Heavier precipitation,more intense and longer droughts….

CLIMATE CHANGE

ATMOSPHERIC AEROSOL GREEN HOUSE GASES (GHGs)

GLOBAL WARMINGGLOBAL DIMMING

GLOBAL AVERAGES OF THE CONCENTRATIONS OF CARBON DIOXIDE, METHANE, NITROUS OXIDE, CFC-12 AND CFC-11

These gases account for about 97% of the direct warming effect of the long-l ived greenhouse gases since 1750. The remaining 3% is contributed by an assortment of 10 minor halogen gases. ( Source NOAA, Annual Greenhouse Gas Index )

CO2N2O

CH4 CFCs

Retrieval & Monitoring of Atmospheric Retrieval & Monitoring of Atmospheric Green House Gases (GHGs) through Green House Gases (GHGs) through remote sensingremote sensing

Debasish Chakraborty Roll No. – 4843Division of Agricultural Physics

RETRIEVAL: To find or extract stored information.

MONITORING: To watch & check over a period of time in order to see how any phenomena develops/changes so that one can take necessary action. So, monitoring of Green House Gases(GHGs) over the globe is a spatiotemporal property.

GHGs MeasurementConventional

Remote sensing

Standard type of technique to measure GHGs

VIALS

GC analysis

ECD, FID detectors

Gas storage

Gas accumulation over t ime

Closed chamber – Gas Chromatographic analysis, IRGA

Advantage:

Technique is simple

Can be handled with short training

Very accurate

Limitation:

Limited spatial distr ibution

Sampling error

Closed chamber – Gas Chromatographic analysis, IRGA

SATELLITE MEASUREMENTSADVANTAGE:

Provide Global coverage High temporal resolution Data with sufficient precision is becoming available - Multi-purpose missions-SCIAMACHY,AIRS -Missions dedicated to GHGs-GOSAT/JAXA ; launched on jan,2009

LIMITATION:

Absolute measurement of physical parameters Several disturbances (sensor cal/val, clouds, aerosol etc) Retrieval needs complex algorithms Asks for expertise

Ground Based Project : FLUXNET LIMITATION

DISCONTINUITY OF MEASUREMENTS

COORDINATION BETWEEN STATIONS

UPSCALING METHODS

LIMITED AREA COVERAGE

SATELLITE MONITORING COMBINED WITH THIS GROUND BASED PROJECTS CAN BE A BETTER

OPTION

ATMOSPHERIC SCIENCE SPACEBORNE INSTRUMENTS

CURRENTLY WORKING SATELLITES

MID & THERMAL INFRARED REGION(TIR & MIR) HIRS(2002) - NOAA AIRS(2002) - NASA

IASI(2006) - EUMETSAT

DETECTION

Thermal radiation emitted from surface & atmosphere (3.6 to 15µm)

ADVANTAGE:Day & night measurement is possible

DISADVANTAGE:Lack of sensit ivity in lower troposphere

CURRENTLY WORKING SATELLITES

UV/VIS/NIR/SWIR REGION SCIAMACHY(2002) - ESA TANSO(2009) - JAXA OCO(2009) - NASA

DETECTION

Reflected, backscattered, transmitted & emitted from surface & atmosphere (240 to 2400 nm)

DISADVANTAGE:Restricted to day only

ADVANTAGE:Sensit ivity constant with height & maximum near the surface

ABSORPTION BANDS OF DIFFERENT CONSTITUENTS

POSSIBLE ERROR SOURCES

EVALUATED IN ADVANCE: Spectroscopic parameters Solar spectra

CORRECTED BY ADDITIONAL INFORMATION

Cloud covered scene

Aerosol covered scene

Surface elevation

Surface spectra

Water vapor

Temperature

Cirrus effect can be cancelled by 760 nm(O 2 band) and 2000nm (H 2O saturated spectral

region).

MEASURED DATA FILTERING FOR NOISE REMOVAL

FILTERING ITEMSSolar Zenith Angle

Cloud Estimation

Aerosol at high Altitudes

Filtered spectra

Aerosol Transport Model(ex.SPRINTARS)

Cloud

Input Spectra

ATMOSPHERE R T MODEL

INITIAL CONSTITUENTS

TEMPERATURE

PRESSURE

ALBEDO

DATA PROCESSING

SYNTHESIZED SPECTRA

FILTERED SPECTRA

Yokota et. al

VALIDATION

CASE STUDY-I

STUDY AREA: Boreal forests (Novosibirsk region) & the region of Surgut

SENSOR USED: AIRS AMSU-A

RADIATIVE TRANSFER MODEL: SARTA

RUSSIAN METEOROLOGY AND HYDROLOGY Vol. 34 No. 4 2009

1. SELECTION OF C02 SENSITIVE CHANNELS

CO2-sensit ive channels at low sensit ivity to interfering factors Nine LW-channels in the spectral range of 699–705 cm –1

