1 Precision of 1x10 15 moleculescm 2 05 ppb in the PBL Approach 3 Pandoras for 1 month 4 seasons Contract requirement Most approaches to using the data assumewill work better if the observations have little bias or a Gaussian distribution of bias ID: 784099
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Slide1
TEMPO NO
2
Validation
Ron Cohen, UC Berkeley
Slide21. Precision of 1x10
15 molecules/cm
2 (~0.5 ppb in the PBL)
Approach: ~3
Pandoras for 1 month; 4 seasons
Contract requirement
Slide3Most 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).
NO
2
Validation issues
Slide4Los Angeles: WRF-Chem
Slide5from Choi et al. 2014
observations
modeled fit
1σ variation range
Particulate Matter
(co-emitted with CO
2
, NO
x
, CO, …)
Slide6NASA 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°
NO
2
profile shape
Annually
2° × 2.5°
WRF-Chem, Monthly 4 × 4 km
2
(CA&NV)
12 x 12 km
2 U.S.CloudsOMI cloud productMODIS cloud product
Russell et al., Atmos Chem & Phys 11, 8543-8554, 2011
http://behr.cchem.berkeley.edu/
/
Slide7Terrain Reflectivity (Albedo)
NASA Standard Product June 2008
BEHR June 2008
MODIS True Color
SP NO
2
June 18, 2008
OMI Monthly Albedo
MODIS 8 day Albedo
Russell et al., Atmos Chem & Phys, 2011
Slide8Terrain Reflectivity (Albedo)
Russell et al., Atmos Chem & Phys, 2011
Histogram of systematic errors
Slide9NO
2
profile shape
Russell et al., Atmos Chem & Phys, 2011
Histogram of systematic errors
Slide10The BEHR product is generally higher in urban regions and lower in rural regions than the operational products
BEHR
% Difference
Standard Product
Russell et al., Atmos Chem & Phys, 2011
Slide11Trends in cities are similar while trends at power plants are more variable
Russell et al.,
ACP
2012
47 cities, 23 power plants!
Slide12Example: look in remote places with uniform (but low) NO
2
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.
NO
2
Validation Strategies
Check all possible avenues for internal consistency
Slide13OMI Berkeley High-resolution Retrieval (BEHR)
0
1
2
3
4
5
6
7
8
9 10x1015NO2 (molecules cm–2)May–October 2005–2006
Slide14NO
2
Validation Strategies
Additional “conventional data”
Aircraft/ground based experiments e.g. DISCOVER; KORUS
Surface network
additional PANDORA’s
Slide15NO
2
Validation Strategies
“unconventional data”
Slide16CO
2
Emissions
in San Francisco bay area at 1km resolution
Slide17NO
NO
2O3CO
CO
2aerosol
Slide18BEACO
2
N observing network http://beacon.berkeley.edu/
Slide19Vaisala
GMP343 NDIR CO
2 Sensor
Shinyei Grove
ParticulateSensor
Electrochemical O
3
, NO, NO
2
& CO
Sensors
Slide20BEACO
2N CO
2 2013Sites:
Laurel Korematsu HeadRoyceBurckhalter Kaiser ODowd ElCerritoPrescott
CollegePrep
StLiz
NOakland
Slide21WRF-STILT for day bridge was closed
Alex
Turner
10 km
10 km
Slide22NO
2 Validation Strategies
“other unconventional data?”
Profiling with small sensors and
dronesLIDARSSondes