Deriving Information on Surface Conditions from Column and VERtically Resolved Observations Relevant to Air Quality

A NASA Earth Venture campaign intended to improve the interpretation of satellite observations to diagnose near-surface conditions relating to air quality

Objectives:

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

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

3. Examine horizontal scales of variability affecting satellites and model calculations

Investigation Overview

Deployment Strategy

Systematic and concurrent observation of column-integrated, surface, and vertically-resolved distributions of aerosols and trace gases relevant to air quality as they evolve throughout the day.

NASA UC-12 (Remote sensing)Continuous mapping of aerosols with HSRL and trace gas columns with ACAM

NASA P-3B (in situ meas.)In situ profiling of aerosols and trace gases over surface measurement sites

Ground sitesIn situ trace gases and aerosolsRemote sensing of trace gas and aerosols (Pandora, AERONET)OzonesondesAerosol lidar observationsTethersondes, NO2 sondes

Three major observational components:

Deployment Locations

Maryland, July 2011 Houston, September 2013

California, Jan-Feb 2013 Colorado, Jul-Aug 2014

Flight Hours and Sampling Statistics

Location P-3B Hours (Flight Days)

King Air Hours (Flight days)

Other aircraft participation

Baltimore-Washington 104 (14) 101 (14) UMD Cessna

California, Central Valley

84 (10)10 (2) - PODEX 70 (10)

NASA ER-2 Alpha JetUC-Davis Mooney

Houston 71 (9) 68 (9)23 (3) - GEO-CAPE NASA LaRC Falcon

Denver, Northern Front Range 93 (16) 89 (17) NCAR C-130

NASA LaRC Falcon

Total for all campaigns 352 (49) 328 (50)

Location P-3B Spirals King Air Overflights Missed Approaches

Baltimore-Washington 254 342 0

California, Central Valley 170 307 166

Houston 195 341 105

Denver, Northern Front Range 214 451 108

Total for all campaigns 833 1441 379

Aerosol Characterization

Fresno

Bakersfield

550 nm Scattering (Mm-1)0 100 200 300

10000

9000

8000

7000

6000

5000

4000

3000

2000

1000

0Day in January

AO

D

PM

2.5

(ug

/cm

3)

Examples of surface PM2.5, Aeronet AOD,P-3B in situ profiles of scattering, and HSRL observations during the California campaign.

Trace Gas Characterization

Examples of Pandora column-integrated observations NO2 and in situ profiles by the P-3B from the Colorado deployment

Comparison of ACAM and P-3B CH2O (Maryland deployment)

Ozonesondes

28 July0750-1030 LT

In addition to traditional ozonesondes, partners also launched tethersondes for O3 (NOAA, Bryan Johnson) and NO2 (KNMI, Deb Stein-Zweers)

Tethersondes

NO2

O3

Final Disposition of Data

A Digital Object Identifier has been obtained for the DISCOVER-AQ data to enable data discovery as well as attribution and referencing in publications:

doi:10.5067/Aircraft/DISCOVER-AQ/Aerosol-TraceGas

This doi points to the Langley ASDC, the long-term archival site for the DISCOVER-AQ data (https://eosweb.larc.nasa.gov/project/discover-aq/discover-aq_table)

NO2 O3

P-3B in situ Low HighP-3B + surface Moderate High

Pandora Moderate LowCMAQ Moderate Moderate

NO2 O3

P-3B in situ Low LowP-3B + surface Moderate Low

Pandora Low LowCMAQ Moderate Low

NO2 O3

P-3B in situ Low LowP-3B + surface Moderate Low

Pandora Low N.S.CMAQ High Low

NO2 O3

P-3B in situ Low N.S.P-3B + surface Low N.S.

Pandora High HighCMAQ High Moderate

Summary of Column vs. Surface Correlations for NO2 and O3

Low correlation: R < 0.4; Moderate: R=0.4-0.8; High: R=0.8-1.0; N.S. = not significant

Maryland California

Texas Colorado

Clare Flynn, U. Maryland

Surface vs Column O3 at BAO (Erie, CO): All DAQ/FRAPPE days

66% of 1500 m AGL column and surface O3 values agree within 10 ppbv, almost entirely after midday, showing lower tropospheric column O3 to be a good predictor of surface conditions.

34% exhibit O3 column exceeding surface values due to shallow mixing in the morning, elevated plumes, and thunderstorm outflows leading to significant vertical gradients of ozone in the lower atmosphere

Christoph Senff, NOAA TOPAZ Lidar

08 09 10 11 12 13 14 15 16 17 18MDT

ΔO3 ≤ 10 ppbv:

66% of all cases

N = 2294

Relationships Between AOD and Surface PM2.5

Crumeyrolle et al. (2014) showed that AOD as measuredby vertical integration of the extinction from the P-3Bwas well correlated with the product of surface PM2.5,the height of the buffer (BuL) layer, and a hydration factor.

The BuL is the transition layer between the BL and the FT with a pronounced gradient of aerosol concentrations from the typically higher concentration observed in the BL and typically cleaner conditions observed in the FT.

