Application of model output statistics to the GEM-AQ high resolution air quality forecast

The TROPOspheric Monitoring Instrument (TROPOMI) on-board the Sentinel-5 Precursor satellite (launched on 13 October 2017) is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral ranges. The measured spectra are used to retrieve total columns of trace gases, including nitrogen dioxide (NO2). In this study, Pandora NO2 measurements made at three sites located in or north of the Greater Toronto Area (GTA) are used to evaluate the TROPOMI NO2 data products, including the standard Royal Netherlands Meteorological Institute (KNMI) NO2 data product and a research data product developed by Environment and Climate Change Canada (ECCC) using a high-resolution regional air quality forecast model (used in the airmass factor calculation).TROPOMI pixels located upwind and downwind from the Pandora sites were analyzed using a new wind-based validation method that increases the number of coincident measurements by about a factor of five compared to standard techniques. Using this larger number of coincident measurements, this work shows that both TROPOMI and Pandora instruments can reveal detailed spatial patterns (i.e., horizontal distributions) of local and transported NO2 emissions, which can be used to evaluate regional air quality changes. The TROPOMI ECCC NO2 research data product shows improved agreement with Pandora measurements compared to the TROPOMI standard tropospheric NO2 data product, demonstrating the benefit of using the high-resolution regional air quality forecast model to derive NO2 airmass factors.

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The LQ-WARN Project – Development of a Model Output Statistics Product for Air Quality Warnings

10.5194/egusphere-egu21-882 ◽

2021 ◽

Author(s):

Sabine Robrecht ◽

Andreas Lambert ◽

Stefan Gilge

Keyword(s):

Air Quality ◽

Meteorological Parameters ◽

Phase 1 ◽

Weather Prediction ◽

Model Output ◽

Operational Mode ◽

Quality Models ◽

Air Quality Forecast ◽

Direct Model ◽

Model Output Statistics

In order to reach legal air quality limits, several municipalities in Germany have decided to take actions if concentrations of NO2 and Particulate Matter (PM) exceed certain thresholds. The decision for concrete measures is usually based on observations or use the Direct Model Output (DMO) of air quality models. However, due to large biases of state-of-the-art numerical air quality models, the skill of DMO forecasts to predict periods of polluted air up to four days ahead is very limited.The project LQ-WARN aims to develop a system for warning of poor air quality based on Model Output Statistics (MOS). Therefore, air quality related observations, model results provided by the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological parameters from the ECMWF numerical weather prediction model are used as predictors to forecast the air quality by applying Multiple Linear Regression (MLR). In this way MOS equations are calculated for four seasons. The final forecast product will comprise post-processed probabilistic as well as deterministic (e.g. mass concentration) parameters for the species NO2, O3, PM10 and PM2.5. Forecasts will be available for several hundred German locations and cover lead times up to 96 hours.Here, we show first results of our phase 1 MOS prototype, for which observational, meteorological and empirical predictors are applied. Despite of the preliminary exclusion of CAMS predictors, the verifications of the MOS equations imply a considerable reduction of variance and a significant reduction of RMSE (Root Mean Square Error) compared to the climatological values for all four species. Hence, the MOS system can already provide a reasonably good air quality forecast. Furthermore, our analysis of used meteorological predictors, enables a detailed analysis of the importance of specific meteorological parameters for improved statistical air quality forecasts. &#160;As an outlook we will provide detailed information about the final phase 2 LQ-WARN product, which will also include the MOS predictors of CAMS and is expected to be launched in pre-operational mode by 2022.

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Impact of urban parameterization on high resolution air quality forecast with the GEM – AQ model

Atmospheric Chemistry and Physics ◽

10.5194/acp-12-10387-2012 ◽

2012 ◽

Vol 12 (21) ◽

pp. 10387-10404 ◽

Cited By ~ 10

Author(s):

J. Struzewska ◽

J. W. Kaminski

Keyword(s):

