Satellite Measurements of CO 2 : A peek into the mathematics of the mission! Annmarie Eldering on behalf of the OCO-2&3 teams November 20, 2012

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1 Satellite Measurements of CO 2 : A peek into the mathematics of the mission! Annmarie Eldering on behalf of the OCO-2&3 teams November 20, 2012

2 Talk Overview What is OCO-2?? Science questions that motivate OCO-2 and OCO-3 Overview of OCO-2 measurement approach Optimal Estimation Retrieval Approach Dealing with Real Data pre-screening Post retrieval data selection Science Use of Initial Data Flux inversions Summary Slide 2 UCLA CAS Nov., 2012

OCO-2 OCO-2 stands for Orbiting Carbon Observatory NASA satellite project one instrument on one spacecraft Designed to measure the atmospheric column of

3 OCO-2 OCO-2 stands for Orbiting Carbon Observatory NASA satellite project one instrument on one spacecraft Designed to measure the atmospheric column of carbon dioxide (CO2), globally This is the second one we are building (hence -2), since there was a launch failure of the first one Slide 3 UCLA CAS Nov., 2012

4 Overview of the Carbon Cycle Slide 4 UCLA CAS Nov., 2012

5 With a few more details Natural and anthropogenic Slide 5 UCLA CAS Nov., 2012

The Mystery of the Missing CO 2 Humans have added >200 Gt C to the

atmosphere Where are the sinks that are absorbing over half of the CO 2?

Why does the CO 2 buildup vary from year to year with nearly uniform

6 The Mystery of the Missing CO 2 Humans have added >200 Gt C to the atmosphere since 1958 Less than half of this CO 2 is staying in the atmosphere Where are the sinks that are absorbing over half of the CO 2? Land or ocean? Eurasia/North America? Why does the CO 2 buildup vary from year to year with nearly uniform emission rates? How will these CO 2 sinks respond to climate change? Data from LeQuere et al., 2009? Slide 6 UCLA CAS Nov., 2012

7 A Closer Look at the Missing CO 2 Long-term temporal changes of carbon cycle are important and can not be addressed by OCO-2 alone need measurements on multi-year timescales of key drivers of atmospheric variability (eg., ENSO) OCO-3 focus areas Continue X CO2 measurements started by OCO-2 Reduce uncertainty of carbon flux of terrestrial biosphere and oceans Sample urban centers with largest emissions of fossil fuel CO 2 (opportunistic) China FF CO2 (2020)! Fossil Fuel 9.1±0.5 Land use 0.9±0.7! Land sink 2.6±1.0! Atmo 5.0±0.2! Ocean 2.4±0.5! Global Carbon Project, 2011 Slide 7 UCLA CAS Nov., 2012

OCO-2: The Future Lead of the A-Train Constellation The Orbiting Carbon Observatory - 2 (OCO-2) Watching The Earth Breathe Mapping CO2 From Space OCO-2 will lead the A-Train in the slot just ahead of

8 OCO-2: The Future Lead of the A-Train Constellation The Orbiting Carbon Observatory - 2 (OCO-2) Watching The Earth Breathe Mapping CO2 From Space OCO-2 will lead the A-Train in the slot just ahead of GCOM-W1 NASA s Orbiting Carbon Observatory (OCO-2) is designed to return space-based measurements of atmospheric carbon dioxide (CO 2 ) with the sensitivity, accuracy and sampling density needed to quantify regional scale carbon sources and sinks and characterize their variability. Slide 8 UCLA CAS Nov., 2012

9 Global Measurements Every 16 Days Slide 9 UCLA CAS Nov., 2012

$fraction, X CO2 over$ sunlit hemisphere

10 Measuring CO 2 from Space Collect spectra of CO 2 & O 2 absorption in reflected sunlight over the globe Retrieve variations in the column averaged CO 2 dry air mole fraction, X CO2 over sunlit hemisphere Validate measurements to ensure X CO2 precision of 1-2 ppm ( %) Initial Surf/Atm State Generate Synthetic Spectrum OCO/AIRS/GOSAT New State (inc. X CO2 ) Instrument Model Inverse Model Tower FTS Aircraft X CO2 Flask Slide 10 UCLA CAS Nov., 2012

