Using Satellite Data to Improve Operational Air Quality Forecasting Capabilities

1

JCSDA Seminar SeriesMay 22, 2013

College Park, MD

Shobha KondraguntaNOAA/NESDIS Center for Satellite Applications and Research

Contributions from: Arlindo Da Silva (NASA), Jinping Huang (SAIC@EMC), Ed Hyer (NRL), Hyuncheol Kim (UMD-

CICS@ARL), Sarah Lu (SAIC@EMC), Jeff McQueen (EMC), Brad Pierce (NESDIS), Ivanka Stajner (NWS/OST), Jian Zeng

(ERT@STAR), Xiaoyang Zhang (UMD-CICS@STAR), Qiang Zhao (IMSG@STAR)

National Air Quality Forecast Capability

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lung function changes, immune cell responses, heart rate or heart rate variability responses

Asthma attacks, medication use, symptoms

Doctor visits

Hospital Admissions

Death

Adversity of Effects

Proportion of Population Affected

• Improving the basis for air quality alerts• Providing air quality information for people

at risk

NWS Prediction Capabilities: • Operations:

Ozone nationwide: expanded from EUS to CONUS (9/07), AK (9/10) and HI (9/10)

Smoke nationwide: implemented over CONUS (3/07), AK (9/09), and HI (2/10)

Dust over CONUS: (3/12)

• Developmental testing: Components for particulate matter (PM) forecasts

JCSDA Atmospheric Composition Working Group Focus

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Global and regional aerosol model improvements Correct sources (e.g., fire emissions) Improve initial conditions via data

assimilation

Use of satellite data in diagnosing issues with model physics and chemistry Near real time verification

Talk Outline4

Part 1: GOES/MODIS Aerosol Optical Thickness (AOD) assimilation in CMAQ Funded by GOES-R program

Part 2: Development of global biomass burning emissions product for NGAC – a joint activity between NASA, NESDIS, and NCEP Funded by JCSDA and PSDI

Part 3: Aerosol product development for NWS HYSPLIT operational air quality forecast verification Funded by NWS and GOES-R

Part 4: Global aerosol model assimilation

GOES/MODIS Aerosol Optical Thickness (AOD) assimilation in CMAQ

Part 15

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Background

NWS uses NOAA-EPA developed CMAQ model to provide air quality forecast guidance.

Ozone forecast is operational

PM2.5 forecast is under development Previous studies showed that CMAQ model generally under-

predicts PM2.5 concentrations in summer. Association between satellite derived Aerosol Optical Depth

(AOD) and ground level PM2.5 concentration has a potential to improve PM2.5 prediction with AOD assimilation.

Objective of this study is to test if satellite-derived AOD product assimilation will help improve surface PM2.5 predictions.

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Copy of a Developmental Version of Air Quality Prediction System

A copy of NCEP developmental PM version air quality forecast system CONUS (5x) domain: 442x265 with 12 km resolution and 22 hybrid sigma

layers Aerosol variables: 26 Community Radiative Transfer Model (CRTM) used to compute AOD

NCEP NMM

PREMAQ

CMAQBCON ICON

EPA NEI

CONCOutput

… + AODCalculation

AODRetrievals

CONCAdjustment

AODAnalysis

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GOES AOD

Retrieved from the GOES visible imagery 4 km X 4 km horizontal resolution Every 30 minutes during the sunlit portion of the day Total column AOD w/o aerosol type or size distribution information

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Objective Analysis of Satellite-derived AOD

Cressman style successive correction scheme

Two passes with reducing radius of influence 1st pass: R0 = 4 grid-length (48 km)

2nd pass: R0 = 2 grid-length (24 km)

0

0220

220

for 0

for

)(

RRW

RRRR

RRW

W

W

ii

ii

ii

i

guessi

obsiiguessana

Grid Point

Grid point to evaluate

Observation

Radius of influence

Ri R0

Distance of observation

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An AOD Analysis Example

guess

anaguessana CC

Number, surface area and mass concentrations of all aerosol species in all layers are adjusted the same way, so that the size, type and vertical distributions from the first guess are all kept the same

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Analysis-Forecast Cycling Scheme

forecast to 48 hours

forecast to 48 hours

forecast to 48 hoursAOD

retrievals

forecast to 48 hours

forecast to 48 hours

DA

AODretrievals

DA

AODretrievals

DA

data assimilation window consisting of several analysis-forecast mini-cycles

Case I: Urban/Industrial Haze12

BASE DA-GOES

July 30-August 6, 2006 Eastern U.S. Hot and humid weather Ozone and PM violations