Chu et al., 2015 proposed the following relationships for predicting surface PM2.5 given AOD observationsfrom satellite, HSRL, AERONET, etc.:

ta :AOD at 0.55 mmf(RH): hydration factor (function of relative humidity) Aerosol extinction cross section per

unit dry massHLH: Haze layer height = top of BuLMPE: mean PBL extinction

Errors in AMFs/retrievals from a-priori NO2 profiles

km

surface

ii fwAMF8

wi scattering weightsfi NO2 shape factors

► AMF calculated for ACAM (air-borne spectrometer

located at ~8km) but is also relevant for tropospheric

NO2 column retrievals

Surface reflectivities: 0.1 to 0.14 at 0.01

steps

Solar zenith angles: 10° to 80 at 10°steps

Aerosol optical depths: 0.1 to 0.9 at 0.1

steps

Lok Lamsal

Padonia, MD Retrieval errors based on 11 AM campaign mean profiles

NO2 profiles and AMFs: Summary plot

NO2 tropospheric column retrievals are highly sensitive to diurnal variation in a-priori profile shapes.

L. Lamsal

Follette-Cook et al. (2015) Atmos. Environ.• Quantified and compared the spatial and temporal variability

seen in the Maryland/DC DISCOVER-AQ P-3B trace gas data and in a regional WRF/Chem simulation

• Compared variability with TEMPO/GEO-CAPE precision requirements

• Found that the variability simulated for the July 2011 campaign period was large enough to be meaningfully resolved by a TEMPO-type geostationary instrument

Additional analyses:• Comparing the variability observed in MD campaign with the

variability observed during the other DISCOVER-AQ campaigns

Spatial and Temporal Variability

Geographic coverage of differences that fall above the PRs at several distances

O3 – 0-2 km CO - 0 – 2 km

7/29/2011 2 pm EDT

PR = 1.7 DU PR = 0.91 e17 molec/cm2

These results indicate that the TEMPO instrument would be able to observe O3 air quality events over the Mid-Atlantic area, even on days when the violations of the air quality standard are not widespread.

M. Follette-Cook

Geographic coverage of differences that fall above the PRs at several distances

• The maximum differences in NO2 are in the morning and early evening, so 2 pm could be considered a minimum in what would be observable

• Despite that, the major features in the tropospheric column field can be seen in the plot of the visible differences at 4 – 8 km distances

• The entire field is almost visible at distances greater than 20 km

• This suggests that TEMPO will be able to satisfactorily observe the spatial variability of this species

7/29/2011 2 pm EDT

PR = 1x1015 molec/cm2

NO2 - Tropospheric column

Percentiles – O3

MD CA TX CO

TEMPO PR

MD, TX, and CO 75th percentile curves above the PR at distances of 8 (TX), 12 (CO), and 16 km (MD)

All campaigns 95th percentile curves above the PR

Percentiles – NO2

MD CA TX CO

• MD shows the lowest NO2 variability of any campaign

• Follette-Cook et al. (2015) concluded that the variability during the MD campaign was large enough to be well-resolved by TEMPO

• Therefore, with respect to variability, TEMPO’s NO2 PR is more than sufficient for these other regions as well

Surface Ozone over Bay vs Land

Possible reasons:

D. Goldberg, UMD

21

Stratospheric Intrusions during DISCOVER-AQLesley Ott, Bryan Duncan, Anne Thompson

LARGE and AERONET Teams

• Absorption aerosol optical depth (AAOD) was calculated from in-situ measurements for each vertical profile

• Comparisons with AERONET yielded significantly lower in-situ AAOD values at each of the DISCOVER-AQ urban sites

• This discrepancy is compounded by model overestimates in remote regions by factors of 2-5 (Schwarz et al., 2013)

• No evidence was observed to support model up-scaling by Bond et al. (2013)

Profiles of Aerosol Absorption Suggest Model/AERONET Over-predictions

Key Scientific Outcomes

• Surface-Column correlations are a mixed bag, but there is indication that lower tropospheric ozone (especially in the afternoon) can sometimes be trusted to infer surface values.

• Spatial variability analyses show that TEMPO precision requirements are adequate to detect air quality violations.

• Model representation of diurnal evolution in profile structure is expected to be an important source of error for TEMPO retrievals.

• Pandora validation of TEMPO diurnal variability would appear to require no more than a couple of instruments in a given metropolitan area.

• Formaldehyde shows promise as a proxy for ozone production.

• Validation of AERONET AAOD reveals a high bias that may be responsible for overprediction of black carbon by global models.

• Strong correlations are observed between AOD and PM2.5, but the scaling factors vary widely based on BL depth, humidity, and aerosol composition (hygroscopicity).

• Reactive nitrogen chemistry in air quality models needs to be improved.

• Ozone can be highest over unmonitored areas downwind (e.g., Chesapeake Bay).

• A single lidar may be sufficient to characterize the vertical distribution of aerosols over distances of up to 100 km.