State of the Dark-Target aerosol retrieval on multiple satellite sensors

Robert C. Levy (NASA-GSFC) and the ”Dark-Target” retrieval team

Shana Mattoo, Virginia Sawyer, Rich Kleidman (SSAI)Yingxi Shi, Pawan Gupta (USRA)Falguni Patadia, Yaping Zhou (MSU) Lorraine Remer (UMBC)Reed Espinosa (NASA – NPP)

Aerosols (why do we care?)

• They affect visibility• They affect human health and morbidity• They enable clouds and precipitation• They have roles in Earth’s chemical cycles (carbon,

sulfate, etc)• They have roles in biology (e.g. transport nutrients)• They directly impact the radiative budget• They are both natural and manmade• They are inhomogeneous in space and time• Their distributions are changing• The science of aerosols is truly “interdisciplinary”

Haze over Maryland: Marufu, Doddridge, Taubman, Dickerson

For Aerosol Optical Depth

GCOS Aerosol CDR* Requirements

*CDR = Climate Data Record

Stability means ”drift per decade less than X” .

Also requires: multi-decade (e.g. 30+ year data record)

Of course, not only AOD is relevant for air quality and climate.. But only discuss AOD here.

CDR = Climate Data Record

Canada Smoke over Eastern U.S. 2002

http://earthobservatory.nasa.gov/

Dark-Target: A “Single View” aerosol algorithm

May 4, 2001; 13:25 UTCLevel 1 “reflectance”

What a sensor observes

OCEAN

GLINT

LAND

May 4, 2001; 13:25 UTCLevel 2 “product”

AOD1.0

0.0

Attributed to aerosol (AOD)

“Established 1997” by Kaufman, Tanré, Remer, etc)“Modified 2005, 2010, 2013, 2015” by Remer, Levy, Gupta, etc

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DT

Separate logic over land and oceanRetrieve: AOD at 0.55 mm, spectral AOD, etc

Can run in near-real-time (NRT; takes 2 minutes)

So where are we?

MODIS C6/C6.1 product

• Compare both land and ocean products to AERONET, separately• Validation: 66% are within

“Expected Error” (EE) defined as• Land: ±(0.15t + 0.05)• Ocean: ±(0.10t + 0.04)

•We are getting close to CDR accuracy requirements! • But….

AERONET: Level 2 (Quality Assured)

0.67

0.22

OCEAN GLINT MASK

LAND

May 4, 2001; 13:25 UTCLevel 2 “Granule”

AOT1.0

0.0

Terra-Aqua (DT) has global offset of 0.015

Independent of time difference:

0 60 120 180 240 300 360Longitude (degrees)

0 60 120 180 240 300 360Longitude (degrees)

Satellite overpass time (Local Solar Time)

Terra: 10:30 (Descending) Aqua: 13:30 (Ascending)

90.0

60.0

30.0

0.0

-30.0

-60.0

-90.0

Terra - Aqua: ~3 hours

Difference (Local Solar Time)

More than 4.5 hours!

Only 1.5 hours!

Earlier

Later

Later

Earlier

And this offset drifts (C6):

• Terra-Aqua global offset of ~0.01-0.02 (approx. 13%)– Appears to be bias, but hard to prove and to fix.

• Seasonal pattern/differences over land is different in later years (post 2011).

• The trending over ocean has no physical interpretation• Offset varies a bit less (improved calibration?) for C6.1 (but still remains)

MODIS Collection 6.1

• For DT retrieval, very few overall changes– Terra-Aqua difference remains ~ 0.015 or 20%

– Drifting of this difference is reduced (at least thru 2016).

– Some local changes (e.g. improved urban aerosol retrieval)

– 3 km retrieval is also updated…

• DB retrieval has more significant changes. This is reflected in the DT-DB merge product

Urban Retrievals in MODIS 6.1

Surface reflectance correction as a function of urban %

→Significant reduction in AOD bias

Implemented in C6.1

Is local, will not affect Terra-Aqua difference

(DISCOVER-AQ, Summer 2011 in Maryland, USA)

Urban %

Gupta et al., 2016, AMT

MODIS-AQUA Data from CONUS Region

MODIS C6.1 schedule/status(modis-atmosphere.gsfc.nasa.gov/documentation/collection-61)

• Current: forward-processing mode for both Terra and Aqua -MODIS since Oct 19.

• C6.1 includes: – L1B (calibration): MxD02, MxD03– upstream Level 2 (MxD35 cloud mask), – L2 – clouds (MxD06): – L2 - aerosols: DT and DB 10-km, and DT 3-km.– Level 3 – Aerosols/Clouds/Water Vapor (MxD08)

• Plan for re-processing (starting Oct 19): – Terra (2000-2017) complete by mid-Dec 2017– Aqua (2002-2017) complete by end of Mar 2018

• Also Near-Real-Time (NRT) processing for LANCE/Worldview applications (including GMAO data assimilation).

Beyond MODIS

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• Terra (17) and Aqua (almost 15) have both have well-exceeded their planned mission lifetimes.