DA-GOES

BASE

DA-MODIS

BASE

Byun et al., Efficacy of incremental reduction of input uncertainties to improve air quality predictions, Air pollution modeling and its application, 2007

Model Dynamics13

Model predicted AOD agrees with in situ observations better for assimilation run than base run

PBL dynamics appear to modulate surface PM2.5 concentrations

Towards Operational Implementation

Target NEMS/NMM‐B inline AQ model Developed at NCEP under ESMF

framework Based on CMAQ/CB05/AERO5

Generate proxy IC datasets with current MET and CMAQ model outputs for DA development

Phased approach to build up the AQ DA capability Use column AOD as analysis variable.

Model variables will be adjusted according to the AOD increment

Accomplishments:GOES AOD converters (encoder and decoder)

to/from BUFR formatCMAQ IOAPI to/from NEMSIO convertersCRTM-CMAQ module for AQ concentrations to

AOD calculationBackground error statistics

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Part 1: Summary

An Analysis-Forecast Cycling scheme based on Cressman style objective analysis method was developed to assimilate GOES AOD and MODIS AOD products in CMAQ model.

AOD assimilation studies show a significant impact on CMAQ PM2.5 predictions For industrial/urban episode, assimilation of hourly GOES AODs

significantly improves the surface PM2.5. PBL dynamics appear to modulate surface PM2.5 concentrations For wild fire episode, improvements to surface PM2.5 predictions were

marginal. A lack of fire emissions and dependence on model vertical profile led to over prediction of PM2.5 near the surface in the assimilation run.

Path forward NCEP terminated all aerosol assimilation activities in 2012. STAR migrating the assimilation system to its computers to do stand-

alone experiments. NWS considering re-initiating aerosol assimilation activities.

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Development of global biomass burning emissions product for NGAC – a joint activity between NASA, NESDIS, and NCEP

Part 216

Overview of NOAA GFS Aerosol Component (NGAC)

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Model Configuration:

Forecast model: Global Forecast System (GFS) based on NOAA Environmental Modeling System (NEMS), NEMS-GFS

Aerosol model: NASA Goddard Chemistry Aerosol Radiation and Transport Model, GOCART

Phased Implementation:

Dust-only guidance is established in Q4FY12

Dust, sea-salt, OC/BC, and sulfate aerosol forecast once real-time global smoke emissions are developed and tested (NWS/NCEP-NESDIS/STAR-NASA/GSFC collaboration)

Near-Real-Time Dust Forecasts

5-day dust forecast once per day (at 00Z), output every 3 hour, at T126 L64 resolution

ICs: Aerosols from previous day forecast and meteorology from operational GDAS

Acknowledge: Development and operational implementation of NGAC represents a successful “research to operations” project sponsored by NASA Applied Science Program and JCSDA

Global biomass burning emissions

Joint NASA/GMAO, NESDIS/STAR, and NWS/NCEP project to: Develop near real time biomass burning emissions product

covering the whole globe from polar and geostationary satellites for NEMS-GFS-GOCART Globally, biomass burning is one of the primary sources of

aerosols; burning varies seasonally, geographically and is either natural (e.g., forest fires induced by lightning) or human induced (e.g., agricultural burning for land clearing). Satellites can provide this information on a real time basis.

Develop and deploy a global aerosol prediction system that can in the future assimilate satellite-derived atmospheric composition parameters

Meet Research (NASA) to Operations (NOAA) goals of the JCSDA QFED code transition from NASA to NOAA

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Annual Global Biomass Burning Aerosol Emissions from Satellite-derived Fire Radiative Power (FRP)

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MTS-2MeteosatGOES-E and GOES-W

INSAT-3D can help fill this data gap

No coverage over high latitudes from

geostationary satellites

Key:PM2.5: Particulate mass for particles smaller than 2.5 um in sizeDOY: Day of the YearKg: Kilograms

Zhang, X. Y, S. Kondragunta, J. Ram, C. Schmidt, H-C. Huang, Near-real time global biomass burning emissions product from geostationary satellite constellation, JGR, 2012

Flowchart for blended Polar and Geo biomass burning emissions

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Terra+Aqua MODIS fire detections

