The capability of different satellite observing configurations to resolve fine-scale methane emissions

Transcription

1 University of California at Berkeley The capability of different satellite observing configurations to resolve fine-scale methane emissions Alexander J. Turner1,2, Daniel J. Jacob2, Joshua Benmergui2, Jeremy Brandman3, Laurent White3, & Cynthia A. Randles3 1UC Berkeley, 2Harvard University, 3ExxonMobil Research and Engineering Company 217 AGU Fall Meeting Funded by ExxonMobil, DOE ARPA-E, and the Miller Institute at UC Berkeley December 13, 217

2 The importance of fine-scale methane sources Contribution to US emissions (%) Emissions (tons h -1 ) Gridded EPA inventory Quantile Top 1% of grid cells make up ~3% of emissions in the EPA inventory Jacob, Turner, et al. (216)

3 The importance of fine-scale methane sources Emissions (tons h -1 ) Contribution to US emissions (%) How can different satellite observing systems resolve fine-scale sources? 35 N 34 N 33 N 32 N 31 N Gridded EPA inventory EDF Barnett Shale Methane Inventory Quantile.5 Top 1% of grid 3 N cells make up ~3% of emissions 1 W 99 W 98 W 97 W 96 W 95 W in the EPA inventory Jacob, Turner, et al. (216) Methane flux (μmol m -2 s -1 )

4 Details of the WRF-STILT modeling 4 nested WRF domains with nudging to NARR (in outermost domain) Hourly STILT trajectories from every 1.3 km 12 vertical levels (including a surface level) for STILT trajectories

5 Resulting footprints for the satellite observations Use these footprints to construct the H matrix that maps from emissions to concentrations

6 Footprints for the whole observing system

7 Simulating methane column enhancements 35 N EDF Barnett Shale Methane Inventory CH 4 = enhancement 34 N 33 N 32 N 31 N Methane flux (μmol m -2 s -1 ) 3 N 1 W 99 W 98 W 97 W 96 W 95 W

8 Simulating methane column enhancements footprint CH 4 = Hx enhancement emissions

9 Quantifying the information content of the observing system cost function (Bayesian with Gaussian errors): J (x) = 1 2 (y Hx)T R 1 (y Hx)+ 1 2 (x x a) T B 1 (x x a ) posterior solution: ˆx = x a + H T R 1 H + B 1 1 {z } posterior covariance matrix posterior error covariance matrix: Q =(H T R 1 H {z } observations Fisher information matrix: F = H T R 1 H + B 1 {z} ) 1 prior H T R 1 (y Hx)

10 Quantifying the information content of the observing system Fisher information matrix: F = H T R 1 H Example cost functions Bayesian: Least-squares: LASSO: Tikhonov: (y Hx) T R 1 (y Hx)+(x x a ) T B 1 (x x a ) (y Hx) T R 1 (y Hx) (y Hx) T R 1 (y Hx)+ P i x i (y Hx) T R 1 (y Hx)+ x T x Eigenvalues of F can tell us about the information content of the observing system

11 Comparing different satellite observing configurations Flux threshold (µmol m -2 s -1 ) 1 1 Information content for constant sources Eigenvalues of EDF inventory Info EPA inventory Configuration TROPOMI GeoCARB (daily) GeoCARB GeoCARB (hourly) hi-res Ranked flux patterns Flux threshold (µmol m -2 s -1 ) Information content for variable sources 1 1 1/21/ EDF inventory EPA inventory Info Configuration GeoCARB (daily) GeoCARB GeoCARB (hourly) hi-res Ranked flux patterns Large scales (basin-scale) Small scales (~ km 2 ) Can directly compare different observing systems

12 Can interrogate the importance of various design parameters Information content (weekly) Information content (daily) Constant sources 1 returns per day median 1-σ 2-σ Instrument precision (ppb) Temporally variable sources 1 returns per day Instrument precision (ppb) Constant sources 4 ppb precision Return times per day Temporally variable sources 4 ppb precision Return times per day Quantifies the importance of precision and sampling frequency Information content (weekly) Information content (daily) *GeoCARB-like resolution

13 The capability of satellite observing systems to resolve fine-scale emissions Emissions (tons h -1 ) Contribution to US emissions (%) Gridded EPA inventory Quantile Flux threshold (µmol m -2 s -1 ) 1 1 Information content for constant sources Eigenvalues of EDF inventory EPA inventory Info Configuration 5 TROPOMI 98 GeoCARB (daily) 286 GeoCARB 961 GeoCARB (hourly) 2221 hi-res Ranked flux patterns Information content (daily) Temporally variable sources 1 returns per day median 1-σ 2-σ Instrument precision (ppb) Important points from this work: 1) Fine-scale sources make up a large fraction of the anthropogenic emissions 2) A week of TROPOMI obs can constrain the mean emissions in the Barnett Shale 3) GeoCARB constrains constant sub-basin scale sources 4) Quantifying fine-scale, transient sources will require better than 6 ppb precision