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

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Presentation on theme: "The “Afternoon” constellation of satellites make near simultaneous measurements to better understand important parameters related to climate change."— Presentation transcript:

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3 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 14.55 orbits per day,@ 24.7 degree separation. Orbits match the World Reference System 2 (WRS-2) reference grid (Developed for LANDSAT).

4 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

5 “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.

6 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

7 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 (Overhead @ 21:00 UTC) –Varying Pixel Sizes (12x13 km 2 at center of swath) –Desired Region at Edges?

8  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

9 OMI: American vs Dutch Results

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

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

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

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

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

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

16 OMI vs. AIRPACT Results

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

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

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

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

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

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

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

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

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

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

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

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

30 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).

31 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

32 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.

33 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

34 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.

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

36 References: http://aura.gsfc.nasa.gov/instruments/ http://www.nasa.gov http://www.temis.nl/airpollution/no2.html http://www.temis.nl/docs/AD_NO2.pdf http://www.temis.nl/products/no2.html http://www-calipso.larc.nasa.gov/about/atrain.php Data Access: http://www.temis.nl/airpollution/no2col/omi_data.php?year=2006 http://disc.sci.gsfc.nasa.gov/data/datapool/OMI/ sftp://zephyr.cmer.wsu.edu

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