tropical climate.

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At Kanpur, the AERONET site is located at Indian Institute of Technology Kanpur campus which

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is 17 km away from the center of Kanpur .Kanpur is a representative site of the Ganga basin in terms of

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the weather conditions and atmospheric seasonal variability. This location also has sub tropical climate.21

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At Pune, the site is located at Indian Institute of Tropical Meteorology (IITM), which is 100 km away from west coast of India. It has tropical wet and dry climate.31

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3. Synoptic Meteorology

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The meteorological parameters for year 2009-12 (Figure. 2) over Jaipur site is retrieved from

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National Center for Environmental Prediction (NCEP) – National Center for Atmospheric

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Research (NCAR) reanalysis data. The rainfall values taken from Department of Water

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Resource, Government of Rajasthan;(http://waterresources.rajasthan.gov.in/Daily_Rainfall_Data

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/Rainfall_Index.htm).

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According to local meteorology, the seasons has been differentiated in three major classes, viz, dry (December to March), pre-monsoon (April-June), and post-monsoon (October and November).

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Dry season is generally characterized by decrease in humidity and wind speed. We see in the

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figure 2 that there is decrease in humidity up-to march and then the humidity starts increasing. The pre-

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monsoon season in the region is characterized by warm winds originating from western part of Rajasthan.

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This in turn results in increase in temperature, wind speed and humidity. Monsoon season (July-


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September) is characterized by decrease in temperature and more increase in humidity as the south west

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monsoon reaches Rajasthan. During monsoon, region receives scanty to normal rainfall in western and

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eastern parts respectively. The post monsoon months is characterized by decrease in temperature and low

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humidity(~40-50%). This continues until the January of dry season after which the temperature gradually

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starts increasing.

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4. Data Analysis and Methodology

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4.1 Aerosol Robotic Network (AERONET)

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The Aerosol Robotic Network (AERONET) consists of automatic tracking CIMEL Sun photometer/sky

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radiometers located at more than 500 ground sites around the world currently. The CIMEL sunphotometer

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is a multi-channel, automatic sun-and-sky scanning radiometer that measures the direct solar irradiance

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and sky radiance at the Earth's surface only during daylight hours (sun above horizon)

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application of the cloud screening and quality control procedures described by Smirnov et al. 2000(41).

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During study period, an AERONET CIMEL sun/sky photometer was operated at the Jaipur ground site.

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Measurements are taken at pre-determined discrete seven wavelengths in the visible and near-IR parts of

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the spectrum to determine atmospheric transmission and scattering properties at every fifteen minutes.

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In this study, in-situ hourly averaged level 2.0 τAERONET data are computed at 0.550 µm to match the

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spectral resolution of τMODIS.

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37-40

after

4.2 MODerate resolution Imaging Spectro-radiometer (MODIS)

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MODIS sensors on board the Earth Observing System (EOS) Terra and Aqua polar-orbiting

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satellites measures the Earth reflecting solar and emitted terrestrial radiances in 36 high resolution

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channels from 0.4 – 14.0 µm with a spatial resolution of 250 m, 500 m, 1 km depending on the

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wavelength. Its large swath of 2330 km and large spectral coverage (36 channels) make it highly suitable

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for characterization of aerosol properties. 8,42


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The spatial resolution of MODIS pixel is 10 x 10 km. The overall accuracy of the τMODIS over 15

However, depending on the assumptions on

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land is estimated to be 15% with a minimum of 0.05.

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surface types and aerosol optical properties, the accuracy may differ for specific regions of the world.

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This paper also examines AOT received from observations in blue channel where surface 30

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reflectance is relatively high

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products, bias and assumptions, algorithms used in deep blue and standard MODIS products (τMODIS) can

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be found elsewhere. 9, 15, 30, 32-33, 40, 43-44

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called as Deep Blue AOT 550nm. Details about the MODIS, its retrieval

4.3 Methodology The present paper discusses the validation of level 2 Collection Version 5.1 MODIS retrieved

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product (τMODIS) and Deep Blue AOT550nm (τDB550nm) against AERONET (2009-12) observations.