• With luck, they will last until 2022. • But for climate, we need to continue the MODIS record, with no “jumps”

VIIRS!Visible-Infrared Imager Radiometer Suite

aboard Suomi-NPP (and future JPSS)

• The NOAA operational products are “too different” from MODIS for climate research.

• Both DT and DB algorithms are ported

To develop “continuity” we port algorithms!(Example: DT from MODIS→VIIRS)

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• Deal with differences in wavelengths (gas corrections/Rayleigh, etc)

• Deal with differences in resolution, etc. • Retrieve on VIIRS (compared with retrieval on MODIS):

Wavelength bands & surface spectra

Levy et al., 2015

VIIRS compared to MODIS-Aqua

There is slight offset of VIIRS compared to MODIS-Aqua. Still exploring why?

MODIS-Terra vs MODIS-Aqua vs SNPP-VIIRS

Terra (10:30, Descending) Aqua (13:30, Ascending)

Give a man one watch….Give a man two watches?Give a man three watches?

VIIRS (13:30, Ascending)

MODIS-T vs MODIS-A (6.1) vs VIIRS-SNP

VIIRS-SNPP has small high bias compared to MODIS-Aqua but less bias than Terra

Also, NPP vs Aqua has opposite seasonal cycle from Terra vs Aqua. Weird

”Trending” of Terra-Aqua offset is reduced in 6.1

MODIS/VIIRS: “Match files”

Example: 0.86 mm channel over “clear” skyCloud Optical Properties: 0.86 µm Channel Radiometry

20 April 2016IRS16, Platnick et al.

Spectral Response FunctionsMODIS B2

VIIRS ~3-4%

more reflective

than expected

VII

RS M

7

VII

RS C

OT

MODIS COT MODIS COT

MO

DIS

CO

T w

/b2 +

3%

bia

s

Retrieved

VIIRS vs MODIS

COT scatterplot

MODIS COT w/3%

increase in reflectance

vs. baseline MODIS COT

0.80 0.82 0.84 0.86 0.88 0.90 (l: mm)

VIIRS M7MODIS B2

T

1.0

0.8

0.6

0.4

0.2

0.0

Transmission

Sayer et al., AMT 2016

VIIRS 865 should be multiplied by:

1.10

1.05

1.00

0.95

0.90

Year‘12 ’13 ’14 ’15 ‘16

Vic

ario

us

Gai

n t

o a

pp

ly

VIIRS-SNPP “beta” schedule/status

• Current: testing mode. We have already processed two years– Whether calibration coefficients (VIIRS vs Aqua)

should be included

• This “beta” will include: – L1B (calibration), – upstream Level 2 (MVCCM cloud mask), – L2 – clouds: – L2 - aerosols: DT separate (6 km), VNPAERDT– L2 – aerosols: DB separate (6 km). – Level 3 – Still in development.

VIIRS on SNPP (and beyond) should include all updates (e.g. 6.1) for MODIS. Plan for re-processing of entire mission

Schedule is TBD, but hopefully soon!Note, our recent proposal was NOT SELECTED for funding, so….

VIIRS-JPSS1

Towards consistent global aerosol using DT

But what’s missing?

% deviation in hourly AOD at 0.44 mm (left y axis) and Angstrom

exponent (AE: 0.44/0.87 mm ; right y axis) relative to the daily

mean in four seasons in Mexico City.

From: Zhang, Y., Yu, H., Eck, T. F., et al, (2012). Aerosol daytime variations over North and South America derived from multiyear AERONET measurements, J. Geophysical Research.

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Dark Target Aerosol Retrieval Algorithm

Polar Orbiting Sensors Geo & Beyond

MO

DIS

-Te

rra

MO

DIS

-Aq

ua

VII

RS-

SNP

P

AH

I 8 &

9G

OES

-REP

IC

17+ Years (1999)

14+ Years (2003)

6+ Years (2011)

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Port the DT algorithm (AHI example)

http://www.data.jma.go.jp/

Breaking the Temporal Barrier: 15-Day DT retrieval on AHI (May, 2016)

Jun

e 2

01

6, A

ll H

ou

rs

Tracking events

Diurnal Cycle of AODs (from KORUS-AQ, 2016)

AERONETAHI

9 11 13 15 17

(UTC and Korea Local Time)

KORUS_Taehwa KORUS_Olympic_ParkXiangHe

9 11 13 15 17 9 11 13 15 17

Merge LEO/GEO? Global/Regional?

• How many additional sensors do we need to observe climatology (and diurnal cycle and transport) of global aerosol?

• Subject of a recently selected (MEaSUREs) proposal.

The DT family

Long and wide aerosol climatology

• Understanding global aerosol is crucial• AOD is one of the essential climate variables we want to measure• With VIS/NIR/SWIR instruments such as MODIS, we get sort of a global

view of AOD and spectral AOD.• Stitching MODIS and VIIRS and into future JPSS, we will get time series.• Add Asian sensors/algorithms (maybe Euro too?) to increase coverage • With GEO we will get some of the time dependence. • With fine-tuning of algorithms, and even more care to radiometric

calibration, we approach climate data accuracy and stability for AOD.

And fill in gaps!