C b di id b i f IAS Carbon dioxide observation from IAS Carbon dioxide observation from IAS S Del Bianco U Cortesi M Gai S. Del Bianco, U. Cortesi, M. Gai Istituto di Fisica Applicata "Nello Carrara" del Consiglio Nazionale delle R Istituto di Fisica Applicata Nello Carrara del Consiglio Nazionale delle R INTRODUCTION INTRODUCTION Th ESA h j t “A li ti f KLIMA Al ith t CO Rti l f The ESA research project “Application of KLIMA Algorithm to CO 2 Retrieval from IASI/METOP-A Observations and Comparison with TANSO-FTS/GOSAT Products” aimed to develop a dedicated software, based on the KLIMA inversion algorithm (originally to develop a dedicated software, based on the KLIMA inversion algorithm (originally proposed by IFAC CNR for the 6 cycle of ESA Earth Explorer Core Missions) suited for proposed by IFAC-CNR for the 6 cycle of ESA Earth Explorer Core Missions), suited for CO i l d i d i h ESA id b d i l i G id CO 2 retrieval and integrated into the ESA grid-based operational environment Grid Processing On-Demand (G-POD) to process Level 1 data acquired by the Infrared Atmospheric Sounding Interferometer (IASI) and to perform a comparison with Thermal Atmospheric Sounding Interferometer (IASI) and to perform a comparison with Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer And Near -infrared Sensor for carbon Observation Fourier Transform Spectrometer ( ) b d f h h b i lli ( ) l (TANSO-FTS), on board of the Greenhouse gases Observing SATellite (GOSAT), Level 2 data. data. I d t bt i bl it t b lk i IASI dt h t it t In order to obtain a reasonable capacity to bulk processing IASI data, we choose to integrate the KLIMA code into the G-POD system. For this reason, we investigated an optimized version of the KLIMA algorithm aiming at developing a non-operational retrieval code with version of the KLIMA algorithm, aiming at developing a non operational retrieval code with adequate features for the integration on the G POD system The optimized version of adequate features for the integration on the G-POD system. The optimized version of KLIMA i l d h b l d d i d h G POD i l KLIMA retrieval code has been completed and integrated on the G-POD operational environment and is available for bulk processing of IASI data. Using the KLIMA inversion code integrated into the ESA G-POD it was possible to perform an extensive comparison of code integrated into the ESA G-POD, it was possible to perform an extensive comparison of a selected set of IASI meas rements collocated ith SWIR TANSO FTS obser ations a selected set of IASI measurements collocated with SWIR TANSO-FTS observations. As part of an extension of the collaboration between IFAC-CNR and the GOSAT team in As part of an extension of the collaboration between IFAC CNR and the GOSAT team in response to the second Research Announcement we performed a comparison over a response to the second Research Announcement, we performed a comparison over a t ll d d dt t f ll td IASI d TIR TANSO FTS b ti temporally reduced dataset of collocated IASI and TIR TANSO-FTS observations over land and ocean. In this work, we present first results and preliminary conclusions from this activity activity . Fi 1 Gl b l f CO d th f ll Fi 2 Ttl f KLIMA Fi 3 A d CO Figure 1: Global map of CO 2 averaged over the full id f 2° 2° i l Figure 2: Total error of KLIMA- IASI CO lti li d b th Figure 3: Averaged CO f th f t t year on a grid of 2°×2° pixels IASI CO 2 multiplied by the square f h 2 l d f i of the surface temperatu 1 K root of the χ 2 plotted as a function 1 K of the surface temperature COMPARISON ACTIVITIES COMPARISON ACTIVITIES KLIMA IASI L2 EUMETSAT IASI L2 d KLIMA IASI L2 vs. EUMETSAT IASI L2 