QFEDv2

Simulate AOD using NEMS-GFS-GOCART

Geostationary satellite fire detections

MODIS fire FRP with cloud adjustment

MODIS fire emissions

MODIS AOD

Calibrate Fire emissions

Scaling MODIS fire emissions

Simulating diurnal FRP

GOES Fire emissions

Scaling GOES fire emissions

GBBEP-GeoQFED: Quick Fire Emission Dataset from MODIS fire data

GBBEP-Geo: Global Biomass Burning Emissions Product from Multiple Geostationary Satellites

Blended Emissions

NGAC Run with no Biomass Burning Emissions

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AOD

NGAC Run with GBBEP22

AOD

NGAC Run with QFED23

AOD

MODIS24

AOD

July 2011 Forecast with Biomass Burning Emissions

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Scaling Satellite-derived Emissions

26 Europe and Africa

Flowchart for blended Polar and Geo biomass burning emissions

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Terra+Aqua MODIS fire detections

QFEDv2

Simulate AOD using NEMS-GFS-GOCART

Geostationary satellite fire detections

MODIS fire FRP with cloud adjustment

MODIS fire emissions

MODIS AOD

Calibrate Fire emissions

Scaling MODIS fire emissions

Simulating diurnal FRP

GOES Fire emissions

Scaling GOES fire emissions

GBBEP-GeoQFED: Quick Fire Emission Dataset from MODIS fire data

GBBEP-Geo: Global Biomass Burning Emissions Product from Multiple Geostationary Satellites

Blended Emissions

Flowchart for blended Polar and Geo biomass burning emissions

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Calibrate Fire emissions

Scaling MODIS fire emissions

Scaling GOES fire emissions

Blended Emissions

• Scaling factors are region and biome dependent but static.

• Blended emissions will be generated daily at NESDIS/OSPO for NGAC.

• Scaling factors need to be re-generated only if there is a new satellite replacing an old satellite.

Part 2: Summary29

• Scaling factors for biomass burning emissions obtained in this study similar to other studies. For example NRL scales biomass burning emissions in its Navy Aerosol Analysis and Prediction System (NAAPS) by a factor of 3;

• NESDIS and NASA has to consider the extension of QFED dataset from using MODIS to NPP VIIRS;

• NGAC operational implementation of global biomass

burning emissions to occur in 2015 but development and parallel test forecast system to occur in 2013-2014 time period.

Aerosol product development for NWS HYSPLIT operational air quality forecast verification

Part 330

GOES Smoke Concentration Product (1)

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hkmc

Animation of Smoke Plume Detection

Original AOD Image Smoke AOD Image

• Column average smoke concentration (µg/m3) using AOD and fire hot spots from GOES

Uses source apportionment and pattern recognition techniques to isolate smoke aerosols from other type of aerosols

Smoke mass concentration (mc) is obtained using AOD (τ), mass extinction efficiency (k), and aerosol height (h)

• Product specifications Name: ASDTA Satellites: GOES-East and GOES-West

(includes Alaska and Hawaii) Accuracy: 40% Spatial resolution: 0.15o

Temporal resolution: hourly Latency: one day Data format: binary file, GRIB file, JPEG

imagery Data availability: 2007 - present

http://www.ssd.noaa.gov/PS/FIRE/ASDTA/asdta.html Zeng, J., S. Kondragunta, and I. Stajner, An automated smoke detection and tracking algorithm, to be submitted to GRL, 2013

NWS testing of smoke verification for Alaska

using NESDIS GOES-W smoke product

GOES Smoke Concentration Product (2)

http://airquality.weather.gov/

July 13, 2009 17Z – 18Z

Forecast Observation

Aqua MODIS Dust Mask Product (1)

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• Column average dust concentration (µg/m3) Uses Aqua MODIS deep blue AOD and

an independently derived dust flag Dust mass concentration (mc) is obtained

using AOD (τ), mass extinction efficiency (k), and aerosol height (h)

• Product specifications Name: MODIS Dust Mask Satellites: Aqua Accuracy: 70% Spatial resolution: 0.1o

Temporal resolution: daily Latency: one day Data format: netCDF4 and GRIB1 Data availability: 2013 - present

