Quantifying methane point sources from fine-scale satellite observation of atmospheric plumes

Transcription

1 Quantifying methane point sources from fine-scale satellite observation of atmospheric plumes Daniel J. Varon 1,2, Daniel J. Jacob 1, Jason McKeever 2, Yan Xia, Yi Huang 1 Harvard University, Cambridge MA. 2 GHGSat, Inc., Montréal QC. McGill University, Montréal QC. GHGSat presentations/posters: A4N-8, Stéphane Germain: Quantifying Industrial Methane Emissions from Space with the GHGSat-D Satellite, Th 15:26, AG-245, Jason McKeever: GHGSat-D: Greenhouse gas plume imaging and quantification from space using a Fabry-Perot imaging spectrometer, W 1:4, Hall D-F. 1

2 Methane is emitted by a very large number of small point sources AVIRIS airborne remote sensing observations: Frankenberg et al., (216) 2

3 Conventional methane-observing satellites have limited ability to quantify point sources GOSAT/TROPOMI specs: Column precision:.1-1% * * Pixel resolution: 1-1 km 1.5 km 1 km 7km GOSAT pixel TROPOMI pixel AVIRIS imagery courtesy of Dr. Andrew Thorpe, NASA JPL

4 Conventional methane-observing satellites have limited ability to quantify point sources GOSAT/TROPOMI specs: * Column precision:.1-1% * Pixel resolution: 1-1 km 1.5 km 1.5 km 1 km GOSAT pixel GHGSat imaging resolution AVIRIS imagery courtesy of Dr. Andrew Thorpe, NASA JPL

5 Conventional methane-observing satellites have limited ability to quantify point sources GOSAT/TROPOMI specs: * Column precision:.1-1% * Pixel resolution: 1-1 km 1.5 km 1 km GHGSat design specs: * 1-5% column precision * 5-m pixels * Targeted 12-km domains 1.5 km GOSAT pixel GHGSat imaging resolution AVIRIS imagery courtesy of Dr. Andrew Thorpe, NASA JPL

6 Conventional methane-observing satellites have limited ability to quantify point sources GOSAT/TROPOMI specs: * Column precision:.1-1% * Pixel resolution: 1-1 km 1.5 km 1 km How do we quantify emissions from point sources Observation specs: using fine-resolution satellite imagery of plumes? * * * 1-5% column precision 5-m pixels Targeted 12-km domains 1.5 km GOSAT pixel GHGSat imaging resolution AVIRIS imagery courtesy of Dr. Andrew Thorpe, NASA JPL

7 WRF-LES: Large Eddy Simulations of methane point sources at 5-m resolution LES ensemble of 15 simulations. 4

8 GHGSat pseudo-observations produced by column integration & addition of noise Q = 1 t h!1, < = 1% #1!4 Q = 1 t h!1, < = % #1! Q = 1 t h!1, < = 5% #1!! km kg m! km kg m! ( ) kg kg m!2!2 5

9 Gaussian plume model OK for coarse-resolution satellite imagery but not for GHGSat m pixels -m pixels m pixels -m pixels km 4.2 kg m =.49 = km km = Pearson correlation coe cient (Gaussian vs. LES enhancements) Gaussian plume OK for OCO-2 or TROPOMI, but not for GHGSat. 6

10 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] 7

11 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] Taking this one step further... Q = 1 IME = U U eff =E ective wind speed [m s 1 ] ) eff L IME = dissipation time scale [s 1 ] L = dissipation length scale [m] 7

12 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] Taking this one step further... Q = 1 IME = U U eff =E ective wind speed [m s 1 ] ) eff L IME = dissipation time scale [s 1 ] L = dissipation length scale [m] Synthetic plume observation Separating Outputsignal of t-test fromprocedure background After median and Gaussian -lters 7

13 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] Taking this one step further... Q = 1 IME = U U eff =E ective wind speed [m s 1 ] ) eff L IME = dissipation time scale [s 1 ] L = dissipation length scale [m] L = p A M = q N j=1 A j Synthetic plume observation Separating Outputsignal of t-test fromprocedure background After median and Gaussian -lters 7

14 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] Taking this one step further... ) Q = 1 IME = U? U eff =E ective wind speed [m s 1 ] eff L IME = dissipation time scale [s 1 ] L = dissipation length scale [m] L = p A M = q N j=1 A j Synthetic plume observation Separating Outputsignal of t-test fromprocedure background After median and Gaussian -lters 7

15 Relating plume mass to emissions: Integrated Mass Enhancement (IME) approach Frankenberg et al., (216): IME = NX j=1 j A j j = jth column enhancement [kg m 2 ] A j = jth pixel area [m 2 ] Taking this one step further... ) Q = 1 IME = f(u 1) L IME U eff =E ective wind speed [m s 1 ] = dissipation time scale [s 1 ] L = dissipation length scale [m] L = p A M = q N j=1 A j Synthetic plume observation Separating Outputsignal of t-test fromprocedure background After median and Gaussian -lters 7

16 Inferring Ueff from local knowledge of the 1-m wind speed First guess for dissipation time scale = 5 U eff vs. Ū 1 Training plumes Polynomial fit line 1:1 line High-frequency U 1 data average 1-m wind Ū 1 4 U eff = f(ū1) Uef f (m/s) 2 = L U eff 1 Q = IME Yes <? No Ū 1 (m/s) 8

17 Evaluate retrieval uncertainty using test plumes Retrieved Q vs. True Q Retrieved Q (t h 1 ) Retrieved Q= 1% vs. True Q Test plumes 1:1 line Retrieved Q (t h 1 ) Retrieved Q (t h 1 ) Retrieved Q vs. Q= vs. % True True Q Q Retrieved Q (t h 1 ) Retrieved Q vs. True Q = 5% Residual standard deviation as % of Q Residual standard deviation 1% % 5% 1 2 True Q (t h 1 ) True True Q (t Q h(t 1 ) h 1 ) 1 2 True Q (t h 1 ) Q (t h 1 ) * * * Q>1th 1 : 1-% noise has similar performance Q>1.5 th 1 : 1-5% noise has similar performance Q>.5 th 1 : accounts for 75% of GHGRP emissions 9

18 What if you don t have local observations of U1? MesoWest winds (m s 1 ) h averages 6/217 Winds 1:1 line Regression line GEOS-FP winds (m s 1 ) Averaging time = (min) Total Representation Uncertainty GEOS-FP U 1 (m s!1 ) Representation uncertainty (% of GEOS-FP wind) 1

19 Quantifying methane point sources from fine-scale satellite observation of atmospheric plumes Daniel J. Varon 1,2, Daniel J. Jacob 1, Jason McKeever 2, Yan Xia, Yi Huang 1 Harvard University, Cambridge MA. 2 GHGSat, Inc., Montréal QC. McGill University, Montréal QC. GHGSat presentations/posters: A4N-8, Stéphane Germain: Quantifying Industrial Methane Emissions from Space with the GHGSat-D Satellite, Th 15:26, AG-245, Jason McKeever: GHGSat-D: Greenhouse gas plume imaging and quantification from space using a Fabry-Perot imaging spectrometer, W 1:4, Hall D-F.