A public research testbed for assimilating remote sensing products in operational air quality forecasting

Greg Carmichael, Scott Spak

Air quality management contacts: Joe Hoch, Wisconsin DNR Matthew Johnson, Iowa DNR Mike Koerber, LADCO

Motivations

Applied Science 1. Limited model skill for fine particle concentrations during

wintertime episodes: how to improve?

2. AQ-WX feedbacks for AQ & WX forecasts

3. Value in assimilating remote sensing for AQ? • atmospheric composition

• land surface

• meteorology

• individual vs. synergistic effects?

Operational forecasting

4. No current resource for AQ managers

5. Public benchmark data archive to inform planning & forecasters

6. Integrated forecasting—support products for transportation, energy production, hazards

Connecting AQAST to ongoing efforts

Upcoming opportunities for AQAST: + Workshop on mesoscale chemistry components for Earth Observing Systems + Int. Workshop on Air Quality Forecasting Research

MOST NEEDED: Height of the planetary boundary

layer Soil moisture and temperature

profiles High resolution vertical profiles of

humidity Measurements of air quality and

atmospheric composition above the surface layer

2009 LADCO Winter Nitrate Study

30

20

10

0

Concentr

ation (

µg m

-3)

Mil all h

ours

May

all ho

urs

Mil ep

isod

es

May

episo

des

Mil no

n ep

isod

es

May

non

episo

des

Other (obs) NH4 (obs)

NO3 (obs)

SO4 (obs)

NH3(g) as NH4 (obs)

HNO3(g) NO3 (obs)

Other (mod) NH4 (mod)

NO3 (mod)

SO4 (mod)

NH3(g) NH4 (mod)

HNO3(g)NO3 (mod)

1.0

0.8

0.6

0.4

0.2

0.0

-0.2

Con

cent

ratio

n (µ

g m

-3)

Mil a

ll hours

May a

ll hours

Mil e

pisodes

May e

pisodes

Mil n

on episo

des

May n

on episo

des

0

20

40

60

80

100

1/1

/09

1/8

/09

1/1

5/0

9

1/2

2/0

9

1/2

9/0

9

2/5

/09

2/1

2/0

9

2/1

9/0

9

2/2

6/0

9

3/5

/09

3/1

2/0

9

3/1

9/0

9

3/2

6/0

9

PM

2.5

(µg

m-3

)

Mil PM2.5(h)_obs Mil PM2.5(h)_model Mil epi

J-I J-II J-III J-IV F-I F-II F-III F-IV M-I M-II III M-IV

(a)

Encouraging CMAQ skill for episode prediction and aerosol speciation despite strong biases for ammonia and nitric acid precursor gases

PBL dynamics are crucial to model skill

-1 0 1 2 3 4 5 6-30

-20

-10

0

10

20

30

40

wind speed bias (m/s)

PM

2.5

bia

s

Jan

Feb

Mar

0 500 1000 1500 2000 2500 3000 3500-30

-20

-10

0

10

20

30

40

Modeled PBL Ht (24 hr avg)

PM

2.5

bia

s

Jan

Feb

Mar

0 500 1000 1500 2000 2500 3000 3500-0.5

0

0.5

1

1.5

2

2.5

Modeled PBL Ht (24 hr avg)

EC

bia

s (

24

hr

av

g)

Jan

Feb

Mar

Shallow model mixed layer and low bias in wind speed drive overprediction in primary and secondary PM2.5 concentrations during wintertime fine particle events. RH bias also a factor.

PM

2.5

bia

s P

M2.

5 b

ias

WRF PBLH (m) WRF PBLH (m)

EC

bia

s

Wind speed bias (m/s)

• Builds on recent urban & regional field campaign and event forecasting experience

• Midwest domain @ 4 km/1 km resolves clouds & urban areas

• MODIS land cover updates

• PNNL WRF-Chem with CBM-Z + MOSAIC – interactive weather, chemistry, aerosols

– aerosol direct, semi-direct, indirect effects

• Comprehensive, evaluated emissions

– LADCO 2008 & NEI 2008 anthropogenic

– MEGAN biogenics

– MODIS daily 1 km point fires, plume rise

• RAQMS boundary conditions

+ AQAST team suggestions

Model structure & inputs to inform the next generation of forecasts

DO3 D T2m (K)

D urban area DPBL (m)

GSI 3DVAR for initial conditions, 2011-2012 & beyond

Remote sensing

• Clouds: MODIS @ 5km – Total precipitable water vapor

– cloud effective emissivity

– cloud top temperature?

– cloud phase?

• Snow: NSIDC SNODAS @ 1 km – Snow depth

– Snow pack temperature

• Eventual extension – CALIPSO

– NPOESS

In situ observations – Soil moisture

• Iowa AgClimate network • Soil Climate Analysis Network

– Trace gases & aerosols • EPA Airnow PM2.5, O3 • TOMS column O3, NO2 • Iowa Air Monitoring Network

(including NH3!)

– Meteorology • NCAR standard GSI ingest • radar • upper air soundings

–PBL height –LIDAR aerosol ,wind profiles

Assimilating remote sensing & in situ meteorological +chemical fields

2008 hourly evaluation at 1.33 km over Chicago

Building a base configuration through retrospective analyses

8

Month Wind Speed (m/s) Wind Direction (º) Temperature (ºC)

Bias RMSE r2 Bias r2 Bias RMSE r2

January -0.01 1.72 0.72 -1.01 0.69 -1.76 3.30 0.91

February -0.19 1.66 0.80 -2.20 0.55 2.91 0.04 0.91

March 0.08 1.71 0.71 -1.60 0.62 1.56 2.63 0.87

April 0.34 1.88 0.80 0.34 0.45 2.86 3.32 0.89

May 0.20 1.77 0.74 -3.05 0.67 2.71 3.17 0.87

June 0.44 1.94 0.62 1.07 0.46 3.13 3.43 0.84

High resolution urban modeling with lake breeze meets current state/RPO SIP modeling performance metrics

• air quality - PM2.5 & ozone

- NAAQS exceedances

- PRB

• aerosol-climate effects

• carbon cycle

• renewable energy

• road conditions

• floods, agriculture

Integrated forecast goals

Toward 4DVAR in high resolution coupled Earth System modeling for prediction, process studies, climate, decision support, and policy applications: 2015-2030

4 km WR surface wind speed during October 2010 “bomb cyclone” event

Year 1 Plans

• Develop a 72-hour operational forecast system

• Compile voluminous input data

• Automate daily ingest, 3DVAR, evaluation

• Test AQ impacts from met assimilation

• Distribute maps & data download

• Extend to wind & solar forecasts

• Evaluate and refine configuration

• Support your tiger team activities

• Iowa EPSCoR wind farm research testbed

• PCBs in Chicago and the Great Lakes

• WRF-Chem aerosol-cloud assimilation

– Cloud optical depth (MODIS, GOES) alters droplet concentration and effective radius

– AOD + COD constrain aerosols above clouds

New synergistic activities

Expected integration in the next year