# Presentation: 135466

## Simultaneous forecasts of air quality and weather using WRF/Chem&Ensemble air quality modeling and its economic value 

- Georg A. Grell, Steven E. Peckham,
- Mariusz Pagowski
- The Cooperative Institute for Research in
-  Environmental Sciences,
- The Cooperative Institute for Research in
- the Atmosphere
- and
- NOAA Earth System Research Laboratory, Global System Division

## Outline

- WRF Chemistry model (WRF/Chem)
- Current status, latest additions
- Ensemble  modeling during ICARTT/NEAQS experiment

## WRF/Chem

-  “Online” (also called “inline”) chemistry
- Consistent: all transport done by meteorology model
- Same vertical and horizontal coordinates (no horizontal and vertical interpolation)
- Same physics parameterization for subgrid scale
- No interpolation in time
- Easy handling (Data management)
- Least amount of computing time if only doing one simulation

## 
Directly involved in major WRF/Chem development 

NOAA/ESRL
Georg Grell, Steven Peckham, Stuart McKeen

PNNL 
Jerome Fast, Bill Gustafson, Rahul A. Zaveri, James C. Barnard

NCAR
Bill Skamarock

Rainer Schmitz (University of Chile – Santiago, Chile)
Marc Salzmann (Max Planck Institute for Chemistry – Mainz, Germany)

And
Many more national and international collaborators

About 250 registered users

- Directly involved in major WRF/Chem development
- _NOAA/ESRL_
- Georg Grell, Steven Peckham, Stuart McKeen
- _PNNL _
- Jerome Fast, Bill Gustafson, Rahul A. Zaveri, James C. Barnard
- _NCAR_
- Bill Skamarock
- Rainer Schmitz (University of Chile – Santiago, Chile)
- Marc Salzmann (Max Planck Institute for Chemistry – Mainz, Germany)
- And
*Many more national and international collaborators*
- About 250 registered users

## WRF/Chem Chemistry Package – V2.1.2

- Chemical mechanisms:
- RADM2, Carbon Bond (CBMZ)
- Photolysis (coupled with hydrometeors and aerosols):
-  Madronich, Fast-j (coupled to aerosols and microphysics)
- Deposition:
- Dry deposition (coupled with soil/veg scheme, “flux-resistance” analogy)
- Simplified wet deposition by convective parameterization
- Biogenic emissions:
- Guenther – online calculation based on USGS landuse, T and radiation
- BEISv3.11 (modify reference fields produced from complex landuse data)

**Notes:**
Simplified wet deposition by convective parameterization (scavenging factor of .6 for aerosols, no aqueous-phase chemistry involved)

## WRF/Chem Aerosols – V2.1.2

- Modal approach – Binkowski and Shankar 1995, Ackermann et al. 1998, Schell et al. 2001
- Modal Aerosol Dynamics Model for Europe (MADE)
- modified to include Secondary Organic Aerosols (SOA)
- Sectional approach - Zaveri et al., 2005, 2006
- Model for Simulating Aerosol Interactions and Chemistry (MOSAIC)          (4 or 8 bins)

## WRF/ChemV2.1.2: Aerosol direct effect

- Aerosols coupled to the Goddard atmospheric
- radiation scheme

- Surface temperature difference from run with/without aerosols

- From Chung et al. 2007, submitted

-  Aerosol absorption induce cloud changes via radiation but no account for effect on nucleation in clouds

- July 23, 2004, 2100Z

## Comparison with 350 Ozone monitors during ICARTT/NEAQS experiment – 8hr peak

| Model | r
coefficent | Mean bias
ppbv | RMSE
(ppbv) | Skill
(%) |
| --- | --- | --- | --- | --- |
| CMAQ/ETA
(2004 real-time) | 0.63 | 13.4 | 17.9 | 24% |
| NOAA FSL, WRF/Chem-1
(2004 real-time) | 0.67 | 14.3 | 20.9 | 24% |
| NOAA FSL, WRF/Chem-2.0 | 0.73 | 3.4 | 11.6 | 61% |
| NOAA/ESRL,WRF/Chem-2.0.3 | 0.72 | 11.5 | 16.3 | 30% |
| NOAA/ESRL,WRF/Chem-2.1.2 | 0.83 | 6.4 | 11.2 | 73% |
| NOAA/ESRL, WRF/Chem V2.2 | 0.83 | 3.3 | 9.8 | 81% |

