retrieval & monitoring of atmospheric green house gases (gh gs) through remote sensing
DESCRIPTION
Climate change is one of the most important global environmental challenges of this century. Green House Gases (GHGs) are the main culprit for this problem. Though much of research has already been done about the distribution and sources (and sinks) of GHGs , still much more uncertainties are present. Currently, there are only a few satellite instruments in orbit which are able to measure atmospheric GHGs. The High Resolution Infrared Radiation Sounder (HIRS), the Atmospheric InfraRed Sounder (AIRS), and the Infrared Atmospheric Sounding Interferometer (IASI) perform measurements in the thermal infrared (TIR) spectral region. But these are having low sensitivity to lower troposphere. In contrast to this, the sensitivity of instruments measuring reflected solar radiation in the near-infrared (NIR)/shortwave infrared (SWIR) spectral region is much more constant (with height) and shows maximum values near the surface. At present, SCIAMACHY aboard ENVISAT launched in 2002 and TANSO (Thermal And Near infrared Sensor for carbon Observation) aboard GOSAT (Greenhouse gases Observing SATellite) launched in 2009 are the only orbiting instruments measuring in NIR region. Among all the algorithms the WFM-DOAS algorithm (Weighting Function Modified Differential Optical Absorption Spectroscopy) developed at the University of Bremen for the retrieval of trace gases from SCIAMACHY (Buchwitz et al.2005) is mostly used. This is based on the principle of differential detection of radiance in gaseous absorption channels with respect to neighboring atmospheric transparent spectral channels (not influenced by gas) to detect the conc. of desired gas. But scattering at aerosol and/or cloud particles remains a major source of uncertainty for SCIAMACHY XCO2 retrievals(Houweling 2005, Schneising 2008).Of late with the use of new merged fit window approach scientists have come up with less than 0.5 ppm error in the estimation of CO2 in the presence of thin cirrus cloud(Reuter, Buchwitz et. al. 2010). Schneising et. al.,2007,retrieved d three year’s column-averaged CO2 dry air mole fraction from the SCIAMACHY instrument using the retrieval algorithm WFM-DOAS version 1.0, with precision of about 2 ppm. In India a study was undertaken to compare the atmospheric methane concentration pattern from SCIAMACHY with the vegetation dynamics from SPOT, showed fairly good correlation of methane emission with the rice cultivation(Goroshi et. al.).TRANSCRIPT
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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