Use of Satellite Data to Improve the Physical Atmosphere in Air Quality Decision Models

Use of Satellite Data to Improve the Physical Atmosphere in Air Quality

Decision Models

AQAST Project

Physical Atmosphere Panel Meeting

April 25-26, 2012

Atlanta, GA

Richard McNiderArastoo Pour Biazar (or Arastoo McBiazar)

University of Alabama in Huntsville

Physical Atmosphere Advisory Team

Wayne Angevine - NOAA – Boundary Layer Observations

Bright Dornblauser – State of Texas – Regulator Model Evaluation

Mike Ek/Jeff McQueen – NOAA – Land Surface Modeling

Georg Grell – NOAA – Clouds and Modeling

John Nielsen-Gammon – Texas A&M – Model Evaluation

Brian Lamb – Washington State University – Emissions/ Model Evaluation

Pius Lee – NOAA – Air Resources Laboratory – Air Quality Forecasting

Jon Pleim – US EPA – Boundary Layer Modeling

Nelsen Seaman – Penn State University – Meteorological Modeling

Saffett Tanrikulu - SF Bay Area Air Quality District – Meteorological Modeling

Also had participation from Local and Regional Air Quality Community in and around Atlanta

Brenda Johnson – EPA Region IVRichard Monteith – EPA Region IVSteve Mueller – Tennessee Valley AuthorityJustin Walters – Southern CompanyJim Boylan – Georgia Environmental Protection DivisionTao Zeng - Georgia Environmental Protection DivisionLacy Brent – Discovery AQ/U. MarylandKiran Alapaty – EPA-NERLJim Szykman- EPA- NERLTed Russell - Georgia Tech Talat Odman – Georgia TechMaudood Khan – University Space Research AssociationScot t Goodrick – U.S. Forest Service

Physical Atmosphere Can Significantly Impact Atmospheric Chemistry and Resulting Air Quality

Most Importantly the Physical Atmosphere Can Impact Control Strategy Efficacy and Response

Temperature, Clouds, Mixing Heights, Humidity and Turbulence Can All Impact Air Quality

SatelliteObservation

Temperature Mixing HeightsClouds

AGENDAAQAST PHYSICAL ATMOSPHERE MEETING

April 25-26, Atlanta Georgia

April 25 12:30 PM Lunch

2:00 PM Introductions

2:15 PM Background and Charge – Dick McNider

2:45 PM Physical Issues and Shortcomings in Physical Atmosphere Modeling for SIP or Forecasting (10-15 minute presentations) General

Nelson SeamanSaffet TanrikuluJames BoylanScott Goodrick

Wayne Angevine

4:00 Break

4:15 Physical Issues and Shortcomings in Physical Atmosphere Modeling for SIP or Forecasting (continued)General

Pius LeeBright Dornblauser Jeff McQueenSteve MuellerLacey Brent (Discovery AQ)Maudood Khan

Clouds and PhotolysisArastoo BiazarKiran Alapaty

6:00 PM Recap and Adjourn

6:30 -8:30 PM Reception

April 26 8:00AM -8:30AM Continental Breakfast

8:30 AM Physical Issues and Shortcomings in Physical Atmosphere Modeling for SIP or Forecasting (continued) Land Surface –PBL - Emissions

Jon PleimJohn Nielsen-GammonTed Russell Brian Lamb

9:30 AM Discussion of Use of Satellite Information to Improve the Physical Atmosphere

Overview – Dick McNider Land Surface – Jon Pleim, Jeff McQueen, Maudood KhanClouds and Photolysis– Arastoo Biazar, Kiran Alapaty, Saffet TanrikuluWinds – Bill Murphrey/ Dick McNider/Seaman

10:30 AM Break

10:45 AM Discussion of Use of Satellite Information to Improve the Physical Atmosphere

General ( Participation by all)

12:00 NOON Lunch

1:00 PM Selection of Priorities – Lead (Dick McNider) Participation by All

2:00 PM Formation of Application Paths and Team Formation 3:00 PM Recap and Adjourn

The presentations by both members of the panel and by local participants brought up a wide variety of topics

1. Coastal clouds in California2. Nighttime Mixing in Houston and Atlanta3. Winds for forest fire smoke transport in Georgia4. Snow cover in Spring in West (photolysis and land

surface energetics)5. Tropospheric/Stratospheric exchange for

background ozone in the Pacific Northwest6. Topographic effects on 8 hour standards7. Urban/Rural bias in NO2 which may be related to

physical atmosphere in Mid-Atlantic8. Representativeness of SIP Meteorology in Georgia

Categorization Summary

Clouds for Photolysis, Vertical Mixing and Aqueous Chemistry Angevine, ‐Tanrikulu, Biazar, Alapaty, Boylan

Stable Boundary Layer for Vertical Mixing, Winds, Cold Pooling - Seaman, Boylan, Lee, Russell, Lamb

Land Surface for Fluxes and Deposition - Angevine, DornBluaser, Pleim, Tanrikulu, Lee

