Richard McNider Arastoo Pour Biazar (or Arastoo McBiazar) University of Alabama in Huntsville

Transcription

1 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 McNider Arastoo Pour Biazar (or Arastoo McBiazar) University of Alabama in Huntsville

2 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

3 Also had participation from Local and Regional Air Quality Community in and around Atlanta Brenda Johnson EPA Region IV Richard Monteith EPA Region IV Steve Mueller Tennessee Valley Authority Justin Walters Southern Company Jim Boylan Georgia Environmental Protection Division Tao Zeng - Georgia Environmental Protection Division Lacy Brent Discovery AQ/U. Maryland Kiran Alapaty EPA-NERL Jim Szykman- EPA- NERL Ted Russell - Georgia Tech Talat Odman Georgia Tech Maudood Khan University Space Research Association Scot t Goodrick U.S. Forest Service

4 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 Clouds Temperature Mixing Heights Satellite Observation

5 April 25 12:30 PM Lunch 2:00 PM Introductions AGENDA AQAST PHYSICAL ATMOSPHERE MEETING April 25-26, Atlanta Georgia 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 Seaman Saffet Tanrikulu James Boylan Scott Goodrick Wayne Angevine 4:00 Break

6 4:15 Physical Issues and Shortcomings in Physical Atmosphere Modeling for SIP or Forecasting (continued) General Pius Lee Bright Dornblauser Jeff McQueen Steve Mueller Lacey Brent (Discovery AQ) Maudood Khan Clouds and Photolysis Arastoo Biazar Kiran Alapaty 6:00 PM Recap and Adjourn 6:30-8:30 PM Reception

7 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 Pleim John Nielsen-Gammon Ted 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 Khan Clouds and Photolysis Arastoo Biazar, Kiran Alapaty, Saffet Tanrikulu Winds Bill Murphrey/ Dick McNider/Seaman 10:30 AM Break

8 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

9 The presentations by both members of the panel and by local participants brought up a wide variety of topics 1. Coastal clouds in California 2. Nighttime Mixing in Houston and Atlanta 3. Winds for forest fire smoke transport in Georgia 4. Snow cover in Spring in West (photolysis and land surface energetics) 5. Tropospheric/Stratospheric exchange for background ozone in the Pacific Northwest 6. Topographic effects on 8 hour standards 7. Urban/Rural bias in NO2 which may be related to physical atmosphere in Mid-Atlantic 8. Representativeness of SIP Meteorology in Georgia

10 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

11 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

12 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

13 Stable Nocturnal Boundary Layer

14 James Boylan Time series O 3 Mon Tue Wed Thur Fri Sat Sun O 3

15 Russell-Odman Ozone in Houston 8-h Ozone Concentration (ppm) Original K zz 0.08 Measured /2 Clinton Dr 0.06 Simulated Modified K zz Date (July 2006 CDT) /2 Clinton Dr Measured Simulated Date (July 2006 CDT) Driven by reanalysis of nocturnal boundary layer mixing

16 Texas Dornblaser Lee Stable regime Model deficiency: mismatch in night time decoupling? regional average surface wind speed for period June 4 12 (dark color) and June 26 July 3 (light color). Frequent surface wind-speed high-bias Similarity theory for surface layer; e.g. Ulrike Pechinger et al. COST 710, 1997 AQAST Physical Atmosphere Meeting, April 25-26, Atlanta GA 16 Night time high wind-speed bias Occurred repeatedly for many days right after sunset

17 1-h Ozone Concentration Russell-Odman Original K zz Modified K zz

18 Ramifications Ted Russell Significantly changes model performance Less effect on peak ozone Still non-zero Major effect on primary/pseudo-primary species concentrations EC, CO, NO 2, PM 2.5 New standards raise importance of NO 2. Use of models in health effects research raise importance of bias, diurnal variation

19 Brian Lamb Cold pool modeling from Avey, Utah DEQ) 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

20 Sub-Km Modeling of the Stable Boundary Layer Combined modeling and observation studies Nittany Valley, Central PA WRF smallest domain (0.444 km horizontal resolution) 10 km scale Observation Network 20

21 Sub-Km Modeling of the Stable Boundary Layer Releases at one-hour intervals from Site 9 at 5 m AGL a) UTC b) UTC c) UTC d) UTC e) UTC f) UTC 21