- Continuous model improvement over the last 2 years

**Comparison with 350 Ozone monitors during ICARTT/NEAQS experiment – 8hr peak**

- Evaluation period: July 12 – July 31, 2004

## Additions  in WRF/Chem V2.2

- Kinetic PreProcessor (KPP), MPI Mainz
- Improved convective (non-resolved) transport, coupling of convective parameterization with atmospheric and photolysis radiation (ESRL/GSD)
- Non-resolved and resolved aqueous phase chemistry, wet deposition, (NOAA/ARL/EPA, PNNL,ESRL/GSD)
- 2-way nesting (PNNL, ESRL/GSD)
- Cloud-aerosol interaction (indirect effect) with Lin et al. 6-class microphysics scheme (PNNL)
- Lateral boundary conditions from global models (U of Chile, ESRL/GSD)
- Urban parameterizations (NCAR, coming soon  Spain)
- Positive definite advection (NCAR)
- NMM and ARW dynamic cores (ESRL/GSD)
- Offline version  for the ARW core (to be released shortly, C-DAC, India and ESRL/GSD))

##    
From 6 July to 30 August 2004.
Over 350 observation sites measuring 1-hr average ozone concentrations -  16480 observations.
Only 87 exceedances of 85 ppbv threshold,  no exceedances of 125 ppbv threshold.

- From 6 July to 30 August 2004.
- Over 350 observation sites measuring 1-hr average ozone concentrations -  16480 observations.
- Only 87 exceedances of 85 ppbv threshold,  no exceedances of 125 ppbv threshold.

- ICARTT/NEAQS(International Consortium for Atmospheric Research on Transport and Transformation/        New England Air Quality Study)

##  
Models participating 
AURAMS (MSC, Canada) 
CHRONOS (MSC, Canada)
CMAQ (EPA, USA) 
MAQSIP at two resolutions (BAMS,  USA)
STEM-2K3 (U of Iowa, USA)
WRF/Chem (NOAA/ESRL, USA)

Different chemical mechanisms, meteorological drivers, emission inventories,  horizontal and vertical  resolutions.

24-hour forecasts issued at 0000 UTC (0600 UTC – CMAQ)

- Models participating
- AURAMS (MSC, Canada)
- CHRONOS (MSC, Canada)
- CMAQ (EPA, USA)
- MAQSIP at two resolutions (BAMS,  USA)
- STEM-2K3 (U of Iowa, USA)
- WRF/Chem (NOAA/ESRL, USA)
- Different chemical mechanisms, meteorological drivers, emission inventories,  horizontal and vertical  resolutions.
- 24-hour forecasts issued at 0000 UTC (0600 UTC – CMAQ)

- ICARTT/NEAQS

## Economic Value of Forecasts 

-                                                   Observed
-                                   Yes                 No
-                 Yes           a (C)                b (C)
- Forecast
-                  No            c (L)                d  (0)
- C - cost of preventive action
- L  - loss due to lack of prevention

- Contingency table:

- Probabilistic forecasts: need to assign thresholds for probability.
- Decision analytic model: Murphy (1977)

## Economic Value of Forecasts 

-  Relative economic value of forecasts - Richardson (2000, 2003)

## Economic Value of Forecasts (ECV)

-        Optimum decision level (ECV maximized) when forecast
-                        probability equals cost-loss ratio.

## Economic Value of Forecasts 

-  1hr daily
-  max O3

-  8hr daily
-  max O3

-  50 ppbv

-  70 ppbv

-  85 ppbv

-                      black - individual  models, green - ensemble average, blue - deterministic ensemble DLR,
-                                                       red – probabilistic, purple – probabilistic DLR

## Conclusions

-  Economic value of deterministic forecasts derived from the
-    ensemble of models is superior to the results obtained for
-    the individual models.
-  Maximum economic value is achieved by converting forecasts
-    of ensemble members to probabilistic framework.  The
-    economic value of probabilistic forecasts is superior to the
-    deterministic forecasts over a wide range of cost-loss ratios
-    and for nearly all thresholds analyzed.

## Outlook

-    Data Assimilation (3DVAR, 4DVAR)
-    Global WRF/Chem
-    SMOKE emission module
-    Aerosols – direct and indirect effects
- Contact: _[www.wrf-model.org/WG11](http://www.wrf-model.org/WG11)_, User Group and Support