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Science

Mission

DISCOVER-AQ, a NASA Earth Venture program funded mission, stands for Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality.

In recent years, progress in reaching air quality goals has begun to plateau for many locations. Furthermore, near-surface pollution is one of the most challenging problems for Earth observations from space. However, with an improved ability to monitor pollution from satellites from DISCOVER-AQ, scientists could make better air quality forecasts, more accurately determine the sources of pollutants in the air and more closely determine the fluctuations in emissions levels. In short, the more accurate data scientists have at hand, the better society is able to deal effectively with lingering pollution problems.

The campaign employed NASA aircraft to make a series of flights, with scientific instruments on board to measure gaseous and particulate pollution, beginning in 2011. The series of flights made by NASA Langley’s King Air and NASA’s P-3B commenced over Baltimore-Washington, D.C. in 2011. Other flights included Houston (2013); San Joaquin Valley, CA (2013); and Denver (2014). Measurements were taken in concert with ground observations in order to shed light on how satellites could be used to make similar, consistent measurements over time, with the ultimate goal of putting better data in the hands of policymakers and elected officials.

DISCOVER-AQ was a collaboration among scientists at NASA’s Langley Research Center in Hampton, Va.; NASA’s Goddard Space Flight Center in Greenbelt, Md.; NASA’s Ames Research Center, outside San Francisco; and multiple universities.

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



 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

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


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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