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4.3.1 Procedure for Extracting AOT

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The extraction of pixel from MODIS header file is done with HDFlook.45 The AOT derived from

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MODIS is the spatial average at pixel surface whereas it is of point location for AERONET. The

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probability of coincidence when the AERONET point location AOT and, AOT derived from center pixel

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value of MODIS matches is very low.

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below to get the desired value from MODIS.

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For spatial window,

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Length=AERONET_SITE (Latitude) ± SW (Spatial window size in degrees)

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Width= AERONET_SITE (Longitude) ± SW (Spatial window size in degrees)

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Where SW (0.05, 0.10, 0.125, 0.25, 0.375, 0.50)

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Temporal window estimation is done by taking the MODIS overpass scan time ± temporal window size

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(0.5, 1.0, 2.0, 3.0 hour).

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So we used different spatial and temporal windows as shown

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A rectangular box is considered with centre at AERONET site by taking Length and Width. The

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average pixel values are calculated after considering all the quality checked pixels. Those pixel values


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which were found within the mean and standard deviations are studied else filtered out. Every Pixel was

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also sub-sampled to find out the direction. It was found that the most of the pixels lie within North East

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Direction (Towards Indo-gangetic plain). Larger windows can introduce undesirable results due to surface

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or aerosol type heterogeneity and also there is a possibility for light cirrus cloud contamination. Smaller

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windows can results into very less number of pixels as MODIS has a spatial resolution of 10 Km × 10

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Km. Spatial window of size ±0.05 (10 Km × 10 Km) will only correspond to 1 pixel by 1 pixel, size

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(±0.10) to 2× 2 pixel (20 Km × 20 Km) and so on. 12 But 2 × 2 is a very small sample for considering

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the average value of τMODIS.. Moreover, average travel speed of aerosol front is 50 Km/hr. This has been

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visually estimated by TOMS daily sequence aerosol index images (http://jwocky.gsfc.nasa.gov/aerosols/

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aermovie.html). So, the 50 Km × 50 Km ( 5 × 5 pixel) or ±0.25 spatial window will match 1-hour

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sunphotometer data segment well.12 This is also found in our case as most of MODIS observations lies

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between 1 hour of standard overpass time (10:30 am and 1:30pm IST).

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The percent change of number of observation when going from spatial window of size ±0.20 to

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±0.25 is also very large, almost half (~48 %). So spatial windows size of ±0.25 degree is considered

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overall as it gives better results in terms of correlation, count (Minimum 5 Number of pixels for MODIS

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and 2 for AERONET), standard deviation, Details are shown in Table S1.

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All the spatial statistics in the rest of paper are considered based on spatial window size of ±0.25(≈ 50

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KM × 50 KM) or 5 pixel × 5 pixel averaged over 1 hour time.

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The same criterion has been followed for extraction of pixels in Deep Blue AOT550nm (τDB550nm).

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For validation purpose, τAERONET (500 nm) has been interpolated to a common wavelength 550 nm using the power law. 28, 31

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4.3.2 Error Envelope Criterion for inter-comparison of MODIS vs AERONET

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Since the launch of MODIS it is seen that MODIS measures AOT with an Expected Error or Error

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Envelope (EE). The previous MODIS validation studies also signify consideration of an EE for τMODIS

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which is represented by equation (1).

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AOT – |EE| ≤ τMODIS ≤ AOT + |EE| 15, 16,31

(Equation 1)


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where and

EE = ± 0.05 ± 0.20× AOT over land EE = ± 0.03 ± 0.05× AOT over ocean

So we have also performed the validation by the direct retrieved AOT from MODIS with EE over Jaipur in section 5.1.1 and 5.1.2, respectively.