)]()([100 4404121044041210 nmnmnmnm 'R/'RlogR/RlogDBDI

)]([10 21341210 nmnm R/RlogNDAI

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MYD.A2011190.2120

MYD.A2011193.2015

MYD.A2011198.2035

MYD.A2011199.2115

MYD.A2011205.2040

MYD.A2011206.2120

MYD.A2011206.2125

MYD.A2011213.2130

MYD.A2011214.2035

MYD.A2011226.2100

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V6.3.4 (1µg/m3)

FMS

(%)

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• Threshold concentration > 1 µg/m3, for average dust in the column• Tracking threat scores, or figure-of-merit statistics: (Area Pred ∩ Area Obs) / (Area Pred U Area Obs)• Initial skill target 0.05

Aqua MODIS Dust Mask Product (2)

Part 3: Summary

Routine use of NESDIS smoke and dust product by NWS to verify operational forecasts: ASDA work started in

2005. GOES-E product became operational in 2008 and GOES-W in 2009.

Aqua MODIS dust mask work started in 2008. Product became operational in 2012.

NESDIS continuing to refine the dust mask algorithm and applying it to SNPP VIIRS

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VIIRS Dust Mask

VIIRS ASDA

Global Aerosol Model Assimilation

Part 436

Navy Global Aerosol Forecasting

• Assimilation system is “2D-VAR” for total column AOD based on NAVDAS 3DVAR system

• Large development effort required for producing DA-quality products from off-the-shelf MODIS data

• Operational AOD assimilation reduces RMS error in analyzed AOD by >50%

• Operational at FNMOC from September 2009 (over ocean)

• Land and ocean MODIS assimilated in operations since February 2012

• Publications about aerosol DA

• Zhang, J. L., et al.: Evaluating the impact of assimilating caliop-derived aerosol extinction profiles on a global mass transport model, Geophys. Res. Lett., 38, L14801, doi:/10.1029/2011gl047737, 2011.

• Zhang, J. L., et al.: A system for operational aerosol optical depth data assimilation over global oceans, J. Geophys. Res.-Atmos., 113, D10208, D10208, doi:/10.1029/2007jd009065, 2008.

The NRL-UND MODIS Level 3 Aerosol Optical Depth Product

14 April 2011 38 of 25

• Thanks to NASA Air Quality Applied Science Team for supporting transition of this product to LANCE

• Produced in NRT by NASA LANCE, <4 hour latency

• Available now: ESDT name is ‘MxDAODHD’

• Publications about AOD data quality• Shi, Y., et al.: Critical evaluation of the modis deep blue

aerosol optical depth product for data assimilation over north africa, Atmospheric Measurement Techniques, 6, 949-969, doi:10.5194/amt-6-949-2013, 2013.

• Campbell, J. R., et al.: Evaluating nighttime caliop 0.532 mu m aerosol optical depth and extinction coefficient retrievals, Atmospheric Measurement Techniques, 5, 2143-2160, 10.5194/amt-5-2143-2012, 2012.

• Hyer, E. J., et al.: An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of modis collection 5 optical depth retrievals, in: Atmospheric Measurement Techniques, European Geophysical Union, 379-408, 2011.

• Shi, Y., et al.: An analysis of the collection 5 modis over-ocean aerosol optical depth product for its implication in aerosol assimilation, Atmos. Chem. Phys., 11, 557-565, 10.5194/acp-11-557-2011, 2011.

• Zhang, J. L., Reid, J. S., and Holben, B. N.: An analysis of potential cloud artifacts in modis over ocean aerosol optical thickness products, Geophys. Res. Lett., 32, L15803, doi:10.1029/2005GL023254, 2005.

• Zhang, J. L., and Reid, J. S.: Modis aerosol product analysis for data assimilation: Assessment of over-ocean level 2 aerosol optical thickness retrievals, J. Geophys. Res.-Atmos., 111, D22207, doi:10.1029/2005JD006898, 2006.