Six SW-channels in the spectral range of 1939–2017 cm –1

THE STUDY HAS TWO PARTS:

∆TB(i) = δTB( i) +δqH2O TB(i) + δqO3TB(i) + δqTB(i) + . . . . + ξ i

2. AIRS DATA INVERSION

Analysis of satell i te data to sample cloud free measurement or measurements reduced to cloud cleared condit ions (http://disc.gsfc.nasa/AIRS/data)

Inverse problem in respect to Xco 2 is solved numerically using the Gauss-Newton iteration algorithm, two independent estimates of Xco2 (LW) & Xco2 (SW) are estimated by AIRS data

Sampling of estimates Xco 2(LW) & Xco2(SW) derived for t ime interval and the sounding area are subject to spatiotemporal f i l tering

The results of aircraft CO 2measurements (spatial ly coincident and quasi- synchronous with satel l i te )at dif ferent alt i tudes are used for comparison

The systematic biases is calculated by -

δ(ᾳ) = [TBobs (ᾳ) - TB

calc (ᾳ)], ᾳ= 1, . . . ., n,

The standard deviat ions (SD) of Xco 2(sat) from the aircraft observations at alt i tudes 7 and 3 km were calculated to estimate the errors of the results of the satell i te sounding. The SD are 1.5 and 1.2 ppm compared to the aircraft CO2 observations at alt i tudes 7 and 3 km, respectively.

Fig: comparison of satell i te(2) and aircraft data of 7000 m (1) & 3000 m (3)

RESULT COMPARISON

Novosibirsk

Surgut

CASE STUDY-II

STUDIED GAS: Methane(CH 4)

SENSOR USED: SCIAMACHY (Channel 8 – 2260 to 2385 nm )

RADIATIVE TRANSFER MODEL: SCIATRAN

RADIATIVE TRANSFER ALGORITHM: WFM-DOAS

Atmos. Chem. Phys. Discussion., 4,2004

THE WFM-DOAS RETRIEVAL ALGORITHM

Based on fitt ing a l inearised radiative transfer model I imod

plus a low order polynomial P i to the algorithm of the ratio of a measured nadir radiance & solar irradiance spectrum,i.e. observed sun-normalised radiance I i

obs . The WFM-DOAS equation can be written as-

|In I iobs (V t ) – [ In I i

mod (V) + ∑δ I imod / δv j /( v j – v j ) + P i (am )]|2 = |RES i|2 →min

The f it parameters are the desired “trace gas vertical column V j” and the polynomial coeff icient a m”.

Fit parameters are determined by LEAST SQUARE method

PRINCIPLE: Differential detection of radiance in gaseous absorption channels with respect to neighbouring atmospheric transparent spectral channels (not influenced by gas) ,to detect the conce. Of desired gas.

Parameters:

Cloud condition- UV PMD(Polarization Measurement Device) SCIAMACHY

Standard atmospheric condition-CH 4, CO2 current concentration

Tropospheric and stratospheric condition- aerosol

Surface albedo and solar zenith angle

Surface elevation

Water vapour column and temperature profi le shift

The reference spectra was generated by-

Radiative transfer model- SCIATRAN

WFM-DOAS CH 4 VERTICAL COLUMN RETRIEVAL ERROR(A) USING SIMULATED MEASUERMENTS

ERROR B-TEMPERATURE PROFILE SHIFT is included

RESULT DISCUSSION

Tab; Comparison of SCIAMACHY WFM-DOAS v 0.5 with ground based FTS measurement.N= no. of SCIAMACHY measurements compared with FTS.

Result And Discussion

Fig. Methane column averaged mixing ratios as retr ieved from SCIAMACHY WFM-DOAS V 0.5.

RESULT DISCUSSION

APPLICATION

STUDY AREA: Low and mid lat itudes of Northern Hemisphere

SENSOR USED: SCIAMACHY (1558 to 1594 nm)

ALGORITHM USED : WFM-DOAS version 1.0

Atmos. Chem. Phys. Discuss.,7,2007

ALGORITHM: WFM-DOAS version 1.0

An improved version (Schneising et al. , 2007). The main problems of the previous version WFMDv0.4 (Buchwitz et al. ,) was solved using spectra with improved calibration Better consideration of surface spectral ref lect ivity variabil i ty I t is no longer required to apply a quite large empirical scal ing factor as was necessary for WFMDv0.4.

QUALITY FILTERING OF SCIAMACHY :

For cloud detection the measured oxygen column(755 to 775 nm) and PMDs is used.

