Evaluation of data assimilation techniques for a mesoscale meteorological model and their effects on air quality model results

2008 ◽  
Vol 1 ◽  
pp. 012033
Author(s):  
A Amicarelli ◽  
C Gariazzo ◽  
S Finardi ◽  
A Pelliccioni ◽  
C Silibello
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Mesoscale Meteorological Model ◽  
Evaluation Of Data

The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

2008 ◽  
Vol 47 (7) ◽  
pp. 1853-1867 ◽  
Author(s):  
Tanya L. Otte
Keyword(s):  
Air Quality ◽  
Meteorological Conditions ◽  
Daily Maximum ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Model Simulations ◽  
Dynamic Analyses ◽  
The Impact ◽  
Over Time

Abstract It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

scholarly journals A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods

2017 ◽  
Vol 10 (12) ◽  
pp. 4743-4758 ◽  
Author(s):  
Youhua Tang ◽  
Mariusz Pagowski ◽  
Tianfeng Chai ◽  
Li Pan ◽  
Pius Lee ◽  
...  
Keyword(s):  
United States ◽  
Air Quality ◽  
Data Assimilation ◽  
Mean Squared Error ◽  
Optimal Interpolation ◽  
Air Quality Model ◽  
Quality Model ◽  
Multi Scale ◽  
Modis Aod ◽  
Environmental Prediction

Abstract. This study applies the Gridpoint Statistical Interpolation (GSI) 3D-Var assimilation tool originally developed by the National Centers for Environmental Prediction (NCEP), to improve surface PM2.5 predictions over the contiguous United States (CONUS) by assimilating aerosol optical depth (AOD) and surface PM2.5 in version 5.1 of the Community Multi-scale Air Quality (CMAQ) modeling system. An optimal interpolation (OI) method implemented earlier (Tang et al., 2015) for the CMAQ modeling system is also tested for the same period (July 2011) over the same CONUS. Both GSI and OI methods assimilate surface PM2.5 observations at 00:00, 06:00, 12:00 and 18:00 UTC, and MODIS AOD at 18:00 UTC. The assimilations of observations using both GSI and OI generally help reduce the prediction biases and improve correlation between model predictions and observations. In the GSI experiments, assimilation of surface PM2.5 (particle matter with diameter < 2.5 µm) leads to stronger increments in surface PM2.5 compared to its MODIS AOD assimilation at the 550 nm wavelength. In contrast, we find a stronger OI impact of the MODIS AOD on surface aerosols at 18:00 UTC compared to the surface PM2.5 OI method. GSI produces smoother result and yields overall better correlation coefficient and root mean squared error (RMSE). It should be noted that the 3D-Var and OI methods used here have several big differences besides the data assimilation schemes. For instance, the OI uses relatively big model uncertainties, which helps yield smaller mean biases, but sometimes causes the RMSE to increase. We also examine and discuss the sensitivity of the assimilation experiments' results to the AOD forward operators.

scholarly journals Comparisons of ground-based tropospheric NO<sub>2</sub> MAX-DOAS measurements to satellite observations with the aid of an air quality model over the Thessaloniki area, Greece

2017 ◽  
Vol 17 (9) ◽  
pp. 5829-5849 ◽  
Author(s):  
Theano Drosoglou ◽  
Alkiviadis F. Bais ◽  
Irene Zyrichidou ◽  
Natalia Kouremeti ◽  
Anastasia Poupkou ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Optical Absorption Spectroscopy ◽  
Monitoring Site ◽  
Air Quality Model ◽  
Ozone Monitoring Instrument ◽  
Quality Model ◽  
Meteorological Model ◽  
Tropospheric No2 ◽  
Ozone Monitoring

