TEMPO NO2 Validation

Ron Cohen, UC Berkeley

1. Precision of 1x1015 molecules/cm2 (~0.5 ppb in the PBL)

Approach: ~3 Pandoras for 1 month; 4 seasons

Contract requirement

Most approaches to using the data assume/will work better if the observations have little bias (or a Gaussian distribution of bias).

We want the data to be unbiased with respect to viewing and solar zenith angles (time of day), cloudiness, aerosol, albedo (several comments about this yesterday).

NO2 Validation issues

Los Angeles: WRF-Chem

from Choi et al. 2014

observationsmodeled fit1σ variation range

Particulate Matter(co-emitted with CO2, NOx, CO, …)

NASA standard BEHR Terrain pressure High-res terrain

database, center of OMI footprint

High-res terrain database, average over OMI footprint

Terrain reflectivity

Monthly 1° × 1° MODIS, 8 day 0.05° × 0.05°

NO2 profile shape

Annually 2° × 2.5° WRF-Chem, Monthly 4 × 4 km2 (CA&NV)12 x 12 km2 U.S.

Clouds OMI cloud product MODIS cloud product

Russell et al., Atmos Chem & Phys 11, 8543-8554, 2011

http://behr.cchem.berkeley.edu//

Terrain Reflectivity (Albedo)

NASA Standard Product June 2008 BEHR

June 2008

MODIS True Color

SP NO2 June 18, 2008

OMI Monthly Albedo MODIS 8 day Albedo

Russell et al., Atmos Chem & Phys, 2011

-120.5 -120 -119.5 -119 -118.5 -11840

40.5

41

41.5

42

Terrain Reflectivity (Albedo)

Russell et al., Atmos Chem & Phys, 2011

Histogram of systematic errors

NO2 profile shape

0 0.05 0.1 0.150

0.5

1

1.5

2

2.5

3

Normalized NO2

He

igh

t (k

m)

Urban

Rural

Russell et al., Atmos Chem & Phys, 2011

Histogram of systematic errors

The BEHR product is generally higher in urban regions and lower in rural regions than the operational products

BEHR % DifferenceStandard Product

Russell et al., Atmos Chem & Phys, 2011

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Co

lum

n N

O2 (

mo

lec/

cm2 )

Year

Denver, CO

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Year

Los Angeles, CA

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Year

Atlanta, GA

05 06 07 08 09 10 110

0.5

1

1.5

Year

No

rma

lize

d N

O2

All Cities

05 06 07 08 09 10 110

0.5

1

1.5

Year

No

rma

lize

d N

O2

All Power Plants

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Co

lum

n N

O2 (

mo

lec/

cm2 ) Intermountain, UT

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Four Corners, NM

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Seminole, FL

Trends in cities are similar while trends at power plants are more variable

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Co

lum

n N

O2 (

mo

lec/

cm2 )

Year

Denver, CO

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Year

Los Angeles, CA

05 06 07 08 09 10 110

0.5

1

1.5

2x 10

16

Year

Atlanta, GA

05 06 07 08 09 10 110

0.5

1

1.5

Year

No

rma

lize

d N

O2

All Cities

05 06 07 08 09 10 110

0.5

1

1.5

YearN

orm

aliz

ed

NO

2

All Power Plants

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Co

lum

n N

O2 (

mo

lec/

cm2 ) Intermountain, UT

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Four Corners, NM

05 06 07 08 09 10 110

2

4

6x 10

15

Year

Seminole, FL

Russell et al., ACP 2012

47 cities, 23 power plants!

Example: look in remote places with uniform (but low) NO2 columns and make sure observed variation is geophysical sensible—not driven by viewing angle etc.

Stare at one location for an hour (at midday) and check that clouds moving across the scene don’t affect the interpretation.

Examine repeats at a power plant with near constant emissions and check that there is little variation of NO2 with time of day.

NO2 Validation StrategiesCheck all possible avenues for internal consistency

OMI Berkeley High-resolution Retrieval (BEHR)0 1 2 3 4 5 6 7 8 9 10x1015

NO2 (molecules cm–2)

May–October 2005–2006

NO2 Validation Strategies

Additional “conventional data”

Aircraft/ground based experiments e.g. DISCOVER; KORUS

Surface network

additional PANDORA’s

NO2 Validation Strategies

“unconventional data”

CO2 Emissions in San Francisco bay area at 1km resolution

NONO2

O3

COCO2

aerosol

BEACO2N observing network http://beacon.berkeley.edu/

Vaisala GMP343 NDIR

CO2 Sensor

Shinyei GroveParticulate

Sensor

Electrochemical O3, NO, NO2 & COSensors

BEACO2N CO2 2013

Sites: LaurelKorematsu HeadRoyceBurckhalter Kaiser ODowd

ElCerritoPrescott CollegePrep StLiz

NOakland

WRF-STILT for day bridge was closed

Alex Turner

10 km

10 km

NO2 Validation Strategies

“other unconventional data?”

Profiling with small sensors and drones

LIDARS

Sondes