Surface objective analyses (O 3,PM2.5,NO 2 ) for data assimilation Alain Robichaud/ Richard Ménard ARQI: air quality research division (Division de la recherche en qualité de l’air)

Outline Objectives Theory, methodology Project OA (ARQI/AQMAS/CMDA) Applications and derived products Plans for

Overall objectives: Develop a regional chemical data assimilation Align the chemical data effort with the main stream meteorological data assimilation effort while adapting to the particularities of chemical fields in order to produce the best chemical analysis/forecast OA is only a first step – not the ultimate goal The main stream meteorological data assimilation system is based on representations of errors by covariances Hybrid EnKF-VAR for 3D regional/global For surface assimilation: CALDAS

Purposes and applications of OA Objective analysis using optimal interpolation is an essential first step in building up an assimilation system (such as EnKF). Producing maps of objective analysis (OA) on a regular basis is motivated by many factors such as: 1) to initialize numerical models at regular time interval (usually every 6 or 12 hours), i.e data assimilation 2) nowcasting purposes (AQ real-time forecasting) 3) help to trace back possible model bugs (or bugs in the observation system) 4) produce long series of re-analyses for specific purposes (i.e. climatology), study of trends which are more robust (no missing data in OA due to the intelligent spatio-temporal interpolator) 5) potentially useful for mapping health indices (AQHI), or specialized environmental indices or pollutant loadings on ecosystems,

Theory OA is a problem of statistical optimization: minimizing the analysis error variance X a = (1-K)X b + Ky o (scalar form) rearranging and introducing H (interpolation required) X a = X b + K(y o –HXb) with K = (HP f ) t * (H(HP f ) t +R) -1 ( same equation as seq EnKF) where X a : objective analysis field matrix ( dimension grid model) X b : trial field (forecast or first guess from model) K : gain matrix (weight matrix) ( dimension grid model X NS) y o : observation vector (dimension NSX1). H: interpolation operator for model at the station location (cubic semi- Lagrangian or linear). Resolution 15 km grid (GEM-MACH), 21 km (CHRONOS)

Ozone objective Analysis : Current formulation OI type analysis solver where (local parameters) estimated by local fitting of O-F on 3-hours intervals (from previous year data sets ) and are global parameters estimated online by a method of maximum likelihood and chi2 adjustment particularities - some local and temporal variability of the error statistics (but based on previous year-hourly innovation statistics) has been accounted - online estimation (current analysis) of correlation length-scale and and global adjustment factor for representativeness error

Getting error stats FOAR modeling: covariance of OmP vs distance Hollinsgworth and Lönnberg, 1986 (H-L) L ci … Assumptions: 1) follow exp decreasing function 2) width of bins is fixed 3) cutoff distance 900 kms Background error

- direct inversion (by Cholesky decomposition) of the innovation matrix and thus limited to ~1500 obs but we plan for next year to perform batch processing and eventually by SVD of innovations in an EnKF - statistical estimation of obs and background error variance – a time consuming process that often requires manual intervention. This procedure will be partially replaced by an online estimation of error variances using a combination of Desrozier’s and maximum likehood approaches - Non gaussian distribution of errors – relevant for aerosols. We plan to use the modified lognormal distribution (three paramaters – not two !!) as shown by daSilva (GMAO) Drawbacks

For aerosols Accounting for error of representativeness is fundamental Non-Gaussian errors Attribution of increments to primary or secondary aerosols is still not not well known daSilva, GMAO MODIS

Project OA Transfer to operations: objective analysis of surface ozone and PM2.5 D. Anselmo et P.A.Beaulieu (AQMAS) G. Verner, L. Veillette, Y. Zaitseva, I. Provost (CMDA) ARQI (A. Robichaud,R. Ménard)

Monthly stats OA (from Yulia’s web page) J U L Y OCTOCT Ozone PM2.5 GEM-MACH oper 15 km

Applications 1) Data assimilation (surface AQ) 2) Diagnosing biases and model errors 3) AQHI mapping 4) Building climatology (O 3 and PM2.5) 5) Others (oil sand monitoring, deep stratospheric intrusions to SFC, etc.)

