The “Afternoon” constellation of satellites make near simultaneous measurements to better understand important parameters related to climate change.

Slides:

Advertisements

Similar presentations

The 7th Meeting of the Ozone Research Managers, Geneva, Switzerland May, 2008 Satellite Ozone Monitoring and Application Research in China Huang.

Advertisements

Martin G. Schultz, MPI Meteorology, Hamburg GEMS proposal preparation meeting, Reading, Dec 2003 GEMS RG Global reactive gases monitoring and forecast.

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.

Improving the View of Air Quality from Space Jim Crawford Science Directorate NASA Langley.

OMI follow-on Project Toekomstige missies gericht op troposfeer en klimaat Pieternel Levelt, KNMI.

Earth System Science Teachers of the Deaf Workshop, August 2004 S.O.A.R. High Earth Observing Satellites.

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.

A U R A Satellite Mission T E S

A statistical method for calculating the impact of climate change on future air quality over the Northeast United States. Collaborators: Cynthia Lin, Katharine.

Results from the OMPS Nadir Instruments on Suomi NPP Satellite

DIRECT TROPOSPHERIC OZONE RETRIEVALS FROM SATELLITE ULTRAVIOLET RADIANCES Alexander D. Frolov, University of Maryland Robert D. Hudson, University of.

Variability of Total Column Ozone During JAN JUN 2011: Consistency Among Four Independent Multi-year Data Records E.W. Chiou ADNET Systems Inc.,

Ground and Satellite Observations of Atmospheric Trace Gases George H. Mount Laboratory for Atmospheric Research WSU 13 April 2007.

Data assimilation of trace gases in a regional chemical transport model: the impact on model forecasts E. Emili 1, O. Pannekoucke 1,2, E. Jaumouillé 2,

ATS 351 Lecture 8 Satellites

The AIRPACT-3 Photochemical Air Quality Forecast System: Evaluation and Enhancements Jack Chen, Farren Thorpe, Jeremy Avis, Matt Porter, Joseph Vaughan,

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.

National Aeronautics and Space Administration Jet Propulsion Laboratory California Institute of Technology Pasadena, California Observing System Simulation.

1 Surface nitrogen dioxide concentrations inferred from Ozone Monitoring Instrument (OMI) rd GEOS-Chem USERS ` MEETING, Harvard University.

My first look at Tropospheric NO 2 data for the Pacific NW. Whats that in Nevada? Why are the Dutch and US. data sets so different? Dutch OMIU.S. OMI.

The AIRPACT-3 Photochemical Air Quality Forecast System: Evaluation and Enhancements Jack Chen, Farren Thorpe, Jeremy Avis, Matt Porter, Joseph Vaughan,

Evaluation of the AIRPACT2 modeling system for the Pacific Northwest Abdullah Mahmud MS Student, CEE Washington State University.

Assimilation of EOS-Aura Data in GEOS-5: Evaluation of ozone in the Upper Troposphere - Lower Stratosphere K. Wargan, S. Pawson, M. Olsen, J. Witte, A.

Chapter 2: Satellite Tools for Air Quality Analysis 10:30 – 11:15.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

The Earth and its Atmosphere This chapter discusses: 1.Gases in Earth's atmosphere 2.Vertical structure of atmospheric pressure & temperature 3.Types of.

Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),

Ozone Detection and Monitoring A Satellite Tutorial By: Gabriel Langbauer.

Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.

AQUA AURA The Berkeley High Spatial Resolution(BEHR) OMI NO2 Retrieval: Recent Trends in NO2 Ronald C. Cohen University of California, Berkeley $$ NASA.

Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.

Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,

Algorithms and chemical data assimilation activities at Environment Canada Chris McLinden Air Quality Research Division, Environment Canada 2 nd TEMPO.

EOS CHEM. EOS CHEM Platform Orbit: Polar: 705 km, sun-synchronous, 98 o inclination, ascending 1:45 PM +/- 15 min. equator crossing time. Launch date.

Summer Institute in Earth Sciences 2009 Comparison of GEOS-5 Model to MPLNET Aerosol Data Bryon J. Baumstarck Departments of Physics, Computer Science,

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

EOS CHEM. EOS-CHEM Platform Orbit: Polar: 705 km, sun-synchronous, 98 o inclination, ascending 1:45 PM +/- 15 min. equator crossing time. Launch date.

A Progress Report on Combining MODIS and CALIPSO Aerosol Data for Direct Radiative Effect Studies Jens Redemann, Qin Zhang, Philip Russell, John Livingston,

From TOMS to OMI Reflections on 15 years of NASA/KNMI/FMI Collaboration Pawan K Bhartia Earth Sciences Division- Atmospheres NASA Goddard Space Flight.

AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.

Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie.

Development and Preliminary Results of Image Processing Tools for Meteorology and Air Quality Modeling Limei Ran Center for Environmental Modeling for.

1 Ground-level nitrogen dioxide concentrations inferred from the satellite-borne Ozone Monitoring Instrument Lok Lamsal and Randall Martin with contributions.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Infrared Temperature and.