products A perfect coincidence in time and space exists with respect to operational data delivered by EUMETSAT The comparison between KLIMA and EUMETSAT datasets is difficult EUMETSAT . The comparison between KLIMA and EUMETSAT datasets is difficult b t i htf d l ti d t d th b f ti t f because straightforward correlations do not emerge and the absence of an error estimate for one of the two results prevents a clear conclusions [Cortesi et al., 2013]. KLIMA IASI L2 vs SWIR TANSO FTS GOSAT L2 products KLIMA IASI L2 vs. SWIR TANSO-FTS GOSAT L2 products Th diff t t t i h b d Three different strategies have been used: • Co-located comparison: comparison of the CO 2 total column retrieved from observations 2 of IASI and TANSO-FTS made in contiguous locations in time and space [Cortesi et al of IASI and TANSO FTS made in contiguous locations in time and space [Cortesi et al., 2013]; 2013]; d i i f h l l d i bl il • Averaged comparison: comparison of the CO 2 total column averaged on a suitable spatial and time interval (Fig. 4); • Seasonal variation comparison: comparison of the seasonal variations of CO from • Seasonal variation comparison: comparison of the seasonal variations of CO 2 from M h 2010 t Fb 2011 (Fi 5) March 2010 to February 2011 (Fig. 5). In the case of the averaged comparison the distribution of the differences between the two datasets shows a negative 7,3 ppm bias of KLIMA-IASI, with a standard deviation of 7 ppm datasets shows a negative 7,3 ppm bias of KLIMA IASI, with a standard deviation of 7 ppm and an un accounted error of KLIMA IASI of about 6 ppm with respect to the retrieval error and an un-accounted error of KLIMA-IASI of about 6 ppm with respect to the retrieval error d h CO h i i bili T ki i h diff A i K l and to the CO 2 atmospheric variability . Taking into account the different Averaging Kernels , the negative bias is reduced to 1,17 ppm, as showed in Fig. 4. KLIMA IASI L2 vs TCCON products KLIMA IASI L2 vs. TCCON products As in the previous case, the comparison with TCCON stations shows a negative bias [Cortesi et al., 2013]. [Cortesi et al., 2013]. CONCLU CONCLU The main outcomes of the project can be summarized in terms of the results we have already c The main outcomes of the project can be summarized in terms of the results we have already c i ft ti iti in future activities: • Development and implementation of a non-operation inverse model for the retrieval of CO 2 p 2 • Fits of good quality obtained from KLIMA wide-band and multi-target analysis; observed ge Fits of good quality obtained from KLIMA wide band and multi target analysis; observed ge • On average negative bias of KLIMA retrieved CO relative to other instruments; the average • On average, negative bias of KLIMA retrieved CO 2 relative to other instruments; the average h i ih S A SO S h d d f A AS f • The comparison with SWIR TANSO-FTS showed an un-accounted error of KLIMA-IASI of • The un-accounted continuum retrieval adopted for the comparison with TIR TANSO-F • The new retrieval settings used for the TIR will be applied in the comparison with SWI • The new retrieval settings used for the TIR will be applied in the comparison with SWI Th h ih h kD N k Si h f Chib Ui i f h ll b i i id The authors wish to thank Dr. Naoko Saitoh of Chiba University for the collaboration in provid U. Cortesi et al. (2013) Finale Report of the Project ‘Sensitivity analysis and application of KL SI d i i h TANSO FTS SI and comparison with TANSO-FTS SI and comparison with TANSO-FTS i L M Laurenza and F Barbara i, L.M. Laurenza, and F. Barbara Ricerche Via Madonna del Piano 10 50019 Sesto Fiorentino (Firenze) Italy Ricerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy ANALISYS OF IASI DATA WITH KLIMAALGORITHM ANALISYS OF IASI DATA WITH KLIMA ALGORITHM W l d ttl f 240 000 IASI t i G POD ti ti i We analyzed a total of 240.000 IASI spectra using G-POD computing resources, retrieving the CO 2 both on land and on water and during day and night for a global geographical 2 coverage coverage. Th i i i Fi 1 h h lbl f CO d h f ll The coverage is given in Fig. 1 where the global map of CO 2 averaged over the full year on a grid of 2°×2° pixels is shown. In the retrievals, the forward model calculations fit well the spectra observed by IASI with residual differences comparable with the spectral noise The spectra observed by IASI with residual differences comparable with the spectral noise. The resid als are generall m ch smaller than the spectral noise Fe isolated atmospheric residuals are generally much smaller than the spectral noise. Few isolated atmospheric 2 features show average residuals that are larger than the spectral noise. The χ 2 values show a correlation with Earth’s surface temperature and are close to unity at low temperatures, correlation with Earth s surface temperature and are close to unity at low temperatures, while increase monotonically up to 3 at the highest temperatures Values greater than 3 while increase monotonically up to 3 at the highest temperatures. Values greater than 3 i ll bt bi tl l td i d t b bl itd t d occasionally occur, but being mostly located in desert areas are probably associated to sand storm scenarios. The retrieval error of CO varies as a function of the surface temperature with values of 2 The retrieval error of CO 2 varies as a function of the surface temperature with values of 2 hi h d 20 l B ii f IASI ppm at high temperature and up to 20 ppm at low temperature. Best precision of IASI observations is obtained in warm seasons and at low latitudes and the worst precision is obtained in the cold seasons and at high latitudes A conservative estimate of the total obtained in the cold seasons and at high latitudes. A conservative estimate of the total ti l i bt i d b lti l i th ti l ith th t f th 2 retrieval error is obtained by multiplying the retrieval error with the square root of the χ 2 (see Fig. 2). Fig 3 shows the average of all retrieved CO values as a function of surface temperature Fig. 3 shows the average of all retrieved CO 2 values as a function of surface temperature. O l d ( d it ) hi h f t t i ltd ith l t th h Over land (red points) a higher surface temperature is correlated with plant growth when a CO 2 sink is expected, on the other hand over ocean (blue points) a lower surface 2 temperature increases the solubility of CO 2 into the sea and produces a sink temperature increases the solubility of CO 2 into the sea and produces a sink. O f ti Fi 4 S tt di f Fi 5 S l i ti f th CO f M h 2010 t O 2 as a function bi d Figure 4: Scatter diagrams of th CO2 KLIMA TANSO Figure 5: Seasonal variation of the CO 2 from March 2010 to Fb 2011 i N th H i h Th th ure binned over the CO2 KLIMA vs TANSO- FTS 2 i 2° 2° February 2011 in Northern Hemisphere. The average on the H i h f h CO i d b KLIMA IASI ( d i ) FTS v .2 comparison on a 2°x2° Hemispheres of the CO 2 retrieved by KLIMA-IASI (red points) pixel grid related to the whole is compared with the Hemisphere average of SWIR TANSO- year FTS CO 2 products; green points refer to L2 TANSO-FTS v .2 original products while blue points refer to v .2 smoothed KLIMA IASI L2 TIR TANSO FTS GOSAT L2 d t KLIMA IASI L2 vs. TIR TANSO-FTS GOSAT L2 products A different retrieval set-up has been adopted in this comparison: • Retrieval of CO profile 010/03/08 coincident IASI and