Richard Ménard

Assimilation of satellite observations of long‐lived chemical species in global chemistry transport models

Use of data assimilation techniques such as optimal interpolation or the Kaiman filter in global chemistry transport models (CTM) is becoming more common. However, owing to high computational requirements, it is often difficult to apply these techniques to multidimensional models containing extensive photochemical schemes. We present a sequential assimilation approach developed for use with general global chemistry transport models. It allows fast assimilation and mapping of satellite observations and provides estimates of analysis errors. The suggested data assimilation scheme evolved from the one described by Levelt et al. [1998]. It is a variant of the suboptimal Kaiman filter and is based on ideas described by Menard et al. [2000] and Menard and Chang [200O]. One of the most important features of the developed scheme is its ability to routinely estimate variance of the analysis and to predict variance evolution in the model. The developed technique (or its variants) has been successfully interfaced with a number of different global models and used for assimilation of several types of measurements, including aerosol extinction ratios. Some of these experiments are described by Lamarque et al. [1999] and W. D. Collins et al. (Forecasting aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX, submitted to Journal of Geophysical Research, 2000, hereinafter referred to as Collins et al., submitted manuscript, 2000). We illustrate the method using assimilation of ozone observations made by the Upper Atmosphere Research Satellite/Microwave Limb Sounder in the three-dimensional chemistry transport model ROSE [Research for Ozone in the Stratosphere and its Evolution; Rose and Brasseur, 1989].

Ambient PM2.5, O3, and NO2 Exposures and Associations with Mortality over 16 Years of Follow-Up in the Canadian Census Health and Environment Cohort (CanCHEC)

Background Few studies examining the associations between long-term exposure to ambient air pollution and mortality have considered multiple pollutants when assessing changes in exposure due to residential mobility during follow-up. Objective We investigated associations between cause-specific mortality and ambient concentrations of fine particulate matter (≤ 2.5 μm; PM2.5), ozone (O3), and nitrogen dioxide (NO2) in a national cohort of about 2.5 million Canadians. Methods We assigned estimates of annual concentrations of these pollutants to the residential postal codes of subjects for each year during 16 years of follow-up. Historical tax data allowed us to track subjects’ residential postal code annually. We estimated hazard ratios (HRs) for each pollutant separately and adjusted for the other pollutants. We also estimated the product of the three HRs as a measure of the cumulative association with mortality for several causes of death for an increment of the mean minus the 5th percentile of each pollutant: 5.0 μg/m3 for PM2.5, 9.5 ppb for O3, and 8.1 ppb for NO2. Results PM2.5, O3, and NO2 were associated with nonaccidental and cause-specific mortality in single-pollutant models. Exposure to PM2.5 alone was not sufficient to fully characterize the toxicity of the atmospheric mix or to fully explain the risk of mortality associated with exposure to ambient pollution. Assuming additive associations, the estimated HR for nonaccidental mortality corresponding to a change in exposure from the mean to the 5th percentile for all three pollutants together was 1.075 (95% CI: 1.067, 1.084). Accounting for residential mobility had only a limited impact on the association between mortality and PM2.5 and O3, but increased associations with NO2. Conclusions In this large, national-level cohort, we found positive associations between several common causes of death and exposure to PM2.5, O3, and NO2. Citation Crouse DL, Peters PA, Hystad P, Brook JR, van Donkelaar A, Martin RV, Villeneuve PJ, Jerrett M, Goldberg MS, Pope CA III, Brauer M, Brook RD, Robichaud A, Menard R, Burnett RT. 2015. Ambient PM2.5, O3, and NO2 exposures and associations with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ Health Perspect 123:1180–1186; http://dx.doi.org/10.1289/ehp.1409276

Data Assimilation and Information

In this introductory chapter we provide an overview of the connection between the data assimilation methodology and the concept of information, whether embodied in observations or models. In this context, we provide a step by step introduction to the need for data assimilation, culminating in an easy to understand description of the data assimilation methodology. Schematic diagrams and simple examples form a key part of this chapter.

Multi-pollutant surface objective analyses and mapping of air quality health index over North America

Air quality, like weather, can affect everyone, but responses differ depending on the sensitivity and health condition of a given individual. To help protect exposed populations, many countries have put in place real-time air quality nowcasting and forecasting capabilities. We present in this paper an optimal combination of air quality measurements and model outputs and show that it leads to significant improvements in the spatial representativeness of air quality. The product is referred to as multi-pollutant surface objective analyses (MPSOAs). Moreover, based on MPSOA, a geographical mapping of the Canadian Air Quality Health Index (AQHI) is also presented which provides users (policy makers, public, air quality forecasters, and epidemiologists) with a more accurate picture of the health risk anytime and anywhere in Canada and the USA. Since pollutants can also behave as passive atmospheric tracers, they provide information about transport and dispersion and, hence, reveal synoptic and regional meteorological phenomena. MPSOA could also be used to build air pollution climatology, compute local and national trends in air quality, and detect systematic biases in numerical air quality (AQ) models. Finally, initializing AQ models at regular time intervals with MPSOA can produce more accurate air quality forecasts. It is for these reasons that the Canadian Meteorological Centre (CMC) in collaboration with the Air Quality Research Division (AQRD) of Environment Canada has recently implemented MPSOA in their daily operations.

Improvements to the Regional Deterministic Air Quality Analysis System for Ozone and PM2.5 at the Surface at the Canadian Meteorological Center

In February 2013, in collaboration with the Air Quality Research Division, the Canadian Meteorological Centre (CMC) implemented into operations a new surface analysis for air quality species (ozone and PM2.5). The Regional Deterministic Air Quality Analysis (RDAQA) generates analyses every hour using the operational GEM-MACH Regional Air Quality Deterministic Prediction System (48 h forecasts on a domain with 10-km horizontal grid spacing and 80-vertical levels) to provide the trial fields. Surface observations are from Canadian regional data providers and the US EPA/AIRNow Program. An optimal interpolation scheme adapted to air quality is used to blend model and observations. A verification of the RDAQA shows major reductions in the error variance and bias of the analysis with respect to the model forecasts as compared to observations.

The NOy budget above Eureka, Nunavut from ground-based FTIR measurements, space-based ACE-FTS measurements, and the CMAM-DAS, GEM-BACH, and SLIMCAT models

Reactive nitrogen species, NOy, play an important role in stratospheric chemistry. Using a Bruker 125HR FTIR installed at Eureka, Nunavut, ACE-FTS satellite data, and model simulations, we study the NOy budget for this Arctic site.

Assimilation and inverse modeling of MOPITT CO observations

Remotely sensed observations are inherently incomplete, and some method must be used to obtain a complete and global state of the atmosphere. Data assimilation is a method that combines an atmospheric model with observations in a dynamically and chemically coherent state of the atmosphere. The degree to which measurements contradicts model-predicted fields can also be an indicator of problems with the instrument, the measurement technique, the inversion, or of the model. The research presented here aims towards obtaining the chemical state of the atmosphere with an emphasis on global atmospheric pollution in the troposphere using observations from MOPITT.