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5. Results and Discussions

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5.1 Statistical Analysis

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5.1.1 Inter-comparison without Error Envelope Criterion

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The graphs in Figure 3a show the comparison between τMODIS and τAERONET during the period

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April 2009 to March 2012 for Jaipur. A significant correlation is found for dry (0.78) and post-monsoon

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(0.76) when there is almost no dust loading. While a correlation of 0.71 is found for pre-monsoon during

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heavy dust-loading period. The mean and standard deviation of τ MODIS is 0.53 ±0.33, 0.32 ±0.19 and 0.36

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±0.28 while τ

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post-monsoon respectively. The variation in dry season is less compare to pre or post monsoon. Pre and

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post monsoon retrieval process is often problematic by aerosol in homogeneity especially bright surfaces

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and cloud contamination.

AERONET

is 0.47 ± 0.17, 0.36 ± 0.15 and 0.40 ±0.24 during pre-monsoon, dry season and

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Figure 3a also shows that τMODIS overestimates τAERONET during pre-monsoon. More points lie

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above trend line (red solid line) which shows overestimation (≈59%) during the season. Dust Storms are

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frequent (source Thar Desert) over Jaipur. The presence of dust particles in the atmosphere reduces the

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transparency which in turn reduces the reflectance and the τMODIS is overestimated.21 Coarse mode volume

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concentration also increases during this period as it is a season of dust loading and surface is dry during

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this time. This may also be the reason for overestimation. Also it is shown in the figure that more points

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lies below the trend line (blue and green solid line). τMODIS underestimates the τAERONET by 67 % and by 62

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% approximately during dry and post-monsoon seasons, respectively over Jaipur.

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overestimates τAERONET during pre-monsoon season where the mean error lies within a range of


The τMODIS

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+0.06±0.24. MODIS underestimates the τAERONET with a -0.04±0.12 and -0.05±0.18 during the dry season

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(Dec-Mar) and post-monsoon (Oct-Nov), respectively. All the statistics are significant to 2 decimal places

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and confidence interval is 95%. Fine mode particles are dominant than coarse mode particles during post

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monsoon and dry seasons. Surface reflectance is the largest bias during non-dust period. During low dust

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loading period the atmosphere is more transparent and thus surface reflectance is overestimated which in

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turn results in underestimation of the τMODIS. The Fractional Bias (FB) for the three seasons are 0.15,-0.11,

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0.12 for the dry, pre-monsoon and post monsoon, respectively. Negative FB value indicates that MODIS

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overestimates AERONET whereas positive FB indicates MODIS underestimates AERONET data. The

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average AOT lies in the range of 0.2-0.6 for all the seasons for both AERONET and MODIS.

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The τMODIS overestimate τAERONET (Figure 3b) during all the seasons over Gual Pahari. The inter-

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comparison has been carried out for the duration April 2009 to January 2010 as AERONET site is

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operational during this time only. More number of points is above trend line. It overestimates with

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0.10±0.14, 0.10±0.15, 0.06±0.22 for pre-monsoon, post-monsoon and dry season respectively. MODIS

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overestimates τAERONET for high AE (α=1.1) and underestimate τAERONET for low AE (α=0.8) in most of the

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cases. The FB for the Dry, Pre and Post seasons are -0.07, -0.16 and -0.15.

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MODIS overestimate the τAERONET during all the seasons over Kanpur (Figure 3c). It overestimates with

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0.30±0.23, 0.13±0.19, 0.06±0.14 for pre-monsoon, post-monsoon and dry season respectively. Kanpur

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site has major industrial activities. Due to the rapid industrialization, the polluted/fine particles and hence

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the absorption coefficient is large which leads to overestimation of τMODIS. for all the seasons. FB is

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highest for the Kanpur in all the other sites. The FB for the dry, pre- and post-monsoon seasons are -0.11,

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-0.41 and -0.16. The work by Tripathi et al 2005 (21) suggests that there is underestimation in post-

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monsoon (Sept-Nov) and winter (Dec-Feb) and overestimation in pre-monsoon (Mar-May) for the year

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2004.

