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Using Objective Analysis for the Assimilation of Satellite-Derived Aerosol Products to Improve PM2.5 Predictions over Europe. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050763] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
We used the objective analysis method in conjunction with the successive correction method to assimilate MODerate resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) data into the Chimère model in order to improve the modeling of fine particulate matter (PM2.5) concentrations and AOD field over Europe. A data assimilation module was developed to adjust the daily initial total column aerosol concentrations based on a forecast-analysis cycling scheme. The model is then evaluated during one-month winter period to examine how such a data assimilation technique pushes the model results closer to surface observations. This comparison showed that the mean biases of both surface PM2.5 concentrations and the AOD field could be reduced from −34 to −15% and from −45 to −27%. The assimilation, however, leads to false alarms because of the difficulty in distributing AOD550 over different particle sizes. The impact of the influence radius is found to be small and depends on the density of satellite data. This work, although preliminary, is important in terms of near-real time air quality forecasting using the Chimère model and can be further developed to improve modeled PM2.5 and ozone concentrations.
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Robichaud A. Surface data assimilation of chemical compounds over North America and its impact on air quality and Air Quality Health Index (AQHI) forecasts. AIR QUALITY, ATMOSPHERE, & HEALTH 2017; 10:955-970. [PMID: 29142613 PMCID: PMC5660843 DOI: 10.1007/s11869-017-0485-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Accepted: 05/17/2017] [Indexed: 05/18/2023]
Abstract
The aim of this paper is to analyze the impact of initializing GEM-MACH, Environment and Climate Change Canada's air quality (AQ) forecast model, with multi-pollutant surface objective analyses (MPSOA). A series of 48-h air quality forecasts were launched for July 2012 (summer case) and January 2014 (winter case) for ozone, NO2, and PM2.5. In this setup, the GEM-MACH model (version 1.3.8.2) was initialized with surface analysis increments (from MPSOA) which were projected in the vertical by applying an appropriate fractional weighting in order to obtain 3D analyses in the lower troposphere. Here, we have used a methodology based on sensitivity tests to obtain the optimum vertical correlation length (VCL). Overall, results showed that for PM2.5, more specifically for sulfate and crustal materials, AQ forecasts initialized with MPSOA showed a very significant improvement compared to forecasts without data assimilation, which extended beyond 48 h in all seasons. Initializing the model with ozone analyses also had a significant impact but on a shorter time scale than that of PM2.5. Finally, assimilation of NO2 was found to have much less impact than longer-lived species. The impact of simultaneous assimilation of the three pollutants (PM2.5, ozone, and NO2) was also examined and found very significant in reducing the total error of the Air Quality Health Index (AQHI) over 48 h and beyond. We suggest that the period over which there is a significant improvement due to assimilation could be an adequate measure of the pollutant atmospheric lifetime.
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Affiliation(s)
- Alain Robichaud
- Air Quality Research Division, Environment and Climate Change Canada, 2121 Trans-Canada Highway, Dorval, QC H9P 1J3 Canada
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Ménard R, Deshaies-Jacques M, Gasset N. A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2016; 66:874-895. [PMID: 27104336 DOI: 10.1080/10962247.2016.1177620] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Revised: 03/29/2016] [Accepted: 04/01/2016] [Indexed: 06/05/2023]
Abstract
UNLABELLED An objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth-Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and the [Formula: see text] diagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, or [Formula: see text] should be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values. IMPLICATIONS This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme.
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Affiliation(s)
- Richard Ménard
- a Modeling and Integration Section, Air Quality Research Division , Environment and Climate Change Canada , Dorval , Quebec , Canada
| | - Martin Deshaies-Jacques
- a Modeling and Integration Section, Air Quality Research Division , Environment and Climate Change Canada , Dorval , Quebec , Canada
| | - Nicolas Gasset
- a Modeling and Integration Section, Air Quality Research Division , Environment and Climate Change Canada , Dorval , Quebec , Canada
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Robichaud A, Ménard R, Zaïtseva Y, Anselmo D. Multi-pollutant surface objective analyses and mapping of air quality health index over North America. AIR QUALITY, ATMOSPHERE, & HEALTH 2016; 9:743-759. [PMID: 27785157 PMCID: PMC5054062 DOI: 10.1007/s11869-015-0385-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 11/23/2015] [Indexed: 05/23/2023]
Abstract
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.