Abstract. One of the main issues arising from the comparison of ground-based and satellite measurements is the difference in spatial representativeness, which for locations with inhomogeneous spatial distribution of pollutants may lead to significant differences between the two data sets. In order to investigate the spatial variability of tropospheric NO2 within a sub-satellite pixel, a campaign which lasted for about 6 months was held in the greater area of Thessaloniki, Greece. Three multi-axial differential optical absorption spectroscopy (MAX-DOAS) systems performed measurements of tropospheric NO2 columns at different sites representative of urban, suburban and rural conditions. The direct comparison of these ground-based measurements with corresponding products from the Ozone Monitoring Instrument onboard NASA's Aura satellite (OMI/Aura) showed good agreement over the rural and suburban areas, while the comparison with the Global Ozone Monitoring Experiment-2 (GOME-2) onboard EUMETSAT's Meteorological Operational satellites' (MetOp-A and MetOp-B) observations is good only over the rural area. GOME-2A and GOME-2B sensors show an average underestimation of tropospheric NO2 over the urban area of about 10.51 ± 8.32  ×  1015 and 10.21 ± 8.87  × 1015 molecules cm−2, respectively. The mean difference between ground-based and OMI observations is significantly lower (6.60 ± 5.71  ×  1015 molecules cm−2). The differences found in the comparisons of MAX-DOAS data with the different satellite sensors can be attributed to the higher spatial resolution of OMI, as well as the different overpass times and NO2 retrieval algorithms of the satellites. OMI data were adjusted using factors calculated by an air quality modeling tool, consisting of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the Comprehensive Air Quality Model with Extensions (CAMx) multiscale photochemical transport model. This approach resulted in significant improvement of the comparisons over the urban monitoring site. The average difference of OMI observations from MAX-DOAS measurements was reduced to −1.68 ± 5.01  ×  1015 molecules cm−2.

scholarly journals Evaluate of air pollution dispersion and propose planing scenerios to reduce air pollution for livestock activities in Tan Thanh district, Ba Ria – Vung Tau province

2019 ◽  
Vol 2 (2) ◽  
pp. 26-37 ◽  
Author(s):  
Dung Minh Ho ◽  
Bang Quoc Ho ◽  
Thang Viet Le
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Agricultural Sector ◽  
Ambient Air ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Pollution Dispersion ◽  
Air Pollution Dispersion

Livestock is one of the main activities of the agricultural sector in Tan Thanh district, Ba Ria – Vung Tau province. Beside of pollution sources such as waste water, solid waste, livestock activity in Tan Thanh district, Ba Ria - Vung Tau province in recent years has caused air pollution in the livestock area and surrounding area. This research was carried out to evaluate the process of air pollution dispersion from livestock activities based on applying the TAPM meteorological model and AERMOD air quality model. The results showed that the maximum concentrations of air pollutants from livestock area such as NH3, H2S and CH3SH exceeded the National Technical Regulation on Ambient Air Quality (average hour) in the centre of Tan Thanh district, such as Toc Tien commune, part of Tan Phuoc and Phuoc Hoa communes, is 505 μg/m3; 57.4 μg/m3 and 111 μg/m3, respectively. Phu My district and other suburban communes (Hac Dich, Song Xoai, Chau Pha, Tan Hoa, Tan Hai, My Xuan, etc.) have distribution of lower concentrations of air pollutants. Base on the present results of modeling, the authors have proposed livestock development scenarios to control air pollution from this activity, contributing to environmental protection for Tan Thanh district.

scholarly journals Comparisons of ground-based tropospheric NO<sub>2</sub> MAX-DOAS measurements to satellite observations with the aid of an air quality model over Thessaloniki area, Greece

2016 ◽  
Author(s):  
Theano Drosoglou ◽  
Alkiviadis F. Bais ◽  
Irene Zyrichidou ◽  
Natalia Kouremeti ◽  
Anastasia Poupkou ◽  
...  
Keyword(s):  
Air Quality ◽  
Transport Model ◽  
Monitoring Site ◽  
Air Quality Model ◽  
Quality Model ◽  
Meteorological Model ◽  
Multi Scale ◽  
Tropospheric No2 ◽  
Urban Monitoring ◽  
The Difference