TEMIS user workshop, Frascati, 8-9 October 2007 TEMIS – VITO activities Felix Deutsch Koen De Ridder Jean Vankerkom VITO – Flemish Institute for Technological.

Methods for Incorporating Lightning NO x Emissions in CMAQ Ken Pickering – NASA GSFC, Greenbelt, MD Dale Allen – University of Maryland, College Park,

NASA/GSFC Tropospheric Ozone Residual M. Schoeberl NASA/GSFC M. Schoeberl NASA/GSFC.

OVERVIEW OF ATMOSPHERIC PROCESSES: Daniel J. Jacob Ozone and particulate matter (PM) with a global change perspective.

SATELLITE OBSERVATIONS OF ATMOSPHERIC CHEMISTRY Daniel J. Jacob.

OMPS Products Applications Craig Long NOAA/NWS/NCEP Climate Prediction Center SUOMI NPP SDR Product Review -- 23/24 October NCWCP Auditorium.

Validation of OMI NO 2 data using ground-based spectrometric NO 2 measurements at Zvenigorod, Russia A.N. Gruzdev and A.S. Elokhov A.M. Obukhov Institute.

Status of the Development of a Tropospheric Ozone Product from OMI Measurements Jack Fishman 1, Jerald R. Ziemke 2,3, Sushil Chandra 2,3, Amy E. Wozniak.

Retrieval of Vertical Columns of Sulfur Dioxide from SCIAMACHY and OMI: Air Mass Factor Algorithm Development, Validation, and Error Analysis Chulkyu Lee.

Evaluation of model simulations with satellite observed NO 2 columns and surface observations & Some new results from OMI N. Blond, LISA/KNMI P. van Velthoven,

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Requirement: Provide information to air quality decision makers and improve.

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.

Ozone PEATE 2/20/20161 OMPS LP Release 2 - Status Matt DeLand (for the PEATE team) SSAI 5 December 2013.

Validation of OMI and SCIAMACHY tropospheric NO 2 columns using DANDELIONS ground-based data J. Hains 1, H. Volten 2, F. Boersma 1, F. Wittrock 3, A. Richter.

TES and Surface Measurements for Air Quality Brad Pierce 1, Jay Al-Saadi 2, Jim Szykman 3, Todd Schaack 4, Kevin Bowman 5, P.K. Bhartia 6, Anne Thompson.

A Brief Overview of CO Satellite Products Originally Presented at NASA Remote Sensing Training California Air Resources Board December , 2011 ARSET.

1 “Air Quality Applications of Satellite Data” Shobha Kondragunta NOAA/NESDIS Center for Satellite Applications and Research Aura Science Team Meeting,

Impact of OMI data on assimilated ozone Kris Wargan, I. Stajner, M. Sienkiewicz, S. Pawson, L. Froidevaux, N. Livesey, and P. K. Bhartia   Data and approach.

OMI Nitrogen Dioxide Workshop Ellen Brinksma, Folkert Boersma.

notepacket information

A.Liudchik, V.Pakatashkin, S.Umreika, S.Barodka

Surface Pressure Measurements from the NASA Orbiting Carbon Observatory-2 (OCO-2) Presented to CGMS-43 Working Group II, agenda item WGII/10 David Crisp.

Comparison of GOME-2 and OMI surface UV products

The Successor of the TOU

Diurnal Variation of Nitrogen Dioxide

Presentation transcript:

The “Afternoon” constellation of satellites make near simultaneous measurements to better understand important parameters related to climate change. The satellites greatly complement each other, making measurements of aerosols, clouds, temperature, relative humidity, and radiative fluxes. Daily global coverage is achieved in orbits per 24.7 degree separation. Orbits match the World Reference System 2 (WRS-2) reference grid (Developed for LANDSAT).

SpacecraftMain Area of Measurement within Earth’s AtmosphereInstruments Carried AquaSolid, liquid and gaseous forms of waterAIRS/AMSU-A/HSB, AMSR- R, CERES, MODIS CloudSatCloud Profiling RadarCPR CALIPSOLIDAR used to understand aerosols and cloudsCALIOP, IIR, WFC PARASOLPolarimetry to distinguish aerosols.POLDER AuraChemistry, pollutants, and greenhouse gasesHIRDLS, MLS, OMI, TES OCOColumn integrated concentration of carbon dioxide3 grating spectrometers

“Ozone Monitoring Instrument” - continues the TOMS record Measures total ozone, as well as other ozone-related chemistry. Capable of mapping pollution products on urban to super-regional scales. Contributed to the EOS Aura mission by the Netherlands's Agency for Aerospace Programs in collaboration with the Finnish Meteorological Institute. Uses hyperspectral imaging in a “push-broom” mode to observe solar backscatter radiation in the visible and ultraviolet, using a wide-field telescope feeding two imaging grating spectrometers with CCD. Tropospheric NO 2 columns are derived from satellite observations based on slant column NO 2 retrievals with the DOAS technique, and the KNMI combined modelling/retrieval/assimilation approach.

