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Science Objectives

 

Science Objective 1: Relate column observations to surface conditions for aerosols and key trace gases O3, NO2, and CH2O

  1. How well do column and surface observations correlate?
  2. What additional variables (e.g., boundary layer depth, humidity, surface type) appear to influence these correlations?
  3. On what spatial scale is information about these variables needed (e.g., 5 km, 10 km, 100 km) to interpret column measurements?
Expected outcome: Improved understanding of the extent to which column observations (as observed from space) can be used to diagnose surface conditions

SO1 Figure: 20090603-20090801 Correlation between AIRNOW 1 hour PM2.5 and MODIS AOD Image caption: Correlations between MODIS AOD and surface PM2.5 vary widely across the U.S. with poorer correlations being more typical in the west. Credit: IDEA Team, http://www.star.nesdis.noaa.gov/smcd/spb/aq/


Science Objective 2: Characterize differences in diurnal variation of surface and column observations for key trace gases and aerosols

  1. How do column and surface observations differ in their diurnal variation?
  2. How do emissions, boundary layer mixing, synoptic transport, and chemistry interact to affect these differences?
  3. Do column and surface conditions tend to correlate better for certain times of day?
Expected Outcome: Improved understanding of diurnal variability as it influences the interpretation of satellite observations from both LEO and GEO perspectives and improved knowledge of the factors controlling diurnal variability for testing and improving models

CLICK HERE for FULL SIZED IMAGE (opens in new window)>> CMAQ Surface and Column NO2 Plotted as a Function of Hour Image caption: Diurnal variation in column integrated and surface NO2 are expected to exhibit important differences as shown in this simulation by EPA’s CMAQ model for Houston, Texas.
Credit: J. Fishman and D. Byun.


Science Objective 3: Examine horizontal scales of variability affecting satellites and model calculations

  1. How do different meteorological and chemical conditions cause variation in the spatial scales for urban plumes?
  2. What are typical gradients in key variables at scales finer than current satellite and model resolutions?
  3. How do these fine-scale gradients influence model calculations and assimilation of satellite observations?
Expected outcome: Improved interpretation of satellite observations in regions of steep gradients, improved representation of urban plumes in models, and more effective assimilation of satellite data by models

CLICK HERE for FULL SIZED IMAGE (opens in new window)>> Science Objective 3 Figure 1 Image caption: Example calculations of HOx radicals and O3 production for a range of NOx abundances.   Precise location of the peak and general behavior vary with radical source strength.   Greatest sensitivity tends to fall near 1000 pptv of NOx which is considered a threshold for polluted conditions.
Credit: J. Crawford, NASA Langley Photochemical Box Model.




 
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