TIR TANSO-FTS measure geoloca • Retrieval of CO 2 profile 80 010/03/08 coincident IASI and TIR TANSO FTS measure geoloca IASI • Use of the same vertical retrieval grid 40 60 eg.] IASI TANSO-FTS • Un accounted continuum retrieved simultaneously 0 20 40 e [de • Un-accounted continuum retrieved simultaneously 40 -20 0 itude -60 -40 Lati -80 -180 -150 -120 -90 -60 -30 0 30 60 90 120 150 180 IASI and TIR TANSO-FTS CO 2 Profile Difference 180 150 120 90 60 30 0 30 60 90 120 150 180 Longitude [deg.] 10 0 Averaged Differences Averaged Differences Standard Deviation IASI and TIR TANSO-FTS CO 2 Total Column 400 2 IASI 395 IASI TANSO-FTS 1 r] 390 m] 10 1 Bar 385 [pp [mB 380 385 O 2 re 375 380 C sur 370 375 10 2 res 370 -80 -60 -40 -20 0 20 40 60 80 10 Pr Latitude [deg.] The un accounted continuum retrieval The un-accounted continuum retrieval th td i th removes the un-accounted error in the KLIMA results. The new retrieval 10 3 settings will be applied in the -6-5-4-3-2-1 0 1 2 3 4 5 6 settings will be applied in the comparison with SWIR TANSO FTS CO 2 vmr [ppm] comparison with SWIR TANSO-FTS GOSAT d TCCON L2 d t GOSAT and TCCON L2 products. USIONS USIONS consolidated and of the open and new issues we are investigating at present or plan to address consolidated and of the open and new issues we are investigating at present or plan to address profile and total column from IASI/MetOp-A observations eographical and seasonal variability often in good agreement with expectations eographical and seasonal variability often in good agreement with expectations ed comparison with SWIR TANSO FTS shows a bias of 7 3 ppm ed comparison with SWIR TANSO-FTS shows a bias of -7,3 ppm f b 6 ih h i l d h CO h i i bili f about 6 ppm with respect to the retrieval error and to the CO 2 atmospheric variability FTS products removes the un-accounted error in the KLIMA results IR TANSO-FTS GOSAT and TCCON L2 products IR TANSO-FTS GOSAT and TCCON L2 products di h dd f TANSO FTS TIR d ding the used dataset of TANSO-FTS TIR data. LIMA algorithm to GOSAT and OCO validation’ (ESA-ESRIN/Contract n. 21612/08/I-OL).
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C b di id b i f IASCarbon dioxide observation from IASCarbon dioxide observation from IASS Del Bianco U Cortesi M GaiS. Del Bianco, U. Cortesi, M. Gai
Istituto di Fisica Applicata "Nello Carrara" del Consiglio Nazionale delle RIstituto di Fisica Applicata Nello Carrara del Consiglio Nazionale delle R
INTRODUCTIONINTRODUCTION
Th ESA h j t “A li ti f KLIMA Al ith t CO R t i l fThe ESA research project “Application of KLIMA Algorithm to CO2 Retrieval fromIASI/METOP-A Observations and Comparison with TANSO-FTS/GOSAT Products” aimedpto develop a dedicated software, based on the KLIMA inversion algorithm (originallyto develop a dedicated software, based on the KLIMA inversion algorithm (originallyproposed by IFAC CNR for the 6 cycle of ESA Earth Explorer Core Missions) suited forproposed by IFAC-CNR for the 6 cycle of ESA Earth Explorer Core Missions), suited forCO i l d i d i h ESA id b d i l i G idCO2 retrieval and integrated into the ESA grid-based operational environment GridProcessing On-Demand (G-POD) to process Level 1 data acquired by the Infraredg ( ) p q yAtmospheric Sounding Interferometer (IASI) and to perform a comparison with ThermalAtmospheric Sounding Interferometer (IASI) and to perform a comparison with ThermalAnd Near infrared Sensor for carbon Observation Fourier Transform SpectrometerAnd Near-infrared Sensor for carbon Observation Fourier Transform Spectrometer( ) b d f h h b i lli ( ) l(TANSO-FTS), on board of the Greenhouse gases Observing SATellite (GOSAT), Level 2data.data.
