Biomass Burning Emissions Measured from Recently Updated MOPITT Products J. X. Warner 1, J.C. Gille 1, D. P. Edwards 1, M. Deeter 1, D. Ziskin 1, L. Emmons.

Slides:



Advertisements
Similar presentations
MOPITT CO Louisa Emmons, David Edwards Atmospheric Chemistry Division Earth & Sun Systems Laboratory National Center for Atmospheric Research.
Advertisements

Global, Regional, and Urban Climate Effects of Air Pollutants Mark Z. Jacobson Dept. of Civil & Environmental Engineering Stanford University.
Improved Automated Cloud Classification and Cloud Property Continuity Studies for the Visible/Infrared Imager/Radiometer Suite (VIIRS) Michael J. Pavolonis.
CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric.
GEOS-5 Simulations of Aerosol Index and Aerosol Absorption Optical Depth with Comparison to OMI retrievals. V. Buchard, A. da Silva, P. Colarco, R. Spurr.
NRL09/21/2004_Davis.1 GOES-R HES-CW Atmospheric Correction Curtiss O. Davis Code 7203 Naval Research Laboratory Washington, DC 20375
NO X Chemistry in CMAQ evaluated with remote sensing Russ Dickerson et al. (2:30-2:45PM) University of Maryland AQAST-3 June 13, 2012 Madison, WI The MDE/UMD.
Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,
Transpacific transport of pollution as seen from space Funding: NASA, EPA, EPRI Daniel J. Jacob, Rokjin J. Park, Becky Alexander, T. Duncan Fairlie, Arlene.
On average TES exhibits a small positive bias in the middle and lower troposphere of less than 15% and a larger negative bias of up to 30% in the upper.
Trans-Pacific transport of Asian dust and pollution: Accumulation of biomass burning CO in subtropics and dipole structure of transport Junsang Nam 1,
Satellite Remote Sensing of Surface Air Quality
Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.
Direct Radiative Effect of aerosols over clouds and clear skies determined using CALIPSO and the A-Train Robert Wood with Duli Chand, Tad Anderson, Bob.
Great Basin Ozone Problem Measurements indicate high ozone concentrations in the Great Basin. Back trajectory analysis and satellite remote sensing will.
 Among all CO source/sink terms, the loss due to CO reaction with OH and the emission from biomass burning appear to be main causes for seasonal fluctuation.
ACKNOWLEDGEMENTS We are grateful to the MOPITT team, especially the groups at University of Toronto and the National Center for Atmospheric Research (NCAR),
(#694) Monitoring the Hawaii Volcano Plume From Satellite By John Porter School of Ocean Earth Science and Technology, University of Hawaii, Honolulu,
Terra Observations of Tropospheric Aerosol and CO from Biomass Burning
M. Van Roozendael, AMFIC Final Meeting, 23 Oct 2009, Beijing, China1 MAXDOAS measurements in Beijing M. Van Roozendael 1, K. Clémer 1, C. Fayt 1, C. Hermans.
Developing a High Spatial Resolution Aerosol Optical Depth Product Using MODIS Data to Evaluate Aerosol During Large Wildfire Events STI-5701 Jennifer.
Occurrence of TOMS V7 Level-2 Ozone Anomalies over Cloudy Areas Xiong Liu, 1 Mike Newchurch, 1,2 and Jae Kim 1,3 1. Department of Atmospheric Science,
Orbit Characteristics and View Angle Effects on the Global Cloud Field
Using MODIS fire count data as an interim solution for estimating biomass burning emission of aerosols and trace gases Mian Chin, Tom Kucsera, Louis Giglio,
TOP-DOWN CONSTRAINTS ON REGIONAL CARBON FLUXES USING CO 2 :CO CORRELATIONS FROM AIRCRAFT DATA P. Suntharalingam, D. J. Jacob, Q. Li, P. Palmer, J. A. Logan,
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
OMI total-ozone anomaly and its impact on tropospheric ozone retrieval Jae Kim 1, Somyoung Kim 1, K. J. Ha 1, and Mike Newchurch Department of Atmospheric.
Introduction Invisible clouds in this study mean super-thin clouds which cannot be detected by MODIS but are classified as clouds by CALIPSO. These sub-visual.
Randall Martin Space-based Constraints on Emissions of Nitrogen Oxides With contributions from: Chris Sioris, Kelly Chance (Smithsonian Astrophysical Observatory)
MONTHLY CO AND FIRE COUNTS IN THREE NORTHERN HEMISPHERE REGIONS AND IN THREE LOW LATITUDE REGIONS  With MOPITT CO data and ATSR fire count data, CO emission.
Operational assimilation of dust optical depth Bruce Ingleby, Yaswant Pradhan and Malcolm Brooks © Crown copyright 08/2013 Met Office and the Met Office.
Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie.
The effect of pyro-convective fires on the global troposphere: comparison of TOMCAT modelled fields with observations from ICARTT Sarah Monks Outline:
Results Figure 2 Figure 2 shows the time series for the a priori and a posteriori (optimized) emissions. The a posteriori estimate for the CO emitted by.
USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005.
Characterization of Aerosols using Airborne Lidar, MODIS, and GOCART Data during the TRACE-P (2001) Mission Rich Ferrare 1, Ed Browell 1, Syed Ismail 1,
Desert Aerosol Transport in the Mediterranean Region as Inferred from the TOMS Aerosol Index P. L. Israelevich, Z. Levin, J. H. Joseph, and E. Ganor Department.
Water Vapour & Cloud from Satellite and the Earth's Radiation Balance
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
Retrieval of Methane Distributions from IASI
Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET) Thomas F. Eck (Code 618 NASA GSFC) and Brent N. Holben (Code.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
Synergy of MODIS Deep Blue and Operational Aerosol Products with MISR and SeaWiFS N. Christina Hsu and S.-C. Tsay, M. D. King, M.-J. Jeong NASA Goddard.
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
Retrieval of Vertical Columns of Sulfur Dioxide from SCIAMACHY and OMI: Air Mass Factor Algorithm Development, Validation, and Error Analysis Chulkyu Lee.
MOPITT during INTEX David Edwards Louisa Emmons, Gabriele Pfister, John Gille, Dan Ziskin, Debbie Mao Atmospheric Chemistry Division NCAR.
How accurately we can infer isoprene emissions from HCHO column measurements made from space depends mainly on the retrieval errors and uncertainties in.
Radiative transfer in the thermal infrared and the surface source term
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
This report presents analysis of CO measurements from satellites since 2000 until now. The main focus of the study is a comparison of different sensors.
Assimilation of Satellite Derived Aerosol Optical Depth Udaysankar Nair 1, Sundar A. Christopher 1,2 1 Earth System Science Center, University of Alabama.
A Brief Overview of CO Satellite Products Originally Presented at NASA Remote Sensing Training California Air Resources Board December , 2011 ARSET.
Convective Transport of Carbon Monoxide: An intercomparison of remote sensing observations and cloud-modeling simulations 1. Introduction The pollution.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
PRELIMINARY VALIDATION OF IAPP MOISTURE RETRIEVALS USING DOE ARM MEASUREMENTS Wayne Feltz, Thomas Achtor, Jun Li and Harold Woolf Cooperative Institute.
International Ocean Color Science Meeting, Darmstadt, Germany, May 6-8, 2013 III. MODIS-Aqua normalized water leaving radiance nLw III.1. R2010 vs. R2012.
number Typical aerosol size distribution area volume
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
ECMWF/EUMETSAT NWP-SAF Satellite data assimilation Training Course
INTERCONTINENTAL TRANSPORT: CONCENTRATIONS AND FLUXES
J. Kar (UT), H. Bremer (UB), James R. Drummond (UT), F
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Using dynamic aerosol optical properties from a chemical transport model (CTM) to retrieve aerosol optical depths from MODIS reflectances over land Fall.
+ = Climate Responses to Biomass Burning Aerosols over South Africa
Continental outflow of ozone pollution as determined by ozone-CO correlations from the TES satellite instrument Lin Zhang Daniel.
Transpacific satellite and aircraft observations of Asian pollution: An Integration of MOPITT and TRACE-P Colette L. Heald, Daniel J. Jacob, Arlene M.