Abstract. The main issue arising from the comparison of ground-based and satellite measurements is the difference in spatial representativeness, which for locations with inhomogeneous spatial distribution of pollutants may lead to significant differences between the two datasets. In order to investigate the spatial variability of tropospheric NO2 within a sub-satellite pixel, a campaign which was lasted for about six months was held at the greater area of Thessaloniki, Greece. Three DOAS/MAX-DOAS systems performed measurements of tropospheric NO2 columns at different sites representative of urban, sub-urban and rural conditions. The direct comparison of these ground-based measurements with corresponding OMI/Aura and GOME-2/MetOp-A and GOME2/MetOp-B products showed good agreement only over the rural area. GOME2A and GOME2B sensors show an average underestimation of tropospheric NO2 over the urban area of about 9.12 ± 7.33 × 1015 and 9.58 ± 8.21 × 1015 molecules cm−2, respectively. The mean difference between ground-based and OMI observations is significantly lower (6.03 ± 6.04 × 1015 molecules cm−2), mainly due to the higher spatial resolution of OMI. OMI data were adjusted using factors calculated by an air quality modelling tool, consisting of the Weather Research and Forecasting (WRF) mesoscale meteorological model and the Comprehensive Air Quality Model with Extensions (CAMx) multi-scale photochemical transport model. This approach resulted to significant improvement of the comparisons over the urban monitoring site. The average negative difference of OMI observations from Phaethon measurements was reduced to 1.15 ± 6.32 × 1015 molecules cm−2.

scholarly journals What Can We Expect from Data Assimilation for Air Quality Forecast? Part II: Analysis with a Semi-Real Case

2019 ◽  
Vol 36 (7) ◽  
pp. 1433-1448 ◽  
Author(s):  
Bertrand Bessagnet ◽  
Laurent Menut ◽  
Florian Couvidat ◽  
Frédérik Meleux ◽  
Guillaume Siour ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Observational Data ◽  
Vertical Direction ◽  
Pollutant Emissions ◽  
Air Quality Model ◽  
Quality Model ◽  
Initial State ◽  
Air Quality Forecast ◽  
Maximum Benefit

AbstractAssimilation of observational data from ground stations and satellites has been identified as a technique to improve air quality model results. This study is an evaluation of the maximum benefit expected from data assimilation in chemical transport models. Various tests are performed under real meteorological conditions; the injection of various subsets of “simulated observational data” at the initial state of a forecasting period is analyzed in terms of benefit on selected criteria. This observation dataset is generated by a simulation with perturbed input data. Several criteria are defined to analyze the simulations leading to the definition of a “tipping time” to compare the behavior of simulations. Assimilating three-dimensional data instead of ground observations clearly adds value to the forecast. For the studied period and considering the expected best favorable data assimilation experiment, the maximum benefit is higher for particulate matter (PM) with tipping times exceeding 80 h; for ozone (O3) the gain is on average around 30 h. Assimilating O3 concentrations with a delta calculated on the first level and propagated over the vertical direction provides better results on O3 mean concentrations when compared with the expected best experiment corresponding to the injection of the O3 “observations” 3D dataset, but for maximum O3 concentrations the opposite behavior is observed. If data assimilation of secondary pollutant concentrations provides an improvement, assimilation of primary pollutant emissions can have beneficial impacts when compared with an assimilation of concentrations, after several days on secondary pollutants like O3 or nitrate concentrations and more quickly for the emitted primary pollutants. An assimilation of ammonia concentrations has slightly better performances on nitrate, ammonium, and PM concentrations relative to the assimilation of nitrogen or sulfur dioxides.

scholarly journals Using 3DVAR data assimilation system to improve ozone simulations in the Mexico City basin

2008 ◽  
Vol 8 (24) ◽  
pp. 7353-7366 ◽  
Author(s):  
N. Bei ◽  
B. de Foy ◽  
W. Lei ◽  
M. Zavala ◽  
L. T. Molina
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Mexico City ◽  
Mesoscale Model ◽  
Three Dimensional ◽  
Air Quality Model ◽  
Quality Model ◽  
Penn State ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. This study investigates the improvement of ozone (O3) simulations in the Mexico City basin using a three-dimensional variational (3DVAR) data assimilation system in meteorological simulations during the MCMA-2003 field measurement campaign. Meteorological simulations from the NCAR/Penn State mesoscale model (MM5) are used to drive photochemical simulations with the Comprehensive Air Quality Model with extensions (CAMx) during a four-day episode on 13–16 April 2003. The simulated wind circulation, temperature, and humidity fields in the basin with the data assimilation are found to be more consistent with the observations than those from the reference deterministic forecast. This leads to improved simulations of plume position, peak O3 timing, and peak O3 concentrations in the photochemical model. The improvement in O3 simulations is especially strong during the daytime. The results demonstrate the importance of applying data assimilation in meteorological simulations for air quality studies in the Mexico City basin.

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