I d t bt i bl it t b lk i IASI d t h t i t tIn order to obtain a reasonable capacity to bulk processing IASI data, we choose to integratethe KLIMA code into the G-POD system. For this reason, we investigated an optimizedy , g pversion of the KLIMA algorithm aiming at developing a non-operational retrieval code withversion of the KLIMA algorithm, aiming at developing a non operational retrieval code withadequate features for the integration on the G POD system The optimized version ofadequate features for the integration on the G-POD system. The optimized version ofKLIMA i l d h b l d d i d h G POD i lKLIMA retrieval code has been completed and integrated on the G-POD operationalenvironment and is available for bulk processing of IASI data. Using the KLIMA inversionp g gcode integrated into the ESA G-POD it was possible to perform an extensive comparison ofcode integrated into the ESA G-POD, it was possible to perform an extensive comparison ofa selected set of IASI meas rements collocated ith SWIR TANSO FTS obser ationsa selected set of IASI measurements collocated with SWIR TANSO-FTS observations.
As part of an extension of the collaboration between IFAC-CNR and the GOSAT team inAs part of an extension of the collaboration between IFAC CNR and the GOSAT team inresponse to the second Research Announcement we performed a comparison over aresponse to the second Research Announcement, we performed a comparison over at ll d d d t t f ll t d IASI d TIR TANSO FTS b titemporally reduced dataset of collocated IASI and TIR TANSO-FTS observations overland and ocean. In this work, we present first results and preliminary conclusions from this, p p yactivityactivity.
Fi 1 Gl b l f CO d th f ll Fi 2 T t l f KLIMA Fi 3 A d COFigure 1: Global map of CO2 averaged over the fullid f 2° 2° i l
Figure 2: Total error of KLIMA-IASI CO lti li d b th
Figure 3: Averaged COf th f t tyear on a grid of 2°×2° pixels IASI CO2 multiplied by the square
f h 2 l d f iof the surface temperatu1 Kroot of the χ2 plotted as a function 1 K
of the surface temperature
COMPARISON ACTIVITIESCOMPARISON ACTIVITIES
KLIMA IASI L2 EUMETSAT IASI L2 dKLIMA IASI L2 vs. EUMETSAT IASI L2 productsA perfect coincidence in time and space exists with respect to operational data delivered byp p p p yEUMETSAT The comparison between KLIMA and EUMETSAT datasets is difficultEUMETSAT. The comparison between KLIMA and EUMETSAT datasets is difficultb t i htf d l ti d t d th b f ti t fbecause straightforward correlations do not emerge and the absence of an error estimate forone of the two results prevents a clear conclusions [Cortesi et al., 2013].p [ ]
KLIMA IASI L2 vs SWIR TANSO FTS GOSAT L2 productsKLIMA IASI L2 vs. SWIR TANSO-FTS GOSAT L2 productsTh diff t t t i h b dThree different strategies have been used:• Co-located comparison: comparison of the CO2 total column retrieved from observationsp p 2
of IASI and TANSO-FTS made in contiguous locations in time and space [Cortesi et alof IASI and TANSO FTS made in contiguous locations in time and space [Cortesi et al.,2013];2013];
d i i f h l l d i bl i l• Averaged comparison: comparison of the CO2 total column averaged on a suitable spatialand time interval (Fig. 4);a d t e te va ( g. );
• Seasonal variation comparison: comparison of the seasonal variations of CO from• Seasonal variation comparison: comparison of the seasonal variations of CO2 fromM h 2010 t F b 2011 (Fi 5)March 2010 to February 2011 (Fig. 5).
In the case of the averaged comparison the distribution of the differences between the twog pdatasets shows a negative 7,3 ppm bias of KLIMA-IASI, with a standard deviation of 7 ppmdatasets shows a negative 7,3 ppm bias of KLIMA IASI, with a standard deviation of 7 ppmand an un accounted error of KLIMA IASI of about 6 ppm with respect to the retrieval errorand an un-accounted error of KLIMA-IASI of about 6 ppm with respect to the retrieval error
d h CO h i i bili T ki i h diff A i K land to the CO2 atmospheric variability. Taking into account the different Averaging Kernels ,the negative bias is reduced to 1,17 ppm, as showed in Fig. 4.g , pp , g
KLIMA IASI L2 vs TCCON productsKLIMA IASI L2 vs. TCCON productsAs in the previous case, the comparison with TCCON stations shows a negative bias[Cortesi et al., 2013].[Cortesi et al., 2013].
