# Presentation: 480526

## A Strategy for Research in the

**Application of Dynamic Data Assimilation**
**to Air Quality Forecasting**
- William R. Stockwell1,2, John M. Lewis2,3
- and S. Lakshmivarahan4
- Department of Chemistry, Howard University1; Division of Atmospheric Sciences, Desert Research Institute2; National Oceanic and Atmospheric Administration /National Severe Storm Laboratory3; School of Computer Science, University of Oklahoma4

## The Vision

**The Vision**
**To Provide the Nation with accurate and time-resolved 4-day forecasts of ozone, PM2.5 and visibility.**

## Atmospheric Chemistry

**Atmospheric Chemistry**

## Key to Data Assimilation is the Construction of a Background Error Covariance Matrix

**How to determine the correlation between the errors for each variable when we don’t know the true state?**

**Note that for atmospheric chemistry the “true state” actually involves thousands of variables!**

**The development of a ****background error covariance matrix will require extensive parameterization of the chemistry.**

## NOx Emissions

**NO****x**** Emissions**

**U.S. EPA**

## VOC Emissions

**VOC Emissions**

**U.S. EPA**

## Biogenic VOC Emissions

**Biogenic VOC Emissions**

**U.S. EPA**

## Biogenic Hydrocarbon Emissions

**CH****4**

**NO**

**NH****3**

**isoprene**

******-pinene**

**limonene**

******-pinene**

## Tropospheric O3 Chemistry

**O****3**** Formation**
**	****NO****2**** + h******** ******** O(****3****P) + NO**
**	**** ****O(****3****P) + O****2**** (+ M) ******** O****3**** (+ M)**
**Ozone Destruction**
**	****NO + O****3****  ******** NO****2**** + O****2**

**Steady-State Ozone Concentrations**
**	****d[NO]/dt = J[NO****2****] - k[NO][O****3****] ******** 0**
**	****[O****3****] = {J / k} {[NO****2****] / [NO]}**

## HO Radical Production

**	****O****3**** + h******** ******** O(****1****D) + O****2**
**	****O(****1****D) + N****2**** (+O****2****) ******** O****3**
**	****O(****1****D) + O****2**** (+O****2****) ******** O****3**
**	****O(****1****D) + H****2****O ******** 2 HO******

## Alkane Oxidation

**	****HO******** + CH****3****CH****3**** ******** H****2****O + CH****3****CH****2******
**	****CH****3****CH****2********  + O****2**** ******** CH****3****CH****2****O****2******** **
**	****CH****3****CH****2****O****2******** + NO ******** CH****3****CH****2****O******** + NO****2**
**	****CH****3****CH****2****O******** + O****2**** ******** CH****3****CHO + HO****2******
**	****HO****2******** + NO ******** HO******** + NO****2**

## Air Quality Equations

** ****t ****	****= time**
** ****c****i ****	****= concentration of i****th**** species**
** ****V****H**** ****	****= horizontal wind vector**
** ******** ****	****= net vertical entrainment rate**
** ****z ****	****= terrain following vertical coordinate **
** ****h ****	****= layer interface height**
** ******** ****	****= atmospheric density**
** ****K ****	****= turbulent diffusion coefficient**

## Ozone Formation

**Ozone Formation**

## NOx and VOC Dependence

**NO****x**** and VOC Dependence**

## Relative Sensitivity of Ozone to Reaction Rate Constants.  Initial total reactive nitrogen concentration is 2 ppb and total initial organic compounds is 50 ppbC.

## Rate constant for the O3 + NO reaction with upper and lower bounds.

- Data from DeMore et al. (1997).

**O****3**** + NO Reaction**

**O****3**** + NO Reaction**

**CH****3****O****2**** + HO****2**** Reaction**

**CH****3****O****2**** + HO****2**** Reaction**

## Uncertainties in rate parameters for HO• with alkenes.

**The closed circles represent the nominal value while the crosses represent the approximate 1********.**

## Mean values and 1 uncertainties of maximum incremental reactivity values for selected hydrocarbons determined from Monte Carlo simulations (Yang et al., 1995).

- Yang et al., 1995

## Comparison of Peterson flux with measured 4 actinic flux from UC-Davis, Sunol and DRI sites for noon September 17, 2000.

**Comparison of Peterson flux with measured 4******** actinic flux from UC-Davis, Sunol and DRI sites for noon September 17, 2000.**

## Photolysis rate parameters of NO2 measured at UC-Davis, Sunol and DRI for episode September 17 to 21, 2000.

**Photolysis rate parameters of NO****2**** measured at UC-Davis, Sunol and DRI for episode September 17 to 21, 2000.**

## 3-D Modeling Studies

**Goal**
- Determine what spatial resolution is required to effectively monitor lower tropospheric ozone from space.
**Why?**
- Satellite observations have the potential to provide an accurate picture of atmospheric chemistry.  A key question when designing new satellite instruments is what spatial resolution is required to effectively monitor air quality from space.
**How?**
- Perform meteorological (MM5) and air chemistry (CAMx) model simulations at 4, 8, 12, and 16km resolutions.
- Produce variograms with the GSLIB Geostatistical Software Library to calculate the spatial length scales of ozone.