Winds for Transport and Dilution - Dornblaser, Lee, Odman

Mixing Heights for Dilution and Plume Rise - McQueen, Goodrick

Topography – Seaman, Mueller, Lamb

Snow Cover for Land Surface and Photolysis Tanrikulu‐

Tropospheric/Stratospheric Exchange for Ozone Background - Lamb, Biazar

Potential For Use of Satellite Data For Improvement and/or Verification Clouds for Photolysis, Vertical Mixing and Aqueous Chemistry Angevine, Tanrikulu, ‐Biazar, Alapaty, Boylan - VERY HIGH

Stable Boundary Layer for Vertical Mixing, Winds, Cold Pooling - Seaman, Boylan, Lee, Russell, Lamb - MODERATE

Land Surface for Fluxes and Deposition - Angevine, DornBluaser, Pleim, Tanrikulu, Lee - HIGH

Winds for Transport and Dilution - Dornblauser, Lee, Odman – MODERATE

Mixing Heights for Dilution and Plume Rise - McQueen, Goodrick – LOW/MODERATE

Topography – Seaman, Mueller, Lamb - LOW

Snow Cover for Land Surface and Photolysis Tanrikulu ‐ VERY HIGH

Tropospheric/Stratospheric Exchange for Ozone Background - Lamb, Biazar – MODERATE/HIGH

Based on Importance to Physical Atmosphere and Potential for Use of Satellite Data Selected Three Major Themes

1. Clouds

2. Stable Boundary Layer

3. Land Surface

Stable Nocturnal Boundary Layer

Time seriesO3

O3

Mon Tue Wed Thur Fri Sat Sun

James Boylan

Ozone in HoustonOriginal Kzz

8-h

Ozo

ne C

once

ntra

tion

(ppm

)

0

0.02

0.04

0.06

0.08

12 13 14 15 16 17 18 19 20 21 22 23

MeasuredSimulated

9525 1/2 Clinton Dr

Date (July 2006 CDT)

0

0.02

0.04

0.06

0.08

12 13 14 15 16 17 18 19 20 21 22 23

MeasuredSimulated

Date (July 2006 CDT)

9525 1/2 Clinton Dr

Modified Kzz

Driven by reanalysis of nocturnal boundary layer mixing

Russell-Odman

AQAST Physical Atmosphere Meeting, April 25-26, Atlanta GA 16

Model deficiency: mismatch in night time decoupling?

Stable regime

regional average surface wind speed for period June 4 – 12 (dark color) and June 26 – July 3 (light color).

Night time high wind-speed bias Occurred repeatedly for many daysright after sunset

Frequent surface wind-speed high-bias

Similarity theory for surface layer; e.g. Ulrike Pechinger et al. COST 710, 1997

Texas – Dornblaser – Lee

1-h Ozone Concentration

Original Kzz Modified Kzz

Russell-Odman

Ramifications

• Significantly changes model performance– Less effect on peak ozone

• Still non-zero– Major effect on primary/pseudo-primary species

concentrations• EC, CO, NO2, PM2.5

– New standards raise importance of NO2.– Use of models in health effects research raise importance of

bias, diurnal variation

Ted Russell

Cold pool modeling

Routine application of prognostic meteorological models including the Fifth-Generation NCAR/Penn State Mesoscale Model (MM5) and Weather Research and Forecasting Model (WRF) with a variety of different physics options, initialization input, vertical and horizontal resolutions, and nudging approaches have failed to replicate the degree and persistence of stagnant meteorological conditions. (Baker et al., 2011, ES&T).

AIRPACT Forecasts don’t capture elevated wintertime PM2.5 levels• stagnant valley meteorology•woodstove emissions

from Avey, Utah DEQ)

Brian Lamb

20

Sub-Km Modeling of the Stable Boundary Layer

Combined modeling and observation studies Nittany Valley, Central PA

10 km scale

WRF smallest domain (0.444 km horizontal resolution) Observation Network

21

a) 0500-0700 UTC b) 0600-0800 UTC c) 0700-0900 UTC

e) 0600-0800 UTC f) 0700-0900 UTCd) 0500-0700 UTC

Releases at one-hour intervals from Site 9 at 5 m AGL

Sub-Km Modeling of the Stable Boundary Layer

Tu

ssey R

idge

Path Forward

Explore mixing formulations for stable boundary layer and role of resolution with MODIS skin temperatures as evaluation metric.

0 0.1 0.2 0.3 0.4 0.5 0.60

0.2

0.4

0.6

0.8

1

1.2

England-McNiderDuynkerkeBeljaars-HoltslagLouis

F h(Ri)

Ri

Coarse grid models

Theory

Use MODIS Skin Temperatures for Model Evaluation

GOES Derived Skin Temperature MODIS Derived Skin Temperature

Nocturnal boundary layer formation dependent on topography has implications for 8 hour attainment at high elevations.

Steve Mueller

CO profiles from P3 upwind, over, and downwind of Nashville (symbols)

Tracer profile from 1D cloud-aware PBL model (early version of TEMF)

Lower panel shows what happens when cloud-induced mixing is not present

CO profiles from P3 upwind, over, and downwind of Nashville (symbols)

Tracer profile from 1D cloud-aware PBL model (early version of TEMF)

Lower panel shows what happens when cloud-induced mixing is not present

Wayne Angevine

Cloud Mixing Changes Effective PBL Height

Southeast Land cells

10X10 cells Over RDU

Surface InsolationDiff: (KFC-BASE)W/m^2

Kiran Alapaty

Tests in Texas showed changes in cloud locations and radiative properties can change ozone by 70ppb

Too Many Options Not Enough Information on Performance!