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Pune is less polluted in comparison to Kanpur and Gual Pahari. During the pre-monsoon period

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MODIS overestimates the τAERONET (0.09±0.14) as coarse mode particles are dominant due to abundant


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dust loading over Pune (Figure 3d). However, during the post-monsoon (-0.01±0.11) and dry seasons (-

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0.01 ±0.11), fine mode particles are dominant than coarse mode particles which leads to under-estimation

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of τMODIS. The FB for the dry, pre- and post- monsoon seasons are 0.09,-0.21 and 0.03 respectively. A

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good coincidence is shown between MODIS and AERONET observations by More et al., 2013 (31)

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during pre-monsoon but for winter (Dec-Mar) MODIS underestimation is reported for the same site.

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5.1.2 Inter-comparison with Error Envelope Criterion

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The correlation is again determined by considering τ MODIS with EE (Equation 1) as suggested by

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validation studies.19-20 The observations which follows Equation 1 are only considered for statistical

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analysis.

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The correlation (0.95, 0.89, 0.94), FB (0.05, 0.00, 0.00), Normalized mean square error i.e.

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NMSE (0.04,0.03,0.04) between τMODIS and τAERONET with EE improves considerably for dry, pre and

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post monsoon seasons respectively. Similar procedure has been applied for Kanpur, Pune and Gual

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Pahari.

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For Jaipur without EE there is overestimation in pre-monsoon and underestimation in other

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seasons. However, the FB comes very close to 0 and there is underestimation in all seasons namely dry,

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pre, post (0.05, 0.00, 0.00) when EE criterion is followed. For Pune, there is underestimation in dry

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(FB=0.01) and overestimation in pre (FB= -0.12) and post season (FB= -0.05). For Gual Pahari and

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Kanpur, without EE and with EE there is overestimation in all the seasons. The FB for Gual Pahari and

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Kanpur are -0.08,-0.11,-0.11 and -0.07,-0.16,-0.10 for dry, pre and post monsoon, respectively. The

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correlations for all the site locations also improved considerably.

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The average of τMODIS and τAERONET at 550 nm has been shown in Figure 4 over Jaipur, Gual

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Pahari, Kanpur and Pune, respectively and results have been tabulated. For Jaipur, the average of τMODIS

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and τAERONET is higher during the pre-monsoon and less during all other seasons. The average AOT i.e

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τMODIS and τAERONET for all the seasons lies within the range of 0.2-0.6. The average AOT ( τMODIS and


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τAERONET) lies within the range of 0.4-0.8 for Kanpur and Gual Pahari and 0.2-0.6 for Pune during all the

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seasons.

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Figure 5a shows the Taylor diagram without EE and Figure 5b with EE criterion for all the sites.

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For the purpose of display SD (normalized) values are multiplied by 10 (range is 0.00-0.06) in Figure 5b.

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After using the EE criterion the SD (normalized) lies in the range of 0.03-0.05 which was earlier 0.1-0.3.

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For Jaipur, the value of NMSE, MAE and RMSE during pre-monsoon season are 0.24, 0.16 and 0.24,

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respectively without EE criterion while after applying EE criterion the values comes to 0.031, 0.068 and

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0.082 respectively. Our results show that the correlation has improved significantly after EE criterion is

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followed. RMSE error also reduces to 0.05-0.06. Also, the average AOT of Jaipur lies in between 0.2-0.6

302

before and after applying EE criterion i.e Equation 1, which ensures the integrity of data. Inter-

303

comparison of MODIS retrievals with respect to AERONET suggests that the MODIS observations over

304

Jaipur are validated and acceptable. The comparative results between data series give a representative

305

pattern of the uncertainties that needs attention for additional parameterization for present MODIS

306

algorithms.