11 Atmospheric Spectra Measurements in 3 bands - O2 A-band - Weak CO2 band - Strong CO2 band Example of Weak CO2 spectra Slide 11 UCLA CAS Nov., 2012

12 What Causes the Absorption? Slide 12 UCLA CAS Nov., 2012

Retrieving X CO2 from OCO-2 Spectra Interpolated Meteorology Level 1B Data T P q Pre-Processing Filter OCO-2 Retrieval Algorithm Optimal Estimation Full Physics 3-band (ABO2, WCO2, SCO2) Altitude

13 Retrieving X CO2 from OCO-2 Spectra Interpolated Meteorology Level 1B Data T P q Pre-Processing Filter OCO-2 Retrieval Algorithm Optimal Estimation Full Physics 3-band (ABO2, WCO2, SCO2) Altitude State Vector First Guess & Covariance Cloud &Aerosol Optical Properties! # aer Aerosol CO 2 Mixing Ratio Cloud Latitude X CO2 Retrieval Algorithm Post-Processing Filter Forward Radiative Transfer Model Instrument Model Inverse Model Final State and X CO2 Update State Vector? Gas Cross Sections!! 2 "p # aer $ XCO2 Altitude # aer Aerosol CO 2 Mixing Ratio T P q Slide 13 UCLA CAS Nov., 2012

14 The equations of the retrieval algorithm The spectrum, or measurement vector y, is expressed symbolically as y = F(x) +! = Kx +! where x is the state vector, F is the forward model, and! is the vector of spectral errors due to the measurement and forward model (model of the radiative transfer). Generally, the expected value of the state vector x e This can be recast as x e = (K T S e -1 K + S a -1 ) -1 (K T S e -1 y + S a -1 x a ) Or x e = x a + (K T S e -1 K + S a -1 ) -1 K T S e -1 (y - Kx a ) x e = x a + S a K T (KS a K T + S e ) -1 (y - Kx a ) x a is the a priori state, S a is the covariance of a a priori, S e is the measurement error covariance, K is the weighting function matrix or Jacobian, (K ij = df i (x)/dx ij ) Slide 14 UCLA CAS Nov., 2012

15 Why am I showing you all this?? We use a Bayesian approach to the inverse problem Measurement error assumed to be Gaussian -2 ln P(y x) = (y Kx) T S e -1 (y Kx) + c 1 The prior knowledge of x is described as a Gaussian pdf -2 ln P(x) = (x x a ) T S a -1 (x x a ) + c 2 Therefore -2 ln P(x y) = = (y Kx) T S e -1 (y Kx) + (x x a )T S a -1 (x x a ) + c 3 This can be recast, to find that And K T S e -1 y + S a -1 x a = (K T S e- 1K + S a -1 )x e x e = x a + S a K T (KS a K T + S e ) -1 (y - Kx a ) Slide 15 UCLA CAS Nov., 2012

16 The practical approach to optimal estimation The solution of the OCO-2 inverse method is the state vector with maximum a posteriori probability, given the measurement y. To find the state vector that produces the maximum a-posteriori probability, we minimize the following standard cost function! 2 :! 2 = (y F(x)) T S e -1 (y F(x)) + (x a - x) T S a -1 (x a x) where F(x) is the forward model, K is the weighting function matrix, or Jacobian, x a is the a priori state vector, S a is the a priori covariance matrix, S e is the measurement covariance matrix. We do this with a Levenberg Marquardt method of iterations, where " i determines the step size x i+1 = x i + (KK T + " i I) -1 K T [y-f(x i )] Slide 16 UCLA CAS Nov., 2012