22 Path Forward Explore mixing formulations for stable boundary layer and role of resolution with MODIS skin temperatures as evaluation metric F h (Ri) Coarse grid models England-McNider Duynkerke Beljaars-Holtslag 0.4 Louis Theory Ri

23 Use MODIS Skin Temperatures for Model Evaluation

24 GOES Derived Skin Temperature MODIS Derived Skin Temperature

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

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27 Cloud Mixing Changes Effective PBL Height CO profiles from P3 CO profiles from P3 upwind, over, and upwind, over, and downwind of Nashville downwind of Nashville (symbols) (symbols) Tracer profile from 1D Tracer profile from 1D cloud-aware PBL model cloud-aware PBL model (early version of TEMF) (early version of TEMF) Lower panel shows what Lower panel shows what happens when cloudinduced mixing is not happens when cloudinduced mixing is not present present Wayne Angevine

28 Kiran Alapaty 10X10 cells Over RDU Surface Insolation Diff: (KFC BASE) W/m^2 Southeast Land cells

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

30 Too Many Options Not Enough Information on Performance!

31 Kiran Alapaty It Rains Cats & Dogs in a Clear Sky!!! (for convective clouds in WRF) Radiative effects were not included for WRF subgrid scale clouds.

32 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!

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

34 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 cloud Provide dynamical cloud support and cloud clearing (McNider and Biazar 2012)

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38 Land Surface

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

40 Land surface Top-level soil temperature and moisture BLLAST, 30 June 2011, 14Z

41 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 / Norman et al (ALEXI)

42 Pleim-Xiu - Land surface energy budget T t E s E g r E tr R = stb = 2π CT n s τ qs ( Tsat ρ 1 fveg β p ( wg ) Ra + ( q ) s ( Tsat ) q ρ fvegσ R + R F ( R H LE) ( T T ) ( ) ( ) q ) 1 = a Rbw 1 = a a bw ρ 1 ( ) ( q ) s ( Tsat ) q 1 σ 1 = a fveg Ra + Rbw + R stb Rst minlai ( PAR) F2 ( w2 ) F3 ( RH s ) F2 2 ( T a Soil moisture )

43 Soil Moisture Nudging w t g ( a f ) ( a f ) T T + RH RH = α α 1 2 w 2 = β1 2 t β ( a f ) ( a f ) T T + RH RH Nudge according to model bias in 2-m T and RH compared to surface air analysis

44 T-2m bias relative to analysis for January 2006 T dt 2 = N T 2 ( T T ) 2m Q v -2m bias relative to analysis for January 2006 obs

45 Mean bias for 2m T August km domain: Most around -1 to +1 Positive bias: N and W regions Negative bias: S, E 1km: Most Negative within Negative bias: high along the coast 4km: Most around: -0.5 to +0.5 Negative bias: high along the coast

46 McNider et al Surface Energy Budget Bulk Heat Capacity dtg 1 = ( RN + H + G + E ) dt C Short-wave radiation obtained from Satellite b Evaporative Heat Flux = dt dt G G E Satellite Cb + dt Satellite dt model E m Morning C b = dt dt G Satellite / dt dt G model Evening

47 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. Satellite Observation Assimilation Control

48 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

49 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

50 Pleim-Xiu - Land surface energy budget T t E s E g r E tr R = stb = 2π CT n s τ qs ( Tsat ρ 1 fveg β p ( wg ) Ra + ( q ) s ( Tsat ) q ρ fvegσ R + R F ( R H LE) ( T T ) ( ) ( ) q ) 1 = a Rbw 1 = a a bw ρ 1 Use satellite derived albedo and insolation ( ) ( q ) s ( Tsat ) q 1 σ 1 = a fveg Ra + Rbw + R stb Rst minlai ( PAR) F2 ( w2 ) F3 ( RH s ) F2 2 ( T a ) Soil moisture

51 w t g Soil Moisture Nudging Use satellite skin temperatures rather than NWS temperatures ( a f ) ( a f ) T T + RH RH = α α 1 2 w 2 = β1 2 t β ( a f ) ( a f ) T T + RH RH Nudge according to model bias in 2-m T and RH compared to surface air analysis

52 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