OMI Data: Dutch vs American  DUTCH Algorithm –Array Format - HDF –Negative Values –Missing Data Points –Data Available From: May 18, 2006  AMERICAN Algorithm –Matrix Format - HDF- EOS –No Negative Values –All Data Points Given –Data Available From: Sept. 28, 2004

AIRPACT Model vs OMI Data Some of the Challenges Involved When Comparing the Different Data Types  AIRPACT Model –17 Layers Predicted –9025 Data Points per Layer over Region –Mixing Ratio Given –Fire Impact Included –Appropriate Layer Must be Chosen (Max Value) –One Data Set per Hour –Equal Pixel Sizes (12x12 km 2 ) –Full Region Available  OMI Data –One Tropo. Value (VCD) –Approximately 5000 Data Points over Region –VCD Conversion (mol/cm 2 ) to PPBV Requires PBL Depth Assumption –One Data Set per Day 21:00 UTC) –Varying Pixel Sizes (12x13 km 2 at center of swath) –Desired Region at Edges?

 Varying Time Periods Selected and Data Obtained for OMI (American & Dutch) and AIRPACT (Early September Displayed)  OMI Vertical Column Depth Troposphere Values Converted to PPBV Using Assumed PBL Height  Max Concentration Value (1 of 17 Layers) from AIRPACT (closest hour) Used for Comparison  OMI (HDF) and AIRPACT Data Converted to Lat/Long Columnar Format  Data Sets Interpolated onto Standard Coordinate Grid Using IGOR Pro  Bias, Ratio, and Stats Analysis Performed on a Per Point Per Day Basis

OMI: American vs Dutch Results

NO 2 in Troposphere – Sept. 3, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

NO 2 in Troposphere – Sept. 4, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

NO 2 in Troposphere – Sept. 5, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

NO 2 in Troposphere – Sept. 6, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

NO 2 in Troposphere – Sept. 7, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

NO 2 in Troposphere – Sept. 8, 2006 OMI (American) OMI (Dutch) Amer. Sub Dutch BIAS

OMI vs. AIRPACT Results

NO 2 in Troposphere – Sept. 3, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

NO 2 in Troposphere – Sept. 4, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

NO 2 in Troposphere – Sept. 5, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

NO 2 in Troposphere – Sept. 6, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

NO 2 in Troposphere – Sept. 7, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

NO 2 in Troposphere – Sept. 8, 2006 OMI (American) AIRPACT Amer. Sub AIRPACT BIAS

Seattle/Portland NO 2 – Sept. 3, 2006 Urban Region – (1000 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Seattle/Portland NO 2 – Sept. 4, 2006 Urban Region – (500 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Seattle/Portland NO 2 – Sept. 5, 2006 Urban Region – (500 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Seattle/Portland NO 2 – Sept. 6, 2006 Urban Region – (500 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Seattle/Portland NO 2 – Sept. 7, 2006 Urban Region – (500 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Seattle/Portland NO 2 – Sept. 8, 2006 Urban Region – (500 meter Vertical Column Assumed) OMI (American)OMI (Dutch)AIRPACT Cloud Cover Fraction

Urban vs. Entire AIRPACT Domain Bias Results Large Number of Zero Values Agree in Entire Domain. Vertical Height Assumption has Strong Influence Consistent Bias Between OMI & AIRPACT evident in Urban area but not in entire domain (Fires).

Meteorological Influence: Cloud Cover and Temperature were analyzed to determine if meteorology directly correlates to differences between AIRPACT and OMI Cloud cover and temperature not related to differences between OMI and AIRPACT (Sept. 3 rd bias vs CFRAC shown. Temperature & CFRAC of other days show similar results) Amer. subAIRPACT CFRA C

Conclusions: American OMI Data Results: -Close Agreement to Airpact when Reasonable PBL Height Assumed -No Negative Values (Some Dutch Contour “lost” to negatives) -More Troposphere Data Available (No “lost” points; Entire AURA life) Dutch OMI Data Results: -Shows higher values in Fire Regions (as predicted) AIRPACT Results: -Often Predicts an Order of Magnitude Higher for NO2 from Fires -Accurately Predicts Urban Concentrations of NO2 -Doesn’t capture rural NO2 reported by OMI algorithms (farms?) Meteorological Factors: -Seem to have no direct correlation to the difference in results.

Possible Reasons for Major Differences: -Accurate Vertical Height (PBL?) Needed (Majority of NO2 probably resides at the bottom of the troposphere in urban areas) -AIRPACT may be over-estimating NO2 concentrations in Fire Regions -OMI may not be detecting fires in small canyon areas

Future Endeavors: Compare other chemical species predicted by AIRPACT to satellite observations (much of the troposphere data has not been released yet) Compare results to actual field measurements Use Variable PBL Depths as Predicted by AIRPACT per point Use a weighted layer average rather than a max layer value for AIRPACT.

Thanks to: Brian Lamb George Mount Joseph Vaughan Everyone else at LAR – WSU NASA & NAAP (Netherlands's Agency for Aerospace Programs)

References: Data Access: sftp://zephyr.cmer.wsu.edu