1. ADAGIO (Atmospheric Deposition Analysis Generated by optimal Interpolation from Observations): Project plans and status
A.S. Cole1, A. Robichaud2, M.D. Moran2, P.A. Makar2, A. Lupu2, M. Shaw1, V. Fortin3, R. Vet1
1Measurements and Analysis Research Section, Environment Canada; 2Modelling and Integration Research Section, Environment Canada; 3Environmental Numerical Weather Prediction Research, Environment Canada
Introduction
II. Measurement Input (cont.)
IV. Optimal Interpolation
The goal of the ADAGIO project is to improve maps of wet, dry and total annual deposition of nitrogen (N) and sulphur (S) in Canada and the United States by combining observed and modelled data.
Measurements of wet deposition and air concentrations of deposited compounds provide the most accurate values at and near measurement sites, but cannot provide reliable regional estimates of deposition where sites are sparse.
Atmospheric models that include meteorology, emissions, transport, chemistry and deposition of key N and S compounds provide complete spatial coverage and the ability to account for non-linear effects of meteorology, topography and chemistry, but often have biases and uncertainties compared to measured concentrations and fluxes.
This project will use optimal interpolation (OI) techniques to combine the complementary advantages of measurements and models.
The measured concentrations of nitrogen and sulphur species in air and precipitation (see Table 1) will be fusion with the predicted concentrations from GEM-MACH using OI techniques. Similarly, measured precipitation amounts from sites across North America (see Figs. 2b and 3) are used to improve precipitation fields from GEM.
These OI techniques essentially minimize the analysis error variance given observation and model errors. as well as the measured and modelled data, additional information are also be needed, such as the error statistics, bias adjustment, and the scale over which the measurements can be considered representative. The analysis can also incorporate built-in quality control procedures to flag irregular data.
Table 1 and Figure 2 summarize the available chemistry measurement networks and species that will be used in the 2010 analysis. A subset of the measurements will be left out of the analysis to be used for post-analysis validation.
Climate stations with precipitation data that are currently used in the Canadian Precipitation Analysis (CaPA) are shown in Figure 3. The higher density of data compared to wet chemistry sites alone will result in the best possible precipitation field.
Figure 5: CaPA 24-hour precipitation fields around the Great Lakes region, May 16, 2014
The Canadian Precipitation Analysis (CaPA) is an operational product that generates near-real-time estimates of 6- and 24-hour precipitation amounts from GEM and ground and radar observations [Mahfouf et al. 2007]. It is continually evolving to incorporate additional data and QC methods. For ADAGIO, precipitation amounts from the precipitation chemistry measurement sites will also be incorporated into the CaPA analysis. The current operational version of CaPA also incorporates radar data; these are not available for 2010 but will be used in future ADAGIO analyses.
Figure 3: Climate stations currently used for precipitation amounts from various networks (SYNOP, METAR, RMCQ, US COOP). Colors represent observed rainfall on a specific day.
I. Overview
An overview of the inputs, model-measurement fusion steps, and outputs of the ADAGIO approach is shown in Figure 1. The initial testing will be done using the year 2010 to align with parallel model inter-comparisons at Environment Canada. In future, the analysis will be used to generate annual deposition maps.
The following sections describe the measurement and model inputs and the optimal interpolation techniques.
Table 1: Sources of compiled air and precipitation measurement data for 2010
Network
Area
Species measured
Sites
Type of network
Frequency of observations
CAPMoN
Canada
Gas: SO2, HNO3;
Particle: SO42-, NO3-, NH4+ 15
Rural
Daily
NAPS
Gas: SO2, NO, NOx
131
Mostly urban
Hourly
(dichotomous)
Gas: SO2 , HNO3
PM2.5: SO42-, NO3-, NH4+ 28
Every 3 days
(24 hr avg.)
AQS US
Gas: SO2, NO2
468
IMPROVE
PM2.5: SO42-, NO3-
172
Mostly
CASTNET
Gas: SO2, HNO3 84
Weekly
Precipitation: SO42-, NO3-, NH4+ 29
NADP/NTN
241
NADP/AIRMoN 7
Daily (when precip occurs)
NBPMN
Canada (New Brunswick) 12
NSPSN
Canada (Nova Scotia) 1
Figure 1: Schematic of ADAGIO methodology for generation of wet, dry and total deposition maps
Precipitation concentration maps
Wet deposition maps
Modelled dry deposition velocities
(GEM-MACH)
Air concentration maps
Dry deposition maps
Total deposition maps
Measured air concentrations
Modelled air concentrations
Measured precipitation
Modelled precipitation (GEM)
Measured precipitation concentrations
Modelled precipitation concentrations
Precipitation maps
Optimal interpolation
(CaPA)
OI methods have also been developed and used successfully for improving concentration maps of air pollutants, such as ozone and fine particulate matter, relative to GEM-MACH alone [Robichaud and Menard 2014]. These methods have now been refined for SO2 and NO2. As seen in Fig. 6, when the resulting analysis and the original model are compared to validation data not used in the analysis, the analysis has lower biases and random errors than the model. The OI approach will be refined and tested for additional air and precipitation species listed in Table 2.
Figure 6: Bias (lower lines) and random error (upper lines) of GEM-MACH (black) and of OI analysis (green) of hourly SO2 for July 2014.
III. Model Input
V. Progress and Next Steps
Modelled inputs to ADAGIO will come from Environment Canada’s GEM (Global Environmental Multiscale) numerical weather prediction model, and its counterpart GEM-MACH (Global Environmental Multiscale model – Modelling Air quality and Chemistry). GEM-MACH adds to GEM in-line chemistry modules and emission inventories in order to simulate chemistry and deposition.
[Something here about v2 specifically?]
While different grid configurations are supported by GEM, the version used will cover North America at 10 km resolution. Hourly outputs of concentrations and fluxes will be aggregated or averaged to measurement periods for the optimal interpolation. Dry deposition velocities of gases and particles from the model will be used directly (Fig. 1).
An updated version of GEM-MACH has completed a 2010 simulation run, with concentrations and deposition velocities archived.
Measurement data for 2010 have been compiled, rolled up to weekly values and, for sulphur species, compared with GEM-MACH predictions. [Add a sentence here about improvements in bias compared to an earlier version?]
Precipitation maps from CaPA are archived for 2010 and will be updated using values from precipitation chemistry sites.
An optimal interpolation methodology has been developed for hourly ozone, SO2, NO2 , fine and coarse particulates for near real time air quality purposes and is currently being adapted for additional sulphur and nitrogen species on longer time scales (Robichaud et al., 2015, submitted to AQAH).
Routine archiving of GEM-MACH concentrations and fluxes will be arranged for future years.
II. Measurement Input
NAPS
NAPSdichot
CAPMoN
IMPROVE
AQS
CASTNET
CAPMoN
NBPMN
NSPSN
NADP
Figure 4: Sample output from GEM-MACH v2: January 2010 average SO2 surface air concentration
Acknowledgements
The authors would like to thank Verica Savic-Jovcic for contributing Fig. 4 and Gary Lear for travelling great distances to provide valuable input. REFERENCES ?
Figure 2: Air (left) and precipitation (right) measurement sites listed in Table 1