INTEX-B flight tracks (April-May 2006)
Presentation transcript:

Biomass Burning Emissions Measured from Recently Updated MOPITT Products J. X. Warner 1, J.C. Gille 1, D. P. Edwards 1, M. Deeter 1, D. Ziskin 1, L. Emmons 1, J. M. Feltz 5, A. Chu 4, E. M. Prins 6, J-L. Attie 3, J. Drummond 2 1 National Center for Atmospheric Research 2 University of Toronto Department of Physics 3 Laboratoire d'Aerologie, Toulouse, France 4 MODIS aerosol team, NASA GSFC, Greenbelt, MD, USA 5 CIMSS, University of Wisconsin, Madison, Wisconsin, USA 6 NOAA/NESDIS/ORA Advanced Satellite Products team, University of Wisconsin, Madison Wisconsin, USA Introduction: The Measurement Of Pollution In The Troposphere (MOPITT) instrument on board EOS Terra is designed to measure the tropospheric CO and CH 4 at a nadir- viewing geometry. The measurements are taken at 4.7um in the thermal region, and 2.3um and 2.2um in the solar region for CO mixing ratio retrieval, CO total column amount and CH 4 column amount retrieval, respectively. MOPITT has been collecting data for over two years and will be providing CO mixing ratio profiles and integrated total column amount for the period of March, 2000 to April, This poster briefly summarizes the MOPITT measurement principle, data processing algorithm, and currently available data products. MOPITT recently released V3 products are discussed and some example of the validation results are shown. A few examples of MOPITT measurements of biomass burning events are also shown and discussed. Elevated CO plumes are correlated with fire events from GOES WF_ABBA (The GOES Wildfire Automated Biomass Burning Algorithm) (Prins et al, 2001), and MODIS aerosols (Kaufman and Tanre, 1998). Measurement Principle: MOPITT CO and CH 4 channels use Correlation Radiometry (Drummond, 1989) that modulates at a longer and a shorter cell path. An average (A) and a difference (D) transmission are calculated in each modulation. The A signals represent the background radiation and are used for cloud detection and to determine other surface properties, and the D signals represent the target gas absorption/ emission and are used for retrievals, as shown in Figure 1. The various channels use different cell pressures and their weighting function peak at different altitudes in the atmosphere and, hence, produce CO profile information. Equivalent Average & Difference Transmission Cell transmission I Cell transmission II Average Transmission Difference Transmission Level-2 Algorithm: After the Level 0-1 Processor has converted instrument counts into calibrated channel radiances, the Level 1-2 Processor retrieves the final geophysical CO and CH 4 data products. In the Level-2 processor, the meteorological data and climatology are interpolated in time and space to match L1 observation. Then L1 data and the ancillary data are passed to the cloud detection and only the clear pixels are passed to the retrieval module. MOPITT latest version V3 processor uses a hybrid cloud detection method that incorporates MODIS cloud mask and MOPITT radiance (Warner et al., 2000). The clear pixels based on MODIS cloud mask are passed directly to the retrieval. Since MOPITT weighting functions peak at the mid- and high-troposphere, the radiances are less sensitive at lower atmosphere. MOPITT CO retrieval over very low cloud are recovered, and thus, the global CO coverage has been doubled. MOPITT retrieval uses a maximum likelihood method that incorporates our prior knowledge of the physical and statistical variability of the trace gas distribution in the atmosphere to choose the solution. The trace gas variability is expressed in the form of the a priori vertical profile and covariance matrix. The retrieved profile x ret can then be expressed as a linear combination of the ‘true’ profile x and the a priori profile x a. Data Products: A "provisional" version of the MOPITT CO Retrievals (V3) is now available at the Langley DAAC. See for more information. MOPITT CO profiles and total column amount will be provided for the period of March, 2000 to April, The current processing will start at Nov., 2000 due to the availability of the MODIS cloud mask data. MOPITT encountered a cooler failure in May, 2001 that resulted the loss of half of MOPITT channels. Only CO mixing ratio maps in the higher troposphere will be provided after the anomaly (May, 2001 to current). V3 Upgrades: Calibration was improved to average ground sampling and to calculate the average and difference radiances. Approximately forty percent of global coverage was recovered from the previous versions when the first 2 stares (4 pixels per stare) in each 5 stare data package were eliminated due to uncertainties. The global CO coverage is also doubled due to the data usage over low cloud. In addition, by the application of MODIS cloud mask, the data over polar regions is extended to about 85 degree North and South. Figure 2 shows a comparison of data coverage between a pre-V3 version (V2) (a) and a provisional version of V3 (b) MOPITT CO total column amounts for Jan. 1 -3, A validation example of V3 products against aircraft measurements is shown in Figure 3 for CO mixing ratio at 6 vertical levels. Summary: MOPITT provisional V3 products begin to be available at Langley DAAC. MOPITT CO measurement is sensitive to biomass burning emissions. Elevated MOPITT CO plumes correlate well with MODIS aerosols from the same source that are characterized as small mode aerosols. MOPITT products are essentially insensitive to large model