CONCLUCONCLUThe main outcomes of the project can be summarized in terms of the results we have already cThe main outcomes of the project can be summarized in terms of the results we have already ci f t ti itiin future activities:• Development and implementation of a non-operation inverse model for the retrieval of CO2 pp p p 2 p• Fits of good quality obtained from KLIMA wide-band and multi-target analysis; observed geFits of good quality obtained from KLIMA wide band and multi target analysis; observed ge• On average negative bias of KLIMA retrieved CO relative to other instruments; the average• On average, negative bias of KLIMA retrieved CO2 relative to other instruments; the average
h i i h S A SO S h d d f A AS f• The comparison with SWIR TANSO-FTS showed an un-accounted error of KLIMA-IASI of• The un-accounted continuum retrieval adopted for the comparison with TIR TANSO-Fp p S• The new retrieval settings used for the TIR will be applied in the comparison with SWI• The new retrieval settings used for the TIR will be applied in the comparison with SWI
Th h i h h k D N k S i h f Chib U i i f h ll b i i idThe authors wish to thank Dr. Naoko Saitoh of Chiba University for the collaboration in providU. Cortesi et al. (2013) Finale Report of the Project ‘Sensitivity analysis and application of KL( ) p j y y pp
SI d i i h TANSO FTSSI and comparison with TANSO-FTSSI and comparison with TANSO-FTSpi L M Laurenza and F Barbarai, L.M. Laurenza, and F. BarbaraRicerche Via Madonna del Piano 10 50019 Sesto Fiorentino (Firenze) ItalyRicerche, Via Madonna del Piano 10, 50019 Sesto Fiorentino (Firenze), Italy
ANALISYS OF IASI DATA WITH KLIMA ALGORITHMANALISYS OF IASI DATA WITH KLIMA ALGORITHM
W l d t t l f 240 000 IASI t i G POD ti t i iWe analyzed a total of 240.000 IASI spectra using G-POD computing resources, retrievingthe CO2 both on land and on water and during day and night for a global geographical2 g y g g g g pcoveragecoverage.
Th i i i Fi 1 h h l b l f CO d h f llThe coverage is given in Fig. 1 where the global map of CO2 averaged over the full year ona grid of 2°×2° pixels is shown. In the retrievals, the forward model calculations fit well theg p ,spectra observed by IASI with residual differences comparable with the spectral noise Thespectra observed by IASI with residual differences comparable with the spectral noise. Theresid als are generall m ch smaller than the spectral noise Fe isolated atmosphericresiduals are generally much smaller than the spectral noise. Few isolated atmospheric
2features show average residuals that are larger than the spectral noise. The χ2 values show acorrelation with Earth’s surface temperature and are close to unity at low temperatures,correlation with Earth s surface temperature and are close to unity at low temperatures,while increase monotonically up to 3 at the highest temperatures Values greater than 3while increase monotonically up to 3 at the highest temperatures. Values greater than 3
i ll b t b i tl l t d i d t b bl i t d t doccasionally occur, but being mostly located in desert areas are probably associated to sandstorm scenarios.
The retrieval error of CO varies as a function of the surface temperature with values of 2The retrieval error of CO2 varies as a function of the surface temperature with values of 2hi h d 20 l B i i f IASIppm at high temperature and up to 20 ppm at low temperature. Best precision of IASI
observations is obtained in warm seasons and at low latitudes and the worst precision ispobtained in the cold seasons and at high latitudes A conservative estimate of the totalobtained in the cold seasons and at high latitudes. A conservative estimate of the total
t i l i bt i d b lti l i th t i l ith th t f th 2retrieval error is obtained by multiplying the retrieval error with the square root of the χ2
(see Fig. 2).( g )
Fig 3 shows the average of all retrieved CO values as a function of surface temperatureFig. 3 shows the average of all retrieved CO2 values as a function of surface temperature.O l d ( d i t ) hi h f t t i l t d ith l t th hOver land (red points) a higher surface temperature is correlated with plant growth when aCO2 sink is expected, on the other hand over ocean (blue points) a lower surface2 p , ( p )temperature increases the solubility of CO2 into the sea and produces a sinktemperature increases the solubility of CO2 into the sea and produces a sink.