307 308

Detailed statistical analysis without and with EE is shown in Table 1. Details about the formulas are given in Table S2.

309 310 311 312 313

5.1.3 Inter-comparison with Deep Blue Aqua AOT550nm (τDB550nm) and Aqua AOT

550nm

(τAqua550nm) The correlation between the τMODIS and τAERONET at 550 nm during the pre monsoon season for Jaipur, Gual Pahari, Kanpur and Pune are 0.71, 0.77, 0.72, 0.61 respectively (Figure 3).

314

To find out the performance of deep blue algorithm at these sites, we again compared both

315

τAqua550nm and τDB550nm against τAERONET for all the sites. The correlation between MODIS Deep Blue Aqua

316

AOT550nm (τDB550nm) with τAERONET is found to be 0.75, 0.67, 0.54 and 0.78 within the chosen spatial

317

(±0.25) and temporal window (1 hour average) during pre-monsoon for respective sites. When τAqua550nm


13

Page 15 of 31


318

is compared with τAERONET the correlation is found to be 0.74, 0.84, 0.66 and 0.75 (Table 2). As per the

319

correlation, for pre-monsoon, τDB550nm performs better for Jaipur and Pune, and for Gual pahari and

320

Kanpur, τAqua550nm performed better. Figure 6(a), 6(b), 7(a) and 7(b) show the scatter plot for τDB550nm and

321

τAqua550nm with τAERONET for Jaipur and Pune respectively.

322

The detailed statistical analysis in terms of correlation, FB and NMSE shows that, for Jaipur and

323

Gual Pahari during the pre-monsoon and post monsoon respectively, τDB550nm performs better then

324

τAqua550nm and for other, situation is reverse. Results for the respective sites are shown in Table 2.

325 326

The result from previous seasonal validation studies on MODIS over India suggests an

327

overestimation during pre-monsoon and underestimations during other seasons over Kanpur, 21

328

India. For Pune

329

and under-estimation in post-monsoon and winter seasons. The present study found

330

overestimation during all seasons in Kanpur and Gual Pahari but Pune and Jaipur has

331

overestimation only in pre-monsoon and underestimation during post-monsoon and dry seasons.

332

Furthermore, as the Jaipur site is a semi arid region, we chose to compare the

333

performance of τDB550nm and τAqua550nm with τAERONET for considered sites over India along with a US desert

334

site for the entire duration (2009-12) to check the consistency of our results. This US location is situated

335

in New Mexico at the White Sands Missile Range (WSMR) High Energy Laser Systems Test Facility

336

(HELSTF). This region of South Central Mexico is high desert with annual precipitation around 20 cm

337

and 350 days sunshine. Strong seasonal winds are very frequent during February to May. So dust aerosols

338

along with gypsum from nearby place can cause frequent dust storms. The annual inter-comparison

339

results over Jaipur, Gual Pahari and White Sand HELSTF (US site) suggests that τDB550nm performs better

340

than τAqua550nm in terms of correlation, root mean square error (RMSE) and FB. Our annual validation

341

results contradicts with recent study by Bibi et al. (46) over Jaipur however agrees in case of Kanpur and

31

previous inter-comparison study suggests a good match during pre-monsoon


annual

14


342

Pune i.e. τAqua550nm performs better than τ

343

presented in Table 3.

DB550nm.

Page 16 of 31

The details of annual inter-comparison results are

344 345

As per the overall annual performance is considered, for Jaipur, Gual Pahari and White Sand (US

346

site) τDB550nm with correlation (0.79,0.80,0.58), fractional bias (0.08, -0.03 and 0.26), RMSE

347

(0.22,0.22,0.03) performed better than τAqua550nm with correlation (0.78,0.80,0.62), fractional bias (0.13, -

348

0.12 and -1.34), RMSE (0.22,0.22,0.03) respectively. For Kanpur and Pune, τAqua550nm performed better

349

statistical results. Thus, our recommendation is to use τDB550nm for Jaipur, Gaul Pahari and White Sand

350

sites and τAqua550nm for Kanpur and Pune.