## C.P. Loughner, D.J. Lary, L.C. Sparling, P.de Cola, and W.R. Stockwell

**C.P. Loughner, D.J. Lary, L.C. Sparling, P.de Cola, and W.R. Stockwell**

## The horizontal range or smallest distance where there is no dependence on concentrations in other locations in the east-west and north-south directions were found to be 60 km.

For a satellite platform to effectively monitor lower tropospheric ozone, the Nyquist sampling theorem tells us that it should have a spatial resolution of at least 30 km but preferably near 15 km.

Future work – use same method to determine spatial resolution for other air quality species and find ideal temporal resolutions for air quality species

**For a satellite platform to effectively monitor lower tropospheric ozone, the Nyquist sampling theorem tells us that it should have a spatial resolution of at least 30 km but preferably near 15 km.**
**Future work – use same method to determine spatial resolution for other air quality species and find ideal temporal resolutions for air quality species**

## Lewis et al., 1989, BAMS 70, 24-29

**GUFMEX Ship Track**

## Daytime Aerosol Formation

**Source ******** NO**
**	**
**NO + HO****2**** ******** NO****2**** + HO**
**HO + NO****2**** (+M) ******** HNO****3 ****(+M)**
**HNO****3**** +NH****3**** ******** NH****4****NO****3**** (aerosol)**
**NH****4****NO****3**** ******** Deposition**

**k****E**
**	**
**k****1**
**k****2**
**k****3**
**k****d**

## Rate Expressions for Daytime Aerosol Formation

## For our simple model assume:

**[HO] and [HO****2****] are constant.**

**NO + HO****2**** ******** NO****2**** + HO is a fast reaction.**

**HNO****3**** +NH****3**** ******** NH****4****NO****3**** (aerosol)**
**can be treated as net reaction:**
**HNO****3**** +NH****3**** ******** NH****4****NO****3**

## The model becomes:

## Chemistry Only Model Simplifies to:

**Chemistry Only Model Simplifies to:**

**Integrate with**
**Runge-Kutta method.**

## Chemistry Only

**Chemistry Only**

## Atmospheric Chemistry is Affected by Meteorology

**H (Inversion - Mixing Height)**

**Typically air pollutants near the Earth’s surface**
**are confined within the “boundary layer”.**

**The boundary layer may expand during the**
**daylight hours diluting mixture.**

**Emission Rate**
**Adjusted for H**

**Dilution Rate**

## Modified Atmospheric Chemistry Equations

**Modified Atmospheric Chemistry Equations**

## Slide 33

## Mixed-Layer Model Equations

**“****T Jump”**

**Mixing Height**

**Average Potential Temperature of Layer**

**Surface ********o**
**Air layer ******
**Difference at top ******
**Surface wind-speed V****o**
**Vertical wind velocity w**
**Mixing layer height H**

## Slide 35

## Slide 36

## Slide 37

## Initial Conditions for Monte Carlo Simulations

**Initial chemical concentrations constant.**
**[A] ****o**** = 1.0; [B] = 0.0; [C] = 0.0**
**Surface pressure (P****o****) varies between 950 and 1050 millibar.**
**Initial potential temperature of surface (********o****) and initial average potential temperature of air layer (********o****) varies between 295 and 305 K under the constraint that ********o**** > ********o****.**
**Initial temperature jump (********)****o**** varies between 0.1 and 0.3 K.**
**Initial surface wind-speed (V****o****)varies between 3. and 7 m/s.**
**Vertical velocity (w) varies between 0.0 and 0.2 m/s.**
**Initial mixing layer height (H) varies between 100 and 500 m.**

## Mixing Height

**Mixing Height**

## Emitted Species [A]

**Emitted Species [A]**

## Reacted Species [B]

**Reacted Species [B]**

## Aerosol Species [C]

**Aerosol Species [C]**

## Deposition

**Deposition**

## How well could model parameters be determined

**from “observations”?**

**For example, could deposition parameter (K****d****) and**
**surface potential temperature (********o****) be determined from**
**observations of the potential temperature of air layer (********)**
**and chemical concentrations [A] and [B]?**

**Test by calculating cost function while varying K****d**** and ********.**

**Cost = (********i**** - ********obs****)****2**** + ([A]****i**** - [A]****obs****)****2**** +([B]****i**** - [B]****obs****)****2**

## Cost Function for Kd and o.

**Cost = (********i**** - ********obs****)****2**** + ([A]****i**** - [A]****obs****)****2**** +([B]****i**** - [B]****obs****)****2**

**Cost Function for K****d**** and ********o****.**

**K****d**

**Surface Potential**
**Temperature, ********o**

**Cost**

## Conclusions

**The development of a ****Background Error Covariance Matrix will require extensive parameterization of the chemistry.**
**The chemistry of the real atmosphere involves thousands of chemical species.**
**Each species (in principle) requires a continuity equation.**
**There is significant uncertainty in the chemical parameters.**
**Lack of knowledge and computational resources require that the chemistry to be simplified.**
**The atmospheric system is nonlinear and this leads to complex behavior that is nontrivial even for models with only 7 variables (3 meteorological and 4 chemical).**

## Conclusions

**Sensitivity analysis supports the idea that simplifications can be made.**
**Future research needs to be focused on improving the operational air quality forecasts.**
**But experiments with simple, “toy” models may provide some valuable insights into the interactions of meteorology and chemistry.**