17 Retrieving X CO2 from OCO-2 Spectra Interpolated Meteorology Level 1B Data T P q Pre-Processing Filter OCO-2 Retrieval Algorithm Optimal Estimation Full Physics 3-band (ABO2, WCO2, SCO2) Altitude State Vector First Guess & Covariance Cloud &Aerosol Optical Properties! # aer Aerosol CO 2 Mixing Ratio Cloud Latitude X CO2 Retrieval Algorithm Post-Processing Filter Forward Radiative Transfer Model Instrument Model Inverse Model Final State and X CO2 Update State Vector? Gas Cross Sections!! 2 "p # aer $ XCO2 Altitude # aer Aerosol CO 2 Mixing Ratio T P q Slide 17 UCLA CAS Nov., 2012

18 Preprocessing 1. Preprocessors (find the clouds) 2. Sounding selection (if we can t afford to process it all, find the best data to use ) Slide 18 UCLA CAS Nov., 2012

Pre-Processing Filters The ACOS GOSAT Pre-Processing Filters screen out the following L1B data: Soundings acquired at solar zenith angles > 85 Soundings contaminated by

2011) Fits a clear sky atmosphere to every sounding in the O 2 A band High values of!

19 Pre-Processing Filters The ACOS GOSAT Pre-Processing Filters screen out the following L1B data: Soundings acquired at solar zenith angles > 85 Soundings contaminated by optically-thick clouds A Spectroscopic cloud screening algorithm based on the O 2 A-band (ABO2) is currently being used for GOSAT retrievals (Taylor et al. 2011) Fits a clear sky atmosphere to every sounding in the O 2 A band High values of! 2 and large differences between the retrieved surface pressure and the ECMWF prior indicate the presence of clouds Example A-Band fit Poor fit (! 2 = 9.6) indicates presence of cloud Small residuals and good agreement between retrieved and ECMWF surface pressure indicates cloud free sounding Slide 19 UCLA CAS Nov., 2012

The ACOS Cloud Filter The ABO2 cloud screen works well on optically thick and high and cirrus clouds, but Simulations and comparisons with MODIS cloud products indicate that it misses 20-30% of low

20 The ACOS Cloud Filter The ABO2 cloud screen works well on optically thick and high and cirrus clouds, but Simulations and comparisons with MODIS cloud products indicate that it misses 20-30% of low clouds A modified cloud filter is currently being tested The new screen combines the A-band cloud screen with other screens excluding soundings where: 0.99 < XCO2(1.61 #m)/xco2(2.06 #m) > 1.01 or 0.95 < XH2O(1.61 #m)/xh2o (2.06 #m) > 1.05 Comparisons with simulations indicate that this methods eliminates almost all low clouds Many soundings with large cloud optical depths that are missed by the A-band filter are caught by the X CO2 and X H2O ratio filters. X H O ratio (weak/strong) CO 2 ratio (weak/strong) H2O Ratio 2 (WCO2/SCO2) X CO2 Ratio (WCO2/SCO2) X CO2 Ratio ±1% log Histogram log(total OD) X H2O Ratio ±5% Ln (Optical Depth) log Histogram log(total OD) Ln (Optical Depth) Slide 20 UCLA CAS Nov., 2012

Issue: OCO-2 will gather up to 96 times as much data as GOSAT, but, like GOSAT, up to 80% of the

Challenge: How do we (rapidly) select the best ~6% to process through Level 2?

Used Machine Learning to develop a method that autonomously constructs a sounding selector for

Guarantees uniform spatial and temporal coverage, minimal data distortion, and runs instantly Use

21 Issue: OCO-2 will gather up to 96 times as much data as GOSAT, but, like GOSAT, up to 80% of the soundings are compromised by clouds, ice, other issues. Challenge: How do we (rapidly) select the best ~6% to process through Level 2? Solution: Design a Sounding Selector to find best soundings using L1B data alone. Used Machine Learning to develop a method that autonomously constructs a sounding selector for GOSAT data User specifies X% of data to retain, selector returns which soundings to process Guarantees uniform spatial and temporal coverage, minimal data distortion, and runs instantly Use of Machine Learning for Sounding Selection Depends on two, well-understood, ACOS OCO-2-derived quantities with easily interpreted rules Lukas Mandrake Slide 21 UCLA CAS Nov., 2012