It Rains Cats & Dogs in a Clear Sky!!!(for convective clouds in WRF)

Radiative effects were not included for WRF subgrid scale clouds.

Kiran Alapaty

Inconsistency in Cloud Handling in Models

1. MM5/WRF do not consider sub-grid clouds in radiation calculations.

2. Clouds in MM5/WRF not used in CMAQ (clouds rediagnosed) for wet chemistry mixing.

3. CMAQ photolysis rates not based on CMAQ clouds but on MM5/WRF liquid water profiles.

These inconsistencies make correction difficult!

Satellite data can be used as a metric to test model cloud agreement

Path Forward

1. Insert satellite measures of radiative properties directly in models. Use satellite derived measures of insolation based on satellite clouds

rather than modeled insolation using model clouds (McNider et al. 1995)

Use satellite cloud transmittance in photolysis calculations (Biazar et al. 2007)

2. Improve physical parameterizations using satellite data as performance metric

Correct model radiation (Alapaty et al. 2012) Connect PBL and cloud schemes (Angevine 2012)

3. Assimilate satellite data to improve the location and timing of cloudProvide dynamical cloud support and cloud clearing (McNider and Biazar

2012)

Land Surface

Factors controlling surface temperatures are complex and many models have created complex land use models that in the end require many ill defined parameters.

Land surfaceTop-level soil temperature and moisture

BLLAST, 30 June 2011, 14Z

Air Quality Simulations for SIPs Are Retrospective Studies

Allows use of observations to constrain forecast models

Simple Surface Models Constrained by Observations

1. Pleim Xiu Scheme

2. McNider et al. 1994 / Norman et al. 1995 (ALEXI)

)()()()( 23221

min

as

ststb TFRHFwFPARF

LAIRR

Pleim-Xiu - Land surface energy budget

22 TTLEHRC

tT

snTs ----

bwa

satsgpvegag RR

qTqwfE

-- 1)()(1

bwa

satsvegar RR

qTqfE

- 1)(

stbbwa

satsvegatr RRR

qTqfE-

- 1)(1 Soil moisture

Soil Moisture Nudging

fafag RHRHTTt

w-- 21

fafa RHRHTTt

w-- 21

2

Nudge according to model bias in 2-m T and RH compared to surface air analysis

T-2m bias relative to analysis for January 2006

Qv-2m bias relative to analysis for January 2006

obsmT TTNdtT

-

222

Mean bias for 2m T – August 2006

12km domain: Most around -1 to +1Positive bias: N and W regionsNegative bias: S, E

4km:Most around: -0.5 to +0.5Negative bias: high along the coast

1km:Most Negative within -0.5Negative bias: high along the coast

1( )GN

b

dT R HG Edt C

McNider et al. 1995 Surface Energy Budget

Bulk Heat Capacity Evaporative Heat FluxShort-wave radiation

obtained from Satellite

mG

Satellite

GbSatellite E

dtdT

dtdTCE

-

model

model

/dt

dTdt

dTC G

Satellite

Gb

Morning

Evening

SatelliteObservation

Assimilation Control

Satellite Data Can Provide Many More Opportunities for Data Skin Temperature Assimilation (GOES ~5 km and MODIS ~1 km).

Land characteristics especially in Eastern U.S. fine scale variations.

Model BL Heights (CNTRL)

Aug. 26, 2000, 19:00-21:00 GMT averaged

Model BL Heights (ASSIMALATED)

Aug. 26, 2000, 19:00-21:00 GMT averaged

Path Forward

1. Use satellite skin temperatures in Pleim –Xiu scheme rather than National Weather Service 2 m temperatures

2. Test McNider et al. scheme using new corrections (use of model skin temperatures and aerodynamic temperatures) suggested by Mackaro

)()()()( 23221

min

as

ststb TFRHFwFPARF

LAIRR

Pleim-Xiu - Land surface energy budget

22 TTLEHRC

tT

snTs ----

bwa

satsgpvegag RR

qTqwfE

-- 1)()(1

bwa

satsvegar RR

qTqfE

- 1)(

stbbwa

satsvegatr RRR

qTqfE-

- 1)(1 Soil moisture

Use satellite derived albedo and insolation

Soil Moisture Nudging

fafag RHRHTTt

w-- 21

fafa RHRHTTt

w-- 21

2

Nudge according to model bias in 2-m T and RH compared to surface air analysis

Use satellite skin temperatures rather than NWS temperatures

Teams are being formed for priority areas

1. Clouds ( Pour-Biazar,Alapaty,Nielsen –Gammon)

2. Stable Boundary Layer (McNider, Angevine, Russell,Lee)

3. Land Surface – (Pleim, Angevine, Tanrikulu, McQueen/Ek)

Next Meeting (12-18 mos) will be on West Coast