Impact of data assimilation on AQ forecast Std Dev O-P Mean O-P gain F-test T-test

Impact of data assimilation on AQ forecast Std Dev O-P Mean O-P

Impact of data assimilation on AQ forecast Mean O-P Std Dev O-P

Existing product AQHI at stations at 18Z Formula from Stieb et al JAWMA AQHI=(10/10.4)*(100*(exp( *NO2)-1+exp( *ozone)-1+exp( *PM25)-1))

Model output of AQHI vs AQHI at the station AQHI at the station

Proposed product AQHI map using OA (OA of AQHI and AQHI calculated at the station)

A surface ozone and PM2.5 climatology (using an improved optimal interpolation scheme) Presented at the 94 th Canadian Chemistry Conference and Exhibition Montreal, June 5-9, 2011 Presenter: Alain Robichaud Collaborator/advisor: Richard Ménard

Comparison of sfc climatology with model outputs CHRONOS ANNUAL AVG 2005 CHRONOS + AIRNOW SFC DATA AVG 2005 MOZART ANNUAL AVG GEM-AQ ANNUAL AVG 2005

PM2.5 climatology Randall et al ( ) Satellite derived CHRONOS+ AIRNOW

Remarks about this objective analysis OA system presented here: Is not just mapping of ambient conditions It is also a tool for environmental monitoring, input for data assimilation, tool for diagnosing model’s error & bugs, input for derived products (AQHI mapping, climatology, etc).

Plans for fiscal year Implementation plans Operational implementation of regional surface ozone and PM2.5 hourly analyses should be completed by June 2012 Make recommendations for operational plans on chemical forecast. (chemical imbalance, primary vs secondary aerosols, better QC, code on-line or not, etc) Introduce a change of variable for non-gaussian distribution of errors Update the analysis solver to handle larger data volume of observations Test the online methodology of obs and background error variances (Desrozier’s method combined with maximum likelihood estimation of correlation length-scales) Scientific plans Submit paper on OA with online maximum likelihood estimation of global parameters Submit paper on implementation of CMC error covariances in BASCOE Formal comparison between 4D-Var and EnKF of BASCOE Submit paper on climatology and trend (10 year: )

Additional slides

Assumptions and hypotheses 1) error stats are normal (or log normal) 2) obs error uncorrelated with background error 3) obs error not correlated in time 4) best linear unbiased estimator (BLUE) 5) correlation homogeneous and isotropic BLUE: -No biases in obs/background -Each error is uncorrelated with the state vector -Assumes linearity

Basic equations K = (HP f ) t * (H(HP f ) t +R) -1 1) Calculate H(HP f (k 1,k 2 )) t = α*σ f (k 1 )*σ f (k 2 )*exp { - |x(k 1 )- x(k 2 )|/( β L c } until χ 2 /NS = 1 (tuning on the fly) 2) Calculate (HP f (i,j,k 1 )) t = α*σ f (i,j)*σ f (k 1 )*exp { - |x(i,j)- x(k 1 )|/( β L c } N ~ < 1500 Hypothesis: σ f (i,j) and L c are constant over the whole domain However, a sensitivity analysis was done: it turns out that those 2 parameters are quite sensitive and can be tuned to achieved a better optimization. A A: positive definite (trace (A) > 0; det |A| > 0) Note: X 2 = ע*A -1 *ע t

Cross validation OmP (warm/cold season) Ozone ERN NA Ozone WRN NA Warm season Cold season Mean σ Mean σ Mean σ Mean σ ppbv 1: Model, 2: OA, 3 et 4 OA with adaptative scheme N~3M (10%) 2006 N~1M (10%) CHRONOS

Decrease weight ? of obs Decrease weight ? Of obs OmA < OmP OmP < OmA OmA < OmP PM2.5 Increase Weight of obs ? Increase Weight of obs A posteriori tuning

Decrease weight of obs Std (OmA) > Std (OmP) 2:1 1:1 PM2.5 Increase weight of obs A posteriori tuning

OA system for surface pollutants Compatible with EnKF Demonstrated positive impact on model forecast GEM-MACH and CHRONOS OA system useful for derived products (AQHI), monitoring of oils sands, climatology, nowcasting and others