O f ti Fi 4 S tt di f Fi 5 S l i ti f th CO f M h 2010 tO2 as a functionbi d
Figure 4: Scatter diagrams ofth CO2 KLIMA TANSO
Figure 5: Seasonal variation of the CO2 from March 2010 toF b 2011 i N th H i h Th thure binned over the CO2 KLIMA vs TANSO-
FTS 2 i 2° 2°February 2011 in Northern Hemisphere. The average on theH i h f h CO i d b KLIMA IASI ( d i )FTS v.2 comparison on a 2°x2° Hemispheres of the CO2 retrieved by KLIMA-IASI (red points)
pixel grid related to the whole is compared with the Hemisphere average of SWIR TANSO-year FTS CO2 products; green points refer to L2 TANSO-FTS v.2
original products while blue points refer to v.2 smoothed
KLIMA IASI L2 TIR TANSO FTS GOSAT L2 d tKLIMA IASI L2 vs. TIR TANSO-FTS GOSAT L2 productsp
A different retrieval set-up has been adopted in this comparison:p p p
• Retrieval of CO profile 010/03/08 coincident IASI and TIR TANSO-FTS measure geoloca• Retrieval of CO2 profile 80
010/03/08 coincident IASI and TIR TANSO FTS measure geoloca
IASI
• Use of the same vertical retrieval grid 40 60
eg.]
IASITANSO-FTSg
• Un accounted continuum retrieved simultaneously 0 20 40
e [de
• Un-accounted continuum retrieved simultaneously40
-20 0
itude
-60-40
Lati
-80
-180-150-120 -90 -60 -30 0 30 60 90 120 150 180IASI and TIR TANSO-FTS CO2 Profile Difference
180 150 120 90 60 30 0 30 60 90 120 150 180
Longitude [deg.]100Averaged DifferencesAveraged Differences
Standard Deviation IASI and TIR TANSO-FTS CO2 Total Column
4002
IASI 395
IASITANSO-FTS
1r] 390m]
101
Bar
385[pp
[mB
380
385
O2
re
375
380C
sur
370
375
102res 370
-80 -60 -40 -20 0 20 40 60 8010
Pr
Latitude [deg.]
The un accounted continuum retrievalThe un-accounted continuum retrievalth t d i thremoves the un-accounted error in the
KLIMA results. The new retrieval103
settings will be applied in the-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 settings will be applied in thecomparison with SWIR TANSO FTS
CO2 vmr [ppm]comparison with SWIR TANSO-FTSGOSAT d TCCON L2 d tGOSAT and TCCON L2 products.
USIONSUSIONSconsolidated and of the open and new issues we are investigating at present or plan to addressconsolidated and of the open and new issues we are investigating at present or plan to address
profile and total column from IASI/MetOp-A observationsp peographical and seasonal variability often in good agreement with expectationseographical and seasonal variability often in good agreement with expectationsed comparison with SWIR TANSO FTS shows a bias of 7 3 ppmed comparison with SWIR TANSO-FTS shows a bias of -7,3 ppmf b 6 i h h i l d h CO h i i bilif about 6 ppm with respect to the retrieval error and to the CO2 atmospheric variabilityFTS products removes the un-accounted error in the KLIMA resultsS pIR TANSO-FTS GOSAT and TCCON L2 productsIR TANSO-FTS GOSAT and TCCON L2 products
di h d d f TANSO FTS TIR dding the used dataset of TANSO-FTS TIR data.LIMA algorithm to GOSAT and OCO validation’ (ESA-ESRIN/Contract n. 21612/08/I-OL).g ( )
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