351 352

As per the seasons, for pre-monsoon season for Jaipur, τDB550nm has to be used where τAqua550nm

353

faces difficulty in correct retrieval of aerosol. For other seasons and remaining sites standard MODIS

354

product (τAqua550nm) produce good statistical results. Though this also shows there is further improvement

355

that needs to be done in deep blue algorithm for semi-arid, arid and urban regions so that better

356

characteristics can be obtained. Results obtained in this work can also aid in improvement of MODIS

357

aerosols products that need to be done in updated version of algorithms.

358

359

Acknowledgments

360

We would like to thank the Editor and anonymous reviewers for suggestions that helped in

361

improving substantially the presentation of the revised manuscript. MODIS data were obtained

362

from the Level 2 and Atmosphere Archive and Distribution System (LAADS) at Goddard Space Flight

363

Center (GSFC), (http://ladsweb.nascom.nasa.gov/data/). We also acknowledge the MODIS mission

364

scientists and AERONET group, PI’s of respective site, for the production of the data used in this

365

research effort. We also acknowledge NCEP for providing the reanalysis data.


15

Page 17 of 31


366

Supporting Information Available

367

The Supplementary information contains 2 tables on a total of 4 pages. Spatial and Temporal window

368

selection criterion for MODIS pixels extraction against AERONET observations for Jaipur is illustrated

369

in Table S1. Spatial window size

370

size(Averaged 0.5, 1.0, 2.0, 3.0 ) hr are considered for this evaluation.Table S2 contains the formulas

371

used as performances measures in this study. This information is available free of charge via the Internet

372

at http://pubs.acs.org/ .

±(0.05, 0.10, 0.125, 0.25, 0.375, 0.50) and Temporal window

373 374

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375

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113-126.

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519 520 521 522 523


22


Gual Pahari Jaipur

Kanpur

Pune

Figure 1. AERONET Site(Jaipur,Gual Pahari,Kanpur,Pune) used in the study.


Page 24 of 31

Page 25 of 31


Figure 2. Synoptic Meteorology for year 2009-2012 over Jaipur, Northwestern India. 3(a)



3(b)

3(c)


Page 26 of 31

Page 27 of 31


3(d)

Figure 3. τ

MODIS

vs τ

AERONET

for (a) Jaipur (b) Gual Pahari, (c) Kanpur (d) Pune (AERONET Sites).

MODIS values have been averaged around ±0.25 latitude x ±0.25 longitude box centered over the corresponding location. Black Dash lines indicates AOD+|EE| and AOD- |EE| where EE = ± 0.05 ± 0.20*AOT and Blue dash line shows 1:1 line. Blue, Red and Green solid line shows trend line for Dry, pre-monsoon, post-monsoon respectively The points below the trend line shows underestimation and above shows overestimation by τ MODIS when compared with τ AERONET .

Figure 4. Average AOT during different seasons over Gual Pahari, Jaipur, Kanpur and Pune, respectively.



5(a)

Page 28 of 31

5(b)

Figure 5. Taylor diagrams for all the sites a) without EE criterion b) with EE criterion

6(a)

6(b)

Figure 6(a) Deep blue AOT 550nm Vs AERONET AOT550nm (b) Aqua AOT 550nm Vs AERONET AOT550nm For Jaipur


Page 29 of 31


7(b) Figure 7(a) Deep blue AOT 550nm Vs AERONET AOT550nm (b) Aqua AOT 550nm Vs AERONET AOT550nm For Jaipur