aerosols from sources such as dust storms. Acknowledgement: The NASA Earth Observing System (EOS) Program funded this work under contract NAS MODIS cloud mask and aerosol was provided by NASA DAAC. References: J. R. Drummond, “Novel correlation radiometer: the length-modulated radiometer,” Appl. Opt., 34, pp , 1989 Y. J. Kaufman and D. Tanre, 1998: Algorithm for Remote Sensing of Tropospheric Aerosol from MODIS. MODIS ATBD E. M. Prins, J. Schmetz, L. Flynn, D. Hillger, J. Feltz, 2001: Overview of current and future diurnal active fire monitoring using a suite of international geo- stationary satellites. In Global and Regional Wildfire Monitoring: Current Status and Future Plans (F. J. Ahern, J. G. Goldammer, C. O. Justice, Eds.), SPB Academic Publishing, The Hague, Netherlands, pp J. X. Warner, J. C. Gille, D. P. Edwards, D. C. Ziskin, M. W. Smith, P. L. Bailey, L. Rokke, “Cloud Detection and Clearing for the Earth Observing System Terra Satellite Measurements of Pollution in the Troposphere (MOPITT) Experiment”, Applied Optics: Vol. 40, issue 8, , 2000 Figure 1: Correlation radiometry measurement principle. x ret = Ax + (I - A)x a (Eq. 1) where the Averaging Kernel A represents the measurement sensitivity to the true profile. Figure 2. CO mixing ratio at 700mb for Jan. 1-3, 2001 for MOPITT product V2 (a) and V3 (b). (a) (b) Figure 3. Validation of V3 CO mixing ratio (ppbv) against aircraft measurements at pressure levels of 850, 700, 500, 350, 250, and 150mb. Averaging Kernels: The Averaging Kernels represent the measurement sensitivity to the true profiles, as shown in Eq. 1, and depends on those factors affecting the radiative transfer of the measured signal through the atmosphere. An example of MOPITT averaging kernels is shown in Figure 4 for day (a) and night (b). As shown in (a) and (b), the averaging kernels peak at lower layers in the atmosphere during the day than at night for the same channels. Therefore, MOPITT day retrievals are slightly larger than night retrievals. Explicit consideration of the MOPITT averaging kernel is required to properly compare MOPITT data with trace gas profiles obtained from in-situ instrumentation or from chemistry model output. Figure 4. MOPITT Averaging Kernels for daytime and nighttime. Biomass Burning Emissions Measured from MOPITT: An example plot of MOPITT CO mixing ratio at 700hpa is shown in Figure 5a for April 10, 2001 to April 17, The map covers the area of North and South America, Western Africa, and Atlantic Ocean. MOPITT data was retrieved using a precursor to the V3 processor that incorporated MODIS cloud mask data. Figure 5b shows the MODIS ocean and land aerosol optical depth retrieved from 0.55um wavelength over the same area and for April 7 to April 15, Figure 5c shows the MODIS small/large mode ratio for April 7 to April 15, Figure 6 shows a fire map from GOES-8 WFABBA for April 10-17, 2001 over Central America (left panel) and the AVN average 700hPa streamline analysis for April 12-19, 2001 (right panel). Figure 6. GOES-8 WFABBA fire map for April 10-17, 2001 (left panel) and AVN 700hPa streamline Analysis for April 12-19, Figure 5. a. MOPITT CO mixing ratio at 700hPa (4/10-17/2001), b. MODIS optical depth (4/7-15/2001), and c. MODIS Small/ Large Mode Ratio (4/7-15/01). Numbered boxes 1-4 are the borders for the areas of interests. 1. Yucatan Peninsula fire, 2. Venezuela fire, 3. Africa dust storm and fire, and 4. North America automobile emissions and industrial pollution. 4 a b c In Area 1 the elevated CO and aerosol concentrations over eastern Texas of US are believed to be due to fires over Yucatan Peninsula. The biomass burning emissions from Yucatan Peninsula traveled west and then north as shown by the 700hPa streamlines. In area 2, the elevated CO and aerosol over Venezuela and Columbia and off Columbia coast are transported from fires in Venezuela as shown in Figure 6 left panel. Aerosols emitted from biomass burnings are primarily small mode aerosols, and correlate very well with CO plumes from the same biomass burning source. In area 3, there is a very large area of high aerosol optical depth off the west coast of Africa and only relatively small area of elevated CO is correlated with the small mode aerosol. The elevated CO is due to the fires in Africa from TRMM fire products (not shown here). Evidently, a large portion of the aerosol off Africa is from dust storms indicated by large mode aerosols in figure 5c. In area 4, the aerosols observed off the east coast of the US are primarily due to automobile emission and industrial pollution as characterized by small mode particles. The elevated CO observed by MOPITT correlates well with this outflow. Another example of MOPITT measurement is from the Montana fire emission during Aug. 20, 2000 to Aug. 27, The CO total column is shown in Figure 7b for Aug , 2000 and 7c for Aug , Figure 7a shows the fire pixels detected by WF-ABBA during Aug , 2000, and 7d shows the averaged 700hPa streamline analysis. The fire emission from Idaho and part of Montana was transported by the eastward wind to Montana and North Dakota. This case demonstrates that MOPITT is sensitive to those boreal fires that do not necessarily persist for a long time as in the cases over South America and Africa. Montana Alberta North Dakota Saskatchewan Idaho ab c d Figure 7. a shows the WF-ABBA fire map over Montana fire, b is MOPITT total column for the early part of the fire, c is for the later part of the fire, and d shows the 700mb streamline analysis.