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Affiliation(s)
- Alain Robichaud
- Air Quality Research Division, Environment and Climate Change Canada, 2121 Trans Canada Highway, Dorval, Québec H9P 1J3 Canada
| | - Richard Ménard
- Air Quality Research Division, Environment and Climate Change Canada, 2121 Trans Canada Highway, Dorval, Québec H9P 1J3 Canada
| | - Yulia Zaïtseva
- Canadian Meteorological Centre, Environment and Climate Change Canada, 2121 Trans Canada Highway, Dorval, Québec H9P 1J3 Canada
| | - David Anselmo
- Canadian Meteorological Centre, Environment and Climate Change Canada, 2121 Trans Canada Highway, Dorval, Québec H9P 1J3 Canada
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Tang Y, Chai T, Pan L, Lee P, Tong D, Kim HC, Chen W. Using optimal interpolation to assimilate surface measurements and satellite AOD for ozone and PM2.5: A case study for July 2011. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2015; 65:1206-16. [PMID: 26091206 DOI: 10.1080/10962247.2015.1062439] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
UNLABELLED We employed an optimal interpolation (OI) method to assimilate AIRNow ozone/PM2.5 and MODIS (Moderate Resolution Imaging Spectroradiometer) aerosol optical depth (AOD) data into the Community Multi-scale Air Quality (CMAQ) model to improve the ozone and total aerosol concentration for the CMAQ simulation over the contiguous United States (CONUS). AIRNow data assimilation was applied to the boundary layer, and MODIS AOD data were used to adjust total column aerosol. Four OI cases were designed to examine the effects of uncertainty setting and assimilation time; two of these cases used uncertainties that varied in time and location, or "dynamic uncertainties." More frequent assimilation and higher model uncertainties pushed the modeled results closer to the observation. Our comparison over a 24-hr period showed that ozone and PM2.5 mean biases could be reduced from 2.54 ppbV to 1.06 ppbV and from -7.14 µg/m³ to -0.11 µg/m³, respectively, over CONUS, while their correlations were also improved. Comparison to DISCOVER-AQ 2011 aircraft measurement showed that surface ozone assimilation applied to the CMAQ simulation improves regional low-altitude (below 2 km) ozone simulation. IMPLICATIONS This paper described an application of using optimal interpolation method to improve the model's ozone and PM2.5 estimation using surface measurement and satellite AOD. It highlights the usage of the operational AIRNow data set, which is available in near real time, and the MODIS AOD. With a similar method, we can also use other satellite products, such as the latest VIIRS products, to improve PM2.5 prediction.
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Affiliation(s)
- Youhua Tang
- a NOAA Air Resources Laboratory, College Park , MD , USA
- b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA
| | - Tianfeng Chai
- a NOAA Air Resources Laboratory, College Park , MD , USA
- b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA
| | - Li Pan
- a NOAA Air Resources Laboratory, College Park , MD , USA
- b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA
| | - Pius Lee
- a NOAA Air Resources Laboratory, College Park , MD , USA
| | - Daniel Tong
- a NOAA Air Resources Laboratory, College Park , MD , USA
- b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA
- c Center for Spatial Information Science and Systems , George Mason University , Fairfax , VA , USA
| | - Hyun-Cheol Kim
- a NOAA Air Resources Laboratory, College Park , MD , USA
- b Cooperative Institute for Climate and Satellites , University of Maryland, College Park , MD , USA
| | - Weiwei Chen
- a NOAA Air Resources Laboratory, College Park , MD , USA
- d Northeast Institute of Geography and Agroecology , Chinese Academy of Sciences , Changchun , People's Republic of China
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Pagowski M, Grell GA. Experiments with the assimilation of fine aerosols using an ensemble Kalman filter. ACTA ACUST UNITED AC 2012. [DOI: 10.1029/2012jd018333] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Mallet V. Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation. ACTA ACUST UNITED AC 2010. [DOI: 10.1029/2010jd014259] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Vivien Mallet
- INRIA, Paris-Rocquencourt Research Center; Le Chesnay France
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