Air Quality ◽

High Resolution ◽

Wind Speed ◽

Quality Parameters ◽

Meteorological Conditions ◽

Specific Humidity ◽

Air Quality Forecast ◽

Pollutant Concentrations ◽

The Impact ◽

Quality Forecast

Abstract. The aim of this study is to assess the impact of urban cover on high-resolution air quality forecast simulations with the GEM-AQ (Global Environmental Multiscale and Air Quality) model. The impact of urban area on the ambient atmosphere is non-stationary, and short-term variability of meteorological conditions may result in significant changes of the observed intensity of urban heat island and pollutant concentrations. In this study we used the Town Energy Balance (TEB) parameterization to represent urban effects on modelled meteorological and air quality parameters at the final nesting level with horizontal resolution of ~5 km over Southern Poland. Three one-day cases representing different meteorological conditions were selected and the model was run with and without the TEB parameterization. Three urban cover categories were used in the TEB parameterization: mid-high buildings, very low buildings and low density suburbs. Urban cover layers were constructed based on an area fraction of towns in a grid cell. To analyze the impact of urban parameterization on modelled meteorological and air quality parameters, anomalies in the lowest model layer for the air temperature, wind speed and pollutant concentrations were calculated. Anomalies of the specific humidity fields indicate that the use of the TEB parameterization leads to a systematic reduction of moisture content in the air. Comparison with temperature and wind speed measurements taken at urban background monitoring stations shows that application of urban parameterization improves model results. For primary pollutants the impact of urban areas is most significant in regions characterized with high emissions. In most cases the anomalies of NO2 and CO concentrations were negative. This reduction is most likely caused by an enhanced vertical mixing due to elevated surface temperature and modified vertical stability.

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Impact of urban parameterization on high resolution air quality forecast with the GEM – AQ model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-12-9517-2012 ◽

2012 ◽

Vol 12 (4) ◽

pp. 9517-9551

Author(s):

J. Struzewska ◽

J. W. Kaminski

Keyword(s):

Air Quality ◽

High Resolution ◽

Wind Speed ◽

Quality Parameters ◽

Meteorological Conditions ◽

Anthropogenic Heat ◽

Air Quality Forecast ◽

Pollutant Concentrations ◽

The Impact ◽

Quality Forecast

Abstract. The aim of this study is to assess the impact of urban cover on high-resolution air quality forecast simulations with the GEM-AQ model. The impact of urban area on the ambient atmosphere is non-stationary and short-term variability of meteorological conditions may result in significant changes of the observed intensity of urban heat island and pollutant concentrations. In this study we used the Town Energy Balance (TEB) parameterization to represent urban effects on modelled meteorological and air quality parameters at the final nesting level with horizontal resolution of ~5 km over Southern Poland. Three one-day cases representing different meteorological conditions were selected and the model was run with and without the TEB parameterization. Three urban cover categories were used in the TEB parameterization: mid-high buildings, sparse buildings and a mix of buildings and nature. Urban cover layers were constructed based on an area fraction of towns in a grid cell. To analyze the impact of urban parameterization on modelled meteorological and air quality parameters, anomalies in the lowest model layer for the temperature, wind speed and pollutant concentrations were calculated. Anomalies of the specific humidity fields indicate that the use of the TEB parameterization leads to a systematic reduction of moisture content in the air. Comparison with temperature and wind speed measurements taken at urban background monitoring stations shows that application of urban parameterization improves model results. For primary pollutants the impact of urban areas is most significant in regions characterized with high emissions. In most cases the anomalies of NO2 and CO concentrations are negative. This reduction is most likely caused by an enhanced vertical mixing due to elevated surface temperature and modified vertical stability. Although the outcome from this study is promising, it does not give an answer concerning the benefits of using TEB in the GEM-AQ model in an operational configuration. Additional long term evaluation would be required to better estimate the anthropogenic heat flux and to assess the urban impact in longer time scales (seasonal and annual average).

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Evaluation of the high resolution WRF-Chem air quality forecast and its comparison with statistical ozone predictions

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-1029-2015 ◽

2015 ◽

Vol 8 (2) ◽

pp. 1029-1075 ◽

Cited By ~ 1

Author(s):

R. Žabkar ◽

L. Honzak ◽

G. Skok ◽

R. Forkel ◽

J. Rakovec ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Atmospheric Chemistry ◽

Weather Prediction ◽

Monitoring Site ◽

Linear Processes ◽

Air Quality Forecast ◽

On Line ◽

Forecasting System ◽

Quality Forecast

Abstract. An integrated high resolution modelling system based on the regional on-line coupled meteorology-atmospheric chemistry WRF-Chem model has been applied for numerical weather prediction and for air quality forecast in Slovenia. In the study an evaluation of the air quality forecasting system has been performed for summer 2013. In the case of ozone (O3) daily maxima the first day and second day model predictions have been also compared to the operational statistical O3 forecast and to persistence. Results of discrete and categorical evaluations show that the WRF-Chem based forecasting system is able to produce reliable forecasts, which depending on monitoring site and the evaluation measure applied can outperform the statistical model. For example, correlation coefficient shows the highest skill for WRF-Chem model O3 predictions, confirming the significance of the non-linear processes taken into account in an on-line coupled Eulerian model. For some stations and areas biases were relatively high due to highly complex terrain and unresolved local meteorological and emission dynamics, which contributed to somewhat lower WRF-Chem skill obtained in categorical model evaluations. Applying a bias-correction could further improve WRF-Chem model forecasting skill in these cases.

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