Download presentation

Presentation is loading. Please wait.

Published byDrew Deep Modified about 1 year ago

1
Next-generation OMI SO 2 Retrieval Algorithm based on Principal Component Analysis Can Li 1,2, Joanna Joiner 2, Nick Krotkov 2, P. K. Bhartia 2 1 ESSIC, University of Maryland College Park 2 NASA GSFC 18 th OMI Science Team Meeting KNMI, De Bilt, The Netherlands March 13,

2
Outline Background and Motivation Methodology (Framework) Application to OMI – Results (Planetary Boundary Layer SO 2 ) – Results (Volcanic SO 2 ) Data Continuity: Comparison of OMI and OMPS Next Steps and Conclusions

3
Background and Motivation Motivation: Band Residual Difference (BRD) algorithm fast and sensitive, but large noise and artifacts (only 3 pairs of wavelengths) Objective: develop an innovative approach to utilize the full spectral content from OMI while maintaining computational efficiency Operational OMI SO 2 (Sept – Feb. 2008)

4
Basis – Spectral fitting algorithms First look at the DOAS Equation: Measured sun- normalized radiances Various gas absorbers (O 3, SO 2 etc.) Rayleigh and Mie scattering, surface reflectance etc. The Ring effect Plus additional measurement artifacts terms (e.g., wavelength shift, stray light, etc.) and/or radiance data correction schemes Utilization of the full spectral content, but some terms are difficult to model (e.g., RRS)

5
Methodology (Framework): PCA Instead of explicit modeling of ozone, RRS, and other instrumental features, we use a data-driven approach based on principal component analysis (PCA) with spectral fitting Measured N- value spectrum PCs from SO 2 -free regions, (O 3 absorption, surface reflectance, RRS, measurement artifacts etc.) other than SO 2 absorption Pre-calculated SO 2 Jacobians (assuming O 3 profiles, albedo, etc.) SO 2 column amount Fitting of the right hand side to the spectrum on the left hand side -> SO 2 column amount and coefficients of PCs (See Guanter et al., 2012; Joiner et al., 2013; Li et al., 2013)

6
Application to OMI Spectral window: nm – avoid stray light at shorter wavelengths Each row (scan position) processed individually – different characteristics between different rows of the 2-D CCD Each swath processed individually – account for orbit-varying dark current # of PCs determined dynamically – exclude SO 2 -related PCs and avoid overfitting by checking the correlation between PCs and SO 2 Jacobians 1st step: Simple Jacobians similar to those used in operational BRD algorithm for straight-forward comparison Step 1 Ps See Li et al., [GRL, 2013] for details

7
Principle Components and Residuals (a-c) First few PCs Blue line: scaled reference Ring spectrum (d) Least squares fitting residuals for a pixel near Hawaii (Var.% ) (Var.% ) Example PCs from entire row # 11, Orbit (Var.% ) (Var.% 5.32E-5) (Var.% 4.79E-5) PC #1: Mean spectrum PC #2: O 3 absorption PC #3: Surface reflectance (also Ring signature) PCs #4 and #5: likely measurement artifacts, noise (>99.99% variance explained) Smaller residuals with SO 2 Jacobians fitted

8
Results: noise and artifact reduction PCA algorithm reduces retrieval noise by a factor of two as compared with the BRD algorithm SO 2 Jacobians for PCA algorithm calculated with the same assumptions as in the BRD algorithm August, 2006 OMI operational BRD

9
Results: Boundary layer pollution SO 2 eastern U.S., August 2006 PCA Operational BRD PCA algorithm reveals major SO 2 point sources (circles), with much reduced noise and artifacts.

10
Ex. Sudbury, Canada (~220 kt in 2006) Ex. analyzed with pixel averaging (super sampling) reveals details of emission sources [e.g., Fioletov et al., 2011] PCA, 2006BRD, 2006 onlyBRD, One year’s worth of PCA retrievals yield results similar to that from 3-5 years worth of BRD data. Global survey shows that PCA SO 2 removes most artifacts in BRD data without significantly altering signals from real sources (Fioletov and McLinden, personal communication) Largely hidden by artifacts

11
Volcanic SO 2 : Kasatochi eruption August 7-8, 2008 For volcanic SO 2, nonlinearity due to saturation at shorter wavelengths Iteration of SO 2 Jacobians (pre-calculated assuming loadings of DU) Shift of spectral fitting window to longer wavelengths PCA closest to estimated released SO 2 mass of ~2200 kt based on observed decay of SO 2 [Krotkov et al., 2010]

12
Transport of the plume Ln(SO 2 ) August 10, 2008 August 11, 2008 August 12, 2008

13
Our algorithm eliminates the need for explicit instrument-specific radiance correction schemes Test on OMPS: minimal changes to algorithm – biggest is the use of OMPS slit function for Jacobians – spectral window, etc., same as in OMI Reduces the chance of introducing artifacts/biases between different instruments Comparison with OMPS

14
OMI and OMPS comparison OMPS and OMI PCA SO 2 retrievals show good agreement despite somewhat different sampling October, 2013

15
OMI and OMPS comparison Both OMI and OMPS PCA SO 2 retrievals show enhanced SO 2 loading over northern China in January 2013, when severe pollution attracted media and public attention. OMPS, Jan. 2013OMI, Jan. 2013

16
OMI and OMPS comparison OMI and OMPS PCA SO 2 data show similar seasonal patterns and SO 2 signals over eastern India (several coal- fired power plants built in recent years) [Lu et al., 2013].

17
Next Steps Expanded table for SO 2 Jacobians to more accurately account for measurement conditions (e.g., O 3 amount, reflectivity, geometries) Addition of scattering weight to output to allow convenient adjustment of SO 2 column amount based on user-provided profile Inclusion of error estimates Operational implementation, public release – >1 year processed and currently under evaluation – Initial release for boundary layer pollution this year – Improved Jacobians and volcanic data to follow soon – On 12 CPUs, 1-2 days to process a year of OMI data

18
Conclusions Significant improvements in retrieval quality – PCA algorithm uses full spectral content from OMI and similar instruments offering increased temporal resolution and source detection Computation efficiency – over an order of magnitude faster than comparable spectral fitting algorithms; increasingly important given the greater data volumes expected from future missions (e.g., TROPOMI, TEMPO) Maximal data continuity between instruments – no need to develop instrument-specific radiance data correction schemes Flexibility – fitting window can be easily adjusted to optimize sensitivity under different conditions

19
Backups

20
Results: Daily boundary layer SO 2 August 13, 2006 August 14, 2006 August 15, 2006August 16, 2006

Similar presentations

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google