τ R

JAIPUR

PRE-MONSOON POST MONSOON

DRY SEASON

GUAL_PAHARI

PRE-MONSOON

POST MONSOON

DRY SEASON

MODIS

Vs

τ

Page 30 of 31

AERONET

FB

FA2

NMSE

W/O EE

W/ EE

W/O EE

W/ EE

W/O EE

W/ EE

W/O EE

W/ EE

0.78 0.71

0.95 0.89

0.15 -0.11

0.05 0.00

87.06 84.35

100.00 100.00

0.17 0.24

0.04 0.03

0.76

0.94

0.12

0.00

81.40

100.00

0.24

0.04

0.72 0.76

0.92 0.87

-0.07 -0.16

-0.08 -0.11

91.67 97.10

100.00 100.00

0.10 0.07

0.03 0.03

0.84

0.95

-0.15

-0.11

97.73

100.00

0.07

0.03

0.84 0.70

0.94 0.93

-0.11 -0.41

-0.07 -0.16

97.92 83.78

100.00 100.00

0.08 0.27

0.03 0.05

0.81

0.94

-0.16

-0.10

98.44

100.00

0.09

0.03

0.72 0.61

0.92 0.86

0.09 -0.21

0.01 -0.12

95.22 96.71

100.00 100.00

0.11 0.16

0.03 0.04

0.78

0.91

0.03

-0.05

98.55

100.00

0.10

0.04

DRY SEASON

KANPUR


DRY SEASON

PUNE


Table 1. Detailed Statistics Without(W/O) and With(W/ ) Error Envelope criterion.

Where FB

Fractional Bias

NMSE Normalized mean square error

FA2

Factor of 2 observations

R

Correlation


Page 31 of 31


τAERONET

Vs CORRELATION

τAQUA550nm τDB550nm τAQUA550nm τDB550nm τAQUA550nm τDB550nm τAQUA550nm τDB550nm

DRY PREMONSOON POST MONSOON

0.79 0.74 0.83

0.74 0.75 0.85

0.18 0.13 0.04

0.43 -0.17 0.09

0.17 0.22 0.42

0.45 0.17 0.60

JAIPUR

FRACTIONAL BIAS DRY PREMONSOON POST MONSOON NMSE DRY PREMONSOON POST MONSOON

GUAL_PAHARI 0.78 0.84 0.87

KANPUR

0.73 0.67 0.89

0.87 0.66 0.84

PUNE

0.80 0.54 0.81

0.80 0.75 0.87

0.76 0.78 0.78

-0.04 -0.12 -0.10

-0.12 -0.03 -0.02

0.01 -0.20 -0.10

0.14 -0.42 -0.03

0.06 -0.18 -0.02

0.19 -0.31 0.24

0.08 0.07 0.06

0.15 0.10 0.08

0.05 0.19 0.07

0.34 0.37 0.18

0.09 0.08 0.04

0.19 0.16 0.25

Table 2. Detailed Statistics of MODIS Aqua AOT (τAqua550nm) and MODIS Deep Blue Aqua AOT550nm (τDB550nm) against AERONET AOT 550nm(τAERONET)

JAIPUR

τAERONET Vs

GUAL_PAHARI

KANPUR

PUNE

WHITE SAND

τAQUA550nm

τDB550nm

τAQUA550nm

τDB550nm

τAQUA550nm

τDB550nm

τAQUA550nm

τDB550nm

τAQUA550nm

τDB550nm

CORRELATION

0.78

0.79

0.80

0.80

0.78

0.69

0.77

0.69

0.62

0.58

FRACTIONAL BIAS

0.13

0.08

-0.12

-0.03

-0.12

-0.14

-0.03

0.02

-1.34

0.26

RMSE

0.19

0.22

0.19

0.22

0.23

0.36

0.11

0.16

0.22

0.03

Table 3. Detailed Annual Statistics (2009-12) of MODIS Aqua AOT (τAqua550nm) and MODIS Deep Blue Aqua AOT550nm (τDB550nm) against AERONET AOT 550nm (τAERONET)


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