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Focus on NASA EOS instruments, MODIS for now

Global, high resolution 2D AOD analysis

3D increments by means of Local Displacement Ensembles (LDE)

Simultaneous estimates of background bias (Dee and da Silva 1998)

Adaptive Statistical Quality Control (Dee et al. 1999): State dependent (adapts to the

error of the day) Background and Buddy checks

based on log-transformed AOD innovation

Error covariance models (Dee and da Silva 1999): Innovation based Maximum likelihood

GEOS-5 Aerosol Data Assimilation

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NASA DISCOVER-AQAERONET “DRAGON” Stations

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RAQMS has been used to support airborne field missions [Pierce et al, 2003, 2007, 2008], develop capabilities for assimilating satellite trace gas and aerosol retrievals [Pierce et al., 2007, 2008, Fishman et al., 2008, Sunita et al., 2008] and assess the impact of global chemical analyses on regional air quality predictions [Song et al., 2008, Tang et al., 2008]

1. Online global chemical and aerosol assimilation/ forecasting system

2. UW-Madison hybrid coordinate model (UW-Hybrid) dynamical core

3. Unified stratosphere/troposphere chemical prediction scheme (LaRC-Combo) developed at NASA LaRC

4. Aerosol prediction scheme (GOCART) developed by Mian Chin (NASA GSFC).

5. Statistical Digital Filter (OI) assimilation system developed by James Stobie (NASA/GFSC)

WRF-Chem + RAQMS BCWRF no aerosols

SPC 12hr precip and tornado tracks valid 06Z on April 28, 2011

Case Study: Severe weather April 27th, 2011 (Huntsville tornado)

Inclusion of aerosol cloud interactions significantly impacts the predicted precipitation distribution and improves forecast over Huntsville, AL

WRF-CHEM simulations conducted by Pablo Saide in collaboration with Greg Carmichael and Scott Spak (U-Iowa)

Part 4: Summary

NRL and NASA assimilating MODIS aerosol products in their forecast models and are preparing to assimilate VIIRS aerosol products.

Given that these centers are moving forward with global aerosol assimilation due to the importance of impact on weather and regional air quality/visibility predictions, it is important for NWS to consider aerosol assimilation With support from NASA and JCSDA, development work

at NCEP has already taken place. Further development is needed.

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Back-up Slides

Good Moderate Unhealthy

0.1

4

-

0.0

30

00

00

00

00

0

00

01

-0

.0

1

0.2

2

0.0

7

0.2

30.3

0.1

4

0.0

2

Categorical Evaluation45

Good Moderate Unhealthy

1.8

7

0.4

8

0.0

6

1.8

1

0.5

0.2

5

1.6

9

0.6

00

00

00

00

00

000

1

0.1

2

Good Moderate Unhealthy

0.5

5

0.5

1

0.5

2

0.4

0.3

8000

0000000

001

0.4

8

0.3

6

0.8

3000

0000000

001

Good Moderate Unhealthy

0.8

40

00

00

00

00

0

00

1

0.2

4

0

0.8

70

00

00

00

00

0

00

2

0.3

0.1

6

0.8

70

00

00

00

00

0

00

2

0.3

90

00

00

00

00

0

00

1

0.0

2

B

FAR HSS

POD

Good Moderate Unhealthy

0.2 0.2

3

-0

.02

0.5

2

0.2

6

0.0

5

0.5

1

0.3

0.1

0.2

8

0.2

2

-0

.04

Good Moderate Unhealthy

0.7

5000

0000000

002

0.4

6

0

0.6

5000

0000000

002

0.6

1000

0000000

001

0.2

5

0.7

1000

0000000

001

0.5

9

0.2

9

0.7

7000

0000000

002

0.4

4

0

Good Moderate Unhealthy

1.2

2

0.7

7000

0000000

002

0.3

8000

0000000

001

0.7

6000

0000000

002

1.0

8

3.7

9

0.8

8

0.9

8

3.0

8

1.1

9

0.7

5000

0000000

002

1.0

4

Good Moderate Unhealthy

0.3

9000

0000000

001

0.4

1

1

0.1

4

0.4

4

0.9

3

0.1

9

0.4

0.9

1

0.3

6

0.4

1

1

46

Categorical Evaluation

B

FAR HSS

POD

47

Observed

ForecastGood

AQI: 0 – 50Moderate

AQI: 51 – 100UnhealthyAQI: > 100

Good r s t

Moderate u v w

Unhealthy x y z

a=z b=x+y

c=t+w d=r+s+u+v

a=v b=u+w

c=s+y d=r+t+x+z

a=r b=s+t

c=u+x d=v+w+y+z

3-Category Forecasts Evaluation

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Case II: Wild Fire

Sept. 01-09, 2006

US NW Region

BASE

DA-FINE

BASE

DA-MODIS

DA-GOES

BASE

Vertical Profiles of Aerosol Loading

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BASE DA-GOES

CALIPSO