22 Overall Flow of Sounding Selection Slide 22 UCLA CAS Nov., 2012

Machine Learning as a Tool for Debugging/ Research Support GOSAT soundings in the southern hemisphere, where X CO2 has little variability, to explore top

dimensional parameter spaces Not necessarily a global minimum GA s are also quite simple to create and use, and they lend themselves trivially to highly

23 Machine Learning as a Tool for Debugging/ Research Support GOSAT soundings in the southern hemisphere, where X CO2 has little variability, to explore top factors responsible for X CO2 retrievals that are Anomalously high or low Have high scatter Used Genetic Algorithms (GA) methods to explore high dimensional parameter spaces Not necessarily a global minimum GA s are also quite simple to create and use, and they lend themselves trivially to highly parallel processing such as multicore and clustered computer systems Apply GA s to reduce RMS of XCO 2 in the area shown on map Lukas Mandrake Slide 23 UCLA CAS Nov., 2012

24 Details of RFE approach Two-dimensional feature example. Yellow lines represent the max and min thresholds for each of the two subfilters, with only data satisfying the union (green) accepted by the filter and all other data (red) rejected. The Pareto-optimal trade-off curves for dominant Land fixed filters in the southern hemisphere using all available features. Slide 24 UCLA CAS Nov., 2012

25 Key Features Identified for Land and Sea Can we adopt just one set of features?? Slide 25 UCLA CAS Nov., 2012

26 Selection with Two Parameters for All Data Slide 26 UCLA CAS Nov., 2012

27 Warn Level Warn levels are defined by filter thresholds, with each warn level reducing data transparency (throughput) by 5% Slide 27 UCLA CAS Nov., 2012

28 Warn Level To ensure global coverage, we have to select data from different warn levels for different regions. Hot colors are places where there is a lot of high quality data Slide 28 UCLA CAS Nov., 2012

29 Retrieving X CO2 from OCO-2 Spectra Interpolated Meteorology Level 1B Data T P q Pre-Processing Filter OCO-2 Retrieval Algorithm Optimal Estimation Full Physics 3-band (ABO2, WCO2, SCO2) Altitude State Vector First Guess & Covariance Cloud &Aerosol Optical Properties! # aer Aerosol CO 2 Mixing Ratio Cloud Latitude X CO2 Retrieval Algorithm Post-Processing Filter Forward Radiative Transfer Model Instrument Model Inverse Model Final State and X CO2 Update State Vector? Gas Cross Sections!! 2 "p # aer $ XCO2 Altitude # aer Aerosol CO 2 Mixing Ratio T P q Slide 29 UCLA CAS Nov., 2012

30 Screening Data After Retrieval Chris O Dell Slide 30 UCLA CAS Nov., 2012

31 Slide 31 UCLA CAS Nov., 2012

32 Slide 32 UCLA CAS Nov., 2012

33 Slide 33 UCLA CAS Nov., 2012

34 Slide 34 UCLA CAS Nov., 2012

35 Slide 35 UCLA CAS Nov., 2012

36 Slide 36 UCLA CAS Nov., 2012

37 Overview of the Carbon Cycle Slide 37 UCLA CAS Nov., 2012

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Conclusions Remote Sensing measurements of XCO 2 can help us improve our understanding of the flow of carbon in the earth system The measurements

Need an excellent instrument, Accurate and detailed models of how light moves through the atmosphere, Sophisticated retrieval techniques and error

48 Conclusions Remote Sensing measurements of XCO 2 can help us improve our understanding of the flow of carbon in the earth system The measurements must be precise to answer this question! Need an excellent instrument, Accurate and detailed models of how light moves through the atmosphere, Sophisticated retrieval techniques and error characterization, Practical ways to select data for processing and use in science analysis, And close partnership with earth system modelers to answer the science questions. Statistics have a critical role in all aspects of our work Slide 48 UCLA CAS Nov., 2012