Ground alt i tude(pressure) used in simulation by WFM-DOAS increased above 4.1 km.To reject ground scenes with strong aerosol contamination, addit ional f i l t rat ion of the SCIAMACHY XCO 2 measurements using NASA’s Absorbing Aerosol Index (AAI) data product from TOMS/ Earthprobe was done.

Concentrat ion of CO 2 can only be retr ieved over land , not over sea .

Fig. Atmospheric CO 2 over the northern hemisphere during 2003–2005 as retr ieved from SCIAMACHY

satell i te measurements.

FIRST DIRECT OBSERVATION OF ATMOSPHERIC CO 2 IN YEAR TO YEAR FROM SPACE

Fig; Satell i te retrieved XCO 2 and NOAA ESRL Carbon Tracker global assimilat ion system data

Increase in the amplitude of the CO 2 seasonal cycle with the increase in lat i tude

In the retr ieved XCO 2 seasonal cycle an error of 2ppm is seen.

ERROR & IT’S CORRECTION :

The correction equation is – DIF=a + b*AMF

Where, DIF=difference between SCIAMACHY & Carbon Tracker

AMF=1/cos(SZA) + 1/cos(LOS) where, AMF=Air mass factor SZA=Solar zenith angle LOS=Line of sight scan angle

APPLICATION:

STUDY AREA: 50 N to 67.50 S , 54.50 E to 1470 E

SENSOR USED: SCIAMACHY (Channel 8 – 2259 to 2361 nm) with WFM-DOAS V 0.4 SPOT-VEGETATION

ISPRS Archives XXXVIII-8/W3 Workshop Proceedings: Impact of cl imate change on agriculture,2009

METHODOLOGYGlobal weekly ENVISAT-SCIAMACHY CH4 conce. (ppbv) data of 2004 & 2005

Global 10 days composite of SPOT-NDVI products of 2004 & 2005

Computed mean monthly CH4 (ppbv)

Study area was extracted by overlying the region’s boundary and gridded to 0.50 x 0.50 lat itude/longitude grid

Validation using NOAA-CMDL Global view data

Spatial and temporal variabil i ty over study area

CH4 data covering Kharif season (May-Oct) for

2004 & 2005

NDVI data covering Kharif season(May-Oct) for 2004 &

2005

Correlation between CH 4 conce. & NDVI during Kharif season over study area

Computed mean monthly NDVI

Fig 1. Temporal and Spatial Variat ion of Atmospheric CH4

Concentrat ion Over India During 2004 – 05

RESULTS:

Fig2. Temporal Variat ion of Vegetation Over India

During 2004–05

Fig 3. Two year khari f season averaged CH4 conce .

Fig 4. Two year khari f season averaged NDVI.

Fig 5. Correlat ion between CH 4 Conc. and Vegetation During Kharif Season in 2004-05

PROBLEM OF THESE STUDIES:

Scattering at aerosol and/or cloud particles remains a major source of uncertainty for SCIAMACHY XCO 2 retr ievals

The XCO2 retr ieval error may amount to 10% in the presence of mineral dust aerosols. Houweling et al. (2005)

The thin scattering layer with an optical thickness of 0.03 in the upper troposphere can introduce XCO 2 uncertainties of up to several percent. Schneising et al. (2008)

Unfortunately, thin clouds with optical thicknesses below 0.1 cannot easily be detected within nadir measurements in the visible and near infrared spectral region. Reuter et al., 2009; Rodriguez et al.(2007).

Recent Advancements

Algorithm: Merged fit windows approach.

Radiative transfer model : SCIATRAN 3.0

Atmos. Meas. Tech., 3, 209–232, 2010

The measurement vector y consists of SCIAMACHY sun-normalized radiances of two merged fit windows concatenating the measurements in the CO2 and O2 fit window.

= ( , ) + y F x b ԑX = state vectorb = parameter vector&, =ԑ error

The information about these parameters comes mainly from the O2 measurements and is made available in the CO2 band by the merged fit windows approach..

The accuracy for scenes with optically thin cirrus clouds was drastically enhanced compared to a WFM-DOAS like retrieval.

At solar zenith angles of 400, the presence of ice clouds with optical thicknesses in the range of 0.01 to 1.00 contributed with less than 0.5 ppm to the systematic absolute XCO2 error if a perfect forward model is assumed.

RESULTS:

Conclusions: Green House Gases (GHGs) can be measured with good accuracy from satellite data if proper algorithms are applied

Through inverse modeling of measured GHGs we can know in detail about their sources and sinks

Further development in understanding about different factors, their interactions influencing the GHG retrieval and improvement in mathematical methods will surely be able to predict GHGs with better accuracy

Monitoring of GHGs emitted from agricultural practices and activities, wetlands over a region can be done with good accuracy

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