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53 ACOS GOSAT B2.10 X CO2 Retrievals 395 Slide 53 UCLA CAS Nov.,

54 !"#$%&'()*+,-'%./#0,)+%1+,'2%345.% 65.37%8 459 %:;/;% 3+#$?&'+Q"5,&J(7(5+?(&F4+4"&I%$#"56$4G^JBE<,&IF<& 3G7+%&-(%"6&+%?&;"%2&3"%2,&I%$#"56$4G&(D&H(5(%4(,&J+%+?+& -.%/$"&0$.&"4&+7S,&-=0,&IS&1?S,&ISJS&'"5Q"7"G,&IF<& =+.7&=+7*"5&+%?&0$+%2&;"%2,&I%$#"56$4G&(D&>?$%Z.52:,&IS!& ACOS =5"7$*$%+5G&5"6.746&6.22"64&4:+4&& H:"&$%4"%6$4G&+%?&?$645$Z.C(%&(D&4:"&5"45$"#"?&\.9"6&?"P"%?&(%& H:"&+66.*"?&P5$(5&& H:"&?"4+$76&(D&4:"&AKF<H&_ JKY &5"45$"#+76&.6"?& ACOS Final Report 54

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55 g C / m S&'+Q"5,&JBE<& T`3&b<E&+66$*$7+C(%&6G64"*& N""Q7G&JK Y &\.9"6&c&Tde9We&f7+4^7(%g& =JH1&(h`7$%"&45+%6P(54,&A>KF&V&*"4&i"7?6& =5$(5&\.9"6,&+&J+5Z(%H5+@Q"5& -(%"6&j3"%2,&IS&H(5(%4(& T`3&b<E&+66$*$7+C(%&6G64"*&fA>KF`J:"*g& 1(%4:7G&JKY&\.9"6&c&Te&9&Ve&f7+4^7(%g& A>KF`J:"*&45+%6P(54,&A>KF&V&*"4&i"7?6& =5$(5&\.9"6]&&<%4:5(P(2"%$@&\.9"6&D5(*& 6"#"5+7&6(.5@"6,&P7.6&+%%.+77G&Z+7+%@"?& Z$(6P:"5"&\.9"6&D5(*&J<F<& <JKF&#YS[,&8`A+$%&(%7G,&%(&Z$+6&@(55"@C(%& ACOS ACOS Final Report 55

3++,=,";B-'%-?%65.37%8 459 %?*-=% C3.3D+%345.%(9EA@% Frédéric Chevallier Laboratory for Sciences of Climate and the Environment (LSCE) ACOS quality control and bias correction from C. O'Dell.

2005) but the results are presented for subcontinental annual fluxes. Total fluxes are shown (including fossil). Positive values correspond to a source to the atmosphere.

56 3++,=,";B-'%-?%65.37%8 459 %?*-=% Frédéric Chevallier Laboratory for Sciences of Climate and the Environment (LSCE) ACOS quality control and bias correction from C. O'Dell. Ocean glint data included. GOSAT-based inversion vs. Air-sample-based inversion for 2010 and vs. prior fluxes. The inversion works at grid-point weekly scale (Chevallier et al. 2005) but the results are presented for subcontinental annual fluxes. Total fluxes are shown (including fossil). Positive values correspond to a source to the atmosphere. The bars represent the 1-$ Bayesian uncertainty. Some regions show unrealistic budgets: Europe and North American Temperate (too much uptake), South American Tropical and Boreal Eurasia (too large source). ACOS ACOS Final Report 56

57 BACKUP Slide 57 UCLA CAS Nov., 2012

FLUXNET and Remote Sensing Workshop: Towards Upscaling Flux Information from Towers to the Globe

FLUXNET and Remote Sensing Workshop: Towards Upscaling Flux Information from Towers to the Globe Space-Based Measurements of CO 2 from the Japanese Greenhouse Gases Observing Satellite (GOSAT) and the